Methods for predicting prostate cancer and uses thereof

Abstract

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Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a divisional application of Australian Patent Application No. 2020241720, and is related to U.S. Provisional Application No. 62 / 978,184, filed February 18, 2020, and U.S. Provisional Application No. 62 / 819,325, filed March 15, 2019, the disclosure of each of which is incorporated herein by reference in their entirety. [0001a] Preceding applications contained a Sequence Listing which is hereby incorporated by reference in its entirety. The present application contains a sequence listing which has been submitted electronically as an XML document in the ST.26 format and is also hereby incorporated by reference in its entirety. FIELD OF THE INVENTION

[0002] The present invention relates to compositions and methods for diagnosing, prognosing, monitoring, and treating a patient with prostate cancer. In particular, the invention relates to the use of small non-coding RNAs (sncRNAs) such as miRNA and snoRNA as expression signatures for identifying a clinically significant prostate cancer. BACKGROUND

[0003] The current method of screening for prostate cancer includes a digital rectal examination followed by a prostate-specific antigen (PSA) test. The former is invasive and the latter requires drawing of a blood sample from the subject. 20

[0004] Patients with a suspicious DRE and / or an elevated PSA level are subjected to a systematic 12-needle core biopsy or Magnetic Resonance Imaging (MRI)-guided targeted needle biopsy. This standard diagnosis strategy is invasive, imprecise and associated with significant costly morbidities, most notably bacterial infections.

[0005] The PSA test has significant drawbacks. In addition to indicating prostate cancer, 25 elevated PSA levels may also indicate urinary tract infection or prostatitis (an inflammation or the prostate or benign prostatic hyperplasia or BPH). The test overdiagnoses prostate cancer, and many men are unnecessarily subjected to core needle biopsies. The prostate tissue collected during the biopsy is then examined by a pathologist and assigned a Gleason score that assesses 15 2026234022   16 Sep 2026 the grade of the disease. The Gleason score is the sum of two numbers: (1) a primary grade assigned by the pathologist based on the pathologist’s determination of the grade of the tumor in the most common pathology (2) a secondary grade based on the determination of the grade of the tumor in the next most prominent pathology. For each area, a score of one to five is 5 assigned based on how [Text continues on page 2] 2026234022   16 Sep 2026 aggressive the tumor appears and the two numbers are added togther to provide the final Gleason Score. A tumor with cells that appear close to normal is assigned a low Gleason score (six or below, reported as Gleason 3+3) whereas a tumor with cells that appear clearly different from those of a normal prostate is assigned a higher Gleason score (seven or above). Low-grade tumors based on low Gleason scores are less likely to be aggressive; whereas tumors with high Gleason score are more likely to be aggressive and metastasize. There are aspects of the Gleason Scoring System that have been problematic since they were implemented - most noteably the fact that tumors that are Gleason 3+4 and Gleason 4+3 are both reported as Gleason 7, even though the clinical outcomes of these groups are clearly different. A recent refinement of the Gleason Score, referred to as Grade Grouping, has been adopted to eliminate this issue (Grade Group 1 (GG1) encompasses Gleason 3+3; GG2 - Gleason 3+4; GG3 - Gleason 4+3; GG4 - Gleason 4+4 and GG5 - Gleason 5+4 or higher. This change to the scoring system has simplified the reporting of the histopathology of prostate cancer and has eliminated the ambiguity associated with “Gleason 7” tumors, making classifying outcome more straight forward. 15

[0006] Approximately 50-70% of patients recommended for core needle biopsy on the basis of “elevated” PSA (>3 ng / mL) have negative biopsies, while 14% of men with PSA < 3 ng / mL have prostate cancer but are not routinely biopsied because of their low PSA levels. The combination of PSA screening and core needle biopsy is both invasive and has poor performance characteristics, which leaves physicians and patients with no reliable measures on which to base their choices of 20 treatment options. The result is that many men needlessly opt for clinical intervention, very often prostatectomy. It also hinders the development of new prognostic tools since the Gleason Score "gold standard" is not itself a reliable indicator of prostate tumor progression.

[0007] This problem has been recognized for at least 30 years. It remains a major issue today. The intervening years have seen many attempts to develop prognostic markers for aggressive disease, 25 including ploidy, nuclear morphology and nuclear matrix architecture, microarray-based transcriptome analyses, DNA methylation status, and detection of gene fusions such as the TMPRSS2:ETS family fusions. None of these methodologies have proved to be significantly better than Gleason Scores as indicators of prostate tumor progression. Furthermore, they do not identify the cancer stage or grade adequately. 2026234022   16 Sep 2026

[0008] Tests have been developed that are designed to distinguish cancer states using mRNA expression profiles. However, each demonstrates significant shortcomings. First, with only a few exceptions, these assays used tumor material derived from radical prostatectomy specimens, and therefore are at best predictive of early tumor recurrence. While potentially useful for making post-surgical clinical decisions related to continuing clinical decisions, they do not help distinguish prostate cancer grades prior to surgery. Secondly, a number of these genomic approaches have focused on specific pathways that have been implicated in prostate cancer progression, including the androgen receptor (AR) modulated gene expression, epithelial-stromal interactions, and cell cycle. These assays assume that all prostate tumors progress along a common pathway. Other commercially available biomarker assays use mRNA expression profiles generated by real-time PCR of a small subset of genes.

[0009] To date there has been very few genome wide transcriptome studies of sncRNAs in prostate cancer. One study compared the miRNA and snoRNA signatures in (i) freshly frozen radical prostatectomy samples and (ii) adjacent normal tissue from the same patient using Illumina / Solexa 15 deep sequencing and microarray analysis on the Affymetrix miRNA® v.2 microarrays that contains 723 human miRNAs catalogued in Sanger miRBase v.10.1. (Wellcome Sanger Institute). This study provides a valuable data set for comparing the complement of sncRNAs expressed in prostate cancer and peritumoral benign tissue but is not useful for the rational design of a panel of sncRNAs that is prognostic and / or predictive for tumor progression prior to clinical intervention. 20 It is also handicapped as a general screening technology, since the technique requires microdissected flash frozen material that is only available after surgery, and therefore cannot be used for diagnosis.

[00010] Accordingly, an improved method of predicting screening and classifying prostate cancer is needed. The present disclosure relates to a non-invasive (by eliminating or reducing the 25 unnecessary core needle biopsy) method for screening the presence or absence of prostate cancer that is highly sensitive and specific.

[00011] The method also provides a platform for disease management that is useful for the diagnosis, classification, prognosis, and monitoring of the progression and treatment of the disease. The disclosed method is based on the interrogation of a large set of at least 200 small non-coding 2026234022   16 Sep 2026 RNAs (sncRNAs) isolated from urinary exosomes in combination with the Sentinel™ PCa, Sentinel™ CS and Sentinel™ HG tests. The Sentinel™ PCa, Sentinel™ CS and Sentinel™ HG tests are based on algorithmic analyses and comparisons of snRNA sequences catalogued from a large target population having no evidence of prostate cancer (NEPC) or having prostate cancer (GG1-GG5) for the Sentinel™ PCa Test; having low grade cancer (GG1) versus intermediate and high grade cancer (GG2-GG5) for the Sentinel™ CS Test; and having low and favorable intermediate grade cancer (GG1+GG2) versus unfavorable intermediate and high grade (GG3-GG5) cancer for the Sentinel™ HG Test. The three Sentinel™ Tests, that can be performed on a single urine sample, are used to sequentially determine whether a patient has prostate cancer or not, and whether patients with prostate cancer have low- or favorable intermediate-grade disease that can be monitored on active surveillance protocols or high-grade disease that needs immediate treatment. SUMMARY OF THE DISCLOSURE

[00012] In one aspect, the disclosure provides a method of screening a subject for prostate cancer 15 comprising: (i) obtaining a biological sample from the subject, (ii) detecting the aggregate expression profile of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample wherein the collection of sncRNAs comprises SEQ ID NOs: 1-280, (iii) correlating the aggregate expression profile of SEQ ID NOs: 1-280 from the subject by comparing the aggregate expression level of SEQ ID NOs: 1-280 in a training data set from a target population 20 having no evidence of prostate cancer (NEPC) or having prostate cancer; and (iv) classifying the subject as NEPC or has prostate cancer based on the results in (iii). This procedure is embodied in the Sentinel™ PCa test.

[00013] In yet another aspect, the disclosure provides a method of determining whether a patient diagnosed as having cancer has low-grade (GG1) or intermediate or high grade disease (GG2-25 GG5) or not, comprising: (i) obtaining a biological sample from a subject, (ii) detecting the aggregate expression profile of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample wherein the collection of sncRNAs comprises SEQ ID NOs: 281-560, (iii) correlating the aggregate expression profile of SEQ ID NOs: 281-560 from the subject by comparing the aggregate expression profile of SEQ ID NOs: 281-560 in a training data set from a 2026234022.^   16 Sep 2026 target population known to have low risk, low grade (GG1) or intermediate and high grade, intermediate and high risk prostate cancer (GG2-GG5) and (iv) classifying the subject as GG1 or GG2-GG5 based on the results obtained from (iii). This procedure is embodied in the Sentinel™ CS test.

[00014] In yet another aspect, the disclosure provides a method of determining whether a patient diagnosed as having cancer has high-grade (GG3-GG5) or not (low or intermediate grade disease (GG1 + GG2), comprising: (i) obtaining a biological sample from a subject, (ii) detecting the aggregate expression profile of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample wherein the collection of sncRNAs comprises SEQ ID NOs: 561-840, (iii) correlating the aggregate expression profile of SEQ ID NOs: 561-840 from the subject by comparing the aggregate expression profile of SEQ ID NOs: 561-840 in a training data set from a target population known to have high grade, high risk prostate cancer (GG3-GG5) or low- or intermediate-risk cancer (GG1+GG2) and (iv) classifying the subject as GG3-GG5 or GG1+GG2 based on the results obtained from (iii). This procedure is embodied in the Sentinel™ HG test. 15

[00015] In yet another aspect, the disclosure provides a method for treating a prostate cancer comprising: (i) obtaining a biological sample from a subject, (ii) detecting the aggregate expression profile of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample wherein the collection of sncRNAs comprises SEQ ID NOs: 281-840, (iii) correlating the aggregate expression profile of SEQ ID NOs: 1-840 from the subject by comparing the aggregate 20 expression profile of SEQ ID NOs: 281-840 in a training data set from a target populations having NEPC, GG1, GG2, GG3, GG4 or GG5 prostate cancer, (iv) classifying the subject as having low-intermediate-grade, prostate cancer (GG1-GG2) or high-grade prostate cancer (GG3-GG5) based on the results obtained from (iii), and (v) treating the subject classified as having high-risk prostate cancer by administering one or more chemotherapeutic agents, hormones, immunotherapeutic, 25 radiation, cryotherapy, surgery or a combination thereof.

[00016] In a further aspect, the disclosure provides a method for determining the likelihood of survival, disease recurrence or response to treatment for a subject with prostate cancer comprising: (i) obtaining a biological sample from a patient, (ii) detecting the aggregate expression profile of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample wherein ^026234022   16 Sep 2026 the collection of sncRNAs comprises SEQ ID NOs: 1-840, (iii) comparing the aggregate expression profile of SEQ ID NOs: 1-840 after treatment with that prior to treatment, (iv) correlating the aggregate expression profile of SEQ ID Nos: 1-840 from the subject by comparing the aggregate expression profile of SEQ ID Nos: 1-840 in a training data set from a target populations having no evidence of prostate cancer (NEPC) or having prostate cancer and the aggregate expression profile of SEQ ID Nos: 1-840 in a training data set from a target populations having Grade Group 1, 2, 3 or 4-5; and (v) determining the likelihood of survival, disease recurrence or response to treatment in a subject treated for prostate cancer.

[00017] In one aspect, the disclosure provides a method for predicting future prostate cancer in a subject comprising: (i) obtaining a biological sample from a patient, (ii) detecting the aggregate expression profile of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample wherein the collection of sncRNAs comprises SEQ ID NOs: 1-280, (iii) correlating the aggregate expression profile of SEQ ID Nos: 1-280 from the subject by comparing the aggregate expression profile of SEQ ID Nos: 1-280 in a training data set from a target 15 population from a target populations having Grade Group 1, 2, 3 or 4-5; (iv) determining the likelihood of a subject at risk of having Grade Group 2 -5 prostate cancer based on the results obtained from (iii), and (iv) treating the subject predicted with a high risk of developing aggressive prostate cancer by administering one or more chemotherapeutic agents, hormones, immunotherapeutic, radiation, cryotherapy, surgery or a combination thereof. 20

[00018] In another aspect, the disclosure provides a system for determining whether a patient has no cancer or has cancer and classifying the subject with cancer as (i) indolent (low grade, GG1), (ii) intermediate or high grade (GG2-GG5), (iii) low / intermediate risk (GG1-GG2) or (iv) aggressive (high grade, GG3-GG5) prostate cancer comprising at least three processors configured to (a) interrogate sncRNA sequences for informative sequences, (b) determine and compare a 25 Sentinel Score to determine if the subject has prostate cancer or no prostate cancer and to classify the prostate cancer stage group. 2026234022   16 Sep 2026 BRIEF DESCRIPTION OF THE DRAWINGS

[00019] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fees.

[00020] Figure 1: Exosomes are small extracellular, membrane bound vesicles that are formed in the early endosomes and released from cells. Exosomes contain proteins, mRNAs and an array of sncRNAs [miRNAs and C / D box and H / ACA Box small nucleolar RNA (snoRNAs)] that reflect the biology of the cell.

[00021] Figure 2: Using a commercial kit, cell-free urine is collected from the patient. The 10 exosomes in the urine are captured, and the RNA is extracted from the exosomes. The RNA yield is measured using the Qubit assay (ThermoFisher) and the sample quality is assessed using the Agilent 2100 bioanalyzer. The resultant sncRNA levels are interrogated using custom made OpenArray™ (Thermo Fisher) plates that are specifically designed for the miR Scientific Sentinel™ PCa, Sentinel™ CS or Sentinel™ HG Tests. Interrogate is a common term of art for 15 the simultaneous analysis of a large number of sequences in a biological sample. The resultant readout for amplification curves for snoRNA and microRNA (collectively called sncRNAs), are then analyzed and used to diagnose the patient, and when cancer is present to classify the disease, and monitor treatment accordingly.

[00022] Figure 3 illustrates the complexity of using more than a single entity to establish an 20 unbiased statistical approach to identify important interactions to identify individual and combinations of sncRNAs and to correlate the grade grouping or prostate cancer phenotype. (See,

[00048] -

[00051] ).

[00023] Figure 4 shows a schematic for screening and diagnosis of a patient suspected of having prostate cancer. 25

[00024] The patient first provides a urine sample for a three-layered Sentinel™ Analysis. Total urinary exosomal RNA is extracted and interrogated for expression of sncRNAs (SEQ ID NOs: 1280) specific for the Sentinel™ PCa Test; sncRNAs (SEQ ID NOs: 281-560) specific for the 2026234022.^   16 Sep 2026 Sentinel™ CS Test; and sncRNAs (SEQ ID NOs: 561-840) specific for the Sentinel™ HG Test. The expression signature will be used to classify patients into those with prostate cancer, and those without prostate cancer (Sentinel™ PCa Test, 1st layer). Patients with negative score will return every 12 months for monitoring.

[00025] In the second layer, patients with positive Sentinel™ PCa Score (those with prostate cancer) will be subjected to a secondary analysis that classifies them into having clinically insignificant (GG1) tumors or clinically significant tumors (GG2-GG5) using the Sentinel™ CS Test. Patients with clinically insignificant (GG1) tumors will be recommended for active surveillance (AS) and monitored continuously with quarterly Sentinel™ CS tests, to establish that the tumor has not progressed to GG2 or higher. Patients with clinically significant tumors (GG2-GG5) will be referred for immediate therapy.

[00026] For some patients, a third classification layer, the Sentinel™ HG Test, will further classify patients as having GG1-GG2 tumors or GG3-GG5 tumors. This test is designed to identify patients with GG3-GG5 cancers that need immediate intervention. Patients with GG1 or GG2 can be 15 monitored by quarterly Sentinel™ HG Test to identify patients that progress to GG3 and therefore need therapeutic intervention. The availability of the Sentinel™ CS and Sentinel™ HG provides both patients and health care providers with the individualized information for treatment decisionmaking.

[00027] Figure 5 shows the output of the Discovery PCa Experiments. The PCa Discovery Studies 20 use very carefully defined patient cohort [NEPC =89; Cancer (GG1-GG5) =146] with well characterized histopathology to identify the most informative sequences among the 6,599 sncRNAs interrogated on the miR4.0 microarrays.

[00028] Figure 5 (left panel): Scatter plot of no cancer (NEPC) and cancer (GG1-GG5) status in the training data set. Positive Discovery PCa Score is indicative of having prostate cancer and a 25 negative Discovery PCa Score is indicative of no cancer. The cancer status as determined by histopathology of core biopsies is shown in blue (no cancer) and red (cancer) circles.

[00029] Figure 5 (right panel): Identification of informative sncRNAs for the Sentinel™ PCa Test Identification of the most informative sncRNA entities (top 35 are shown, each circle represents a 2026234022.^   16 Sep 2026 single entity) for discriminating between no cancer and cancer status using the proprietary Selection Algorithm. The resultant Sentinel™ PCa Test interrogates 280 sncRNA including the 145 most informative sncRNA sequences, which comprises of 60 snoRNA and 85 miRNA entities as shown in the bar graph. Green: miRNA entities; Yellow: snoRNA entities.

[00030] Figure 6 (left panel) shows the output of the Discovery CS Experiments. The CS Discovery Studies use very carefully defined patient cohorts [GG1=9O; GG2-GG5=56] with well characterized histopathology to identify the most informative sequences among the 6,599 sncRNAs interrogated on the miR4.0 microarrays. Positive Discovery CS Score is indicative of having GG2-GG5 cancer (yellow circles) and a negative Discovery CS Score is indicative of having GG1 cancer, (green circles)

[00031] Figure 6 (right panel): Identification of informative sncRNAs for the Sentinel™ CS Test Identification of the most informative sncRNA entities (top 35 are shown, each circle represents a single entity) for discriminating between GG1 and GG2-GG5 cancer status using the proprietary Selection Algorithm. The resultant Sentinel™ CS Test interrogates 280 sncRNA including the 145 15 most informative sncRNA sequences, which comprises of 66 snoRNA and 130 miRNA entities as shown in the bar graph. Green: miRNA entities; Yellow: snoRNA entities.

[00032] Figure 7 (left panel) shows the output of the Discovery HG Experiments. The HG Discovery Studies use very carefully defined patient cohorts [GG1+GG2=181; GG3-GG5=55] with well characterized histopathology to identify the most informative sequences among the 6,599 20 sncRNAs interrogated on the miR4.0 microarrays. Positive Discovery HG Score is indicative of having GG3-GG5 cancer (purple circles) and a negative Discovery HG Score is indicative of having GG1+GG2 cancer (brown circles).

[00033] Figure 7 (right panel): Identification of informative sncRNAs for the Sentinel™ HG Test. Identification of the most informative sncRNA entities (top 35 are shown, each circle represents a 25 single entity) for discriminating between GG1 and GG2-GG5 cancer status using the proprietary Selection Algorithm. The resultant Sentinel™ CS Test interrogates 280 sncRNA including the 196 most informative sncRNA sequences, which comprises of 66 snoRNA and 130 miRNA entities as shown in the bar graph. Green: miRNA entities; Yellow: snoRNA entities. 2026234022   16 Sep 2026

[00034] Figures 8A-8C show the clinical validation of high throughput OpenArray™ interrogation of urinary exosomal sncRNA using the Sentinel™ PCa Test. The data from a case-control study of 1436 men (836 subjects in the training group used to cross-validate the interrogation of sncRNAs identified in the Discovery PCa phase and 600 independent subjects used in the validation study) are shown.

[00035] Figure 8A: Scatter plot of cancer status in the validation group set that examines 600 patients (300 no cancer; 300 cancer). Classification of no cancer (black circle) and cancer (green circle) patients where a positive Sentinel™ PCa Score is indicative of having prostate cancer and a negative Sentinel™ PCa Score is indicative of no cancer. 10

[00036] Figure 8B: Sorted plot of cancer status in the validation group set that examines 600 patients. Classification of no cancer (black circle) and cancer (green circle) patients where a positive Sentinel™ PCa Score is indicative of having prostate cancer and a negative Sentinel™ PCa Score is indicative of no cancer.

[00037] Figure 8C: Receiver Operator Curve (ROC) for Sentinel™ PCa Test. The ROC curve for 15 the analysis of 600 patients in testing group shown in Figures 8A and 8B was calculated by successively calculating (1-specificity) for different user-defined false negative rates. Performance characteristics reported in Table 6 (see, [000112]) were from the user defined false negative rate of 0.05 (shown in red).

[00038] Figures 9A-9C show the clinical validation of high throughput OpenArray™ interrogation 20 of urinary exosomal sncRNA using the Sentinel™ CS Test. The data from a case-control study of 1436 men (836 subjects in the training group used to cross-validate the interrogation of sncRNAs identified in the Discovery CS phase and 600 independent subjects used in the validation study) are shown.

[00039] Figure 9A: Scatter plot of cancer status in the validation group set that examines 300 25 prostate cancer patients (146 GGl-low grade and 154 GG2-GG5 intermediate and high grade). Classification of low grade (teal circle) and intermediate and high-grade cancer (orange circle) patients where a positive Sentinel™ CS Score is indicative of having high-grade prostate cancer and a negative Sentinel™ CS Score is indicative of low-grade cancer. 2026234022^  16 Sep 2026

[00040] Figure 9B: Sorted plot of cancer status in the validation group set as shown in Figure 9A that examines 300 prostate cancer patients. Classification of low grade (teal circle) and high-grade cancer (orange circle) patients where a positive Sentinel™ CS Score is indicative of having highgrade prostate cancer and a negative Sentinel™ CS Score is indicative of low-grade cancer.

[00041] Figure 9C: Receiver Operator Curve (ROC) for Sentinel™ CS Test. The ROC curve for the analysis of 300 prostate cancer patients, as shown in Figures 9A and 9B was calculated by successively calculating (1-specificity) for different user-defined false negative rates. Performance characteristics reported in Table 6 (see, [000112]) were from the user defined false negative rate of 0.05 (shown in red). 10

[00042] Figures 10A-10C show the clinical validation of high throughput OpenArray™ interrogation of urinary exosomal sncRNA using the Sentinel™ HG Test. The data from a casecontrol study of 1436 men (836 subjects in the training group used to cross-validate the interrogation of the same sncRNAs identified in the Discovery HG phase and 600 independent subjects used in the validation study) are shown. 15

[00043] Figure 10A: Scatter plot of cancer status in the validation group set that examines 300 prostate cancer patients (200 GG1+GG2 low grade and 100 GG3-GG5 intermediate and high grade). Classification of low grade (teal circle) and intermediate and high-grade cancer (orange circle) patients where a positive Sentinel™ CS Score is indicative of having high-grade prostate cancer and a negative Sentinel™ CS Score is indicative of low-grade cancer. 20

[00044] Figure 10B: Sorted plot of cancer status in the validation group set as shown in Figure 10A that examines 300 prostate cancer patients. Classification of low grade (blue circle) and highgrade cancer (red circle) patients where a positive Sentinel™ HG Score is indicative of having high grade prostate cancer and a negative Sentinel™ HG Score is indicative of low-grade cancer.

[00045] Figure 10C: Receiver Operator Curve (ROC) for Sentinel™ HG Test. The ROC curve 25 for the analysis of 300 prostate cancer patients, as shown in Figures 10A and 10B was calculated by successively calculating (1-specificity) for different user-defined false negative rates. Performance characteristics reported in Table 6 (see, [000112]) were from the user defined false negative rate of 0.05 (shown in red). 202623402216 Sep 2026 DETAILED DESCRIPTION

[00046] The present subject matter may be understood more readily by reference to the following detailed description that forms a part of this disclosure. It is to be understood that this invention is not limited to the specific products, methods, conditions or parameters described and / or shown herein, and that the terminology used is for the purpose of describing particular aspect and embodiments by way of example only and is not intended to be limiting of the claimed invention.

[00047] The disclosure relates to a method for screening, diagnosing and treating prostate cancer in a subject. The method provides robust tests for (1) classifying a male patient with unknown prostate cancer status and (2) accurately distinguishing between prostate cancer grades in 10 biological samples from patients. The method is based on the detection and correlation of the aggregate expression profiles of a collection of sncRNAs from the patient’s biological sample to determine if a patient has prostate cancer or not using Sentinel™ PCa Test. For the patient identified as having prostate cancer, the exosomal sncRNA is further interrogated using the Sentinel™ Clinical Significant (CS) Test to distinguish patients with clinically significant or 15 aggressive (GG2-GG5) from those with clinically insignificant or indolent (GG1) prostate cancer, and the Sentinel™ High Grade (HG) Test to identify patients with high grade, high risk (GG3-GG5) prostate cancer.

[00048] The disclosed method is based on an unbiased statistical approach developed to identify important interactions of individual sequences and combinations of sequences that correlate best 20 to a phenotype of interest. The approach is based on (i) the modulating effects of miRNA on mRNA and (ii) the influence of snoRNAs on mRNA translatability through post-transcriptional modification of ribosomal RNAs, tRNAs and other nuclear RNAs, which lead to new protein products that alters protein function and phenotype.

[00049] The disclosed computational / statistical approach analyses urinary exosomal sncRNAs 25 to provide a very granular analysis of the critical associations between sncRNAs that leads to the identification of Sentinel sequences that accurately predict the prostate cancer phenotype. This is illustrated in the accompanying Figure 3. For example, in the single entity analysis, the expression level of individual sncRNAs is correlated to the Grade Group of the prostate cancer 2026234022   16 Sep 2026 (the phenotype). For each sncRNA entity there are two informative outcomes: either an increase in the expression level of the entity relative to the control pathology (e.g., no cancer) or a decreased expression level. No change of expression between the two phenotypes indicates that there is no association (1) with either phenotypes, and (2) the entity is not useful as a marker of either phenotype. Thus, when single entity is used in the analysis, there are only two informative outcomes, leaving all of the possible sncRNA interactions unexplored.

[00050] In the two entities analysis, examining the association of the expression changes for all possible interactions between two entities results in 8 different informative outcomes and 1 non-informative outcome (when neither entity is differentially expressed in their phenotype) (see, Figure 3 “Interrogation of 2 Entities). Thus, in the context of the Sentinel™ Tests, when the association of all possible combinations of two sncRNA entities with a specific grade group are compared, there are 8 different ways that will lead to a meaningful association between pairs of sncRNAs and grade groups. This provides a more detailed analysis that uncovers hidden 15 associations between expression levels of sncRNAs and Grade Grouping.

[00051] Using the same approach of three or four or more sncRNA entities provides a very granular analysis of the association between sncRNA expression and phenotype (grade grouping), making it possible to assess a patient of unknown disease status and predict the 20 individual disease status using the expression levels of urinary exosomal sncRNA selected by the algorithm. Development of Sentinel™ PCa, and Sentinel™ HG Test Sentinel™ CS Platforms

[00052] The Sentinel™ PCa Test is a classification platform or algorithm based on the analysis of 25 a collection of signature sncRNA (i.e., miRNAs and snoRNAs sequences) levels. The predictive value of each sequence is defined via a data-driven Selection Algorithm that is independent of the a priori determined biological role of the sequences in prostate biology. The Selection Algorithm is trained on a dataset consisting of: (1) control subjects who presented in urology for conditions unrelated to prostate cancer; (2) subjects with suspicion of prostate cancer known to not have 30 prostate cancer based on the biopsy results; and (3) patients diagnosed with prostate cancer and whose core needle biopsy histopathology was reported as Grade Groups 1 through 5 (GG1-GG5). 2026234022   16 Sep 2026

[00053] To establish robust datasets for the Sentinel™ Tests, exosomal sncRNAs obtained from the urinary exosomes of these training set of patients were interrogated using the Affymetrix miR 4.0 microarrays to define expression signatures. These studies using selected subjects with well characterized histopathology are referred to as the Discovery PCa, Discovery CS, and Discovery HG Tests. The patients included in the “no cancer” group were carefully selected from age-matched men who were seen at urology clinics for issues unrelated to urological oncology, and from men who had one or more 12-needle diagnostic core needle biopsy that showed no evidence of prostate cancer (NEPC). For patients in the “cancer” cohort, the pathological grade group classification of the core needle biopsies each tumor was thoroughly assessed. These carefully select groups of patients (no cancer and cancer group in different stages of cancer) form the training set in the development of the Discovery PCa, CS and HG Tests. The demographics of the 235 patients used for the Discovery experiments are shown in Table 4 (see, [000103]-[000104]) The Selection Algorithm

[00054] The most informative sncRNA sequences that discriminate between cancer and no cancer 15 were identified using Selection algorithm which determines which sncRNA sequences are differentially represented between patients that do not have prostate cancer (NEPC) and those with prostate cancer (GG1-GG5). This is exemplified below for the Discovery PCa Test. The Selection algorithm tests how well the levels of the urinary exosomal sncRNAs correlate with pathological stage of the disease (cancer / no cancer) in a large population of participants [89 subjects with NEPC 20 and 146 patients with cancer (GG1-GG5)] with carefully defined pathology. The Selection algorithm individually assesses how well each of the 6,599 sncRNAs interrogated on the miR4.0 arrays correlates the known pathology of the tumor. Since many sncRNA are coordinately modulated, the algorithm then assesses all combinations of 2 sncRNAs, 3 sncRNAs or 4 sncRNAs, followed by examination of each individual sncRNA using a leave-one-out strategy to assess the 25 importance of each individual sncRNA in the pathology of the disease. As would be expected leaving out most of the 6,599 sncRNA sequences from the Selection algorithm has no impact on the distinction between having prostate cancer and no prostate cancer because they are not differentially associated with either pathology. The impact of the sncRNAs assessment can be visualized using the importance plot shown in Figure 5 (right panel). The importance plots show 30 the following: (1) some exosomal sncRNAs are present in different levels in different pathologies, 2026234022^  16 Sep 2026 (2) the sncRNAs are snoRNAs and miRNAs indicating that one type of sncRNA is insufficient for the disclosed analysis and (3) the diagnosis using the algorithm does not change with more than 280 sncRNA sequences in the classification assessment.

[00055] The informative sequences for the Discovery CS Test (which differentiates between low-risk (GG1) and intermediate- and high-risk prostate cancer (GG2-GG5), were identified using the appropriate Grade Groups and the same Selection strategy (Figures 6). The patient population for this analysis included 89 subjects with NEPC and 146 patients with cancer (GG1-GG5)]

[00056] The informative sequences for the Discovery HG Test (which differentiates between low-and intermediate-risk (GG1+GG2) and high-grade, high-risk (GG3-GG5) prostate cancers were 10 similarly determined (Figure 7). The patient population for this analysis included 181 patients with GG1+GG2 cancer and 55 patients with GG3-GG5 cancer. (Figure 7).

[00057] It is important to note that while some of the sncRNAs are common between the tests, their relative importance in the Classification of disease status varies from test to test (i.e.. Discovery PCa Test, CS Test, and HG Test). 15

[00058] For each Test the most informative 280 sncRNAs SEQ. ID NOs: 1-840) were used to design customized OpenArray™ platforms. The OpenArray™ platform for each Sentinel™ Test was further validated in a large case-control study of 1436 patients. The demographics of the subjects used to train and validate the Sentinel™ PCa, Sentinel™ CS and Sentinel™ HG Tests are shown in Table 5 (see [000109]-[000110]). A stratified random sample of 600 subjects was 20 selected to identify the validation dataset; the remaining 836 subjects served as the training dataset. The validation sample of 600 patients was stratified so that an equal number of subjects were biopsy negative versus biopsy positive (300 each), and of the biopsy positive cases, 200 were GG1 + GG2 (146 GG1 and 54 GG2) and 100 GG3 - GG5. The Sentinel™ PCa Test to identify prostate cancer 25

[00059] The Sentinel™ PCa, Sentinel™ CS and Sentinel™ HG Tests are based on a Classification Algorithm that takes as input the sncRNA expression signature for each patient with unknown disease status and produces a Sentinel™ Score; the participant is classified by comparing this 2026234022.^   16 Sep 2026 Score to the pre-determined cutoff value (obtained from cross-validation in the training dataset) that controls sensitivity for classifying a future patient with unknown disease status (but known expression signature), at a user-defined level (typically 95% or greater).

[00060] The Sentinel PCa Score is compared to a calculated cutoff that controls sensitivity for a future patient at a desired level of, for example, 95% to distinguish between having prostate cancer and no prostate cancer for the PCa Test (Figure 7). The Sentinel™ PCa Test utilizes 280 sncRNA (identified by the Discovery PCa Test), of which 145 unique sncRNAs: 60 miRNAs and 85 snoRNAs are highly informative. This defines the classification boundary used to dichotomize patients into prostate cancer or not. The cutoff is determined by the algorithm such that the Sentinel™ PCa score, which dichotomizes the patients into cancer / no cancer, correctly classify the patient as having cancer 19 times out of 20 (i.e., with 95% sensitivity). Table 1: SEQ ID NOs: 1-280 Used in the PCa Test Analysis SEQ ID NO: Sequence Name Sequence 1 hsa-miR-548ac CAAAAACCGGCAAUUACUUUUG 2 MIR6687 GAGAAUGGGGGGACAGAUGGAGAGGACACAGGCUGGCACUGAGGUCCCCUCC ACUUUCCUCCUAG 3 MIR3150 B GAGGGAAAGCAGGCCAACCUCGAGGAUCUCCCCAGCCUUGGCGUUCAGGUGC UGAGGAGAUCGUCGAGGUUGGCCUGCUUCCCCUC 4 MIR 1301 GGAUUGUGGGGGGUCGCUCUAGGCACCGCAGCACUGUGCUGGGGAUGUUGCA GCUGCCUGGGAGUGACUUCACACAGUCCUC 5 MIR548P AUUAGGUUGGUAUAAAAUUAAUUGCAGUUUUUGUCAUUACUUUCAAUAGCA AAAACUGCAGUUACUUUUGCACCAAUGUAAUAC 6 hsa-let- 7b-5p UGAGGUAGUAGGUUGUGUGGUU 7 ENSG000 00222185 UGGACCAAUGAUGUGAAUGGAAUGCAUCUGAAUAAAAAUUAUGAUCAAUCA GUUUUUGGAACAACUGAGGUCCAC 8 MIR578 AGAUAAAUCUAUAGACAAAAUACAAUCCCGGACAACAAGAAGCUCCUAUAGC UCCUGUAGCUUCUUGUGCUCUAGGAUUGUAUUUUGUUUAUAUAU 9 hsa-miR-323a-3p CACAUUACACGGUCGACCUCU 10 hsa-miR-4283 UGGGGCUCAGCGAGUUU 11 MIR4438 UAAGUGUAAACUUAAGGACUGUCUUUUCUAAGCCUGUGCCUUGCCUUUCCUU UGGCACAGGCUUAGAAAAGACAGUCUUUAAGUUUACACUUC 12 MIR 1205 GAAGGCCUCUGCAGGGUUUGCUUUGAGGUACUUCCUUCCUGUCAACCCUGUU CUGGAGUCUGU 13 hsa-miR-6866-3p GAUCCCUUUAUCUGUCCUCUAG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 14 hsa-miR-6839-3p UUGGGUUUUCUCUUCAAUCCAG 15 hsa-miR-8061 CUUAGAUUAGAGGAUAUUGUU 16 hsa-miR-4323 CAGCCCCACAGCCUCAGA 17 MIR6784 UACAGGCCGGGGCUUUGGGUGAGGGACCCCCGGAGUCUGUCACGGUCUCACC CCAACUCUGCCCCAG 18 SNORA2 2 UUGCACAGUGAACACCCAAGUGUGCUUUAUAGUUCCCUUGGCUUUGACCCUG UGCUAGAGCAUUGCCUGCUCUUCUCCUCUGCAUUAAAAGGAAUAUUUAUCCU UUUAAAUGUAUUCAGAAAGCCAGCACAUUA 19 MIR6803 CUCCUCUGGGGGUGGGGGGCUGGGCGUGGUGGACAGCGAUGCAUCCCUCGCC UUCUCACCCUCAG 20 MIR371A GUGGCACUCAAACUGUGGGGGCACUUUCUGCUCUCUGGUGAAAGUGCCGCCA UCUUUUGAGUGUUAC 21 MIR378E CUGACUCCAGUGUCCAGGCCAGGGGCAGACAGUGGACAGAGAACAGUGCCCA AGACCACUGGACUUGGAGUCAGGACAU 22 hsa-miR-6825-5p UGGGGAGGUGUGGAGUCAGCAU 23 ENSG000 00252204 AUGACCUGUGAAACCAAGGGCUCCUAAUGCUAUGACCAAAGACUGAAGCUCU CUAUGAGAUGCCAGCCACUCAAUAGUGCACUUUUUCUGAGAAGAUAUAAGA 24 MIR7847 GUGUCGGCUGUGGCGUGACUGUCCCUCUGUGUCCCCCACUAGGCCCACUGCU CAGUGGAGCGUGGAGGACGAGGAGGAGGCCGUCCACGAGCAAUGCCAGCAU 25 hsa-miR-147b-3p GUGUGCGGAAAUGCUUCUGCUA 26 MIR181A 1 UGAGUUUUGAGGUUGCUUCAGUGAACAUUCAACGCUGUCGGUGAGUUUGGA AUUAAAAUCAAAACCAUCGACCGUUGAUUGUACCCUAUGGCUAACCAUCAUC UACUCCA 27 hsa-miR-424-5p CAGCAGCAAUUCAUGUUUUGAA 28 MIR4712 GACAGGAUUCCAGUACAGGUCUCUCAUUUCCUUCAUGAUUAGGAAUACUACU UUGAAAUGAGAGACCUGUACUGUAUCUGUU 29 hsa-miR-4487 AGAGCUGGCUGAAGGGCAG 30 hsa-miR-18b-3p UGCCCUAAAUGCCCCUUCUGGC 31 ENSG000 00238298 UUAUUUUUGUAGUUGAUGAAUGUGCUGAUUGGGUAUUCUCGUGUGUGUGUG AGGUGCCACCCUCAAACUUUGUUAUGAUGUUGGCACAUUACCCAUCUGAUA 32 hsa-miR-4435 AUGGCCAGAGCUCACACAGAGG 33 hsa-miR- 2114-3p CGAGCCUCAAGCAAGGGACUU 34 hsa-miR-4758-3p UGCCCCACCUGCUGACCACCCUC 35 SNORD1 13-1 AAAGUGAGUGAUGAAUAGUUCUGUGGCAUAUGAAUCAUUAAUUUUGAUUAA ACCCUAAACUCUGAAGUCC 36 hsa-miR- 21 -3p CAACACCAGUCGAUGGGCUGU 37 ENSG000 00239123 AUCCUUUUGUGGUUCAUAAGCAUGAUGAUCAGGUUUUCAGGCAUAUGUGUAC GAUGUGCCUCCUUCAAACUUUGUUAGGAUGCUACCACGCUACCCAUCUGACU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 38 hsa-miR-4680-5p AGAACUCUUGCAGUCUUAGAUGU 39 MIR5580 UGCUGGCUCAUUUCAUAUGUGUGCUGAGAAAAUUCACACAUAUGAAGUGAGC CAGCAC 40 SNORD4 2B GUGCAUAUGAUGGAAAAGUUUUAAUCUCCUGACACUUGUGAUGUCUUCAAAG GAACCACUGAUGCAC 41 MIR365A ACCGCAGGGAAAAUGAGGGACUUUUGGGGGCAGAUGUGUUUCCAUUCCACUA UCAUAAUGCCCCUAAAAAUCCUUAUUGCUCUUGCA 42 SNORD1 14-21 UGGAUCAAUGAUGACCACUGGUGGCGUAUGAGUCAUAUGUGAUGAAUACGU GUCUGGAACUCUGAGGUCCA 43 ENSG000 00252945 UGAGUUUUGGGAUGAGACCCUGGAAUAAGUGCUGGACACAGUGCCUGAAUCA GACUGUGGAAAUAUUAAUGUAUUUUAUUUUUACUUA 44 MIR4757 UUCCAGCCCGAGGCCUCUGUGACGUCACGGUGUCUGCGGGAGGAGACCAUGA CGUCACAGAGGCUUCGCGCUCUGAG 45 hsa-miR-1267 CCUGUUGAAGUGUAAUCCCCA 46 SNORD1 14-24 UGGAUCGAUGGUGACUGUUGAUGGCAUAUGACUCACAUAUGAUGAGUACGU AUCUGGAACUCUGAGGUCUG 47 ENSG000 00238676 AUCCUUUUGUACUUGGUAAGCAUGAUGAUUGGGUUUUUAUGCUUAUAUAUG AGACAUGCUUGUCUCAAAUCUUGUUACAGCACAUUACCCUUCCUACU 48 hsa-miR-628 -3p UCUAGUAAGAGUGGCAGUCGA 49 MIR7158 GGCUCAAUCUCUGGUCCUGCAGCCUUCUGCCUUUGGCUUUCUGAAGCGAGCU GAACUAGAGAUUGGGCCCA 50 ENSG000 00238605 GUCCUUUUGUAGUCCAUAAGCAUGGUGAUUUGGUUUCAUGCUCAUGUGUCAG AUAUGCUUCCCUCAAACCUUGUUACAGCAUCAUCACAUUACCUGUUUGAUG 51 ENSG000 00212618 UUCUUACAAAUCUAAAUGUGCUUUGAUGCAAGUAUAUUUGAAUCCCUUUCCA UCUGAUAACUGAGCAAAAUAAUA 52 hsa-miR-429 UAAUACUGUCUGGUAAAACCGU 53 hsa-miR-338-5p AACAAUAUCCUGGUGCUGAGUG 54 hsa-miR-3650 AGGUGUGUCUGUAGAGUCC 55 hsa-miR-520d-5p CUACAAAGGGAAGCCCUUUC 56 SNORD2 8 GUCAGAUGAUUUGAAUUGAUAAGCUGAUGUUCUGUGAGGUACAAAAGUUAA UAGCAUGUUAGAGUUCUGAUGGCA 57 hsa-miR-34b-3p CAAUCACUAACUCCACUGCCAU 58 hsa-miR-548av-3p AAAACUGCAGUUACUUUUGC 59 ENSG000 00200042 AAAAUUAUACUUUCAGCAAUCAUCUCUAUAGUUUGUUACUAGAGAAGCUUCU GUGAAUGUGUAGAGCACCGGAAACCACAAGGCAAAGGCUCAGCAUUCUCUCC UAAGCGCGAAGCUGGCUCCUGGUGUUGGUUGGCCGCAACUGCCAUUUGCCAU UGAUGAUCAUUCUUCUCUUCCUGUGGUAGAGGAAGAGGGAGAGAAUGCAGU UUGAGUGG 60 ENSG000 00238596 AUCCUUCUGUAGUUCGUGAGCAUGAUGAUUGGGUGCUCACACACAUAUGUGA GAUGUACCACCCUCAAACCUUGUUACAAUGUCAGCACAUUACCCAUCUGACC 61 hsa-miR-1253 AGAGAAGAAGAUCAGCCUGCA 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 62 MIR3910 -2 UUUUUUUGUUGCUUGUCUUGGUUUUAUGCCUUUUAUGUGCCUUGAUAUAAA AGGCAUAAAACCAAGACAAGCAACAGAAAAA 63 MIR4690 GAGCAGGCGAGGCUGGGCUGAACCCGUGGGUGAGGAGUGCAGCCCAGCUGAG GCCUCUGC 64 hsa-miR-192-5p CUGACCUAUGAAUUGACAGCC 65 hsa-miR-302b-5p ACUUUAACAUGGAAGUGCUUUC 66 hsa-miR-668 -3p UGUCACUCGGCUCGGCCCACUAC 67 hsa-miR-4720-5p CCUGGCAUAUUUGGUAUAACUU 68 hsa-miR-223-3p UGUCAGUUUGUCAAAUACCCCA 69 ENSG000 00239018 AUCCUGUUAUGAUUCAUAAGCAUGAUGACUGAGUUUUCACACUCCUGUGUGA GAUGUGCCUCCCUCUAACCUUAUUACAACAUUGACAUCUUACUCAUUUGACA 70 hsa-miR-100-5p AACCCGUAGAUCCGAACUUGUG 71 hsa-miR-124-5p CGUGUUCACAGCGGACCUUGAU 72 ENSG000 00201151 CUAAAACAAUGUCAAUAGUUUUCAUCAACAGCAGUUGAACCUAGUAAGUGUC GAUACUUUGGGUCUGAGUGG 73 hsa-miR-550a-5p AGUGCCUGAGGGAGUAAGAGCCC 74 SNORA3 8B CCCUCCUACAAAGGCAUGUCUAUAGUUCCUUGUCUUUGGACAUGUAAGAAUU GGAGGCAAAGAAAUGUGGACUUGGAGAAAUCUGGGGCCAGCUUGCUCUCCGC AGGCUCAAGAUCAACCAUCCCACAUAG 75 hsa-miR-636 UGUGCUUGCUCGUCCCGCCCGCA 76 hsa-miR-6739-5p UGGGAAAGAGAAAGAACAAGUA 77 hsa-miR-4764-3p UUAACUCCUUUCACACCCAUGG 78 hsa-miR-7851-3p UACCUGGGAGACUGAGGUUGGA 79 ENSG000 00212168 CUGUAAUGAUGUUGCUCAAAUAUCUGACCUGAAAUGAUUAUAUAGACCAAUU UAAUACUGAAGAA 80 SNORA9 UAGCAAGCCUCCAGCGUGCUUGGGUCUGCGGUGACCCUAUGCAUUCCUUCAG UGCUUGCUAGAACAGUUUUGAAACGGUUUGAGGCCUUGCCCUGCUCCAUCCA GAGCAAGGUUAUAGAAAUUUCAGACAAUG 81 hsa-miR-3115 AUAUGGGUUUACUAGUUGGU 82 ENSG000 00252071 AACCUAUGUAUCAUAUCACAUUGGGUUUUCAUGCUCAUGUGUGAGAAGUGCC UCUUUCAAACCUUGUUCUGACACAUUAUCUUACA 83 hsa-miR-6829-5p UGGGCUGCUGAGAAGGGGCA 84 MIR4693 GUUUAAAGAAUACUGUGAAUUUCACUGUCACAAAUUCAAAUAAAGUGAGAG UGGAAUUCACAGUAUUUAAGGAAU 85 MIR3162 CUGACUUUUUUAGGGAGUAGAAGGGUGGGGAGCAUGAACAAUGUUUCUCAC UCCCUACCCCUCCACUCCCCAAAAAAGUCAG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 86 hsa-miR-7152-3p UCUGGUCCUGGACAGGAGGC 87 ENSG000 00252236 GGUCUCUUGUGUCGGCACCUGGGUGGCUUGCCGCCCACACAACCAAAUUAAA AAAUAACACAGAAGGGUAAGGUAAGUCUCCAUUAAACCCAGGAAAGAGACUG GAAAACUCCUCUUUGGAGCCUGUCUAUAGUCACAGGU 88 MIR153- 2 AGCGGUGGCCAGUGUCAUUUUUGUGAUGUUGCAGCUAGUAAUAUGAGCCCAG UUGCAUAGUCACAAAAGUGAUCAUUGGAAACUGUG 89 hsa-miR-7154-3p AGGAGGACAAGUUGUGGGAU 90 hsa-miR-1468-3p AGCAAAAUAAGCAAAUGGAAAA 91 MIR4446 CUGGUCCAUUUCCCUGCCAUUCCCUUGGCUUCAAUUUACUCCCAGGGCUGGC AGUGACAUGGGUCAA 92 hsa-miR-5681a AGAAAGGGUGGCAAUACCUCUU 93 MIR592 UAUUAUGCCAUGACAUUGUGUCAAUAUGCGAUGAUGUGUUGUGAUGGCACA GCGUCAUCACGUGGUGACGCAACAUCAUGACGUAAGACGUCACAAC 94 MIR4521 UCGGCUAAGGAAGUCCUGUGCUCAGUUUUGUAGCAUCAAAACUAGGAUUUCU CUUGUUAC 95 MIR636 UGGCGGCCUGGGCGGGAGCGCGCGGGCGGGGCCGGCCCCGCUGCCUGGAAUU AACCCCGCUGUGCUUGCUCGUCCCGCCCGCAGCCCUAGGCGGCGUCG 96 ENSG000 00238734 AUCCUUUUGUAAUACAUAAGCAUAAUGAUUGGGUUUUUAUGUUCACAUGUU UGAUAUGCCUCCCUCAAAUCCUCUUAUGAUGUCGGCACAUUACCCAUCUGAG G 97 hsa-miR-6087 UGAGGCGGGGGGGCGAGC 98 MIR4498 AGGGCUGGGCUGGCAGGGCAAGUGCUGCAGAUCUUUGUCUAAGCAGCCCCUG CCUUGGAUCUCCCA 99 hsa-miR-6740-5p AGUUUGGGAUGGAGAGAGGAGA 100 hsa-miR-381-5p AGCGAGGUUGCCCUUUGUAUAU 101 hsa-miR-302e UAAGUGCUUCCAUGCUU 102 hsa-miR-4688 UAGGGGCAGCAGAGGACCUGGG 103 hsa-miR-1976 CCUCCUGCCCUCCUUGCUGU 104 ENSG000 00252128 ACUCCAUGAUGAACCCAAAAUGCCAAGUAUAUGACUGAACUUACAAGUGAUA CCAUCUUACGACUGAAGAGU 105 ENSG000 00238922 AUGCUUUUGUAGUUCGUAAGCAUGAUGAUUGGGUUUUCUUGCUCUUGUAUG AGAUGUGCCUCCGUCAUACCUUGGAAACCUGACUUGAAA 106 hsa-miR-6515-5p UUGGAGGGUGUGGAAGACAUC 107 hsa-miR-6838-3p AAGUCCUGCUUCUGUUGCAG 108 MIR892A GCAGUGCCUUACUCAGAAAGGUGCCAGUCACUUACACUACAUGUCACUGUGU CCUUUCUGCGUAGAGUAAGGCUC 109 ENSG000 00251959 ACAAGGGUUCUAAUUUCACUACAUCCCCUCCAAUAUUUGGUAUCUUUCCUUU CUUAAAAAAAUAGCCAGCCUAGUGAGUGUGAAGUGGCAUCUCAAUGUGGUUU UGAUUU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 110 ENSG000 00207100 UGCACUGCGUGGUAUCUGCACUCAGCAGUUUACUCCUGCUAGGGUGUUCAAA GGUCAGUGCCAUAGAAAUCCAGUAUCUGGUUUCAUUGGUUUUCUUGGCUUUG UGCUUGUUAAACCUGGUAUUUCUAUUGAUACAGCA 111 ENSG000 00238843 AUACUUUUGUAGGUCAUAAGCUGAGGAUUGGGUUUUCAUGCUCUUGUGUGA GAUAUGCUUCUCUCAAACCUUCUGACCUGGGCACAUUACCCAGCUAAUG 112 ENSG000 00221496 UUUCUAUAGUUUAUUACCAGAAAAGUUUCUCAGAAUGUGUAGAGCACUGGA AACCAUGAGGAAGAGGCAUAGCGUUCUCUCUUGAGCAUCAAGUUGGCUGUUG GUGUUGCUUUGCUGCAAACGCCAUUUGUCAUUGUCUUCCUUGUCUUCCUUUA GGAGAGUAAGAGGGAGAGGACACAGUCUGGGUAG 113 hsa-miR-4640-3p CACCCCCUGUUUCCUGGCCCAC 114 MIR2117 GCUCUGAUUUACUUCUGUCCGGCAUGGUGAACAGCAGGAUUGGCUGUAGCUG UUCUCUUUGCCAAGGACAGAUCUGAUCU 115 hsa-miR-642b-5p GGUUCCCUCUCCAAAUGUGUCU 116 MIR25 GGCCAGUGUUGAGAGGCGGAGACUUGGGCAAUUGCUGGACGCUGCCCUGGGC AUUGCACUUGUCUCGGUCUGACAGUGCCGGCC 117 ENSG000 00238748 AUCCUUUUGUAGUUCAUAAAUGUGAUAAUUGGGUGUUCACGUGCAUGUAUG AGAUGUCUGAGUCCCUCAAACCUUGUUACAACAUUGGUACAUUACCCAUUUU ACC 118 hsa-miR-6074 GAUAUUCAGAGGCUAGGUGG 119 MIRLET 7F2 CUAUACAGUCUACUGUCUUUCC 120 hsa-miR-6868-3p UUCCUUCUGUUGUCUGUGCAG 121 MIR4320 GACAUGUGGGGUUUGCUGUAGACAUUUCAGAUAACUCGGGAUUCUGUAGCUU CCUGGCAACUUUG 122 ENSG000 00238651 AUCCUUUUGUAGUUUAUAAGCGUGAUGACUGGGGUUUCACGUGCAUGUGUG AAAUGUGCCUUCCCCAAGCCUUGUUAUGACCUCAUUGGAACAUUACCCCUUU GACA 123 hsa-miR-6510-3p CACCGACUCUGUCUCCUGCAG 124 hsa-miR-4312 GGCCUUGUUCCUGUCCCCA 125 hsa-miR-4769-3p UCUGCCAUCCUCCCUCCCCUAC 126 hsa-miR-6728-5p UUGGGAUGGUAGGACCAGAGGGG 127 MIR5192 UUAGUUCCAGCCUCCUGGCUCACCUGGAACCAUUUCUCCUGGGAAGCAUGGU AGCCAGGAGAGUGGAUUCCAGGUGGUGAGGGCUUGGUACU 128 hsa-miR-191-5p CAACGGAAUCCCAAAAGCAGCUG 129 hsa-miR-658 GGCGGAGGGAAGUAGGUCCGUUGGU 130 hsa-miR-5584-3p UAGUUCUUCCCUUUGCCCAAUU 131 MIR 155 CUGUUAAUGCUAAUCGUGAUAGGGGUUUUUGCCUCCAACUGACUCCUACAUA UUAGCAUUAACAG 132 hsa-miR-3687 CCCGGACAGGCGUUCGUGCGACGU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 133 ENSG000 00238807 AUCCUUUUGUAGUUCAUCAGUGUCAUGAGUGGGUUUUCACGCACAUGUGUCA AAUAUGCCUCCCUCAAACUGUUACGUCAUUGGCAUAUUACCUGACGUGAAG 134 hsa-miR-5090 CCGGGGCAGAUUGGUGUAGGGUG 135 hsa-miR-3065-3p UCAGCACCAGGAUAUUGUUGGAG 136 hsa-miR-5697 UCAAGUAGUUUCAUGAUAAAGG 137 hsa-miR-3934-3p UGCUCAGGUUGCACAGCUGGGA 138 ENSG000 00200150 UGAAUCAAUGGUGACCACUGGUGGCAUAUAAGUCAUGGAUGAUGAAUAUGA GAAGAAAAGAAUCUAGGUUUUU 139 ENSG000 00238947 AUCCUUUUGUGGUUCAUCCGCCUGAUGAUUGGGUUUUCAUGCAGACGUGUGA GCUGUGCCUCCCUCAAGCCUUGUUACAACAUCCGACAUCCGCACAUUACCUG UCUGAUG 140 MIR 1289 -2 CCACGGUCCUAGUUAAAAAGGCACAUUCCUAGACCCUGCCUCAGAACUACUG AACAGAGUCACUGGGUGUGGAGUCCAGGAAUCUGCAUUUUUACCCCUAUCGC CCCCGCC 141 hsa-miR-6862-3p CCUCACCCAGCUCUCUGGCCCUCU 142 hsa-miR-487a-3p AAUCAUACAGGGACAUCCAGUU 143 MIR590 UAGCCAGUCAGAAAUGAGCUUAUUCAUAAAAGUGCAGUAUGGUGAAGUCAA UCUGUAAUUUUAUGUAUAAGCUAGUCUCUGAUUGAAACAUGCAGCA 144 hsa-miR-4646-5p ACUGGGAAGAGGAGCUGAGGGA 145 MIR4635 CCGGGACUUUGUGGGUUCUGACCCCACUUGGAUCACGCCGACAACACUGGUC UUGAAGUCAGAACCCGCAAAGUCCUGG 146 SNORD7 4 CUGCCUCUGAUGAAGCCUGUGUUGGUAGGGACAUCUGAGAGUAAUGAUGAAU GCCAACCGCUCUGAUGGUGG 147 hsa-miR-190b-5p UGAUAUGUUUGAUAUUGGGUU 148 MIR4723 AGUUGGUGGGGGAGCCAUGAGAUAAGAGCACCUCCUAGAGAAUGUUGAACUA AAGGUGCCCUCUCUGGCUCCUCCCCAAAG 149 ENSG000 00212397 AUUCUUAAAUGAAUGAUGAAAUACCAAAAAGAAAAAUAAGCAAAGAACAGA UAACAGAAAGAAGCACAGCAAAUACAACAUAAUACUGACAGUAAAAAU 150 MIR3123 AUGGAUUUGAUUGAAUGAUUCUCCCAUUUCCACAUGGAGAGUGGAGCCCAGA GAAUUGUUUAAUCAUGUAUCCAU 151 hsa-miR-29b-l-5p GCUGGUUUCAUAUGGUGGUUUAGA 152 MIR4489 GGGGGUGGGGCUAGUGAUGCAGGACGCUGGGGACUGGAGAAGUCCUGCCUGA CCCUGUCCCA 153 ENSG000 00239197 AAGCAUGGCACACUGGAUGGGCGUUCUGCUUCUCUUUAAAGAGCAUGGAUUU AUCCAUACCAUGUGACAUGAAUGAAAUGAGGAGUUUUCAGGGCUGCCAACCU CUUGGUUAAGGUUCUGUGUAGUAUAUUUCUCCUACAAUA 154 ENSG000 00252380 UGGACCAAUGAUGAGAAUAUGUCAUGAACCAAGGAAUAUGAUUAAUCCAAU UCUGUGUACUGGAGGGUCAAA 155 hsa-miR-4795-5p AGAAGUGGCUAAUAAUAUUGA 156 MIR4685 UAGCCCAGGGCUUGGAGUGGGGCAAGGUUGUUGGUGAUAUGGCUUCCUCUCC CUUCCUGCCCUGGCUAG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 157 hsa-miR-3669 ACGGAAUAUGUAUACGGAAUAUA 158 MIR3202 -2 AUUAAUAUGGAAGGGAGAAGAGCUUUAAUGCUCUGAAAAUGACUCCAAUCA UUAAAGCUCUUCUCCCUUCCAUAUUAAU 159 MIR5694 GCCAACUGCAGAUCAUGGGACUGUCUCAGCCCCAUAUGUAUCUGAAGGCUGA GAAGUCCCAUGAUCCGCACUUGGC 160 hsa-miR-6742-5p AGUGGGGUGGGACCCAGCUGUU 161 hsa-miR-22-3p AAGCUGCCAGUUGAAGAACUGU 162 hsa-miR-6734-3p CCCUUCCCUCACUCUUCUCUCAG 163 ENSG000 00252258 UCCCAUCUCUUAAAUAAAAAGAUUUUUUUUUUAAGAAGUUGUACAUGUGCA AUGGCUGCAAACAGCAGCUUCCUUGGCAGUGUGUGCAGCCUGUUUCUUGUAU GGGUUGCUCUAAGGGACCUUGGAGACAGGC 164 hsa-miR-5787 GGGCUGGGGCGCGGGGAGGU 165 hsa-miR-4442 GCCGGACAAGAGGGAGG 166 ENSG000 00199713 UCAUCAGGUGGGAUAAUCCUUACCUGUUCCUCGUUUUGGAGGGCAGAUAGAA CAGGAUAAUUGGAGUUUGCAUGAUCCAUGAUUAAUGUCUCUGUGUAAUCAG GACUUGCAAACUCUGAUUGUUCAUAUCUGAU 167 hsa-miR-27a-3p UUCACAGUGGCUAAGUUCCGC 168 hsa-miR-4667-5p ACUGGGGAGCAGAAGGAGAACC 169 hsa-miR-7641 UUGAUCUCGGAAGCUAAGC 170 MIR320C 2 CUUCUCUUUCCAGUUCUUCCCAGAAUUGGGAAAAGCUGGGUUGAGAGGGU 171 MIR6759 UAUUGUUGUGGGUGGGCAGAAGUCUGUUUUCUUCAUGGUUUUCUGACCUUU GCCUCUCCCCUCAG 172 hsa-miR-4428 CAAGGAGACGGGAACAUGGAGC 173 MIR3158 -2 AUUCAGGCCGGUCCUGCAGAGAGGAAGCCCUUCCAAUACCUGUAAGCAGAAG GGCUUCCUCUCUGCAGGACCGGCCUGAAU 174 ENSG000 00238306 AUCUUUUUGUGGUUCACAAGUGUGAUGAUUAGGUUUUCAGACUCAUGUGUG AGACAUGCCUUCCUCAAACCUUCUUAUGCUAUCAGCACAUAAUCUGGCUGAC A 175 hsa-miR-513a-3p UAAAUUUCACCUUUCUGAGAAGG 176 ENSG000 00206913 GAGCUUCCAGGAUCACCCCUGCAGAGUGGCUAAUAUUCUGCCAGCUUCGGAA AGGGAGGGGAAGCAAGCCUGGCAGAGGCACCCAUUCCAUUCCCAGCUUGCUU AGUAGCUGGCCAUGGGAAGACACUGUGCAACACUG 177 ENSG000 00207199 UCUUAGUGAUGAAAACUUUGUCCAGUUCUGCUAAUGACUUUAAGUGAUGAU AAACUAUGUCUGAGGGGA 178 ENSG000 00207094 AUCCAAGGCGAUUCCCUCUCCAAGGGGACAUCUAGUGCCCCUCUCAGGAAAG UAGCAACUUGGAAUAGAAUCUGGCAUGCCUAAGGUCUUUGAGGAACAGGGAU GCUUAUUUCCUCUGCCUUCCUUGGCUGCCUACAUAG 179 hsa-miR-3194-3p AGCUCUGCUGCUCACUGGCAGU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 180 ENSG000 00206897 UAGCAAGCCUCCAGCGUGCUUGGGUCUGCAGUGACCCCGUGGAUUCCUACAG GGCUUGCCAGAACAGUUUUGAAAUGGUUUGAGGCCUUGCCGUGCUCCAUGUA GAGCAAGGUUAUAGAAAUUUCAGACAAUG 181 hsa-miR-4639-3p UCACUCUCACCUUGCUUUGC 182 SNORD1 16-16 UGGAUCGAUGAUGACUUUCAUACAUGCAUUCCUUGGAAAGCUGAACAAAAUG AGUGAAAACUCUAUACCGUCAUCCUCGUCGAACUGAGGUCCA 183 MIR 1324 CCUGAAGAGGUGCAUGAAGCCUGGUCCUGCCCUCACUGGGAACCCCCUUCCC UCUGGGUACCAGACAGAAUUCUAUGCACUUUCCUGGAGGCUCCA 184 hsa-miR-611 GCGAGGACCCCUCGGGGUCUGAC 185 ENSG000 00221043 AAGACUCUACUCUCAGGGCUCAUUUCUGUCAUUCAAUACUAGAGAAGUUUCU CUGAAUGUUUAGAGCACUGGAAACCAAACGGAGGAGGCGGGCAUUCUUUCCU GAGCAUGCAGCCAGCUCAUAGUGUUGUUUUGUUGCAGCUGCCGCUUGCCAUU GAUGAUCCUUCUUCUCUUCCUUCAGGGGAGUAAGGAGACGACGCGGUCUUAG UGG 186 hsa-miR-4744 UCUAAAGACUAGACUUCGCUAUG 187 ENSG000 00252805 AAGACUACACUUUCAGGGAUAAUUUCUAUAGUUCAUUACUAGAGAAGUUUCU CUGAAUGUGUAGAGCACCAUAAAAUACAUUUUAUUUUUUAUUUGAGACAGG GUCUCACUCUGUCACCCAAGCUGGAGUGCAGUGG 188 MIR6500 CCUGCCUGCAGAAAGGAGCUAUCCACUCCAGGUGUCCUUUCUUCUGAGAGCU GGACACUUGUUGGGAUGACCUGCCUGCAGGUAGG 189 hsa-miR-4768-5p AUUCUCUCUGGAUCCCAUGGAU 190 MIR4419 B CUCAGGCUCAGUGGUGCAUGCUUAUAGUCCCAGCCACUCUGGAGGCUGAAGG AAGAUGGCUUGAGCCU 191 hsa-miR-7107-3p UGGUCUGUUCAUUCUCUCUUUUUGGCC 192 ENSG000 00239083 AUCCUUCUGUGGCUGAUAUGUGUGAUGAGGGGGUUUUCACACUCUUGCGUGG GACGUGCAACCUCUUUAGAACAGUGGCACAUUACCUGUCCUACA 193 MIR513C GCGUACAGUGCCUUUCUCAAGGAGGUGUCGUUUAUGUGAACUAAAAUAUAAA UUUCACCUUUCUGAGAAGAGUAAUGUACAGCA 194 hsa-miR-627-3p UCUUUUCUUUGAGACUCACU 195 hsa-miR-6780a-5p UUGGGAGGGAAGACAGCUGGAGA 196 ENSG000 00212378 AUGUAAUAAUGUUCAUCAAAUGUCUGACCUGAAAUGAGCAUGUAGACAAGU UAAUUUAACACUGAAGAA 197 MIR559 GCUCCAGUAACAUCUUAAAGUAAAUAUGCACCAAAAUUACUUUUGGUAAAUA CAGUUUUGGUGCAUAUUUACUUUAGGAUGUUACUGGAGCUCCCA 198 SNORA7 OG CUGCAGCCUAUUAAGCCAACUGAGUUCCUUUCCUCAUGGGGGGGCCCAGUGU GCAAUGGCUGCAAACAGCAGCUUCCUUGGUAGUGUAUGCAGCCUGUGUGUUG UAUUGUAUGGGUUGCUCUAAGGGACCCUGGAGACAGUC 199 hsa-miR-548v AGCUACAGUUACUUUUGCACCA 200 hsa-miR-4645-3p AGACAGUAGUUCUUGCCUGGUU 201 hsa-miR-3689d GGGAGGUGUGAUCUCACACUCG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 202 hsa-miR-4798-3p AACUCACGAAGUAUACCGAAGU 203 hsa-miR-7162-3p UCUGAGGUGGAACAGCAGC 204 hsa-miR-145-3p GGAUUCCUGGAAAUACUGUUCU 205 ENSG000 00202268 AAUUGUAUACUUUCAGGGAUCAUUCCAUAGGUUGUUACUAGAGAAGUUUUU UUAGAUGUGUAGAACACUGGAAACCACGAGGAGGAGGCGCAGCAUUCUCUCU UGACCAUGAAGCCGGCUCUUGGUGUUGUUUCAUUGCAACUGUCAUUUGCCAU UGAUGAUCGUUCUCUUCCUCUGGGAGAGUAAGAGGGAGAGGACACAGUUUGA GUGG 206 ENSG000 00238455 AUCCAUUUGUAGUUCAGAAACAUGACUAUUGUCUUUUCAAGCUUAUAUGAGA UCUGGCUCCCUCAAUCCUUGCUAUGAUAUCAGUACAUUACCUGUCUGAUG 207 MIR4420 CUCUUGGUAUGAACAUCUGUGUGUUCAUGUCUCUCUGUGCACAGGGGACGAG AGUCACUGAUGUCUGUAGCUGAGAC 208 SNORD1 15-6 GGGUCAAUGAUGAGAACCUUAUAUUGUUCUGAAGAGAGGUGAUGACUUAAA AAUCAUGCUCAAUAGGAUUACGCUGAGGCCC 209 hsa-miR-4460 AUAGUGGUUGUGAAUUUACCUU 210 hsa-miR-136-5p ACUCCAUUUGUUUUGAUGAUGGA 211 hsa-miR-466 AUACACAUACACGCAACACACAU 212 ENSG000 00201847 ACCACCAGUGAUGAGUUGAAUACUGCCCCAGUCUGAUCAACAUGCGUGAAAG AUAUUUUCUGAGCUGUG 213 hsa-miR-579-5p UCGCGGUUUGUGCCAGAUGACG 214 hsa-miR-550a-3p UGUCUUACUCCCUCAGGCACAU 215 MIR3167 GGCUGUGGAGGCACCAGUAUUUCUGAAAUUCUUUUUUCUGAAAUUCUUCAGG AAGGAUUUCAGAAAUACUGGUGUCCCGACAGCC 216 MIR676 GCAUGACUCUUCAACCUCAGGACUUGCAGAAUUAAUGGAAUGCUGUCCUAAG GUUGUUGAGUUGUGC 217 hsa-miR-2113 AUUUGUGCUUGGCUCUGUCAC 218 ENSG000 00212611 AUACAUGAUGACUUACAUGGACUCUCAUUCAGCUAAUGACUUGCUGCUGAAA CAUGGAAAUCUGAUUUUU 219 hsa-miR-6783-3p UUCCUGGGCUUCUCCUCUGUAG 220 hsa-miR-1265 CAGGAUGUGGUCAAGUGUUGUU 221 MIR4493 CCAGAGAUGGGAAGGCCUUCCGGUGAUUAUCACAGCCAUGCCUUUACCUCCA GAAGGCCUUUCCAUCUCUGUC 222 hsa-miR-4524b-3p GAGACAGGUUCAUGCUGCUA 223 ENSG000 00238475 AUCCUUUUGUAGUUCAUAAGCUUGAUGUUUGAGUUUUCACACUUACGUGUGA AAUGUGCCUCCCUUAAACCUUGUUACUACGUCAGCACAUUACCCAUGAGACA 224 SNORD1 15-1 GUGUUGAUGAUGAGAACCUUAUAUUAUCCUGAAGAGAGGUGAUGACUUAAA AAUCAUGCUCAAUAGGAUUACGCUGAGGCCC 225 MIR8085 CUAGGAGGGAUGGGAGAGAGGACUGUGAGGCAUGGGUGGCUCUAUGGUCAC GCCCAUCUUCCUAC 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 226 SNORD3 1 CUCACCAGUGAUGAGUUGAAUACCGCCCCAGUCUGAUCAAUGUGUGACUGAA AGGUAUUUUCUGAGCUGUG 227 SNORA7 3A UCCAACGUGGAUACACCCGGGAGGUCACUCUCCCCGGGCUCUGUCCAAGUGG CGUAGGGGAGCAUAGGGCUCUGCCCCAUGAUGUACAAGUCCCUUUCCACAAC GUUGGAAAUAAAGCUGGGCCUCGUGUCUGCGCCUGCAUAUUCCUACAGCUUC CCAGAGUCCUGUCGACAAUUACUGGGGAGACAAACCAUGCAGGAAACAGCC 228 MIR4435 -1 AGGCAGCAAAUGGCCAGAGCUCACACAGAGGGAUGAGUGCACUUCACCUGCA GUGUGACUCAGCAGGCCAACAGAUGCUA 229 MIR29B2 CUUCUGGAAGCUGGUUUCACAUGGUGGCUUAGAUUUUUCCAUCUUUGUAUCU AGCACCAUUUGAAAUCAGUGUUUUAGGAG 230 MIR4777 UAGAAUAUUUCGGCAUUCUAGAUGAGAGAUAUAUAUAUACCUCAUAUGUAU AUGGUAUACCUCAUCUAGAAUGCUGUAAUAUUCUA 231 MIR635 CAGAGAGGAGCUGCCACUUGGGCACUGAAACAAUGUCCAUUAGGCUUUGUUA UGGAAACUUCUCCUGAUCAUUGUUUUGUGUCCAUUGAGCUUCCAAU 232 MIR508 CCACCUUCAGCUGAGUGUAGUGCCCUACUCCAGAGGGCGUCACUCAUGUAAA CUAAAACAUGAUUGUAGCCUUUUGGAGUAGAGUAAUACACAUCACGUAACGC AUAUUUGGUGG 233 ENSG000 00201042 CCCUCCUACAAAGGCAUGUCUAUAAUUCCUUGUCUUUGGACAUGUAAGAAUU GGAGGGACAGAAAUGUGGACUUGGAGAAAUCUGGGGCCAGCUUUCUCAUCAC AGGCUCAACAUCAACCAUGCCACAUAG 234 hsa-miR-5009-3p UCCUAAAUCUGAAAGUCCAAAA 235 MIR24-2 CUCUGCCUCCCGUGCCUACUGAGCUGAAACACAGUUGGUUUGUGUACACUGG CUCAGUUCAGCAGGAACAGGG 236 hsa-miR-1296-5p UUAGGGCCCUGGCUCCAUCUCC 237 ENSG000 00238914 AUCCUUUUGUGGUUCAUUAGCUUGAUAUUGGGUUUUCACACUAUUCUAUGAG AUGUGCCUCCCUCAAAACUUGUUACAACAUUGACACAUUACCCUUCUGAUG 238 hsa-miR-544b ACCUGAGGUUGUGCAUUUCUAA 239 ENSG000 00221398 GGGGUGCACUCAGGGCAGGGGGCUUGAAGAACGGCUCCUCUGUUUACGACAC ACUCAACAGGGGUGUGAGGUCACAGUGAUGAGAGGCCCAAACUUGUGGCCUC CCCGUGAACAAAUGCCCUACACAU 240 ENSG000 00253072 UGAUGACACUCUCUGGAAUUGUUACACUACCAUAAUUAAAGUGCACUGAAUC UUUUUCUAUCUGAUGGGGGGGGAAUAAAAUAAUU 241 hsa-miR-6823-3p UGAGCCUCUCCUUCCCUCCAG 242 hsa-miR-4520a-5p CCUGCGUGUUUUCUGUCCAA 243 MIR6074 UACCAACCCCAUGGAAUUUUUACUCACCUUCAGUCAACUGAUUUGCUCUUUG GUGGAGAUAUUCAGAGGCUAGGUGGAGAUAGAGGUAGCCUUGAGGGUGGGU GUGG 244 hsa-miR-4746-3p AGCGGUGCUCCUGCGGGCCGA 245 hsa-miR-7846-3p CAGCGGAGCCUGGAGAGAAGG 246 hsa-miR-5004-3p CUUGGAUUUUCCUGGGCCUCAG 247 ENSG000 00238595 AUCCUCUUGCAGUUCAUAAGCAUGAUGAUUGGGUUUUCACACUCCUGUGUGA AAUGUACCUUCCUCAAACCUUUUUAUAACAUCAGCACAUUACCGAACAUGAA A 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 248 MIR4638 GACUCGGCUGCGGUGGACAAGUCCGGCUCCAGAACCUGGACACCGCUCAGCC GGCCGCGGCAGGGGUC 249 MIR 1296 ACCUACCUAACUGGGUUAGGGCCCUGGCUCCAUCUCCUUUAGGAAAACCUUC UGUGGGGAGUGGGGCUUCGACCCUAACCCAGGUGGGCUGU 250 MIR 1255 Bl UACGGAUGAGCAAAGAAAGUGGUUUCUUAAAAUGGAAUCUACUCUUUGUGA AGAUGCUGUGAA 251 ENSG000 00200051 GGUCAAUGAUGAGCUGACAUGUAUUCUGAAUCUAAAGUUGAUUAUUAGUAC UUUAGUUCUAGAAUUACUGAGACAUG 252 hsa-miR-4309 CUGGAGUCUAGGAUUCCA 253 hsa-miR-578 CUUCUUGUGCUCUAGGAUUGU 254 hsa-miR-3606-5p UUAGUGAAGGCUAUUUUAAUU 255 hsa-miR-6741-3p UCGGCUCUCUCCCUCACCCUAG 256 hsa-miR-133b UUUGGUCCCCUUCAACCAGCUA 257 ENSG000 00251858 UUAUUUAUCUGACAGACCUGCAGCAGUUACUGGAUGCUGUUAAAGUUUCCAC UACAGAUGCAAGAAAAGUGUCCCACACUUUCUGUCUGUCUGAUUGUGACAGC UAAGAUUAAAUCAGGUAGGACAGUA 258 ENSG000 00252256 UAUUCCAAUGAUGCAAGUGUGUCGUGAACUAAGGAUUAUGAUUAAUCCAGU UUUGUAGCUAGAGGGAUUUU 259 hsa-miR-3688-5p AGUGGCAAAGUCUUUCCAUAU 260 MIR3199 -2 GUGACUCCCAGGGACUGCCUUAGGAGAAAGUUUCUGGAAUGUCAGAACUUCC AGAAACUUUCUCCUAAGGCAGUCCCUGGAGUCAC 261 MIR548 W GGUUGGUGCAAAAGUAACUGCGGUUUUUGCCUUUCAACAUAAUGGCAAAACC CACAAUUACUUUUGCACCAAUC 262 SNORD6 0 AGUCUGUGAUGAAUUGCUUUGACUUCUGACACCUCGUAUGAAAACUGCACGU GCAGUCUGAUUAUUUAGCAAGACUGAGGCUU 263 MIR433 CCGGGGAGAAGUACGGUGAGCCUGUCAUUAUUCAGAGAGGCUAGAUCCUCUG UGUUGAGAAGGAUCAUGAUGGGCUCCUCGGUGUUCUCCAGG 264 MIR4447 GUUCUAGAGCAUGGUUUCUCAUCAUUUGCACUACUGAUACUUGGGGUCAGAU AAUUGUUUGUGGUGGGGGCUGUUGUUUGCAUUGUAGGAU 265 hsa-miR-2110 UUGGGGAAACGGCCGCUGAGUG 266 ENSG000 00207274 CUGCAGCCAAUUAAGCCAACUGAGUUCCUUUCCUUGUGGGGGCCCAGUGUGC AAUGGCUGCACACAGCAGCUUCCUUGGUAGUGUACACAGCCUGUUGGUUGUA UGGGUUGCUCUGAGGGACCUUGGAGACAGGC 267 hsa-miR-7162-5p UGCUUCCUUUCUCAGCUG 268 hsa-miR-597-3p UGGUUCUCUUGUGGCUCAAGCGU 269 MIR147B UAUAAAUCUAGUGGAAACAUUUCUGCACAAACUAGAUUCUGGACACCAGUGU GCGGAAAUGCUUCUGCUACAUUUUUAGG 270 MIR4264 AAAGCUGGAUACUCAGUCAUGGUCAUUGUAACAUGAUAGUGACAGGUACUGG GUAAGACUGCAUAG 271 hsa-miR-512-3p AAGUGCUGUCAUAGCUGAGGUC 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 272 ENSG000 00201329 AAUGCUAUACUUUCAUGGGUCAUUUCUAUAGUUUGUUAUUAGAGAAGUUUC UCUGAAUGUGUUGAGCACCAGAAACCACGAGGAGAUGCAGCAUUCUCUCCUG AACGGGAAGCCAGCUUUUGGCAUUGCUUUGAUGCAACUACCAUUUGCCAUUG AUGGCAAUGCAUCGCUUCCUCUAGGAGUGUAAGAGGGAGUGGAUGCAGUCAG AGUGG 273 MIR548A H AGGUUGGUGCAAAAGUGAUUGCAGUGUUUGCCAAUAAAAGUAAUGACAAAA ACUGCAGUUACUUUUGCACCAGCCC 274 hsa-miR-6851-5p AGGAGGUGGUACUAGGGGCCAGC 275 SNORD3 8B UCUCAGUGAUGAAAACUUUGUCCAGUUCUGCUACUGACAGUAAGUGAAGAUA AAGUGUGUCUGAGGAGA 276 MIR101- 2 ACUGUCCUUUUUCGGUUAUCAUGGUACCGAUGCUGUAUAUCUGAAAGGUACA GUACUGUGAUAACUGAAGAAUGGUGGU 277 MIR6866 CCAUUUUAGAGGCUGGAAUAGAGAUUCUUGAGGCUUGGAAGAGUAAGGAUC CCUUUAUCUGUCCUCUAG 278 hsa-miR-3193 UCCUGCGUAGGAUCUGAGGAGU 279 hsa-miR-3131 UCGAGGACUGGUGGAAGGGCCUU 280 ENSG000 00223027 CCUCAUUUUCUUGGCAGGAACUUGUAGUCCCACUCCCUGUUAUGUACAGAGG CAAAGGGAAGAGCUCUGGCCCCCUUGGCAUGUCUUUGGAGCCAUGCAGCUUC CCGUCUGCCAGUUCUAUCCUCAAGCACCAGGACACCA The Sentinel™ CS Test to identify low grade (indolent) prostate cancer

[00061] The Sentinel™ Clinically Significant (CS) Test uses a similar classification algorithm to produce a Sentinel™ CS Score that is compared to a calculated cutoff. The cutoff controls 5 sensitivity for a future patient at a desired level (95%), to distinguish between Clinically Significant cancer (GG2-GG5) (if the Sentinel™ CS Score is greater than or equal to the cutoff) and Clinically Insignificant cancer (GG1) (if the Sentinel™ CS Score is less than the cutoff). The algorithm is trained using only the subset of patients known to have prostate cancer in the dataset used to train the Sentinel™ PCa Test. Similarly, using the Classification Algorithm, 280 sncRNAs 10 were used as a basis to define an expression signature for the Sentinel™ CS Test. The Sentinel™ CS Test utilizes 280 sncRNA (identified by the Discovery CS Test, of which 135 unique sncRNAs: 130 miRNA and 66 snoRNAs are highly informative. 2026234022   16 Sep 2026 Table 2: SEQ ID NOs: 281-560 Used in the CS Test Analysis SEQ ID NO: Sequence Name Sequence 281 MIR539 AUACUUGAGGAGAAAUUAUCCUUGGUGUGUUCGCUUUAUUUAUGAUG AAUCAUACAAGGACAAUUUCUUUUUGAGUAU 282 hsa-miR-1184 CCUGCAGCGACUUGAUGGCUUCC 283 hsa-miR-562 AAAGUAGCUGUACCAUUUGC 284 ENSG000 00207215 AAGACUAUACUCUCAGGAAUCAUUUCUAUAGUUUUUUACUAGAGAAA UUUCUCUGAACGUGUAGAGCACUGGAAACCGUGAGGAGAAGCUGCCU UCUCUUCUGAGCAUGAAGUGAGCUCUCAGUGUUGCUUCUCUGCAACUG CCAUUUGCCAUUGAUGAUCGUUCUUCUCUUCCUCUGGGAGAGUAAAA GGGUACAGGAUGCAGUCUGAG 285 SNORD11 6-25 UGGAUCGAUGAUGACUUUAAAAUGGAUCUCAUCGGAAUCUGAACAAA AUGAGUGACCAAAUCACUUCUGUGCCACUUCUGUGAGCUGAGGUCCA 286 hsa-miR- 1180-5p GGACCCACCCGGCCGGGAAUA 287 hsa-miR-4452 UUGAAUUCUUGGCCUUAAGUGAU 288 hsa-miR-4770 UGAGAUGACACUGUAGCU 289 hsa-miR-30d-5p UGUAAACAUCCCCGACUGGAAG 290 MIR518C GCGAGAAGAUCUCAUGCUGUGACUCUCUGGAGGGAAGCACUUUCUGU UGUCUGAAAGAAAACAAAGCGCUUCUCUUUAGAGUGUUACGGUUUGA GAAAAGC 291 MIR4315-1 UGGGCUUUGCCCGCUUUCUGAGCUGGACCCUCUCUCUACCUCUGGUGC AGAACUACAGCGGAAGGAAUCUCUG 292 hsa-miR-590-5p GAGCUUAUUCAUAAAAGUGCAG 293 hsa-miR-30c-5p UGUAAACAUCCUACACUCUCAGC 294 SNORD11 4-30 UGGAUCAAUCAUGACUACUGGUAUUGGAUGGGUCUUCGUCAGUGAAU GCCUAUCUGGAACUCUGAGGUCCA 295 MIR4450 UGUCUGGGGAUUUGGAGAAGUGGUGAGCGCAGGUCUUUGGCACCAUC UCCCCUGGUCCCUUGGCU 296 hsa-miR-3921 UCUCUGAGUACCAUAUGCCUUGU 297 hsa-miR-580-3p UUGAGAAUGAUGAAUCAUUAGG 298 hsa-miR-3122 GUUGGGACAAGAGGACGGUCUU 299 hsa-miR-4520a-5p CCUGCGUGUUUUCUGUCCAA 300 hsa-miR-329-3p AACACACCUGGUUAACCUCUUU 301 hsa-miR-4504 UGUGACAAUAGAGAUGAACAUG 302 MIR4642 CACAACUGCAUGGCAUCGUCCCCUGGUGGCUGUGGCCUAGGGCAAGCC ACAAAGCCACUCAGUGAUGAUGCCAGCAGUUGUG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 303 hsa-miR-146b-3p UGCCCUGUGGACUCAGUUCUGG 304 ENSG000 00206731 UUCUAAAGUGUUGAGUUCAGUCCAGGGUGGAUCCCCUGCUCUGUUAA UUGAACUGGAACAUUUAAACUGGCUAGGCAAAAUGCCUACAUAGAAA GCAUUACUCUUUAUUCAUCCCCAGCCUACAAAA 305 hsa-miR-4722-5p GGCAGGAGGGCUGUGCCAGGUUG 306 hsa-miR-155-5p UUAAUGCUAAUCGUGAUAGGGGU 307 hsa-miR-5088-3p UCCCUUCUUCCUGGGCCCUCA 308 ENSG000 00238334 ACUCUUUUGUAGUUCAUAAGUGUGAUGAUUUGGUGUUCAUGUGAACA UGUGAAACGUGCCACCCUCAAACCUUGUUACAAUGUGGGCAUAUUACC CAUCUGACA 309 MIR526B UCAGGCUGUGACCCUCUUGAGGGAAGCACUUUCUGUUGUCUGAAAGA AGAGAAAGUGCUUCCUUUUAGAGGCUUACUGUCUGA 310 hsa-miR- 1914-5p CCCUGUGCCCGGCCCACUUCUG 311 hsa-miR-195-5p UAGCAGCACAGAAAUAUUGGC 312 MIR6742 GAGGGAGUGGGGUGGGACCCAGCUGUUGGCCAUGGCGACAACACCUG GGUUGUCCCCUCUAG 313 hsa-miR-323b-3p CCCAAUACACGGUCGACCUCUU 314 hsa-miR-3912-3p UAACGCAUAAUAUGGACAUGU 315 MIR203A GUGUUGGGGACUCGCGCGCUGGGUCCAGUGGUUCUUAACAGUUCAAC AGUUCUGUAGCGCAAUUGUGAAAUGUUUAGGACCACUAGACCCGGCG GGCGCGGCGACAGCGA 316 hsa-miR-1343-3p CUCCUGGGGCCCGCACUCUCGC 317 hsa-miR-6825-5p UGGGGAGGUGUGGAGUCAGCAU 318 hsa-miR-188-5p CAUCCCUUGCAUGGUGGAGGG 319 MIR378H ACAGGAACACUGGACUUGGUGUCAGAUGGGAUGAGCCCUGGCUCUGU UUCCUAGCAGCAAUCUGAUCUUGAGCUAGUCACUGG 320 ENSG000 00212626 UGCCUCUGACCUGGGUAGAGUGGCAUCUGGCUGUGACAUUCAUCUCAU AUCAGCCAGGGACAAAGCAACCCCUUGUUUAUUUCAGCUUGGCCUUUU GUCUGUGCCCAUGCCUGGUUCAUGCCUUGGACACACUA 321 ENSG000 00212580 UUCUUAUUGAGCUCCUUUCUGUCUACUGGUGGCAGUCUAUGGAUUUG CACAAGACAAAACUAGCGCUAUUUUACCUUCUGUCUUUAAACAGGUA UAUUUGACUGUUUUGUGAGAAAUUC 322 MIR599 AAAGACAUGCUGUCCACAGUGUGUUUGAUAAGCUGACAUGGGACAGG GAUUCUUUUCACUGUUGUGUCAGUUUAUCAAACCCAUACUUGGAUGA C 323 hsa-miR-4301 UCCCACUACUUCACUUGUGA 324 hsa-miR-4315 CCGCUUUCUGAGCUGGAC 325 hsa-miR-3161 CUGAUAAGAACAGAGGCCCAGAU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 326 MIR4502 AGCCUUUAGCAAGUUGUAAUCUUUUUGCUGAUGGAGGGUCUUGCCUC CAUGGGGAUGGCUGAUGAUGAUGGUGCUGAAGGC 327 SNORD23 UGCCCAGUGAUGACACCAUCCUUGCUCCCCGUGCCCCCCAGGGGCUAU GGGCGACACCAUGGCUGCCCCUGGGCUGGGCCAGUGGGGCCAAUGCCC AGGGGCUGAGGGCA 328 MIR320E GCCUUCUCUUCCCAGUUCUUCCUGGAGUCGGGGAAAAGCUGGGUUGAG AAGGU 329 ENSG000 00238545 ACCCUUCUUAGUUCAUAAGCAUGAUGAUUGGGUUUUCAUACUCAUGU GUGAGAUGUGUCUCUCUCAAACUUUGUGAAAAGUCAGCACAUGACCC AUCUGAUG 330 MIR 1294 CACCUAAUGUGUGCCAAGAUCUGUUCAUUUAUGAUCUCACCGAGUCCU GUGAGGUUGGCAUUGUUGUCUGGCAUUGUCUGAUAUACAACAGUGCC AACCUCACAGGACUCAGUGAGGUGAAACUGAGGAUUAGGAAGGUGUA 331 MIR519C UCUCAGCCUGUGACCCUCUAGAGGGAAGCGCUUUCUGUUGUCUGAAAG AAAAGAAAGUGCAUCUUUUUAGAGGAUUACAGUUUGAGA 332 ENSG000 00212391 UGUUCUGACAUGGGAAGAGUAGCUUCUGGUUGGUGGAGCCCAUCUCA CAUUAGCCAGAGACAAAGCAACACCUUGUUUAUCCCGGCUUGGCUUUU GGCCUGUGUCCAUGACUGGUCCAUACCUUGGACACAUGG 333 ENSG000 00238436 AUCCUUUUGUGGGUCAUAUGCAUGAUGAUUGGGUGUUCACGCACAAG UAUGAGAUGUGCCACCUUUUUACAGCAUUGGCACAUUACCUGUCUGA UG 334 hsa-miR-4431 GCGACUCUGAAAACUAGAAGGU 335 ENSG000 00238948 AUCUUUUUGUGGUUCAUAAGCAUGAUGAUUGGGUUUUCAUACCAUUG UGUAAGAUGUGCCUUUCUCAGACCUUGCCAAAACACUGGCACAUUACC UGUCUGAUA 336 MIR518E UCUCAGGCUGUGACCCUCUAGAGGGAAGCGCUUUCUGUUGGCUAAAA GAAAAGAAAGCGCUUCCCUUCAGAGUGUUAACGCUUUGAGA 337 ENSG000 00201500 UGGACCAAUGAUGAUGACUGGUGGUGUAUGAGUUAAAGGUGAUGAAU AGUAAGUGUCUUUGUUAGUGGCAAGUUCAGA 338 MIR3915 CAAGUUGGCACUGUAGAAUAUUGAGGAAAAGAUGGUCUUAUUGCAAA GAUUUUCAAUAAGACCAUCCUUUCCUCAAUAUUCUGUGGUGUCAUCU UUG 339 hsa-miR-3170 CUGGGGUUCUGAGACAGACAGU 340 hsa-miR-99a-5p AACCCGUAGAUCCGAUCUUGUG 341 hsa-miR-4501 UAUGUGACCUCGGAUGAAUCA 342 hsa-miR-524-3p GAAGGCGCUUCCCUUUGGAGU 343 hsa-miR-33a-3p CAAUGUUUCCACAGUGCAUCAC 344 MIR4669 GCCUCCCUUCACUUCCUGGCCAUCCAGGCAUCUGUGUCUGUGUCCGGG AAGUGGAGGAGGGC 345 MIR 1197 ACUUCCUGGUAUUUGAAGAUGCGGUUGACCAUGGUGUGUACGCUUUA UUUGUGACGUAGGACACAUGGUCUACUUCUUCUCAAUAUCA 346 ENSG000 00252740 UGGAUCAAUGAUGACAAAGUAUCAUGAAUGAGGGAUUGUGAAUAAUC UAUUUUUAUGAACCUGUGGUCAAAU 347 ENSG000 00238368 AUCCUUUUGUGGUUCAUAAGCAUGAUGAUUAGAUUUUCAUGCUAUUG GGUGAGAUAUGCCUUCCUCAGACUUUGUUACAGCAUAGGCACAUUAC AACCUGUCUGAUA 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 348 MIR4673 GUCCAGGCAGGAGCCGGACUGGACCUCAGGGAAGAGGCUGACCCGGCC CCUCUUGCGGC 349 MIR4720 AAGCCUGGCAUAUUUGGUAUAACUUAAGCACCAGGUAAAAUCUGGUG CUUAAGUUGUACCAAGUAUAGCCAAGUUU 350 ENSG000 00212321 AAGGCUAUACUUUCAGGGAUCAUUUUUAUAGCUUAUUACUAGAGGAG UUAAUGUGAAUGUGUAGAGCACCAGAAACCUUGAGGAGGAGGUGCAG CGUUCUCUCCUGAGCAUAAAGCUGGCCCGCAGUAUUGUGUUGCCUCAC UGCAACUGCCAUUUGCCAUUGAUGAUGAUUGUUCUCUUUCACUGAGA GAGUAAGAGGACAGGAUGCAUUCUAACUGG 351 hsa-miR-410-5p AGGUUGUCUGUGAUGAGUUCG 352 hsa-miR-7154-5p UUCAUGAACUGGGUCUAGCUUGG 353 hsa-miR-3943 UAGCCCCCAGGCUUCACUUGGCG 354 ENSG000 00238530 AUCCUUUUGUAGUUCAUAAGGAUGAUGACUGAGUGUUCACACUCGUG UGUGAGAUGUGCCACCCUCAGACCUUGAAAUCUUCAGUCACUCUUGUU AAGUGAAC 355 hsa-miR-4448 GGCUCCUUGGUCUAGGGGUA 356 hsa-miR-6816-3p GAAGGACCUGCACCUUCG 357 hsa-miR-30e-3p CUUUCAGUCGGAUGUUUACAGC 358 MIR525 CUCAAGCUGUGACUCUCCAGAGGGAUGCACUUUCUCUUAUGUGAAAA AAAAGAAGGCGCUUCCCUUUAGAGCGUUACGGUUUGGG 359 hsa-miR-548aw GUGCAAAAGUCAUCACGGUU 360 hsa-miR-4509 ACUAAAGGAUAUAGAAGGUUUU 361 hsa-miR-6775-3p AGGCCCUGUCCUCUGCCCCAG 362 hsa-miR-503 -5p UAGCAGCGGGAACAGUUCUGCAG 363 SNORD58 B CUGCGAUGAUGGCAUUUCUUAGGACACCUUUGGAUUAAUAAUGAAAA CAACUACUCUCUGAGCAGC 364 hsa-miR-8065 UGUAGGAACAGUUGAAUUUUGGCU 365 ENSG000 00200545 AAGACUGUGCUUUCAGGGAUCAUGUCUAUAGUUUGCCACUAGAGAAG UUUUUUUGAACAUGUAGUAGGGCACCAGAAGCACAAGGAAGAGGCAC AGCCUUCUCUCCUGAGCAUGAAUCUGGCUCUUGGUCUUGCUUUGUUCC AGCUACCAUUUGCCAUUGAUUAUGUCCUUCUCUUCCUUCCAGAAAGUA AAAGGGAGAGAAUGCAGUCUGAGUGG 366 ENSG000 00238801 AUCCUUUUGUAGUUCAUAAGCUGAUGGUUGGGUUUUCACGCUCAUGU GUGAGAUGUGUUCCUUCAUAUCUGUCAACACACUACCGGGCUGUUG 367 hsa-miR-6864-5p UUGAAGGGACAAGUCAGAUAUGCC 368 hsa-miR-5587-5p AUGGUCACCUCCGGGACU 369 hsa-miR-3928-3p GGAGGAACCUUGGAGCUUCGGC 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 370 hsa-miR-1537-5p AGCUGUAAUUAGUCAGUUUUCU 371 MIR 182 GAGCUGCUUGCCUCCCCCCGUUUUUGGCAAUGGUAGAACUCACACUGG UGAGGUAACAGGAUCCGGUGGUUCUAGACUUGCCAACUAUGGGGCGA GGACUCAGCCGGCAC 372 ENSG000 00253047 CAAAGUUCUGGAAUUACAGGUGUGAGCCACCGUGCCCAGCAUUUAAA AUUUUAAUAUGUACUUUUUGCAACCCAGAACUCAUUGUUCAGUAUGA GUUUUGAUACAUAUAAGAAGGGAUAUU 373 hsa-miR-548e-3p AAAAACUGAGACUACUUUUGCA 374 hsa-miR-8082 UGAUGGAGCUGGGAAUACUCUG 375 hsa-miR-4638-5p ACUCGGCUGCGGUGGACAAGU 376 MIR8089 AAGGAGCACUCACUCCAAUUUCCCUGGACUGGGGGCAGGCUGCCACCU CCUGGGGACAGGGGAUUGGGGCAGGAUGUUCCAG 377 ENSG000 00238339 AUCCUUUUGUAGUUUAUAAGCAUGAUGAUGGGUGCUCACACUCAUCU GAGAUGUGUCUCCCUCUAAGCCUUGUAACAACAUCAGCACGUUACCCU UCUGAUG 378 hsa-miR-6848-3p GUGGUCUCUUGGCCCCCAG 379 ENSG000 00239144 AGUUUAAAAAAUUUGUUAAGCAUGAUGAUUAACUUUUCACAAUAAUG CAAUAAUGUGUGAGCUAUGCCUCUCUCAAACCUUAUUAUGAUGUUGG CCCAUUACCCAUCUGAUG 380 hsa-miR-5087 GGGUUUGUAGCUUUGCUGGCAUG 381 MIR5583-1 AAACUAAUAUACCCAUAUUCUGGCUAGGUGAUCAUCAGAAUAUGGGU AUAUUAGUUUGG 382 MIR8082 GCCUGUGUGAUGAUGGAGCUGGGAAUACUCUGGGGAGAGAGUCCUCU UUUCAGCUGUAUUUUGCUUCCUUCCCACACAGAC 383 hsa-miR-4639-5p UUGCUAAGUAGGCUGAGAUUGA 384 hsa-miR-548d-5p AAAAGUAAUUGUGGUUUUUGCC 385 ENSG000 00252576 UUAAAUGAUGAUUUUUUUAAACAAAUGUAUCAGAGUGCAUUCAUUCA AAGGAAUGUUGUCUUCUGGCAAGUAAAAAUCCAUGCAG 386 SNORD19 B UUUUGGUUGAAAUAUGAUGAGUGUACAAAAUCUUGAUUUAAGUGAAU GAAAAAUUACAAGAUCCAACUCUGAUUUCAGCCAGAG 387 ENSG000 00238666 AUCCUUUUGUAGUUCAUGAGUGUAAUGAUUGAGUGUUCAUGCACAUG UGUGAGAUAUGCCACCCUUGAACCUUGUUACACCGUUGUCACAUUGCC CGUUUGACA 388 ENSG000 00199363 AGGCAGGAUCUAGUUACAUUGUAGCUGUGAAGUGCUGCAUUGUCUUU GCCCCCUGCUCAAAAUAAAACUGUUACCUUUCAAGCCCUGUCUGCCAU GGUGCUGUAGCAGCAGGGAUGUUUGGUCUCAUACAU 389 hsa-miR-98-3p CUAUACAACUUACUACUUUCCC 390 hsa-miR-5682 GUAGCACCUUGCAGGAUAAGGU 391 MIR7114 UCCGCUCUGUGGAGUGGGGUGCCUGUCCCCUGCCACUGGGUGACCCAC CCCUCUCCACCAG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 392 SNORA16 A UUGGCCCUUAUCGAAGCUGCAGCUGCUUCCGCAUAGCUGCUGUGGUCA AAAAGGAGCCCAGAGUGACAGUUUUCCUUGACGGUCGCCGUUCUGUU UGUUGUAACUGAUCUGCAACAUUUUGGGAAAAUACAGUU 393 hsa-miR-134-5p UGUGACUGGUUGACCAGAGGGG 394 hsa-miR-4475 CAAGGGACCAAGCAUUCAUUAU 395 hsa-miR-6814-5p UCCCAAGGGUGAGAUGCUGCCA 396 MIR323B UGGUACUCGGAGGGAGGUUGUCCGUGGUGAGUUCGCAUUAUUUAAUG AUGCCCAAUACACGGUCGACCUCUUUUCGGUAUCA 397 MIR4266 CCACUGCUGGCCGGGGCCCCUACUCAAGGCUAGGAGGCCUUGGCCAAG GACAGUC 398 hsa-miR-4762-3p CUUCUGAUCAAGAUUUGUGGUG 399 ENSG000 00252525 AAGCUUAUGAUGACGUAAGUGUGACGACAUUGGGUUUUCACGUUCAU GUGUGAGAUGUGCCUCCCUCAAGCCUUAUUACAAUGCCAGUACAUUUU UUUUCCACAUCUGAUG 400 MIR4742 UCAGGCAAAGGGAUAUUUACAGAUACUUUUUAAAAUUUGUUUGAGUU GAGGCAGAUUAAAUAUCUGUAUUCUCCUUUGCCUGCAG 401 hsa-miR-6789-3p CGGCGCCCGUGUCUCCUCCAG 402 hsa-miR-7843-3p AUGAAGCCUUCUCUGCCUUACG 403 hsa-miR-548ar-5p AAAAGUAAUUGCAGUUUUUGC 404 ENSG000 00238732 AUCUUUUCGUAGUUCAUAAGUGUGAUGACUGGGUAUUCAUGCAUGUA UGUGGGAUAUGCCACCCUUGACCCUUGUUACAACAUUAGCACAUUAAC CAUCUGACA 405 MIR8052 UGGAGGGCUGCGGGACUGUAGAGGGCAUGAGCUCAGGAGCUCAGGCC AGCUCAUGGUGCAAGGCCUCUG 406 SNORD11 4-11 UGGACCAGUGAUGGUGACUGGUGGUGUGUGAGUCAUGCACAGUGAAU AUCAUGUGUCUGGAACUCUGAGGUCCA 407 hsa-miR-4524a-5p AUAGCAGCAUGAACCUGUCUCA 408 hsa-miR-3606-3p AAAAUUUCUUUCACUACUUAG 409 SNORD54 UGGCGAUGAGGAGGUACCUAUUGUGUUGAGUAACGGUGAUAAUUUUA UACGCUAUUCUGAGCC 410 SNORD32 A GUCAGUGAUGAGCAACAUUCACCAUCUUUCGUUUGAGUCUCACGGCCA UGAGAUCAACCCCAUGCACCGCUCUGAGA 411 MIR3529 GGCACCAUUAGGUAGACUGGGAUUUGUUGUUGAGCGCAGUAAGACAA CAACAAAAUCACUAGUCUUCCAGAUGGGGCC 412 MIR520G UCCCAUGCUGUGACCCUCUAGAGGAAGCACUUUCUGUUUGUUGUCUGA GAAAAAACAAAGUGCUUCCCUUUAGAGUGUUACCGUUUGGGA 413 MIR4330 AAUUGUCAGCAGGCAAUUAUCUGAGGAUGCAGGAGAGGAAGGGGGCU UCUUUUUGACGCCUACUUCAUCAGCUGCUCCUCAGAUCAGAGCCUUGC AGGUCAGGCC 414 SNORD97 UUGCCCGAUGAUUAUAAAAAGACGCGUUAUUAAGAGGACUUUAUGCU GGAGUUCUUGACGUUUUUCUCUCUUUUCUAUACUUCUUUUUCUUUCU UUGAAUGUCCAGCGUCCUGUGAGCGAAGAUUAUGAGAUAUGAGGGCA A 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 415 MIR640 GUGACCCUGGGCAAGUUCCUGAAGAUCAGACACAUCAGAUCCCUUAUC UGUAAAAUGGGCAUGAUCCAGGAACCUGCCUCUACGGUUGCCUUGGG G 416 hsa-miR-29a-3p UAGCACCAUCUGAAAUCGGUUA 417 MIR5739 GGUUGGCUAUAACUAUCAUUUCCAAGGUUGUGCUUUUAGGAAAUGUU GGCUGUCCUGCGGAGAGAGAAUGGGGAGCCAGG 418 MIR24-2 CUCUGCCUCCCGUGCCUACUGAGCUGAAACACAGUUGGUUUGUGUACA CUGGCUCAGUUCAGCAGGAACAGGG 419 hsa-miR-6774-3p UCGUGUCCCUCUUGUCCACAG 420 MIR 1224 GUGAGGACUCGGGAGGUGGAGGGUGGUGCCGCCGGGGCCGGGCGCUG UUUCAGCUCGCUUCUCCCCCCACCUCCUCUCUCCUCAG 421 hsa-miR-6787-3p UCUCAGCUGCUGCCCUCUCCAG 422 hsa-miR-412-3p ACUUCACCUGGUCCACUAGCCGU 423 hsa-miR-4293 CAGCCUGACAGGAACAG 424 MIR577 UGGGGGAGUGAAGAGUAGAUAAAAUAUUGGUACCUGAUGAAUCUGAG GCCAGGUUUCAAUACUUUAUCUGCUCUUCAUUUCCCCAUAUCUACUUA C 425 ENSG000 00238294 AUUCUUUAGUAGUUCAUAAUGCUAUGAUUGGGUUUCCAUGUGCACAU GUAAGAUGUGCCUCUCUCAAGCCUUGUUGUGACAUCAGCACAUUACCC AUCUGAUG 426 ENSG000 00252458 CUCAUACCUAAACCCAAGAAUCACUUUCUUAUAGUGAUGAUUUAAAC AGAUGCAAACAGCGAGCACAUCUUGUCACCUUUGCGGGACUGUGGCUG UGCCCCUCGCAGUAAAUUUGGAGGUUCUACAUCC 427 MIR3171 UAUAUAUAGAGAUGUAUGGAAUCUGUAUAUAUCUAUAUAUAUGUGUA UAUAUAGAUUCCAUAAAUCUAUAUAUG 428 ENSG000 00253068 UGGAACAAUGAUGAGAGUGUGUCAUGAACCAAGGUUAUGAUUAAUCU AGUUCUGUGCAUCUGAAAUCCGUU 429 hsa-miR-4316 GGUGAGGCUAGCUGGUG 430 MIR379 AGAGAUGGUAGACUAUGGAACGUAGGCGUUAUGAUUUCUGACCUAUG UAACAUGGUCCACUAACUCU 431 ENSG000 00239015 AUCCUUUAGUUCUUAAACAUGACAAUUGGAUGUUUAUGCAUAUGUGU GAGAUGUGUCACCCUUGAACCUUGUUACCAUGUCUGCACAUUACCUAU CUGACA 432 MIR4645 UGAUAGGGAAACCAGGCAAGAAAUAUUGUCUCCUCAAGUUGCGACGA GACAGUAGUUCUUGCCUGGUUUCUCUAUCA 433 hsa-miR-3973 ACAAAGUACAGCAUUAGCCUUAG 434 MIR 184 CCAGUCACGUCCCCUUAUCACUUUUCCAGCCCAGCUUUGUGACUGUAA GUGUUGGACGGAGAACUGAUAAGGGUAGGUGAUUGA 435 ENSG000 00221083 ACAGACUCACUUUGCACCUGGCUGCAGCCUCAUGGGGGUGCUUUUUCC AUGUGCCAGGGAAACAUUCUGGGGUGUUGUGGCUGCCUGACCUAUCA AGGGUGAUGCAGCUGUCUGGGGAUACAGGA 436 ENSG000 00207171 GGCUUGCUGGUGCUUACCACAGGCUGAAUUCUUACACUGACUAUAUA GAAAAGGAGGUAGAGUAAACCUACCCAAUAUACCCCUCAGCCCAGGCU CUGUGCCUGAUCUAUAUUGUGAAUGUGGGAACAUAG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 437 SNORA20 CUUCCCAUUUAUUUGCUGCUUGUAGUCUCACAGUGAUACGAGCAGUU AUACGCAUGGGAUAAAAUAACAUUGGGCCACUGUAAAUUGAGAUGAA GUAACCAUUUUCAUCUCUUCUGCAGGGACUAGACAUUG 438 MIR5002 UCUUCCUCUCUGUCCUCUGGAAUUUGGUUUCUGAGGCACUUAGUAGG UGAUAGCAUGACUGACUGCCUCACUGACCACUUCCAGAUGAGGGUUAC UC 439 MIR649 GGCCUAGCCAAAUACUGUAUUUUUGAUCGACAUUUGGUUGAAAAAUA UCUAUGUAUUAGUAAACCUGUGUUGUUCAAGAGUCCACUGUGUUUUG CUG 440 MIR302A CCACCACUUAAACGUGGAUGUACUUGCUUUGAAACUAAAGAAGUAAG UGCUUCCAUGUUUUGGUGAUGG 441 ENSG000 00223111 UCCAGCAGUAGUCAGCUGUCUGGACAGAACCAUUCCUGGGAUCAUGUU ACACUGCUGGGAGAAGAAUGUCUUCUCUUCAUCCAGUUGCGUCCAUCA CUGUUCUGGUGGUGUCUGGCACUGGUGCAAGGCAGAACUGUGCUUCC UUGAGAGUGUGCUGAGCAUUCACCUUGGCUGCUUGGUUCUAGUCUAG GAGCAGACACA 442 hsa-miR-375 -3p UUUGUUCGUUCGGCUCGCGUGA 443 MIR3678 GAAUCCGGUCCGUACAAACUCUGCUGUGUUGAAUGAUUGGUGAGUUU GUUUGCUCAUUGAUUGAAUCACUGCAGAGUUUGUACGGACCGGAUUC 444 SNORD11 5-1 GUGUUGAUGAUGAGAACCUUAUAUUAUCCUGAAGAGAGGUGAUGACU UAAAAAUCAUGCUCAAUAGGAUUACGCUGAGGCCC 445 hsa-miR- 518f-5p CUCUAGAGGGAAGCACUUUCUC 446 MIR 16-2 GUUCCACUCUAGCAGCACGUAAAUAUUGGCGUAGUGAAAUAUAUAUU AAACACCAAUAUUACUGUGCUGCUUUAGUGUGAC 447 MIR3691 UUGAGGCACUGGGUAGUGGAUGAUGGAGACUCGGUACCCACUGCUGA GGGUGGGGACCAAGUCUGCGUCAUCCUCUCCUCAGUGCCUCAA 448 hsa-miR-3622b-5p AGGCAUGGGAGGUCAGGUGA 449 MIR4441 CAGAGUCUCCUUCGUGUACAGGGAGGAGACUGUACGUGAGAGAUAGU CAGAUCCGCAUGUUAGAGCAGAGUCUCCUUCGUGUACAGGGAGGAGA UUGUAC 450 hsa-miR-510-3p AUUGAAACCUCUAAGAGUGGA 451 MIR454 UCUGUUUAUCACCAGAUCCUAGAACCCUAUCAAUAUUGUCUCUGCUGU GUAAAUAGUUCUGAGUAGUGCAAUAUUGCUUAUAGGGUUUUGGUGUU UGGAAAGAACAAUGGGCAGG 452 hsa-miR-1271-5p CUUGGCACCUAGCAAGCACUCA 453 SCARNA4 ACUGGAGGACUAAGAAGGCUGAGUCUGAUGAAGUAAGACUUUGCUGA UACAUUCCUCCUAGAAAAAAGGGUUGGAGAGAGCAGCCUUCACUGAA GAGUAUCACAGGGCUGACUGUACUACCCAACACUC 454 MIR302E UUGGGUAAGUGCUUCCAUGCUUCAGUUUCCUUACUGGUAAGAUGGAU GUAGUAAUAGCACCUACCUUAUAGA 455 hsa-miR-6790-3p CGACCUCGGCGACCCCUCACU 456 MIR3613 UGGUUGGGUUUGGAUUGUUGUACUUUUUUUUUUGUUCGUUGCAUUUU UAGGAACAAAAAAAAAAGCCCAACCCUUCACACCACUUCA 457 SNORD57 UGGAGGUGAUGAACUGUCUGAGCCUGACCUUGUAGAAUGGAGGCAAA AAAACUGAUUUAAUGAGCCUGAUCC 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 458 hsa-miR-3146 CAUGCUAGGAUAGAAAGAAUGG 459 hsa-miR-222-5p CUCAGUAGCCAGUGUAGAUCCU 460 MIR 13025 UGCCCGGCCUCCCAUUAAAUUGGUUUUUCAGACAAAUCACAAAUUUGU UUAGGUAUAAGUAUAUCCCAUGUAAUCUUUGGGACAUACUUAUGCUA AAAUAAUUGUUCCUUGUUGAUUGGAAAUUUUAAUUUUAAUUAGGUGU CCUGUAUU 461 hsa-miR-6854-3p UGCGUUUCUCCUCUUGAGCAG 462 SNORA15 GCAUGGCCGAAUACUGUGUUUUUAUCAGUAGUUUACACAGCCAGACA CCAUGCAAAAGCAGUCUUCCCUUUAGAAUGACUGAUGGUAUGCUAAG GUUUUUCAUAGCAUAUCAUUAUUAAAGGUGAAUACAAAU 463 hsa-miR-3529-3p AACAACAAAAUCACUAGUCUUCCA 464 MIR106B CCUGCCGGGGCUAAAGUGCUGACAGUGCAGAUAGUGGUCCUCUCCGUG CUACCGCACUGUGGGUACUUGCUGCUCCAGCAGG 465 SNORD11 5-39 GGGUCAAUGAUGAGAAUCUUAUAUUGUCCUGAAGAGAGGUGAUGACU UAAAAAUCAUGCUCAAUAGGAUUACGCUGAGGCCC 466 hsa-miR-590-3p UAAUUUUAUGUAUAAGCUAGU 467 ENSG000 00199787 GCAUGGGUUUGGAUUUAUGAUGGGCUGGAUUCCCUAGGCCUCUCAUA GUACCCCAUGCCAGAGCAAACUGUAGCCCCAACCAUUGCCGGGCCUCU AUGCCUGUAGGCUGCUGGCACUGAAGUGGGUUGCACAGUA 468 hsa-miR-3667-3p ACCUUCCUCUCCAUGGGUCUUU 469 SNORD15 A CUUCGAUGAAGAGAUGAUGACGAGUCUGACUUGGGGAUGUUCUCUUU GCCCAGGUGGCCUACUCUGUGCUGCGUUCUGUGGCACAGUUUAAAGAG CCCUGGUUGAAGUAAUUUCCUAAAGAUGACUUAGAGGCAUUUGUCUG AGAAGG 470 hsa-miR-3657 UGUGUCCCAUUAUUGGUGAUU 471 MIR 129-2 UGCCCUUCGCGAAUCUUUUUGCGGUCUGGGCUUGCUGUACAUAACUCA AUAGCCGGAAGCCCUUACCCCAAAAAGCAUUUGCGGAGGGCG 472 hsa-miR-4515 AGGACUGGACUCCCGGCAGCCC 473 hsa-miR-142-3p UGUAGUGUUUCCUACUUUAUGGA 474 MIR6802 GAGGGCUAGGUGGGGGGCUUGAAGCCCCGAGAUGCCUCACGUCUUCAC CCCUCUCACCUAAGCAG 475 hsa-miR-635 ACUUGGGCACUGAAACAAUGUCC 476 hsa-miR-5007-3p AUCAUAUGAACCAAACUCUAAU 477 MIR6835 UGAUGAGGGGGUAGAAAGUGGCUGAAGCGAGAUGUUUGUCUAAAAGC ACUUUUCUGUCUCCCAG 478 SNORD96 B CCUGGUGAUGACAGACGACAUUGUCAGCCAAUCCCCAUGUGGUAGUGA GGACAUGUCCUGCAGUUCUGAAGG 479 MIR4755 AGAUUCAGCUUUCCCUUCAGAGCCUGGCUUUGGCAUCUAUGAAAGCCA GGCUCUGAAGGGAAAGUUGAAUCU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 480 ENSG000 00238488 AUCAUUUUGCAGCUUAUACAUGUGAUGACUGGGUUUUUUAACUCAUA AGUGAGAUGUGCCUUUCUUACAUCUUAUUAUGACAUUAGUACAUUAC CCAUUUGAUA 481 ENSG000 00238624 AUCCUUCUGUAGUUUAUGAGUGUGAUGAUUGGCUGUUCAUGUGCAUG UAUGAGAUGUGCCACCCUUGAACCUUGUCAUGUCUGAUGUGAAA 482 hsa-miR-4287 UCUCCCUUGAGGGCACUUU 483 ENSG000 00252154 UAGACCAGUGAUGAGAAUCUGUCAUGAACCAAGGAGUAUUAUUAAUC UAAUUCUGUUUACCUGAGAGUUUUAAA 484 SNORD91 B AAGAGCCAAUGAUGUUUUUAUUCAAAAUGUCUGAACCUGUCUGAAGC AUCCCAGUGAUGCAACUUCUGUGUGAUACUGAGGCUUUU 485 ENSG000 00238769 AUCCUUUUGUGGUUCAUAAGCAUGAUGAUUGGGUUUCCACAUUCUUG UGAGAUGGGCCUCCCUCCAACCUUGUUAUGAUGUCAGCACAUUACCCU UGACG 486 MIR3126 AUGAUUAUAUGAGGGACAGAUGCCAGAAGCACUGGUUAUGAUUUGCA UCUGGCAUCCGUCACACAGAUAAUUAU 487 hsa-miR-3907 AGGUGCUCCAGGCUGGCUCACA 488 hsa-miR-4719 UCACAAAUCUAUAAUAUGCAGG 489 MIR612 UCCCAUCUGGACCCUGCUGGGCAGGGCUUCUGAGCUCCUUAGCACUAG CAGGAGGGGCUCCAGGGGCCCUCCCUCCAUGGCAGCCAGGACAGGACU CUCA 490 SNORD11 4-14 UGGACCAAUGAUGACAACUGCCGGCGUAUGAGUGUUGGGUGAUGAAU AAUACGUGUCUAGAACUCUGAGGUCCA 491 MIR4757 UUCCAGCCCGAGGCCUCUGUGACGUCACGGUGUCUGCGGGAGGAGACC AUGACGUCACAGAGGCUUCGCGCUCUGAG 492 MIR4738 GGUCGCAUUUCUCCUUCUUACCAGCGCGUUUUCAGUUUCAUAGGGAAG CCUUUCCAUGAAACUGGAGCGCCUGGAGGAGAAGGGGCC 493 hsa-miR-6859-3p UGACCCCCAUGUCGCCUCUGUAG 494 MIR548A V AAAAGUACUUGCGGAUUUGCCAUCACCUUUACCUUUAAUGGCAAAAC UGCAGUUACUUUUGC 495 MIR4733 GGUCGCUUAAAUCCCAAUGCUAGACCCGGUGGCAAUCAAGGUCUAGCC ACCAGGUCUAGCAUUGGGAUUUAAGCCC 496 MIR3202-2 AUUAAUAUGGAAGGGAGAAGAGCUUUAAUGCUCUGAAAAUGACUCCA AUCAUUAAAGCUCUUCUCCCUUCCAUAUUAAU 497 ENSG000 00252728 UGCACUGAUGACAGUGAACCAUAAACCAAGAAUUAUGAUUUAUCCAG UUCUAUGAAUCUUAAGUCCAUU 498 ENSG000 00238767 AUCCUUUUGUAGUUCAUAAGCGUGAUGACUGUGGUUUCAUGCUUGUG UGUGAGAGAUGGGUGGGCCUCCCUCAAACCUUGUUACGACGUAGGCCC AUUACCCAUCUGACA 499 ENSG000 00199977 CCAAUGUGUAAUAUCCUGGGAUAUCAUUUUUUCUAGGCUUUGUCCAC AUGGCUUAGGGGAGCAUAGGGCUCUGCCCCAUGAUGUACAGUCCCUUU CCUCAGUGUUGGAGAUGAAGCUGGGUCUGGUGUUUGCACUUUCAUAU UCCUGUAGCUUCUCAGAAUCCUGUGGACAGUGACUGGGGAGACAAACC AUGCAGGAAAUAUGU 500 ENSG000 00238564 AUCCUUCUAUAUAGUUCAUAAGCUUGAUGAUCGGUGUUCACACACAU GUGAGAUACGCCACCUGUGAACCUUGUUAGGACAUCAGCACAUUACCC AUCUGACA 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 501 ENSG000 00238970 AUCCUUUUGUAGUUCAUAAGCACAAUGAUUGAAUUUUCAUGCUCAUG UGUGAGAUAUGCCUCACUCCAGUCUUGUUACAGUGUUAGCACAUUACC UAUCUGAUA 502 ENSG000 00252298 UGGACAAAUGAUUAGAUUAGAUUGUGUUAUAAACCAAAGAUUAUAGU UAUUCCAAUUAUGUGCAUUUGAGAUCCACU 503 ENSG000 00200398 CAGUCAGUGUCGAGAACCUUAUAUUGUUCUGAAGAGAGGUGGUGACU UAAAAAUCAUGCUCAAUAGGAUUACGCUGAGGCCC 504 SNORD11 6-16 UGGAUCGAUGAUGACUUUCAUACAUGCAUUCCUUGGAAAGCUGAACA AAAUGAGUGAAAACUCUAUACCGUCAUCCUCGUCGAACUGAGGUCCA 505 ENSG000 00212445 UGUCCUUGACUUGGGUAGAGUGAUGUCUGGUUGGUGCUGCCUAUCUC AUAUAAGCCAGGGACAAAUCAAUGCCUUAUUUAUUCCAGCUUGGCUU UUGGUCUGUGCCCAUACCUGGUUUAUGCCUUGGACACAUGG 506 MIR548I3 CAGAUGGCUCCGAAGUUUACAUCCUAUUAGGUUUGUGCAAAAGUAAU UGCGGAUUUUGCCAUUAAAAGUAAUGGCAAAAAUAGCAAUUAUUUUU GUACCAGCCUAGUAUCUUUUCUCCUUCUACCAAACUUUGUCCCUGAGC CAUCUCA 507 ENSG000 00199851 AAGACUAUACUUUCAGGGAUCAUUUCUACAUUUCCGGGUAAUUUCUU UGAACAUGUGGAGCACCGGAAACCACCAGGAGGAGGCACAGCAUUUUC UCUGGAGCGUGAAGCCAGUUCUUGGUGUUGCUGCAUAGCAACUGCCA UUUGCCUUUGAUGAUCAUUCUUCUUUUCCUUUAGGAGAAUAAGAGGG GGAGAACCCAGUCUGAGGG 508 MIR6776 CGGGCUCUGGGUGCAGUGGGGGUUCCCACGCCGCGGCAACCACCACUG UCUCUCCCCAG 509 hsa-miR-3149 UUUGUAUGGAUAUGUGUGUGUAU 510 ENSG000 00201701 AGGUCAUUUCAAAGAGGUCUUGUGAGGCUGUGAAACCAAGAGCUCUU AACACUGCGACCAAAGAUGGAAGUUCUCUAUAGGAUGCCAUGGCAUU UGAUGGUGCUAUGUUUUCUUGAGGAGAUAUAAGA 511 hsa-miR-4691-3p CCAGCCACGGACUGAGAGUGCAU 512 ENSG000 00238327 AUCCCUUUAUAGUUCCCGAGCAUGACGAUUGGGUGUUCACAUGCAUG UGUGAGAUGUACCACCCUCGCAUCUUGUUAGACGUUGGCACAUUACCC GUCUGACC 513 hsa-miR-3692-3p GUUCCACACUGACACUGCAGAAGU 514 hsa-miR-630 AGUAUUCUGUACCAGGGAAGGU 515 MIR 143 GCGCAGCGCCCUGUCUCCCAGCCUGAGGUGCAGUGCUGCAUCUCUGGU CAGUUGGGAGUCUGAGAUGAAGCACUGUAGCUCAGGAAGAGAGAAGU UGUUCUGCAGC 516 SNORD50 A UAUCUGUGAUGAUCUUAUCCCGAACCUGAACUUCUGUUGAAAAAAAA AAACUUUUACGGAUCUGGCUUCUGAGAU 517 ENSG000 00238625 AUAAUCUUGUAGUUCAUAAGCAUGAUGAUUGCCUUUUCACACUCGUA UGAGAUGUGCCUCCCUUGAACCUUGUUAUGAUGUUGGCACAUUACCCA UCUGAUG 518 SCARNA8 UGGGAGGCUGAUACACAAAUUGGGCUGAAAUACUGCUCUACUUGUCA CCAUGCCUCCCUAGAAUAAACUGCCUUUUGAUGACCGGGACGAAUUGA GUGAAAUCGUAACGGACAGAUACGGGGCAGACAGAU 519 hsa-miR-4756-3p CCAGAGAUGGUUGCCUUCCUAU 520 hsa-miR-6762-5p CGGGGCCAUGGAGCAGCCUGUGU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 521 ENSG000 00239135 AUUCUUUUGCUGUUCGUAAGCAUAAGGAUCAGGUAUUCAUGGUCAUG UGUAAGACGUGCCUCCCUCCAACCUUGUUACGAUGUGGACGUCAGCAC AUACCCAUUUGAUG 522 MIR222 GCUGCUGGAAGGUGUAGGUACCCUCAAUGGCUCAGUAGCCAGUGUAG AUCCUGUCUUUCGUAAUCAGCAGCUACAUCUGGCUACUGGGUCUCUGA UGGCAUCUUCUAGCU 523 ENSG000 00222185 UGGACCAAUGAUGUGAAUGGAAUGCAUCUGAAUAAAAAUUAUGAUCA AUCAGUUUUUGGAACAACUGAGGUCCAC 524 ENSG000 00238557 AGGCCCCUGUAGUUCCCGAGCACGAUGACUGGGUGUUCACGUGCACGU GUGGGAUGUGCCACCCUCUGAACCUUGUUACGAUGUUGGCACAUUACC CUGGACCUGACC 525 MIR592 UAUUAUGCCAUGACAUUGUGUCAAUAUGCGAUGAUGUGUUGUGAUGG CACAGCGUCAUCACGUGGUGACGCAACAUCAUGACGUAAGACGUCACA AC 526 ENSG000 00251778 UUCAAAAAAGACCAUAUAUCCUUGAAGAGUAACUGCUGAACUUAUUC ACUGGCAGUGGGCCUUAUAGCACAGUGAAUGACCAGGUUAGAGACAU GC 527 ENSG000 00200538 AAGACUAUACUUUCAGGGAUCAUUUCUAUAGUUAGUUGCUAGAGAAG UUUCUCUGGACAUGUGGAGCACCAGAAACCAUGAGAAGGAGAUGUAG UGUUCUCUCCGGAGCAUGAAGCUGGCUCUUGGUGUUGCUUCGCUGCAC CUGCCAUUUGCCAUUGACAAUCAUUCUUCUCUUCCUCUGGGAGAGUAA GGAGGAGAGGACACAGUCUGAGUGG 528 hsa-miR-7156-3p CUGCAGCCACUUGGGGAACUGGU 529 MIR5580 UGCUGGCUCAUUUCAUAUGUGUGCUGAGAAAAUUCACACAUAUGAAG UGAGCCAGCAC 530 hsa-miR-6730-3p CCUGACACCCCAUCUGCCCUCA 531 hsa-miR-4672 UUACACAGCUGGACAGAGGCA 532 MIR217 AGUAUAAUUAUUACAUAGUUUUUGAUGUCGCAGAUACUGCAUCAGGA ACUGAUUGGAUAAGAAUCAGUCACCAUCAGUUCCUAAUGCAUUGCCU UCAGCAUCUAAACAAG 533 hsa-miR-4472 GGUGGGGGGUGUUGUUUU 534 MIR770 AGGAGCCACCUUCCGAGCCUCCAGUACCACGUGUCAGGGCCACAUGAG CUGGGCCUCGUGGGCCUGAUGUGGUGCUGGGGCCUCAGGGGUCUGCUC uu 535 hsa-miR-29c-3p UAGCACCAUUUGAAAUCGGUUA 536 ENSG000 00239068 AUCCUUUUGUAGUUCAUGAGCAUGAUGAUUGGUUGUUCACGUACAUG UGUGAGAUGUGUCACCCUCGAACCUUGUGGCAAUGUUGGAAUAUUAC CUGUCUGACA 537 ENSG000 00252443 GAUUCACAGCAGAAAGACAGCUAAUCUAGUGUGCUAGCUGUAGAGCA AGUUUGCUGCAAACACCUCAAGGAGGGUCUCUGGCCAAAUGAGUAGA AUCUGACAGUAAUCCUUGCUAAAAGU 538 ENSG000 00207299 CUACAAUGAUGGCAAUAUGUUUCAUCGACAGCAGUUCACCCAUUGAG UGUUGAUACCGUGGGUCUGAGUGA 539 MIR3158-2 AUUCAGGCCGGUCCUGCAGAGAGGAAGCCCUUCCAAUACCUGUAAGCA GAAGGGCUUCCUCUCUGCAGGACCGGCCUGAAU 540 SNORD11 4-12 UGGACCAAUGAUGACAAAUACCGGCGUAUGAGUCUUGGAUGAUGAAU AAUACGUGUCUGGAACUCUGAGGUCCA 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 541 MIR4531 GCCUAGGAGUCCUUGGUCAGUGGGGACAUGGAGAAGGCUUCUGAGGA 542 MIR6834 GUGAGGGACUGGGAUUUGUGGGGCGAGGAGGGACCUGUACUAGCCAU GGUUCUGAUCACAUAUGUCCCAUCCCUCCAUCAG 543 ENSG000 00201025 GUUUCUGAUGAAGCCUAUGUUGGUAGGGACAACUAAGGUUGUUGAUG AAUGCUAACAGCUCUAACACACACAC 544 MIR451A CUUGGGAAUGGCAAGGAAACCGUUACCAUUACUGAGUUUAGUAAUGG UAAUGGUUCUCUUGCUAUACCCAGA 545 hsa-miR-6807-5p GUGAGCCAGUGGAAUGGAGAGG 546 MIR4477B ACCUCCUCCCGUGAAUCACAAAUGUCCUUAAUAGCAAUCCUUAAAUGC CAUUAAGGACAUUUGUGAUUGAUGGGAGGAGGA 547 MIR548A 0 AACUAUUCUUAGGUUGAUGCAGAAGUAACUACGGUUUUUGCAGUUGA AAGUAAUGGCAAAGACCGUGACUACUUUUGCAACAGCCUAAUAGUUU cu 548 ENSG000 00207027 AUCCAAGGGGAUUCCCUCUCCAAGGGAACAUGCAGUGCCCCUCUCAGG AAAGUAACAACCUGGAAUAGAAUCUGGCAUGCCUAAGGUCUUUGAGG AAUAGAGGAAUAGAGGAUGCUUGUUUCCUCUGCCUUCCUUGGCUGCC UACAUGG 549 MIR 13027 ACAACAUGUUUUUAGGACAUGUAUGUCUGGUGCAAUAAUUGGGACAU ACUUAUGCUAAAAAAAUUAGUGUUC 550 MIR3605 ACUUUAUACGUGUAAUUGUGAUGAGGAUGGAUAGCAAGGAAGCCGCU CCCACCUGACCCUCACGGCCUCCGUGUUACCUGUCCUCUAGGUGGGAC GCUCG 551 MIR588 AGCUUAGGUACCAAUUUGGCCACAAUGGGUUAGAACACUAUUCCAUUG UGUUCUUACCCACCAUGGCCAAAAUUGGGCCUAAG 552 ENSG000 00212532 GUCCUCUGAUGACUUCAUGUUAGUGCCACCUGUCUGGGCCACGGAGAA CCCAUGAUGGAACUGAGAAUCUGAGGAA 553 hsa-miR-5691 UUGCUCUGAGCUCCGAGAAAGC 554 ENSG000 00238506 ACCCUUUUGUAGUUCAUAAGCAGGAUGACUGAGUUUUCAUGCACUUG UGUGAGAUGCGCCUCCCUCAAUGUUGGCACAUUACCUAUCUGAUG 555 MIR553 CUUCAAUUUUAUUUUAAAACGGUGAGAUUUUGUUUUGUCUGAGAAAA UCUCGCUGUUUUAGACUGAGG 556 SNORD3 A AAGACUAUACUUUCAGGGAUCAUUUCUAUAGUGUGUUACUAGAGAAG UUUCUCUGAACGUGUAGAGCACCGAAAACCACGAGGAAGAGAGGUAG CGUUUUCUCCUGACGUGAAGCCGGCUUUCUGGCGUUGCUUGGCUGCAA CUGCCGUCAGCCAUUGAUGAUCGUUCUUCUCUCCGUAUUGGGGAGUGA GAGGGAGAGAACGCGGUCUGAGUGGU 557 hsa-miR-4491 AAUGUGGACUGGUGUGACCAAA 558 hsa-miR-4471 UGGGAACUUAGUAGAGGUUUAA 559 MIR4288 AUGGAGGUGGAGAGUCAUCAGCAGCACUGAGCAGGCAGUGUUGUCUG CUGAGUUUCCACGUCAUUUG 560 SNORA33 AAGCCAGCCAAUGAAUCUGCUUACCUGAUUGUGUUUGUGCAGACAUA CUUUAAAAACUGGCAAUAGUAAAGCCAUGUUACGAGCCUUAAGGACA UUGAAGUCGUUAAGGUCCCUGAGAAUGGCUAUAACAAAU 2026234022.^   16 Sep 2026 The Sentinel™ HG Test to identify patients with High Grade prostate cancer

[00062] For individuals classified as having prostate cancer, a similar approach was used to train and validate the Sentinel™ High Grade (HG) which discriminates between GG1 + GG2 (low- and favorable-intermediate risk cancer) and unfavorable-intermediate and high-risk prostate cancer (GG3-GG5). These informative sequences identified form the basis of the Sentinel™ HG Test. Additional analyses demonstrated that the same cohort of sncRNAs can be used to dichotomize the patients with cancer into GG1+GG2 (low and intermediate risk cancer) from GG3-5 (high risk cancers). This biostatistical analysis forms the basis for the Sentinel™ HG Test, which utilizes 280 sncRNAs, identified by the Discovery HG Test, of which 280 unique sncRNAs: 191 miRNA and 89 snoRNAs are highly informative Table 3: SEQ ID NOs: 561-840 Used in the HG Test Analysis SEQ ID NO: Sequence Name Sequence 561 hsa-miR-2276-3p UCUGCAAGUGUCAGAGGCGAGG 562 hsa-miR-19a-3p UGUGCAAAUCUAUGCAAAACUGA 563 hsa-miR-34a-3p CAAUCAGCAAGUAUACUGCCCU 564 MIR3924 UAAAUGAAAAAGUAGUAGUCAAAUAUGCAGAUCUAUGUCAUAUA UACAGAUAUGUAUAUGUGACUGCUACUUUUUUGUUUA 565 hsa-miR-4293 CAGCCUGACAGGAACAG 566 ENSG000002 06731 UUCUAAAGUGUUGAGUUCAGUCCAGGGUGGAUCCCCUGCUCUGU UAAUUGAACUGGAACAUUUAAACUGGCUAGGCAAAAUGCCUACA UAGAAAGCAUUACUCUUUAUUCAUCCCCAGCCUACAAAA 567 hsa-miR-561-3p CAAAGUUUAAGAUCCUUGAAGU 568 hsa-miR-4762-3p CUUCUGAUCAAGAUUUGUGGUG 569 hsa-miR-409-3p GAAUGUUGCUCGGUGAACCCCU 570 hsa-miR- 5011-3p GUGCAUGGCUGUAUAUAUAACA 571 MIR4315-1 UGGGCUUUGCCCGCUUUCUGAGCUGGACCCUCUCUCUACCUCUGG UGCAGAACUACAGCGGAAGGAAUCUCUG 572 MIR6505 GCAUUGGAAUAGGGGAUAUCUCAGCAUGUUGAGCCCUGUCUCUG GGGAGCUGACUUCUACCUCUUCCAAAG 573 ENSG000002 12338 AUCCAAGGUGAUUCCUUCUCCAAGGGGGACAUCCAGUGACCCUCU CAGGAAGUAGCAACUUGGAAUAGAAUAGUCCAGGAGUUCCAGGA CCAGCCUGGCCAAUAUGG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 574 hsa-miR-2276-5p GCCCUCUGUCACCUUGCAGACG 575 hsa-miR-6819-3p AAGCCUCUGUCCCCACCCCAG 576 hsa-miR-4662a-5p UUAGCCAAUUGUCCAUCUUUAG 577 ENSG000002 39145 AUCUUUUUGUAGUUCAUGAGCGUGAUGACUGAGUGUUCAUGUGC AUGUGUGAGGCGUGCCACCCUUAAACCUUGUUAUAACAUCAGCA CAUUACCCACAUGACA 578 hsa-miR-188-5p CAUCCCUUGCAUGGUGGAGGG 579 ENSG000002 52193 CUGGAGACUAAGAAACCAGUCCUUGAAGUCAAGCUGACUCUGCU UUUAGCCUCCUAAAUUAAAAGAUAGAUAGAAUAGGUCUUGUUUG CAAAAUAAAUUCAAGAUCUACUCAUCUAUCAAUAGCA 580 hsa-miR-130a-3p CAGUGCAAUGUUAAAAGGGCAU 581 hsa-miR- 7151-3p CUACAGGCUGGAAUGGGCUCA 582 ENSG000002 53065 AGCACUUGUGUUUGCUUUUGUUUGACUUGUGGACAAAGACUUAU AGUAGACAGGCACGAAAAAAUAAAUCCUCUUUUGCAACCCAUGA GUUGUUAUACAUGCAAGAAGGAAUAUU 583 hsa-miR-146b-3p UGCCCUGUGGACUCAGUUCUGG 584 hsa-miR-6738-3p CUUCUGCCUGCAUUCUACUCCCAG 585 hsa-miR-98-3p CUAUACAACUUACUACUUUCCC 586 ENSG000002 51838 CUUCUGCUAAGGUUUACACUAUAGAUGCAGGAAAAAAAAUGUCC UCACACUGUCUGUCUGAUUGUGGCAGCUGAGAUUGAAUAGAGAA AUAUAGGG 587 hsa-miR-5090 CCGGGGCAGAUUGGUGUAGGGUG 588 SNORD115-34 GGGUCAAUGAUGAGAACCUUAUAAUGUUCUGAAGAGAGGUGAUG ACUUAAAAAUCAUGCUCAAUAGGAUUACGCUGAGGCCC 589 MIR382 UACUUGAAGAGAAGUUGUUCGUGGUGGAUUCGCUUUACUUAUGA CGAAUCAUUCACGGACAACACUUUUUUCAGUA 590 hsa-miR-6859-3p UGACCCCCAUGUCGCCUCUGUAG 591 hsa-miR-1184 CCUGCAGCGACUUGAUGGCUUCC 592 hsa-miR-6811-5p AUGCAGGCCUGUGUACAGCACU 593 MIR518C GCGAGAAGAUCUCAUGCUGUGACUCUCUGGAGGGAAGCACUUUC UGUUGUCUGAAAGAAAACAAAGCGCUUCUCUUUAGAGUGUUACG GUUUGAGAAAAGC 594 hsa-miR-4504 UGUGACAAUAGAGAUGAACAUG 595 MIR548S UUGCUGCAAAAAUAAUUGCAGUUUUUGCCAUUAUUUUUAAUAAU UAUAAUAAUGGCCAAAACUGCAGUUAUUUUUGCACCAA 596 MIR6761 UCUGCUCUGAGAGAGCUCGAUGGCAGGUGCCUCCGUGUUGCCGA ACCCUCCUACGCUGCUCUCUCACUCCAG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 597 hsa-miR-1282 UCGUUUGCCUUUUUCUGCUU 598 hsa-miR-4439 GUGACUGAUACCUUGGAGGCAU 599 MIR 126 CGCUGGCGACGGGACAUUAUUACUUUUGGUACGCGCUGUGACAC UUCAAACUCGUACCGUGAGUAAUAAUGCGCCGUCCACGGCA 600 ENSG000002 21125 AAGAUGACACUUUGAGGCAUCGUGUCUAUGGUUCAUUACUACAG AAGCUUCUCUGGAUGUGUAAAGCACAGGAAACCAGGCAGAGGAG GCACAGGGUGCUCUCCAGAACGAGAAGCCAGCUCCUGGAGUUGU UUGCUGCAACUGCCAUUCCCCGUUGAUGACCAUGCUCUUCCUUCA GAAGAGGGAGAGUGAGAGGACCAAGUCCAAGUGG 601 hsa-miR-6814-5p UCCCAAGGGUGAGAUGCUGCCA 602 hsa-miR-4657 AAUGUGGAAGUGGUCUGAGGCAU 603 MIR31 GGAGAGGAGGCAAGAUGCUGGCAUAGCUGUUGAACUGGGAACCU GCUAUGCCAACAUAUUGCCAUCUUUCC 604 MIR6754 GGCUGCCAGGGAGGCUGGUUUGGAGGAGUCUGGUGGCCUGUUCU CUUCACCUGCCUCUGCCUGCAG 605 MIR376A2 GGUAUUUAAAAGGUAGAUUUUCCUUCUAUGGUUACGUGUUUGAU GGUUAAUCAUAGAGGAAAAUCCACGUUUUCAGUAUC 606 hsa-miR-200a-3p UAACACUGUCUGGUAACGAUGU 607 hsa-miR-3671 AUCAAAUAAGGACUAGUCUGCA 608 hsa-miR-6775-3p AGGCCCUGUCCUCUGCCCCAG 609 hsa-miR-6862-3p CCUCACCCAGCUCUCUGGCCCUCU 610 ENSG000002 38368 AUCCUUUUGUGGUUCAUAAGCAUGAUGAUUAGAUUUUCAUGCUA UUGGGUGAGAUAUGCCUUCCUCAGACUUUGUUACAGCAUAGGCA CAUUACAACCUGUCUGAUA 611 MIR183 CCGCAGAGUGUGACUCCUGUUCUGUGUAUGGCACUGGUAGAAUU CACUGUGAACAGUCUCAGUCAGUGAAUUACCGAAGGGCCAUAAA CAGAGCAGAGACAGAUCCACGA 612 hsa-miR-423-3p AGCUCGGUCUGAGGCCCCUCAGU 613 hsa-miR-4306 UGGAGAGAAAGGCAGUA 614 ENSG000002 52128 ACUCCAUGAUGAACCCAAAAUGCCAAGUAUAUGACUGAACUUAC AAGUGAUACCAUCUUACGACUGAAGAGU 615 ENSG000002 38932 AUCCUUUUGUAGUUCAUGAGGAUGAUGGUUGGGUGUUUCACACA UGUGUGUGAAAUGUACCACCCUCAAACCUUGUUACAAUGUCAGC ACAUUACCUGCCUGACC 616 MIR96 UGGCCGAUUUUGGCACUAGCACAUUUUUGCUUGUGUCUCUCCGC UCUGAGCAAUCAUGUGCAGUGCCAAUAUGGGAAA 617 hsa-miR-3925-3p ACUCCAGUUUUAGUUCUCUUG 618 hsa-miR-3065-3p UCAGCACCAGGAUAUUGUUGGAG 619 ENSG000002 00026 UCGAUGGGUGGGAUAAUCCUUACCUGUUCCUCGUUUUGGAGGGC AGAUAGAACAUGAUGAUUGGAGAUGCAUGAAAUGUGAUUAAUGC 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence CUCUGCCUAAUCAGGACUUGCAACACCCUGAGUACUCCUCUCUGA U 620 ENSG000002 39155 AUCCUUUUGUAGUUCAUAAGUGUGAUGAUUAGGUUUUCACAUUU GUGUGUGAGAUGUAUCUCCCUCAAACAUUUUAUGACAUCGGCAU AUUAUCCUUCUGAUG 621 hsa-miR-337-3p CUCCUAUAUGAUGCCUUUCUUC 622 hsa-miR-559 UAAAGUAAAUAUGCACCAAAA 623 hsa-miR-580-3p UUGAGAAUGAUGAAUCAUUAGG 624 ENSG000002 00318 AAGACUAUACUUUCAGGGAUCAUUUGUAUAGUUCGUUACUAGAG AAUUUUCUCUGAAUGUGUAGAACACCAGAAACCACAAGGAGGAG GCGCAGCGUUCUCUCCUGAGCGUGAAGCCGGGUCCUGGUGUUGC UUCACUGCAACUGCCAUUUGCCAUUGAUGAUUGUUCUUCUCUUC CUUUGGGAGAGUAAGAGGCAAAGGAUGCAGUCUGAAUGG 625 hsa-miR- 181c-3p AACCAUCGACCGUUGAGUGGAC 626 MIR6808 GGGGCCAGGCAGGGAGGUGGGACCAUGGGGGCCUUGCUGUGUGA CCACCGUUCCUGCAG 627 ENSG000002 38775 AUCUUUUUGUAGUUCAUAAGCAUGAUGAUUAUGUUUUUACAUUC AUGUGUAAGAUGUGCCUCCCUCAAACCUUGUUAUGAUGUCAGCA UAUUACCUGUCUGAUG 628 hsa-miR-3660 ACUGACAGGAGAGCAUUUUGA 629 SNORA33 AAGCCAGCCAAUGAAUCUGCUUACCUGAUUGUGUUUGUGCAGAC AUACUUUAAAAACUGGCAAUAGUAAAGCCAUGUUACGAGCCUUA AGGACAUUGAAGUCGUUAAGGUCCCUGAGAAUGGCUAUAACAAA U 630 hsa-miR-630 AGUAUUCUGUACCAGGGAAGGU 631 ENSG000002 53028 UCCAUCUGUUUGGCAGACCUGGAGCAGUUAGUGUCUGCUGCUAA GGUUUCCAUUACAGAUGUGAGAAAAAAAAGUGUUCUUCUGCUUU CUGUCUGUCUCAGUGGCAACCAAGAUUGAAUGGGGGAUAUGAGA G 632 ENSG000002 38618 AUUCUUUUGUAAUUCAUAAGCAUGAUGACUCGGUAUUCACGUGC AUGUGUGAGAUGUGCCACCCUGGAACCUUGUUGCAACGUCAGCA CAUUAUGGGUCUGACA 633 ENSG000002 52844 AUGACCAAUGGUGAGAGUGUAUCAUGAAGCAAGGAAUGUGAUUA AUCCAGUUCUGUAAACCCAAGUUCCAGU 634 hsa-miR-6079 UUGGAAGCUUGGACCAACUAGCUG 635 ENSG000002 38552 AUCCUUUUGUAGUUCAUGAGCAUGACCAUCGAGUGUUUACAUGC AUGUGUGAGAUAUGACACCUUCUGAACCUUGUUACGGAGUUGGC AUGUUACCCAUCUAACC 636 ENSG000002 12165 AAGAUUAUAUUUUCAGGGAUCAUUUCUAUAGUUUGUCACUAGGG AAGUUCCUCUGAAUGUGUAGAGCACCAGAAACAUGAGGAAGAGG CACAGGGUUCUCUCCUGAGUGUGAAGCUGGCUCUUGGCGCUGCU UUCCUGCAACUGCUAUUUGCCAUUCGUGAUUGUGGAGAGUCAGA GGGAGAGGAUGAUGCAGUCUGAGUGG 637 ENSG000002 38752 AUCCUUUUGUAGUUCAUGAGCAUGAUGAUUGGGUGUUCACGUGC AUGUGUGAGAUGUGACACCCUUGCACAUUACUCGCCUGACC 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 638 hsa-miR-504-5p AGACCCUGGUCUGCACUCUAUC 639 hsa-miR-5688 UAACAAACACCUGUAAAACAGC 640 SNORD23 UGCCCAGUGAUGACACCAUCCUUGCUCCCCGUGCCCCCCAGGGGC UAUGGGCGACACCAUGGCUGCCCCUGGGCUGGGCCAGUGGGGCCA AUGCCCAGGGGCUGAGGGCA 641 MIR6797 CAGCCAGGAGGGAAGGGGCUGAGAACAGGACCUGUGCUCACUGG GGCCUGCAUGACCCUUCCCUCCCCACAG 642 ENSG000002 52682 ACUCACUGAUGAGUAGCUUCUGACUUUCAUUCUGAGUUUGCUGA ACCCAGAUGCCAUUCCUGGGAAGG 643 ENSG000002 38914 AUCCUUUUGUGGUUCAUUAGCUUGAUAUUGGGUUUUCACACUAU UCUAUGAGAUGUGCCUCCCUCAAAACUUGUUACAACAUUGACAC AUUACCCUUCUGAUG 644 MIR3202-1 UAUUAAUAUGGAAGGGAGAAGAGCUUUAAUGAUUGGAGUCAUUU UCAGAGCAUUAAAGCUCUUCUCCCUUCCAUAUUAAUG 645 MIR520F UCUCAGGCUGUGACCCUCUAAAGGGAAGCGCUUUCUGUGGUCAG AAAGAAAAGCAAGUGCUUCCUUUUAGAGGGUUACCGUUUGGGA 646 hsa-miR-6844 UUCUUUGUUUUUAAUUCACAG 647 hsa-miR-7856-5p UUUUAAGGACACUGAGGGAUC 648 MIR6739 GAAUGUGGGAAAGAGAAAGAACAAGUAAAAGGAAUUUUCAUUUU CCAGCCCCUAAUUGUUCUGUCUUUCUCCCAG 649 MIR4521 UCGGCUAAGGAAGUCCUGUGCUCAGUUUUGUAGCAUCAAAACUA GGAUUUCUCUUGUUAC 650 SNORD114-28 UGGAUCGAUGAUGACUGCUGGUGGCGUAUGAGUCAUAUGCGAUG AAUACGUGUCUAGAACUCUGAGGUCCA 651 MIR6867 CCCGGUGUGUGUGUAGAGGAAGAAGGGAAGCUGGGAACCUGACU GCCUCUCCCUCUUUACCCACUAG 652 hsa-miR-1244 AAGUAGUUGGUUUGUAUGAGAUGGUU 653 hsa-miR-23a-3p AUCACAUUGCCAGGGAUUUCC 654 ENSG000002 38889 AUUCUUUUGUAGUUCUUAGGCACGAUGAUUGGGUGUUCAUGUGC AUGUUUGAGAUGUGCCUCCCUCAAACCUUGUUCUUACAUCAGCA CCUUACACGUCUAACA 655 hsa-miR-4272 CAUUCAACUAGUGAUUGU 656 MIR320E GCCUUCUCUUCCCAGUUCUUCCUGGAGUCGGGGAAAAGCUGGGU UGAGAAGGU 657 ENSG000002 38642 AGUUUUUCAUAGUUCAUAAGCAUGAUGAGUGGGUUUUCAUGUUC AUGUGUGAGGUGUGCCUCCCUCAAACCUUGUUAUGAUGUCAACA CAUUGCCCAUCUGAUG 658 ENSG000002 66834 UGCAGAUGAUGUAAAAGAAUAUUUGCUAUCUGAGAGAUGGUGAU GACAUUUUAAACCACCAAGAUCGCUGAUGCA 659 hsa-miR-3130-3p GCUGCACCGGAGACUGGGUAA 660 hsa-miR-7848-3p CUACCCUCGGUCUGCUUACCACA 661 MIR4699 AGCAAUUGGAGAAGAUUGCAGAGUAAGUUCCUGAUUAAGAAAUG GAAUUUACUCUGCAAUCUUCUCCAAUUGCU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 662 MIR770 AGGAGCCACCUUCCGAGCCUCCAGUACCACGUGUCAGGGCCACAU GAGCUGGGCCUCGUGGGCCUGAUGUGGUGCUGGGGCCUCAGGGG UCUGCUCUU 663 MIR3686 CUCACCUCAUUCAUUUACCUUCUCUUACAGAUCACUUUUCUGCAC UGGACAGUGAUCUGUAAGAGAAAGUAAAUGAAAGAGGUGAG 664 hsa-miR-548ar-5p AAAAGUAAUUGCAGUUUUUGC 665 MIR 1203 UCCUCCCCGGAGCCAGGAUGCAGCUCAAGCCACAGCAGGGUGUUU AGCGCUCUUCAGUGGCUCCAGAUUGUGGCGCUGGUGCAGG 666 SNORD115-33 GGGUCAAUGAUGAGAACCGUAUAUUGUCCUGAAGAGCGGUGAUG ACUUAAAAAUAAUGCUCAAUAGGAUUACGCUGAGGCCC 667 hsa-miR-6134 UGAGGUGGUAGGAUGUAGA 668 ENSG000002 52526 AAGACCCUUCAGCUGCAAACAACAGCUUCCUUGGUAGUUUAUGC AGCCUGUUUCUUGUAUGGGCUGCUCUAAGGGACCAUGGAGACAG GC 669 hsa-miR-1537-5p AGCUGUAAUUAGUCAGUUUUCU 670 MIR7977 UUCCCAGCCAACGCACCAAAAAUGAUAUGGGUCUGUUGUCUGGA GAAAC 671 ENSG000002 38843 AUACUUUUGUAGGUCAUAAGCUGAGGAUUGGGUUUUCAUGCUCU UGUGUGAGAUAUGCUUCUCUCAAACCUUCUGACCUGGGCACAUU ACCCAGCUAAUG 672 hsa-miR-4520a-5p CCUGCGUGUUUUCUGUCCAA 673 ENSG000002 38772 UUCCUGUUGGUUCCUAAGUGUGAUGAUUGGGUUUUCACAUUCAU GUGUGACAUGUGCCUCCCUCAAAUCUUGUGAUGAUGUCGGCACG UGACCCAUCUGACG 674 MIR593 CCCCCAGAAUCUGUCAGGCACCAGCCAGGCAUUGCUCAGCCCGUU UCCCUCUGGGGGAGCAAGGAGUGGUGCUGGGUUUGUCUCUGCUG GGGUUUCUCCU 675 hsa-miR-554 GCUAGUCCUGACUCAGCCAGU 676 SNORD87 ACAAUGAUGACUUAAAUUACUUUUUGCCGUUUACCCAGCUGAGG UUGUCUUUGAAGAAAUAAUUUUAAGACUGAGA 677 hsa-miR-624-3p CACAAGGUAUUGGUAUUACCU 678 ENSG000002 52777 UGGACAUUUAUUUUUAUUCAGUUUUUUCUCAAGGUGAAGGUAAC UGUUUGUAGAUGUCCUAGAGAAAUAUUGUAGCUUUCUGUUCACC CUUUGCAACUAAAAAGCAUGGACUGUUCCACUACUGAGAUUU 679 ENSG000002 06649 CUUCCCAUUUAUUUGCUGCUAUAGUCUCAUAAUGAUACAAGCAG UUAUAUGCAUGGGAUAAAAUAAUAUUGGGACAUUGUAAAUUGAA AUGAAGUAACCAUUUUCAUCUUUUCUGCAUGGACAAGACAUUG 680 MIR4534 UGUGAAUGACCCCCUUCCAGAGCCAAAAUCACCAGGGAUGGAGG AGGGGUCUUGGGUACU 681 hsa-miR-4709-3p UUGAAGAGGAGGUGCUCUGUAGC 682 MIR548F4 GAGUUCUAACGUAUUAGGUUGGUGCAAAAGUAAUAGUGGUUUUU GCCAUUAAAAGUAAUGACAAAAACUGUAAUUACUUUUGGAACAA UAUUAAUAGAAUUUCAG 683 ENSG000002 00422 GGUCAAUGAUGUAAUGGCAUGUAUUAGCUGAAUCCAAAGUUGAA GUGAAUUCUAAAAUUACACCAAGACCUU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 684 MIR 16-1 GUCAGCAGUGCCUUAGCAGCACGUAAAUAUUGGCGUUAAGAUUC UAAAAUUAUCUCCAGUAUUAACUGUGCUGCUGAAGUAAGGUUGA C 685 ENSG000002 12391 UGUUCUGACAUGGGAAGAGUAGCUUCUGGUUGGUGGAGCCCAUC UCACAUUAGCCAGAGACAAAGCAACACCUUGUUUAUCCCGGCUU GGCUUUUGGCCUGUGUCCAUGACUGGUCCAUACCUUGGACACAU GG 686 ENSG000002 38983 AUCCCUUUGUAGUUCAUAAGCGUGAUGAUUGGGUGUUCAUGCUC AUACAUGAGCUGUGCCUCCCUCAAGCUUUGUUGUGACAUCAUCA UAUUACCUGUCCGAUG 687 hsa-miR-4722-3p ACCUGCCAGCACCUCCCUGCAG 688 MIR6802 GAGGGCUAGGUGGGGGGCUUGAAGCCCCGAGAUGCCUCACGUCU UCACCCCUCUCACCUAAGCAG 689 hsa-miR-141-5p CAUCUUCCAGUACAGUGUUGGA 690 MIR3124 GCGGGCUUCGCGGGCGAAGGCAAAGUCGAUUUCCAAAAGUGACU UUCCUCACUCCCGUGAAGUCGGC 691 hsa-miR-569 AGUUAAUGAAUCCUGGAAAGU 692 ENSG000002 12604 UGCCCCUGACCUGGGAAGAGAGGGGCCUGGCUGGUGGUAUCCAU CUCAUACCAGCUAGGGAUGAAGAAACCGCUUGCUCAUCCCAGCCU GGCUCCUGGUCUAUGCCCAUGCCUGGUUCGUGCCUUGGACAUAUC A 693 hsa-miR-4487 AGAGCUGGCUGAAGGGCAG 694 MIR2110 CAGGGGUUUGGGGAAACGGCCGCUGAGUGAGGCGUCGGCUGUGU UUCUCACCGCGGUCUUUUCCUCCCACUCUUG 695 MIR5010 GAUCCAGGGAACCCUAGAGCAGGGGGAUGGCAGAGCAAAAUUCA UGGCCUACAGCUGCCUCUUGCCAAACUGCACUGGAUUUUGUGUC UCCCAUUCCCCAGAGCUGUCUGAGGUGCUUUG 696 ENSG000002 51715 UUCACAAUGUCUAUUGAAGGAUCUCAUCACCUUUAGAGAGCUGU GGUCAUGCCCCUUAAAGUGAAUUUGGAGGUUUUAUACCC 697 MIR640 GUGACCCUGGGCAAGUUCCUGAAGAUCAGACACAUCAGAUCCCU UAUCUGUAAAAUGGGCAUGAUCCAGGAACCUGCCUCUACGGUUG CCUUGGGG 698 MIR4424 CUUACAUCACACACAGAGUUAACUCAAAAUGGACUAAUUUUUCC ACUAGUUAGUCCAUUUCAAGUUAACUCUGUGUGUGAUGUAGU 699 ENSG000002 38685 AUUACUUGAAAAUCACUCCCAGGCUUUGGCCAUGGCAGCAGGUG AGAUUCAAGGCCCAGAGCCUCCAGGGCCUCAGCUCACCGCACACU GCCCCGUGUGUGGUGGGGAAACCCAGACCCCAACAGGU 700 hsa-miR-370-5p CAGGUCACGUCUCUGCAGUUAC 701 MIR 1307 CAUCAAGACCCAGCUGAGUCACUGUCACUGCCUACCAAUCUCGAC CGGACCUCGACCGGCUCGUCUGUGUUGCCAAUCGACUCGGCGUGG CGUCGGUCGUGGUAGAUAGGCGGUCAUGCAUACGAAUUUUCAGC UCUUGUUCUGGUGAC 702 ENSG000002 52699 CCUCUUCUCAGAACACUUCCUGGGUCUGAUUGGUGGCCCAGGGA GCUGUCAGAGAAGAGCAGAGCAAAUGGCCUUCACUUUGUAGAUG AGAUGGCAGGAGGGUGGAUUGUUGGUCUCAGUCAGUGGUGGGAC AGAC 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 703 hsa-miR-433-3p AUCAUGAUGGGCUCCUCGGUGU 704 ENSG000002 01448 AAGCAGGAUUCAGACUACAAUAUAGCUGUUAAGUGCUGUAUUGU CAUUCCCCCUGCUCAAAUUAAAGUUGUUUCUUAACUAUACCCAUC UGCUAUUCUGUAGCAGCCAGGGAUGCUUGGUCACAUACAU 705 SNORA43 GCUGUCCUGGACCUGUUGGCACCACAGACAGUUGCUCUGCUGUGC CUGUGGCCUCGGGGCAAAGAGAAAGUGGCGAUUUCUACACUCAG UGCUCGGGAACCAGUGGGCACUGAGAAUGGUUUAUGGCCUGACA UUA 706 ENSG000002 01398 UCGUCAGGUGGGAGAAUUCUUACAUGUUCCUCCUUUUGCAAGGC AGAUUAGAACAUGAUGAUUGGGGUUCGCAUAAUAUGUGAUUAAC GUUUCUGUGUAAUCAGGACUUGCAACAUCCCGAAUGCCCUUACC UGAC 707 ENSG000002 52740 UGGAUCAAUGAUGACAAAGUAUCAUGAAUGAGGGAUUGUGAAUA AUCUAUUUUUAUGAACCUGUGGUCAAAU 708 hsa-miR-654-3p UAUGUCUGCUGACCAUCACCUU 709 MIR3667 UGAGGAUGAAAGACCCAUUGAGGAGAAGGUUCUGCUGGCUGAGA ACCUUCCUCUCCAUGGGUCUUUCAUCCUCA 710 ENSG000002 38978 AUCCUUUUGUGGUUCAUAAGAAUAGGGAUUGGGAUUUCACACUC AUGUGUGAGAUGUGCCUCCCUUAAACCUUAAGAUGUUGGCACAU UACCUAUUUGAUG 711 ENSG000002 38494 AUCCUUUUGUAGUUCAUUAGCAUAAUGAUUGGGUUUUCACACUC AGGCGUGAGAUGUGCCUCUCUCAAACCUUGCUACGAUGUUGGCA CAUUGCCUAUCUGGCA 712 hsa-miR-3606-3p AAAAUUUCUUUCACUACUUAG 713 hsa-miR-335-3p UUUUUCAUUAUUGCUCCUGACC 714 ENSG000002 52458 CUCAUACCUAAACCCAAGAAUCACUUUCUUAUAGUGAUGAUUUA AACAGAUGCAAACAGCGAGCACAUCUUGUCACCUUUGCGGGACU GUGGCUGUGCCCCUCGCAGUAAAUUUGGAGGUUCUACAUCC 715 MIR 1297 UGUUUAUCUCUAGGGUUGAUCUAUUAGAAUUACUUAUCUGAGCC AAAGUAAUUCAAGUAAUUCAGGUGUAGUGAAAC 716 ENSG000002 01329 AAUGCUAUACUUUCAUGGGUCAUUUCUAUAGUUUGUUAUUAGAG AAGUUUCUCUGAAUGUGUUGAGCACCAGAAACCACGAGGAGAUG CAGCAUUCUCUCCUGAACGGGAAGCCAGCUUUUGGCAUUGCUUU GAUGCAACUACCAUUUGCCAUUGAUGGCAAUGCAUCGCUUCCUC UAGGAGUGUAAGAGGGAGUGGAUGCAGUCAGAGUGG 717 MIR30B ACCAAGUUUCAGUUCAUGUAAACAUCCUACACUCAGCUGUAAUA CAUGGAUUGGCUGGGAGGUGGAUGUUUACUUCAGCUGACUUGGA 718 ENSG000002 51778 UUCAAAAAAGACCAUAUAUCCUUGAAGAGUAACUGCUGAACUUA UUCACUGGCAGUGGGCCUUAUAGCACAGUGAAUGACCAGGUUAG AGACAUGC 719 hsa-miR-544a AUUCUGCAUUUUUAGCAAGUUC 720 ENSG000002 01388 UUCUCACCUAAACCCAAGAAUCACUGUUUCUUAUAGCGGUGGUU UAAACAGAGGUGCAAACAGCAAGUGAAUCUCGUCGCCUUUGCGG GGCUGUGGCCAUGCCCCUCAAAGGAAAUUUGGAGGCUCUACAGC C 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 721 MIR767 GCUUUUAUAUUGUAGGUUUUUGCUCAUGCACCAUGGUUGUCUGA GCAUGCAGCAUGCUUGUCUGCUCAUACCCCAUGGUUUCUGAGCA GGAACCUUCAUUGUCUACUGC 722 MIR642A AUCUGAGUUGGGAGGGUCCCUCUCCAAAUGUGUCUUGGGGUGGG GGAUCAAGACACAUUUGGAGAGGGAACCUCCCAACUCGGCCUCU GCCAUCAUU 723 MIR454 UCUGUUUAUCACCAGAUCCUAGAACCCUAUCAAUAUUGUCUCUG CUGUGUAAAUAGUUCUGAGUAGUGCAAUAUUGCUUAUAGGGUUU UGGUGUUUGGAAAGAACAAUGGGCAGG 724 ENSG000002 07094 AUCCAAGGCGAUUCCCUCUCCAAGGGGACAUCUAGUGCCCCUCUC AGGAAAGUAGCAACUUGGAAUAGAAUCUGGCAUGCCUAAGGUCU UUGAGGAACAGGGAUGCUUAUUUCCUCUGCCUUCCUUGGCUGCC UACAUAG 725 MIR4645 UGAUAGGGAAACCAGGCAAGAAAUAUUGUCUCCUCAAGUUGCGA CGAGACAGUAGUUCUUGCCUGGUUUCUCUAUCA 726 MIR3155B CCACUGCAGAGCCUGGGAAGGGAGCUGUCCGGCUCCCCAGGCUCU GCAGUGGGAGG 727 SNORD96B CCUGGUGAUGACAGACGACAUUGUCAGCCAAUCCCCAUGUGGUA GUGAGGACAUGUCCUGCAGUUCUGAAGG 728 ENSG000002 12587 UGCACUUAUGUAUGUUUUUGUUUAACUUGUGGACAAAGACUUUA GGAAAGGUGCAAAAAAUAAAUCUUCUUUUGCAACCCAGAACUCA UUGUUCAGUAUGAGUUUUGAUACAUAUCAGAAUGGAUACU 729 hsa-miR-3186-3p UCACGCGGAGAGAUGGCUUUG 730 ENSG000002 12490 UGCCCCUUUUAAGGUUGACACAGUGCAUUAAGCAGAAGGGUUAA GUAAGUCUCCAUAAAACCCAGAGAAGAGAAUGUAAAGCUCCUCU UUGGAGGAGCUAGACUCCUGUCUGGAGUCACAGCU 731 MIR3622B AGUGAUAUAAUAGAGGGUGCACAGGCAUGGGAGGUCAGGUGAGC UCAGCUCCCUGCCUCACCUGAGCUCCCGUGCCUGUGCACCCUCUA UUGGCU 732 hsa-miR-6762-5p CGGGGCCAUGGAGCAGCCUGUGU 733 MIR3938 AGGAAUUUUUAACCCGAUCACUAGAUUAUCUACAAGGGAAUUUU UUUUUAAUUUAAAAAAUUCCCUUGUAGAUAACCCGGUGGUCAGG UUGGAUGGCUCCAUG 734 ENSG000002 07100 UGCACUGCGUGGUAUCUGCACUCAGCAGUUUACUCCUGCUAGGG UGUUCAAAGGUCAGUGCCAUAGAAAUCCAGUAUCUGGUUUCAUU GGUUUUCUUGGCUUUGUGCUUGUUAAACCUGGUAUUUCUAUUGA UACAGCA 735 hsa-miR-3122 GUUGGGACAAGAGGACGGUCUU 736 hsa-miR-548az-3p AAAAACUGCAAUCACUUUUGC 737 hsa-miR-3165 AGGUGGAUGCAAUGUGACCUCA 738 MIR2277 GUGCUUCCUGCGGGCUGAGCGCGGGCUGAGCGCUGCCAGUCAGCG CUCACAUUAAGGCUGACAGCGCCCUGCCUGGCUCGGCCGGCGAAG cue 739 MIR519C UCUCAGCCUGUGACCCUCUAGAGGGAAGCGCUUUCUGUUGUCUG AAAGAAAAGAAAGUGCAUCUUUUUAGAGGAUUACAGUUUGAGA 740 hsa-miR-891b UGCAACUUACCUGAGUCAUUGA 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 741 hsa-miR-4676-3p CACUGUUUCACCACUGGCUCUU 742 MIR5692C1 UAUAACAUUGUAUAUACCCACUGUGAUAUUAAGAGUAAUAGCUC UCUAGGUUAUUAUGAAUAAUAUCACAGUAGGUGUACACAAUGUU GUA 743 SCARNA15 CUGGAGACUAAGAAAAUAGAGUCCUUGAAAUCAAGCUGACUCUG CUUUUAGCCUCCUAAAUGAAAAGGUAGAUAGAACAGGUCUUGUU UGCAAAAUAAAUUCAAGACCUACUUAUCUACCAACAGCA 744 MIR3972 GCCCAUUUGCCUUGGCUUGGGGUGGCAGUCCUGUGGGAAUGAGA GAUGCCAAACUGGACCUGCCAGCCCCGUUCCAGGGCACAGCAU 745 ENSG000002 01348 CCAAUGUGGAUACACCCAGGAGGUCACUCUCUCCCCAGGCUGUGU CCAAGUAGCAUAGGGGAGCACAGGGCUCUGUCCCCAUGAUGUAC UGUCCUUUUCCAUGACAUUGGAGAUGAAGCUGGACCUCAACUCU GCACAUGCAUAUUCCUACAACUUCUCAGAGUCCUGUGGAUAAUG ACGGAGGAGAGAAACCAUGCAGGAAACAGCC 746 ENSG000002 52040 UGAGAUGAGAUCAUGCCAUUGCACUCCAGCCUGGACGACAGAGC GAGACUUCAUCUCAAAAAAAAAAAAGGAUCCUCAGGGCUGCCAA CCUUAUAGUAGAAGUUGAGGUGGUAGUGGAUUUCUCCUACACAA 747 MIR8074 CCAGUUCCUGAGUUUAUGCAAGAUGCCCAUGGGAGCCCAGAGAC GUCCUAUGGCGAGACUGGCAUGUACUCACACAACUGA 748 hsa-miR-3152-5p AUUGCCUCUGUUCUAACACAAG 749 MIR619 CGCCCACCUCAGCCUCCCAAAAUGCUGGGAUUACAGGCAUGAGCC ACUGCGGUCGACCAUGACCUGGACAUGUUUGUGCCCAGUACUGU CAGUUUGCAG 750 ENSG000002 02275 AUUGUUACAUUGAUAAAAUCAAAUCACCAUCUUUUAGCUAAGCU UGUGCUGGAUUUGCUUUUUUUCUGAUAAAGAUG 751 hsa-miR-759 GCAGAGUGCAAACAAUUUUGAC 752 MIR514A1 AACAUGUUGUCUGUGGUACCCUACUCUGGAGAGUGACAAUCAUG UAUAAUUAAAUUUGAUUGACACUUCUGUGAGUAGAGUAACGCAU GACACGUACG 753 MIRLET7A3 GGGUGAGGUAGUAGGUUGUAUAGUUUGGGGCUCUGCCCUGCUAU GGGAUAACUAUACAAUCUACUGUCUUUCCU 754 hsa-miR-519a-3p AAAGUGCAUCCUUUUAGAGUGU 755 hsa-miR-106b-3p CCGCACUGUGGGUACUUGCUGC 756 SNORD116- 20 UGGAUCGAUGAUGACUUCCAUAUAUACAUUCCUUGGAAAGCUGA ACAAAAUGAGUGAAAACUCUAUACUGUCAUCCUCGUCGAACUGA GGUCCA 757 hsa-miR-4677-3p UCUGUGAGACCAAAGAACUACU 758 ENSG000002 52298 UGGACAAAUGAUUAGAUUAGAUUGUGUUAUAAACCAAAGAUUAU AGUUAUUCCAAUUAUGUGCAUUUGAGAUCCACU 759 MIR493 CUGGCCUCCAGGGCUUUGUACAUGGUAGGCUUUCAUUCAUUCGU UUGCACAUUCGGUGAAGGUCUACUGUGUGCCAGGCCCUGUGCCA G 760 hsa-miR-4538 GAGCUUGGAUGAGCUGGGCUGA 761 ENSG000002 12284 UCAAUAAUGAAAUCUUCUGAUUUGGUGAGAAAUAAUGCCUUAAA AUUACACUCAAUAGGAUUAUGCUGAGG 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 762 hsa-miR-3913-3p AGACAUCAAGAUCAGUCCCAAA 763 MIR4714 AUUUUGGCCAACUCUGACCCCUUAGGUUGAUGUCAGAAUGAGGU GUACCAACCUAGGUGGUCAGAGUUGGCCAAAAU 764 hsa-miR-6838-3p AAGUCCUGCUUCUGUUGCAG 765 hsa-miR-4531 AUGGAGAAGGCUUCUGA 766 hsa-miR-5481 AAAAGUAUUUGCGGGUUUUGUC 767 hsa-miR-326 CCUCUGGGCCCUUCCUCCAG 768 hsa-miR-6830-5p CCAAGGAAGGAGGCUGGACAUC 769 MIR6780A GACACUUGGGAGGGAAGACAGCUGGAGAGUAUGGUCACAGCAGC AUCCUCCUCUGUUUUCUUUCCUAG 770 hsa-miR-4747-5p AGGGAAGGAGGCUUGGUCUUAG 771 hsa-miR-4756-3p CCAGAGAUGGUUGCCUUCCUAU 772 hsa-miR-517a-3p AUCGUGCAUCCCUUUAGAGUGU 773 MIR3160-2 ACCUGCCCUGGGCUUUCUAGUCUCAGCUCUCCUGACCAGCUGAGC UGGAGGAGAGCUGAGACUAGAAAGCCCAGGGCAGGU 774 hsa-miR-103a-3p AGCAGCAUUGUACAGGGCUAUGA 775 hsa-miR-588 UUGGCCACAAUGGGUUAGAAC 776 ENSG000002 12378 AUGUAAUAAUGUUCAUCAAAUGUCUGACCUGAAAUGAGCAUGUA GACAAGUUAAUUUAACACUGAAGAA 777 MIR548BA AAAGGUAACUGUGAUUUUUGCUAUUAGAAAGUAAUGGCAAAAAC UGCAAUUACUUU 778 hsa-miR-1251-3p CGCUUUGCUCAGCCAGUGUAG 779 ENSG000002 38995 AACCAUGAAUGCAAGAAGCGUAUGAUUGGGUUUUCAUGCUCACG UGUGAAAUGGACCACCCUCAAACCUGGUUAUGCUAUCAGCACAU UACCUGUCUGAUG 780 SNORA64 ACUCUCUCGGCUCUGCAUAGUUGCACUUGGCUUCACCCGUGUGAC UUUCGUAACGGGGAGAGAGAGAAAAGAUCUCCUCAGGACCUCGG AUGGGCCUUACUGUGGCCUCUCUUUCCUUGAGGGGUGCAACAGG C 781 hsa-miR-206 UGGAAUGUAAGGAAGUGUGUGG 782 MIR5708 AUUACAGACAUGAGCGACUGUGCCUGACCAAAAGUCAACAUUAA ACAACAAAUCUUGGCCAGGCACAGUGGCUCAUGCCUGUAAU 783 hsa-miR-146a-3p CCUCUGAAAUUCAGUUCUUCAG 784 ENSG000002 38488 AUCAUUUUGCAGCUUAUACAUGUGAUGACUGGGUUUUUUAACUC AUAAGUGAGAUGUGCCUUUCUUACAUCUUAUUAUGACAUUAGUA CAUUACCCAUUUGAUA 785 MIR519D UCCCAUGCUGUGACCCUCCAAAGGGAAGCGCUUUCUGUUUGUUU UCUCUUAAACAAAGUGCCUCCCUUUAGAGUGUUACCGUUUGGGA 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 786 ENSG000002 02498 AGAUCAUUGAUGACUUCCAUAUAUCCAUUCCUUGGAAAGCUGAA CAACAUGAGUGAAAACUCUACUGAAAAAAGAAAAGAAAUGGGAG GCCG 787 hsa-miR-429 UAAUACUGUCUGGUAAAACCGU 788 MIR5687 CCUCACUUAUCUGACUCUGAAAUCUUCUAAAUGGUACCCACUUU AUUUAGAACGUUUUAGGGUCAAAUAAGUACAGG 789 hsa-miR-3922-5p UCAAGGCCAGAGGUCCCACAGCA 790 hsa-miR-6499-5p UCGGGCGCAAGAGCACUGCAGU 791 MIR3683 GGGUGUACACCCCCUGCGACAUUGGAAGUAGUAUCAUCUCUCCCU UGGAUGCUACGAACAAUAUCACAGAAGGUGUACACCC 792 hsa-miR-4515 AGGACUGGACUCCCGGCAGCCC 793 hsa-miR-6884-5p AGAGGCUGAGAAGGUGAUGUUG 794 ENSG000002 02023 CAAAUACAUGAUGAUCUCACCUCAGUUUGAACUCUCUCACUGAU CACUUGAUGACAAUAAAAGAUCUGAUAUUGUG 795 SNORA17 ACUGCCCCUAGAGGCGUUGCAGCUGUGGCUGCCGUGUCACAUCUG UGUCAUUAGGUGGCAGAGAUUAGAGAGGCUAUGUCUACGCUCAG CGUUCUGCCCCGUGAACGUUUGAAUGUUUGAUAGUCUCACACUC 796 ENSG000002 01811 GUCUGCAUUUGAAAGUGAUCAUCAGCUAGCCUGUGUCUUCGUCA UCGAUAGUACAGGCCGGUGAACUGCGCAAAGCAUUUUCUGCAUU UGGAGGGUCCAUCUCUAUCCUUGGAAAUGCUAGUGCUUUUCUCA CA 797 MIR3926-1 AAAAUGGAGCUGGCCAAAAAGCAGGCAGAGACUUUAAAAGCGUC UCUGCCUGCUUUUUGGCCAGCUCCGUUUU 798 ENSG000002 00398 CAGUCAGUGUCGAGAACCUUAUAUUGUUCUGAAGAGAGGUGGUG ACUUAAAAAUCAUGCUCAAUAGGAUUACGCUGAGGCCC 799 MIR 1298 AGACGAGGAGUUAAGAGUUCAUUCGGCUGUCCAGAUGUAUCCAA GUACCCUGUGUUAUUUGGCAAUAAAUACAUCUGGGCAACUGACU GAACUUUUCACUUUUCAUGACUCA 800 hsa-miR-1973 ACCGUGCAAAGGUAGCAUA 801 ENSG000002 38464 AUCCUUUUGUAAGUCAUAAGUGUGAUUGGGUUUUCAUGCUCUUG UGUCAAAUGUGCCUCCCUCAAACCUUGUUACGAAGUGGGCACAC UACCCACCUGAUG 802 SNORD114-10 AAGAUCAAUGAUGACUACUGUUAGUGUAUGAGUUACACAUGAUG AAUACAUGUCUGAAACUCUGAGGUCCA 803 MIR6856 UGGAAAAGAGAGGAGCAGUGGUGCUGUGGCAGUGGCAGAGGUCG CUACAGCCCUGUGAUCUUUCCAG 804 ENSG000002 39111 AACAUUUAAAAAAAUGUAUCAAGGCGUGGUGAUUAGGUUUUCAC ACUCAUGUGUGAGAUGUGCCUCCCUUGAACUUUGUUACAUUGGC ACUUUACCCAUUUGACA 805 hsa-miR-98-5p UGAGGUAGUAAGUUGUAUUGUU 806 MIR 1262 AUCUACAAUGGUGAUGGGUGAAUUUGUAGAAGGAUGAAAGUCAA AGAAUCCUUCUGGGAACUAAUUUUUGGCCUUCAACAAGAAUUGU GAUAU 807 ENSG000001 99262 GUGCAUGUGAUGAAGCAAAUCAGUAUGAAUGAAUUCAUGAUACU GUAAACGCUUUCUGAUGUA 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 808 MIR548AY AGAAGAUGCUUACUACUAGGUUGGUGCAAAAGUAAUUGUGGUUU UUGCAUUUAAAGUAAUGGCCAAAACCGCGAUUACUCUUGCACGA ACCUAACGGUAACACUUCU 809 MIR4754 ACGCGCCUGAUGCGGACCUGGGUUAGCGGAGUGAGGCCCAGUGG UCACCGCCGCCCUCCGCAGGUCCAGGUUGCCGUGCGCAUGUGCCU 810 MIR2113 UUUUCAAAGCAAUGUGUGACAGGUACAGGGACAAAUCCCGUUAA UAAGUAAGAGGAUUUGUGCUUGGCUCUGUCACAUGCCACUUUGA AAA 811 MIR 144 UGGGGCCCUGGCUGGGAUAUCAUCAUAUACUGUAAGUUUGCGAU GAGACACUACAGUAUAGAUGAUGUACUAGUCCGGGCACCCCC 812 ENSG000002 12211 AAGAUUAUAUUUCCAGGGGUCAUUUCUGUGGUUCAUUACUUAAA GGAGUUUCCCCAAGUGUGUAGAGCACUGGAAACCACAGGAAGAU AUGCAAUGUUCUCUCCCGAGCACGAAGCUCGUUCUUGGUGUUGC UUCAUUGCAACUGCCAUUUGCCAUUGAUCAUUGUUUUUUUCUUC CUUUGGGGAGAUUAAGAGGAAGAGGACACAGUCUGAGUGA 813 MIR3135A UCACUUUGGUGCCUAGGCUGAGACUGCAGUGGUGCAAUCUCAGU UCACUGCAGCCUUGACCUCCUGGGCUCAGGUGA 814 ENSG000002 07130 CUCCAUGUGUCUUUGGAACCUGUCAGCUGUGGCAGUUGCCCUUCC UAGCCAUGGAAGAGUAAGUAUAUUCUUGUUUAUUGGCAAAGCUG UCACCAUUUCAUUGGUAUCAGAUUCUGACUUGCACAAGUAACAU uc 815 ENSG000002 38440 AUCCUUUUGAAGUUCAUAAGCAUGGUGAUUGGGUUUUCACACUC AUGUGUGAGAUGUACCACCCUUAAAGCUUGUUAUGAUGUAGGCA CAUUACCCAUCUGACA 816 MIR128-1 UGAGCUGUUGGAUUCGGGGCCGUAGCACUGUCUGAGAGGUUUAC AUUUCUCACAGUGAACCGGUCUCUUUUUCAGCUGCUUC 817 SCARNA5 AGGUCGAUGAUGAUUGGUAAAAGGUCUGAUUGCACUGAAUGUCA CGGUCCCUUUGUUGCCCUCAACUCCCAGCAGCCCAUUUUUUCCCU CCCGUCACAUUUAAGUCAUGUGUAUGGGAUCAUGGAGCAGCUGA UAAUUUGGGAUUCUGUCAGUGUGUGUUUCUGAGAGUGAUCGGCU CACAGCUGACGAGUAUCCAACAAAACCAGUUACACAGGAGACUG ACGAGUGGCAGUCAUGGGUGUGAUGGUGCAUGAUCUCAAGUUUU CAAUCUGAGACCU 818 MIR4432 GCAUCUUGCAGAGCCGUUCCAAUGCGACACCUCUAGAGUGUCAUC CCCUAGAAUGUCACCUUGGAAAGACUCUGCAAGAUGCCU 819 MIR569 GGUAUUGUUAGAUUAAUUUUGUGGGACAUUAACAACAGCAUCAG AAGCAACAUCAGCUUUAGUUAAUGAAUCCUGGAAAGUUAAGUGA CUUUAUUU 820 hsa-miR-586 UAUGCAUUGUAUUUUUAGGUCC 821 hsa-miR-4670-3p UGAAGUUACAUCAUGGUCGCUU 822 ENSG000002 12620 GACUUCUCACUGAGCUUCUUUCUGUCUGUUGCUGGCAGCUUAUG GAUUCAUAUGAGCAGAGAGAAUCACAGAACUAGCAUUACUUUUG UCUUUACAGGAGUAUAUUUGGCUGUCUUGUGAGAUAUUA 823 SNORD114-27 UGGUUCAGUGUUGACUACUGGUGUCGUGUGAGUCAUACAAUGAA UACAUGUCUGGAACUCUGAGGCCCA 824 ENSG000002 51737 AUCCUUUUGCGGUUCAUAAAGAACCAAGAUGACUGGGUUUCAUG CUAAUGCAUGACAUGUGCCUCCCUCAAAUCAUGUUGCCUCAUGG GCUUAUUGGCACAUUACCGUCUGAGG 825 hsa-miR-4472 GGUGGGGGGUGUUGUUUU 2026234022   16 Sep 2026 SEQ ID NO: Sequence Name Sequence 826 hsa-miR-5580-5p UGCUGGCUCAUUUCAUAUGUGU 827 hsa-miR-548n CAAAAGUAAUUGUGGAUUUUGU 828 MIR6841 GUGUUUAGGGUACUCAGAGCAAGUUGUGAAACACAGGUGUUUUU UAACCUCACCUUGCAUCUGCAUCCCCAG 829 MIR4434 UCACUUUAGGAGAAGUAAAGUAGAACUUUGGUUUUCAACUUUUC CUACAGUGU 830 ENSG000002 12558 GUGCCUAAGGUUAACACAGCGCCUUAAGAGGCUAACACAGAAGG GCAAAGUAAGUCUCCAUAAAACCCAGAGAAGACUGUGAACCCCU CUCUGGAUCCUGUCUGGAGUCACAGCU 831 ENSG000002 52517 CCUCAUUGAUUAGUAGCUUCUGACUUUUGUUCUGAGUUUGCUGA AGCUAGAUGCCAUUCCAGAUAAGA 832 hsa-miR-6731-5p UGGGAGAGCAGGGUAUUGUGGA 833 ENSG000002 12270 GUUCAUGAUAAGUAACAUUUCUUCAAUUUGACCUGAUGUGUAUU GAAGAAAACCAGCAUCUGAGG 834 hsa-miR-200c-5p CGUCUUACCCAGCAGUGUUUGG 835 hsa-miR-6514-5p UAUGGAGUGGACUUUCAGCUGGC 836 SNORD115-48 GGGUCAAUGAUGAGAUGUUACCUUGAAGAGAAAUGAUGACGUAA AAAUUAAGUUCAGUUGGAUUACGCUGAGGCCC 837 MIR6779 GAGCUCUGGGAGGGGCUGGGUUUGGCAGGACAGUUUCCAAGCCC UGUCUCCUCCCAUCUUCCAG 838 hsa-miR-4659b-5p UUGCCAUGUCUAAGAAGAA 839 MIR1915 UGAGAGGCCGCACCUUGCCUUGCUGCCCGGGCCGUGCACCCGUGG GCCCCAGGGCGACGCGGCGGGGGCGGCCCUAGCGA 840 MIR573 UUUAGCGGUUUCUCCCUGAAGUGAUGUGUAACUGAUCAGGAUCU ACUCAUGUCGUCUUUGGUAAAGUUAUGUCGCUUGUCAGGGUGAG GAGAGUUUUUG

[00063] The selection of the sncRNAs in the Sentinel™ PCa, CS and HG Tests is independent of PSA, Gleason Score, or biological pathway analysis, and as such is entirely unbiased. Because the algorithm was validated using sncRNA levels obtained from an independent training set made 5 up of a cohort of participants whose core needle biopsy is positive or negative (PCa Test), or patients labeled as either having advanced disease (GG3-5) or not (No evidence of PCa or GG1-1) for the CS Test, (see Table 4, [000103]-[000104] and Table 5 [000109]-[000110]), this statistical methodology minimizes both Type 1 error (false negative) and Type 2 error (false positive), which ensure that the tests rigorously distinguish between none and low-grade cancer, 10 low and intermediate grade cancer and between intermediate and high-grade disease. Based on the 2026234022^  16 Sep 2026 algorithm used in the analysis, the described invention has no false negatives and a very low (<5%) false positive rate.

[00064] Based on the three tests described above, the OpenArray™ platform sequentially interrogates the informative RNA entities present in a single sample of sncRNA extracted from urinary exosomes without compromising sensitivity and specificity of the three tests.

[00065] In one aspect, the disclosure provides a method for diagnosing prostate cancer comprising a platform that allows one to distinguish between clinically significant tumors and indolent tumors and in putting the data based on a subset sncRNAs interrogated into an algorithm that has been validated based on an independent training data set. 10

[00066] In one aspect, the method for diagnosing prostate cancer in a male patient comprising (1) obtaining a biological sample from the patient, (2) detecting the aggregate expression profile of a collection of signature small non-coding RNAs (sncRNAs) that bind to a plurality of nucleic acids or hybridizing probes selected from the group consisting of SEQ ID NOs: 1 - 280; and (3) correlating the aggregate expression profile of the collection of signature sncRNAs using the PCa 15 test to determine whether the patient is at risk for prostate cancer, i.e., having no evidence of prostate cancer or having prostate cancer.

[00067] In another aspect, the disclosure provides a method for screening prostate cancer using the same. In yet another aspect, the disclosure provides a method for predicting the probability of prostate in a subject. 20

[00068] For patients identified to be at risk for prostate cancer (i.e., determined to have prostate cancer), the samples are re-analyzed using the Sentinel™ Clinical Significant (CS) Test to distinguish patients with clinically significant or aggressive prostate cancer (GG2-GG5) from patients with clinically insignificant or indolent (GG1) prostate cancer. In one embodiment, the patient is identified as having aggressive prostate cancer when the aggregate or combined 25 expression profile of a plurality or a collection of signature sncRNAs is higher than or equal to the aggregate expression profile in a prostate cancer biological sample, or identifying the subject as having low-grade prostate cancer when the aggregate expression profile of a collection of 2026234022^  16 Sep 2026 signature sncRNAs is less than or equal to the aggregate expression profile in a low-grade prostate cancer biological sample.

[00069] In one embodiment the biological sample includes, but is not limited to, prostate tissue, blood, plasma, serum, urine, urine supernatant, urine cell pellet, cerebrospinal fluid, semen, prostatic secretions, and prostate cells. In some embodiments, the biological sample is a urine sample. In yet another embodiment, the samples are exosomes isolated from the urine sample. In a preferred embodiment, the sample is sncRNAs isolated from exosomes derived from the urinary sample.

[00070] Exosomes are small extracellular vesicles (EV) that originate in the endosomal 10 compartment of eukaryotic cells. They are found in biological fluids including blood, urine, semen and cerebrospinal fluid. The biogenesis of exosomes is not well-understood; however, it is generally accepted that they arise at the point at which the early endosomal pathway bifurcates to form late endosomes and multi-vesicle endosomes, the first stages of the exosomal pathway. Exosomes contain sncRNAs including miRNAs and small nucleolar RNAs (snoRNAs) which are 15 derived from the cytoplasm and nucleolar region of the cell respectively. The presence of exosomes and EVs in the tumor microenvironment have been associated with malignancy in a number of tumor types including prostate cancer and other cancers.

[00071] In certain embodiments, sncRNAs isolated from exosomes are derived from semen, blood, prostatic secretions and cerebral spinal fluid. In further embodiments, the exosomes are isolated 20 from the cancer cells including prostate cancer cells, lymphocytes, and cells from prostate tissues.

[00072] The method for isolating exosomes is well known in the art and can be carried out using kits such as the Exosome RNA Isolation Kits (Norgen Biotek Corp., Ontario, CA). sncRNA yields can be quantified by fluorimetry (Qubit, Thermo Fisher Scientific), and the quality of the sncRNAs isolated is assessed using an Agilent 2100 bioanalyzer. 25

[00073] The RNAs extracted from the isolated exosomes are a mixture of small RNAs referred collectively as small non-coding RNAs (sncRNAs), which include miRNA, snoRNA, scaRNA, siRNA, snRNA, and exRNA. Due to their size (< 200 nucleotides), the sncRNAs are readily extracted from biological samples, including, for example, Formalin-Fixed Paraffin-Embedded 2026234022.^   16 Sep 2026 (FFPE) tissue or urine. The sncRNAs are not degraded during fixation or extraction, obviating the problems intrinsic in extraction of mRNA from FFPE tissues. Yield of sncRNAs of about 10 ng from the biological samples is sufficient for multiple analyses using the Exosome RNA Isolation Kits of Norgen disclosed above.

[00074] The extracted sncRNAs are reversed transcribed into cDNAs, which are more stable than RNA, thus allowing for longer storage. The resulting cDNAs are hybridized against a selected set or collection of signature sncRNAs probes or genome array or micro-array chips, such as the miR 4.0 arrays (ThermoFisher Scientific) for further analysis. The selection of the informative set of sncRNA probes is independent of PSA, Gleason Score or biological pathways. The selected set or collection of signature sncRNAs comprises SEQ ID NOs: 1-280, 281-560 and 561-840. The number of sncRNA sequences or probes in the set range from 145 - 196, preferably not more than 280 sncRNA sequences for in putting in the Sentinel™ PCa, Sentinel™ CS and Sentinel™ HG Tests. The tests are more precise with up to 280 sncRNA sequences added to the algorithm. Addition of more than 280 sncRNA sequences in the analysis does not increase the precision of 15 the assay.

[00075] Real time-PCR or RT-PCR, often known as qPCR or RT-qPCR, is conducted to quantify the absolute amount of a target sequence or to compare relative amounts of a target sequence between samples. The RT-qPCR monitors amplification of the target in real-time via a targetspecific (probes) fluorescent signal emitted during amplification. Because background 20 fluorescence occurs during most RT-qPCR reactions despite the use of sequence specific probes against the targets, the issue of background fluorescence signal can be addressed by considering two values in real-time PCR: (1) the threshold line (Ct) and (2) the cycle quantification (Cq). value. The threshold line (Ct) is the level of detection when a reaction reaches a fluorescent intensity that is above background levels, that is a point where the reaction curve begins the exponential phase 25 (inflexion point). The Cq or cycle quantification value is the PCR cycle number at which the sample’s reaction curve intersects the threshold line. The Cq, value therefore, indicates how many cycles it took to detect a real signal from the samples, i.e., time to event, where the event is the saturation of the fluorescence, indicating the maximum level of detection. Because RT-qPCR runs provide a reaction curve for each sample, there will be many Cq values. The software in the PCR 30 cycler will calculate and chart the Cq value for each of the samples. Values are inverse to the ^026234022   16 Sep 2026 amount of target nucleic acid in the sample, and correlate to the number of target copies in the sample. Lower Cq values (typically below 29 cycles) indicate high amounts of target sequence. Conversely, higher Cq values (above 38 cycles) indicate lower amounts of the target nucleic acid in the sample. However, the time to event value can be obtained when the slope of the reaction curve becomes zero, rather than using the Cq when the slope is at a maximum. In one embodiment, the sncRNAs are interrogated using RT-qPCR. In another embodiment, the sncRNAs are interrogated using qPCR. In a further embodiment, the sncRNAs isolated from the urinary exosomes are interrogated using the Affymetrix GeneChip™ miRNA 4.0 Array following manufacturer’s instructions.

[00076] These informative sequences obtained from the training set (usually 280 sequences) are then transferred to the OpenArray platform. Patient samples of unknown status are then interrogated on the OpenArray platform and the Sentinel Score is determined using the Classification Algorithm. The status of the patient is determined from the Sentinel Score.

[00077] In one embodiment, the sncRNAs levels from a patient of unknown prostate cancer status 15 are interrogated on the OpenArray platform and Sentinel score from a patient of unknown disease status can then be compared to that of the training set to determine the status of the patient

[00078] In one embodiment, the data obtained from the analysis of sncRNAs levels in the test sample from a patient of unknown prostate disease status (test sample) using, for example, by RT-qPCR on the Affymetrix GeneChip™ miRNA 4.0 Array can be compared to those from healthy 20 patients (no evidence of cancer) or healthy cells obtained from the subject with prostate cancer. The data obtained from the analysis of sncRNA levels from the test sample can also be compared to clinical baselines established by analyzing healthy (no cancer) and non-healthy (with genitourinary cancer) patients, and the non-healthy patients be further categorized into different specific cancer types, which can be further categorized into different stages or severity of the 25 specific cancer type (for example, prostate cancer, etc.) and different stages of the specific disease. In one embodiment, the data obtained from the analysis of sncRNAs expression in the test sample using RT-qPCR on the Affymetrix GeneChip™ miRNA 4.0 Array are compared to those from healthy patients (no evidence of cancer) or the data can also be compared to clinical baselines established by analyzing healthy (no cancer) and non-healthy (with having prostate cancer) 2026234022.^   16 Sep 2026 patients, and the non-healthy patients be further categorized into different stages of prostate cancer (GG1 and GG2-GG5 or GG1+2 and GG3-GG5).

[00079] In some embodiments, the method uses an OpenArray™ technology (ThermoFisher Scientific) to interrogate a panel of sncRNAs (e.g., miRNAs, snoRNAs). The OpenArray™ technology uses a microscope slide-sized plate having 48 subarrays. Each subarray has 64 through-holes, and each hole is 300 pm in diameter and 300 pm deep. The holes are treated with hydrophilic and hydrophobic coatings in order to retain reagents in the through-holes via surface tension. The OpenArray™ technology, with its 3,072 through-hole (48x64), provides a system for streamlining real-time PCR studies that use large number of samples, assays or both. The system thus allows processing of samples for gene expression in large number in short time periods using microquantities of samples and reagents. The method employs an algorithm that relies on the expression level of each of the sncRNAs and the grading of the biopsies (at least 12 core needle biopsies). In the case of prostate cancer, the methodology is independent of serum Prostate Specific Antigen (PSA) levels, Gleason Score (neither of which are meaningful markers of tumor 15 progression), or patient age. The methodology is also independent of any analyses of biological pathways. The present method uses sncRNAs isolated from the subject (e.g., urinary sample, urinary exosome, or prostate tissue samples) to stratify men into those that have prostate cancer (both indolent (clinically insignificant) or aggressive (clinically significant)) and those that do not. This methodology can replace serum PSA as the major screening assay for prostate cancer. 20

[00080] In one aspect, the disclosure provides a method that distinguishes clinically significant prostate cancer based on the aggregate expression profile of a collection of signature sncRNAs interrogated that is subjected to a classification algorithm that is independent of pathology (Gleason Score), tumor volume or PSA. The RNA extracted from biological samples of patients with known cancer outcomes are reverse-transcribed and hybridized against a full-genome array 25   (e-g-, Affymetrix GeneChip miR 4.0) containing sncRNAs. Small non-coding RNAs that are differentially regulated in clinically significant prostate tumors are identified. In one embodiment, the absolute value of the signal from the Open Array identifying the sncRNAs that hybridize to the probes for SEQ ID NOs: 281-561, is compared to the aggregate expression profile found in clinically significant (GG2-5) prostate cancer tumors. In another embodiment, the aggregate value 30 of the signal from the Open Array identifying the sncRNAs that hybridize to the probes for SEQ 2026234022.^   16 Sep 2026 ID NOs: 561-840, is compared to the absolute expression profile found in clinically low and favorable intermediate grade (GG1 + GG2) versus unfavorable intermediate and high grade (GG3-GG5) prostate cancer tumors.

[00081] The disclosed method provides a robust and accurate determination of prostate cancer prognosis in 72-96 hours from the time when the urine sample is received to obtaining a Sentinel Score. In another embodiment, the aggregate expression profile of the identified sncRNAs that bind to SEQ ID NOs: 281-560 and 561-840 is compared to the aggregate expression profile of sncRNAs in clinically significant prostate cancer. In another embodiment, the aggregate expression profile of the identified sncRNAs interrogated that bind to SEQ ID NOs: 281-560 and 561-840 is compared to the aggregate expression profile of sncRNA in clinically significant prostate cancer. In a further embodiment, the aggregate expression profile of the identified sncRNAs that bind to SEQ ID NOs: 281-560 and 561-840 is compared to the aggregate expression profile of sncRNA in clinically significant prostate cancer sample. As a result, appropriate treatment options (or the lack thereof) can be initiated. 15

[00082] The terms relative aggregate expression profile are used interchangeably. The aggregate expression profile of at least a plurality of sncRNAs are combined and compared to the same aggregate expression profile in clinically significant prostate cancer tissue. In some embodiments at least 40 sncRNAs are combined and compared to the same aggregate expression profile in clinically significant prostate cancer tissue. In some embodiments at least 90 sncRNAs are 20 combined and compared to the same aggregate expression profile in clinically significant prostate cancer tissue. In some embodiments at least 150 sncRNAs are combined and compared to the same aggregate expression profile in clinically significant prostate cancer tissue. In some embodiments at least 200 sncRNAs are combined and compared to the same aggregate expression profile in clinically significant prostate cancer tissue. In a preferred embodiment, at least 224 sncRNAs and 25 not more than 280 sncRNAs are combined and compared to the same aggregate expression profile in clinically significantly prostate cancer tissue. In certain embodiments, a higher aggregate expression profile as compared to the aggregate expression profile in a low-grade prostate cancer tissue indicates the patient has aggressive prostate cancer and treatment is required. In other embodiments, an aggregate expression profile equal to or lower than the aggregate expression 2026234022^  16 Sep 2026 profile in a low-grade prostate cancer tissue indicates the patient does not have aggressive prostate cancer and monitoring but not treatment may be required.

[00083] In some embodiments, the aggregate expression profile of selected sncRNAs is an aggregation of various types of modulated expression of the sncRNAs. The modulated expression can be decreased or increased expression profile relative to the same sncRNA in other tissue / tumor types, such as healthy prostate tissue, low-grade prostate cancer tissue, or high-grade prostate cancer tissue.

[00084] In other embodiments, the aggregate expression profile of selected sncRNAs can be an aggregation of the decreased aggregate expression profile of certain sncRNAs as well as an 10 aggregation of the increased aggregate expression profile of other sncRNAs in the same tissue sample. For example, a progression score, or aggregate expression profile of a collection of signature sncRNAs may include one or more sncRNAs with decreased aggregate expression profiles relative to another tissue type or other sncRNAs in the same tissue sample, while one or more of the remaining sncRNAs exhibit increased aggregate expression levels relative to another 15 tissue type or other sncRNAs in the same tissue sample. The aggregate expression profile of the collection of differently modulated sncRNAs provides a sophisticated, unbiased, indication of whether a prostate tumor is clinically significant. Unlike other methods that merely evaluate the presence or absence, or simple increase or decrease of individual target molecules, as compared to normal tissue, the methods disclosed provide a truly unbiased, independent, and multi-variable 20 analysis of a prostate tissue sample thereby allowing for a surprisingly accurate diagnosis of whether a prostate cancer tumor is clinically significant.

[00085] In some aspect, the method provides for the use of the aggregate expression profile of the collection of signature sncRNAs for monitoring metastasis and cancer staging.

[00086] In another aspect, the disclosure provides a method for detecting a urological malignancy 25 based on the aggregate expression profile of the collection of signature sncRNAs interrogated and subject to analysis using the classification algorithm disclosed. In some embodiments the malignancy is cancer of the prostate. 2026234022.^   16 Sep 2026

[00087] The present disclosure provides an algorithm-based molecular diagnostic assay for predicting a clinical outcome for a patient with prostate cancer. The expression level of one or more sncRNAs may be used alone or arranged into functional gene subsets to calculate a quantitative score that can be used to predict the likelihood of a clinical outcome.

[00088] A “quantitative score” is an arithmetically or mathematically calculated numerical value for aiding in simplifying or disclosing or informing the analysis of more complex quantitative information, such as the correlation of certain expression profile of the disclosed sncRNAs or sncRNAs subsets to a likelihood of a clinical outcome of a prostate cancer patient. A quantitative score may be determined by the application of a specific algorithm. The algorithm used to calculate the quantitative score in the methods disclosed may group the expression profile values of the sncRNAs. The grouping of sncRNAs may be performed at least in part based on knowledge of the relative contribution of the sncRNAs according to physiologic functions or component cellular characteristics, such as in the groups discussed herein. A quantitative score may be determined for a sncRNA group (“sncRNA group score” or the Sentinel™ Score). The formation of groups, in 15 addition, can facilitate the mathematical weighting of the contribution of various aggregate expression profile of genes or gene subsets to the quantitative score. The weighting of a sncRNA or sncRNAs group representing a physiological process or component cellular characteristic can reflect the contribution of that process or characteristic to the pathology of the cancer and clinical outcome, such as recurrence or upgrading / upstaging of the cancer. The present invention provides 20 a number of algorithms for calculating the quantitative scores. For example, the Classification algorithm in the present disclosure works the same way for developing the Sentinel Scores for distinguishing different disease states. The Classification algorithm selects different sncRNA sequences from the training data set for clinically significant and insignificant disease states.

[00089] On the other hand, the Selection algorithm test how well the aggregate expression profile 25 of the urinary exosomal sncRNAs correlate with pathological stage of the disease (cancer / no cancer) in a large population of participants with known pathology. The Selection algorithm individually assessed how well each of the 6,599 sncRNAs interrogated on the miR4.0 arrays correlates the known pathology of the participant. It then iteratively assesses all combinations of 2 sncRNAs, 3 sncRNAs or 4 sncRNAs of the 6,599 sncRNAs interrogated by the miR 4.0 Arrays, 30 followed by examination of each individual sncRNA using a leave-one-out strategy to assess the ^026234022   16 Sep 2026 importance of each individual sncRNA in the pathology of the disease. The Sentinel Score for a patient with unknown disease status is then determined by interrogating selected sncRNAs using the Open Array and the clinical status is determined by comparing the score to that from the training data sets. In an embodiment of the invention, an increase in the quantitative score indicates an increased likelihood of a negative clinical outcome.

[00090] Based on the quantitative score and cumulative or absolute or aggregate expression profile, methods of treatment can also be decided. The methods of treating prostate cancer include surgery for complete surgical removal of prostate tissue, administering an effective dose of radiation, and administering a therapeutically effective amount of a medication for the treatment of prostate cancer, or a combination of the above.

[00091] The algorithm-based assay and associated information provided by the practice of the methods of the present invention facilitate optimal treatment decision-making in prostate cancer. For example, such a clinical tool would enable physicians to identify patients who have a low likelihood of having an aggressive cancer and therefore would require no further medical 15 intervention except for a routine follow-up or active surveillance every 3 months, 6 months or 12 months. Patients with no cancer do not require medical intervention return for follow-up once every year. Patients at risk for developing aggressive cancer require medical intervention, which includes but is not limited to treatment with one or more chemotherapeutic agents (e.g., taxotere, cabazitaxel, docetaxel, mitoxantrone, epirubicin, paclitaxel and estramustine, etc.), hormone 20 therapy (e.g., lutenizing hormone releasing hormone agonists to prevent production of testosterone such as leuprorelin, goserelin and triptorelin or anti-androgen drugs that prevent testosterone from reaching the cancer cells, e.g., bicalutaminde and nilutamide), immunotherapeutics, radiation, cryotherapy, surgery or a combination thereof.

[00092] Patients who undergo treatment are monitored using the disclosed method to determine 25 the patients’ response to treatment. In one aspect, the disclosure provide a method for determining the patient’s response to treatment comprising: (i) obtaining a biological sample from a patient, (ii) detecting the aggregate expression profile of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample wherein the collection of sncRNAs comprises SEQ ID NOs: 1-280, 281-560 and 561-840 (iii) correlating the aggregate expression profile of sncRNAs 2026234022   16 Sep 2026 of SEQ ID NOs: 1-280, 281-560 and 561-840 from the subject after treatment by comparing the aggregate expression profile of SEQ ID NOs: 1-280, 281-560 and 561-840 to that prior to treatment, (iv) determining if the patient is responsive to the treatment, and if there is a need for modification of the treatment. In one embodiment, the method further compares the resulting aggregate expression profiles of a signature collection of sncRNA from (iii) above is then compared to the aggregate expression profile of a signature collection of sncRNA for the large training data set from a target population having prostate cancer with known Grade groups to determine if the (a) patient prostate cancer is stable (no apparent change compared to the Grade group), (b) the patient is responsive to the treatment, i.e., patient gets better (the results show tumors that resembles tumors with lower grade group) or (c) the patient is non-responsive (patient gets worse when the results show tumors that resembles tumor of higher Grade group) based on the aggregate expression profile of a collection of signature sncRNAs and Sentinel Score for that Grade group, and if there is a need for modification of the treatment. Treatment modification includes but not limited to adjusting the concentration or amounts of chemotherapeutic agents, 15 radiation, immunotherapeutic or hormone administered, adding or removing one or more of agents used.

[00093] In another aspect, the disclosure provides a method for determining the disease recurrence, disease progression or likelihood of survival based on the aggregate expression profile of a collection of signature sncRNAs comprising SEQ. ID. NOs: 1-280, 281-560 and 561-840 by 20 comparing the aggregate expression profile of SEQ. ID. NOs: 1-280, 281-560 and 561-840 in a training dataset and the patient’s earlier profile.

[00094] In another aspect, the disclosure provides a system for determining whether a patient has no cancer or has cancer and classifying the subject with cancer as (i) indolent (low grade, GG1), (ii) intermediate or high grade (GG2-GG5), (iii) low / intermediate risk (GG1-GG2) or (iv) 25 aggressive (high grade, GG3-GG5) prostate cancer comprising at least three processors configured to (a) interrogate sncRNA sequences for informative sequences, (b) determine and compare a Sentinel Score to determine if the subject has prostate cancer or no prostate cancer and to classify subject determined to have cancer to the various Grade groups, e.g., low grade, intermediate / high grade, low / intermediate risk or aggressive grade cancer. Subjects determined to have no evidence 30 of cancer do not require medical intervention and would return for follow-up once every year. 2026234022^  16 Sep 2026 Subjects determined to have low grade or low / intermediate grade prostate cancer would require no medical intervention except for a routine follow-up or active surveillance every 3, 6, or 12 months, and subjects determined to have intermediate / high grade or aggressive prostate cancer require medical intervention. EXAMPLES

[00095] This and other aspects of the present invention are further illustrated by the following nonlimiting examples. EXAMPLE 1 Study Populations 10

[00096] Two independent patient cohorts were used for the development and validation of the Sentinel™ PCa and Sentinel™ CS Tests. The clinical and demographic characteristics of the 233 participants used to develop the Sentinel™ PCa to classify patients as having cancer or no cancer was based on the statistical analysis of a collection of signatures snRNAs. For patients classified as having cancer, patients with GG1 (indolent, low risk cancer) are distinguished from GG2-5 15 (respectively as intermediate, high-risk and aggressive cancers) using the Sentinel™ CS Tests, which is also based on the statistical analysis of another collection of signatures the sncRNAs using a second Classification algorithm to classify tumors into GG1 versus GG2-5. The sncRNAs in both tests are interrogated by the Affymetrix miR 4.0 array. Urine Collection and Processing 20

[00097] Urine samples for the development of the Sentinel™ PCa and CS Tests and the US-based cohort of the retrospective study were collected on the day of visit for clinical workup at two clinical sites: Albany Medical Center (Albany, NY, USA) and SUNY Downstate Medical Center (Brooklyn, NY, USA). Remaining samples for the retrospective study were retrieved from the 25 GUBioBank, University Health Network, Toronto CA and shipped frozen at -20°C in bulk to the miR Scientific laboratories. Patient information was collected and anonymized as approved by Institutional Review Board at each participating site. Prostate cancer diagnosis was obtained by 2026234022^  16 Sep 2026 histopathological grading of core-needle biopsies; the percentage of tumor per core and number of positive cores were used to assess the grade group (GG).

[00098] Urine samples were centrifuged to remove free cells and debris. RNA was extracted using Exosome RNA Isolation Kits (Norgen Biotek, ON) according to the manufacturer's instructions. sncRNA yields were quantified by fluorimetry (Qubit, Thermo Fisher Scientific) and RNA samples were stored at -80°C until analysis. Microarray analysis of total exosomal sncRNAs

[00099] sncRNAs were interrogated using the Affymetrix GeneChip™ miR 4.0 Array following the manufacturer's instructions. MAIME-compliant raw data files for the 235 patients analyzed on 10 these arrays have been deposited in NCBI’s Gene Expression Omnibus. (Edgar R et al. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acid Res 2002 30:207). Six thousand five hundred and ninety-nine sncRNAs in the training set were interrogated on the Affymetrix GeneChip™ miRNA 4.0 Array. [000100] The small non-coding RNA entities interrogated for each participant were analyzed 15 using proprietary Selection and Classification Algorithms. The most informative sequences for distinguishing between cancer and non-cancer subjects (SEQ ID NOs: 1-280) and between Grade Group 1 and Grade Group 2-5 patients were identified. (SEQ ID NOs: 281-842) QuantStudio OpenArray™-based interrogation of exosomal sncRNAs [000101] cDNA synthesis, / ^^-amplification of selected miRNAs: For analysis of exosomal 20 miRNA, total sncRNA was reverse transcribed in separate reactions with three specific miRNA stem-loop primer pools with the TaqMan™ MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific) as recommended by the manufacturer. The miRNA cDNA pools were enriched individually with Pre-Amp primer pools for 16 cycles (95°C for 10 min, 55°C for 2 min, 72°C for 2 min, 95°C for 15 sec and 60°C for 4 min repeated 16 cycles, 99.9°C for 10 min), and interrogated 25 on the QuantStudio OpenArray™ on three 56-entity sub-arrays following the manufacturer’s recommendations. 2026234022   16 Sep 2026 [000102] cDNA synthesis, pre-amplification and interrogation of selected snoRNAs: Total sncRNA was reverse transcribed with High-Capacity cDNA Reverse Transcription Kit with a single Pre-Amp primer pool (Thermo Fisher Scientific) as recommended by the manufacturer. snoRNA cDNA products were enriched by preamplification (95°C for 10 min, 95°C for 15 sec and 60°C for 4 min repeated for 14 and 18 cycles respectively, and 99°C for 10 min) and interrogated on two 56-entity sub-arrays. Statistical Analysis: [000103] The Sentinel™ PCa Test is based on Classification Algorithm that has been trained on a cohort of participants whose core-needle biopsy is positive or negative. The Classification 10 Algorithm takes as input the sncRNA expression signature for a participant with unknown disease status and produces a Sentinel™ Score; the participant is classified by comparing the Sentinel™ PCa Score to the pre-determined cutoff value that maintains the sensitivity for classifying a future patient with unknown disease status (but known expression signature), at a user-defined level (95% or greater). A second classification algorithm, the Sentinel™ CS Test operates analogously to the 15 Sentinel™ PCa Test. However, the classification algorithm for the Sentinel™ CS Test is trained on a cohort of patients labeled as low grade (GG1) and a second cohort of patients labeled as favorable intermediate to high grade prostate cancer (GG2-GG5). The third classification algorithm, the Sentinel™ HG Test is trained on a cohort of patients determined to be low- and favorable intermediate-risk (GG1+GG2) prostate cancer and a second cohort characterized as 20 unfavorable intermediate risk and high-risk (GG3-GG5) [000104] The Sentinel™ Testing paradigm operates in two or three layers. First, it uses the sncRNA signature from the participant’s urine to input to the classification rule of the Sentinel™ PCa Test to determine if cancer is present; second, for those patients diagnosed with cancer, the Sentinel™ CS Test determines whether the cancer is low risk (GG1) or not; thirdly the Sentinel™ 25 HG Test determine whether the tumor is unfavorable intermediate or high risk (GG3-GG5) or not. (See Figure 4) 2026234022   16 Sep 2026 Table 4: Demographics and Clinical Characteristics of Cohort Used to Develop Classification Algorithm. Control (%) Cancer (%) GG1 GG2 GG3 GG4 GG5 Total 89 (37.9%) 90 (38.3%) 34 (14.5%) 9 (3.8%) 7 (3.0%) 6 (2.5%) Age Range Mean ± SD 23 - 89 65.6 ± 13.0 53 - 83 67.3 ± 6.7 50-81 66.3 ±6.6 60-81 72.1 ±6.6 51-74 63.0 ±7.5 59-74 67.2 ±5.5 BMI Range Mean ± SD 18.9-54.1 29.3 ±5.6 20.7-53.3 28.4 ±4.9 22.0-44.1 28.2 ±3.8 26.1-36.1 30.1 ±3.3 16.0-44.1 29.2 ±8.5 27.5-41.2 32.7 ±5.2 Race NHW NHB not reported 72 3 14 78 7 5 30 4 0 7 1 1 7 0 0 6 0 0 PSA Range Mean ±SD N / A 0.55-28.2 6.3±4.0 2.1-49.9 7.7±7.9 5.6-28.2 10.5±7.4 6.9-40.8 18.8±10.7 5.5-17.8 8.7±4.6 5                 *Under exempt study status, PSA levels were not available for patients with no evidence of cancer. [000105] Table 4 established the training datasets used to develop the Sentinel™ tests. Of the 235 patients, patients included in the “no cancer” cohort (89 patients) were carefully selected from age-10 matched men who were seen at urology clinics for issues unrelated to urological oncology (n=58), and from men who had one or more 12-needle diagnostic core needle biopsies that showed no evidence of prostate cancer (n=30). [000106] Patients in the “cancer” cohort (n=146) were selected based on the histopathology of the core needle biopsies. Of the 146 “cancer” cohort, 90 patients were classified as GG1 cancer, 56 15 patients were classified as GG2-5. [000107] Of the 6,599 microarray sequences from the training data set interrogated using the proprietary Selection Algorithm to separate that are “informative” for the outcome versus those 2026234022.^   16 Sep 2026 that are not, only 400-600 are informative. By outcome, it is meant to mean sncRNA sequences that impact the algorithm when each sequence is added to predict whether the subject of unknown disease status has or has no prostate cancer and the stages of the cancer (indolent versus aggressive). [000108] The statistical analysis used is based on the ability to identify sequences that have hidden associations with outcome that is only observed after conditioning on other sequences. Of the 400600 informative sncRNA sequences, 280 sncRNA sequences were used in the Classification algorithm as a basis to define an expression signature for the Discovery PCa Test (Figure 5), and Discovery HG Test (Figure 7) and Discovery CS Test (Figure 6). The subset of informative sncRNAs considered to be of the highest importance were then identified for each Test (Figures5, 7 and 6, respectively (right panels)). [000109] These 280 sncRNAs were combined to design an OpenArray™ platform that provides the basis for the Sentinel™ PCa and CS Tests. The Sentinel™ PCa Test incorporates the aggregate expression profiles of 84 unique sncRNAs: 60 miRNAs and 24 snoRNAs, for classifying a subject 15 with unknown disease status as having prostate cancer or no prostate cancer. Similarly, the Sentinel™ CS Test utilizes 135 unique sncRNAs: 105 miRNA and 30 snoRNAs for classifying a subject having prostate cancer as having GG1 (indolent) prostate cancer or GG2-GG5 (aggressive) prostate cancer. In addition, 61 sncRNAs (25 miRNAs and 36 snoRNAs) are informative in both Tests. The OpenArray™ platform sequentially interrogates the informative RNA entities present 20 in a single sample of sncRNA extracted from urinary exosomes without compromising sensitivity and specificity of the two tests. EXAMPLE 2 Validation of the Sentinel™ PCa, Sentinel™ CS and Sentinel™ HG Tests in a Case Control Patient Cohort 25   [000110] The performance characteristics of the Sentinel™ PCa and Sentinel™ CS Tests using the OpenArray™ platform was established in a case control study of 1436 patients (Table 5). 2026234022   16 Sep 2026 Table 5: Demographics and Clinical Characteristics of Case-Control Sample Used to Validate the Sentinel™ PCa and Sentinel™ HG Tests. NEPC (%) Cancer (%) GG1 GG2 GG3 GG4 GG5 Total 568 (39.5%) 437 (30.4%) 162 (11.3%) 131 (9.1%) 66 (4.6%) 72 (5.0%) Age Range Mean ± SD 23-90 65.8 ±9.0 46-93 70.3   ± 8.8 50-96 71.1   ± 8.2 49-95 74.2   ± 8.7 50-93 72.4 ± 9.7 54-91 72.7 ±9.5 Race NHW NHB not reported 458 61 49 233 20 184 96 11 55 47 5 79 26 9 31 36 3 33 PSA Range Mean ±SD N / A 0.21     - 108 6.4 ± 6.2 1.24     - 32.0 7.4 ±4.7 1.67    - 138 14.0   ± 19.4 1.93 - 1400 57.6±194.0 1.98-199 25.9 ±36.0 *Under exempt study status, PSA levels were not available for individual patients with no evidence of cancer, all were less than 3.0ng / mL. 5 [000111] The performance characteristics of the Sentinel™ PCa Test were determined in case control cohort of 600 men whose demographics are shown in Table 5. The scatter plot of the Sentinel™ PCa Scores is shown in Figure 8A, with the corresponding Receiver Operator curve (ROC) curve in Figure 8C. As summarized in Table 6, the Sentinel™ PCa Test correctly classifies 10   281 / 300 patients as having cancer and 275 / 300 patients as having no cancer (Sensitivity 93.7%, Specificity 91.7%). [000112] The performance characteristics of the Sentinel™ CS Test were determined in a testing cohort of 600 men. The scatter plot of the Sentinel™ CS Scores is shown in Figure 9 A, with the corresponding Receiver Operator curve (ROC) curve in Figure 9C. As summarized in Table 6, 15 the Sentinel™ CS Test correctly classifies 143 / 154 patients as high grade (GG3-GG5) and 132 / 143 as not high grade (Sensitivity 92.9%, Specificity 90.4%). 2026234022   16 Sep 2026 [000113] The performance characteristics of the Sentinel™ HG Test were determined in a testing cohort of 600 men. The scatter plot of the Sentinel™ HG Scores is shown in Figure 10A, with the corresponding Receiver Operator curve (ROC) curve in Figure 10C. As summarized in Table 6, the Sentinel™ CS Test correctly classifies 94 / 100 patients as high grade (GG3-GG5) and 191 / 200 as not high grade (GG1+GG2) (Sensitivity 94%, Specificity 95.5%). Table 6: Empirical Sensitivity, Specificity, PPV and NPV for Sentinel™ PCa, Sentinel™ CS and Sentinel™ HG Tests Sentinel','! PCa 1-Error Rate Numerator Denominator Proportion 95% lower CI 95% upper CI Sensitivity 281 300 0.937 0.905 0.960 Specificity 275 300 0.917 0.882 0.944 PPV 281 306 0.918 0.884 0.945 NPV 275 294 0.935 0.903 0.959 Sentinel™ CS 1-Error Rate Numerator Denominator Proportion 95% lower CI 95% upper CI Sensitivity 143 154 0.929 0.880 0.962 Specificity 132 146 0.904 0.848 0.944 PPV 143 157 0.911 0.859 0.948 NPV 132 143 0.923 0.871 0.959 Sentinel™ HG 1-Error Rate Numerator Denominator Proportion 95% lower CI 95% upper CI Sensitivity 94 100 0.940 0.880 0.975 Specificity 191 200 0.955 0.919 0.978 PPV 94 103 0.913 0.846 0.956 NPV 191 197 0.970 0.938 0.987 *NPV or Negative Predictive Value is the probability that following a negative test result, that individual 10                 will not have that specific disease. NPV = _____True Negative________ True Negative + False Negative "Sensitivity of a test is the proportion of people who test positive among all those who actually have the disease. 15                 Sensitivity = True Positive True Positive + False Negative ***The specificity of a test is the proportion of people who test negative among all those who do not actually have that disease. Specificity = ______True Negative_________ 20                              True Negative + False Positive ****PPV or Positive Predictive Value is the probability that following a positive test result, that individual will truly have the specific disease. PPV = True Positive True Positive + False Positive 25 2026234022   16 Sep 2026 EXAMPLE 3 Safety and Scientific Validity Study to Identify Clinically Insignificant PCa with Sceintific Sentinel™ Platform. [000114] The purpose the clinical study is to validate the performance characteristics of the Scientific Sentinel™ PCa Test and the Scientific Sentinel™ CS Test to (1) identify patients with prostate cancer in men of age 50-80 years with suspicion of prostate cancer for whom needle biopsy is performed, and (2) to distinguish men of ages 50-80 years with clinically significant prostate cancer (Grade 2 or above) from men with clinically insignificant prostate cancer (Grade Group 1). These classifications will be compared to the results of core needle biopsies, and of 10 radical prostatectomy (where available). The sensitivity, specificity, positive and negative predictive values will be established. This study is a prospective, observational and non-interventional study. The informed participants will provide two or more urine samples over the course of the study and consent to share relevant anonymized clinical data with the study team. [000115] Participants between the age of 50 and 80 years with suspicion of prostate cancer for 15 whom a core-needle biopsy is performed, and otherwise meeting the inclusion and exclusion criteria, will be enrolled and will provide urine samples for the Sentinel™ PCa / CS Tests. The study will evaluate the properties of the Sentinel™ PCa Test and the Sentinel™ CS Test that is based on the disclosed method using Classification Algorithms to identify future patients with prostate cancer and to classify prostate cancer as clinically significant or clinically insignificant. 20   [000116] The "gold standard" assessment of cancer will be made from the results of core needle biopsies: participants with no positive cores will be designated "cancer-free"; participants with cancer in one or more cores will be designated as having "Clinically Insignificant" prostate cancer provided all cores with cancer have no greater than Grade Group 1 histopathology; participants will be designated as having "Clinically Significant" prostate cancer if any cores have Grade 25 Groups 2-5. [000117] Each study participant enrolled will be followed for one year. Participants will provide urine samples during each visit, and all relevant clinical data, including re-biopsies, PSA results and pathology report from radical prostatectomy (if administered as part of clinical care) will be 2026234022^  16 Sep 2026 obtained. The follow-up results, if available will be used for outcome analysis. For each urine sample provided, the Sentinel™ PCa and CS tests will be determined and compared with the available 1 year follow up outcome data to inform the sensitivity, specificity, positive and negative predictive values of the tests. [000118] The Classification Algorithm employed functions by controlling sensitivity at, or above, a pre-specified level, denoted 1-a; for example, the value that has been assumed in this design is a=0.05, so that sensitivity is at least 95% in the population. Note that the value of a represents the false-negative rate of the test, i.e., the test is (incorrectly) negative for a patient who is truly positive. 10   [000119] To describe how the cutoff the Sentinel™ PCa Score is calculated to control sensitivity, for each participant in the training dataset, the Sentinel™ PCa Score will be calculated using the remaining members of the training dataset and only his small non-coding RNA (sncRNA) sequence; that is, the true disease status of each patient in the training dataset will be blinded, thereby mimicking the setting for classification of a future patient. The cutoff used in the 15 Sentinel™ PCa Test is then calculated so that the empirical sensitivity over patients in the training dataset with prostate cancer corresponds to the value that provides an upper one-sided 95% confidence interval for population sensitivity for a future patient of at least 1-a. [000120] With this cutoff for the Sentinel™ PCa Score determined from the training dataset a priori, the corresponding values of sensitivity, specificity, positive and negative predictive values 20 will be calculated, along with a corresponding upper 95% confidence interval, on the prospective participants data accrued in this proposed study, with each biopsy result blinded, i.e., using only the participant's sncRNA sequence. Note that these error rates refer to the classification of a future patient with unknown disease status. [000121] Any patent, patent application publication, or scientific publication, cited in this 25 application, is incorporated by reference in its entirety. 2026234022   16 Sep 2026 Disclosed herein are the following forms: 1.     A method for screening a subject at risk for prostate cancer, the method comprising: (i) obtaining a biological sample from the subject; (ii) detecting the aggregate expression profiles of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample, wherein detecting aggregate expression profiles of the collection of signature sncRNAs comprises hybridizing probes specific for each of the cDNA derived from sncRNAs obtained from the biological sample, wherein at least one of the hybridizing probes selected from the group consisting of SEQ ID NOs: 1-280; (iii) correlating the aggregate expression profiles of SEQ ID NOs: 1-280 from the subject by comparing the aggregate expression profiles of SEQ ID NOs: 1-280 in a training data set from a target population having no prostate cancer or prostate cancer; and (iv) determining the likelihood of a subject at risk for prostate cancer based on the results obtained from (iii) above. 2.     The method of form 1, wherein the expression of sncRNAs in the subject determined to be at risk for prostate cancer is re-analysed and compared to the aggregate expression profiles of SEQ ID NOs: 281-560 in a training data set from a target population with indolent (low grade, GG1) or intermediate or high grade, (GG2-GG5) prostate cancer to further classify the subject as having indolent (low grade, GG1) or intermediate or high grade, (GG2-GG5) prostate cancer. 3.     The method of form 2, wherein the expression of sncRNAs in the subject determined to have aggressive (intermediate or high grade, GG2-GG5) prostate cancer is re-analysed and compared to the aggregate expression profiles of SEQ ID NOs: 561-840 in a training set from a target population with low / intermediate risk (GG1-GG2) or aggressive (high grade, GG3-GG5) prostate cancer to further classify the subject as having low / intermediate grade (GG2-GG5) or aggressive (high grade, GG3-GG5) prostate cancer. 2026234022   16 Sep 2026 4.     The method of form 3, wherein the subject classified as having aggressive (intermediate or high grade, GG2-GG5) prostate cancer is treated with one or more of radical prostatectomy, brachytherapy of the prostate, radiotherapy of the prostate, neoadjuvant hormone therapy and adjuvant hormone therapy. 5.     The method of form 1, wherein the biological sample is cell free urine. 6.     The method of form 1, wherein the biological sample is a urinary exosome. 7.     The method of form 1, wherein the biological sample is a sncRNA extracted from the urinary exosome. 8.    The method of form 1, wherein the sncRNA comprises miRNA, C / D box snoRNA, H / ACA box snoRNA, scaRNA, piRNA, and lncRNA. 9.    The method of form 1, wherein the sncRNA comprises a miRNA and a snoRNA. 10.    The method of form 1, wherein the method for screening a subject at risk for prostate cancer comprises performing an Sentinel™ PCa (PCa) Test, wherein the PCa test comprises the steps of: (i) interrogating sncRNAs sequences of SEQ ID NOs: 1-280 on an Open Array platform for informative sequences; (ii) determining a Sentinel Score that examines the aggregate expression profiles of all the sequences and interaction between the sequences included in a first Classification algorithm; (iii) comparing the Sentinel Score in (ii) for the informative sequences to a Sentinel Score obtained for training data sets from patients known to have prostate cancer or no prostate cancer; and (iv) determining whether the subject has prostate cancer or no prostate cancer. 11. The method of form 10, wherein the method for screening a subject having prostate cancer is classified as having indolent or low grade (GG-1) or intermediate or high grade (GG-2 - GG-5) prostate cancer comprises performing a Sentinel™ Clinical Significance (CS) test, wherein the CS test comprises the steps of: 2026234022   16 Sep 2026 (i) interrogating sncRNAs of SEQ. ID. NOs: 281 -560 in a second round on an Open Array platform for a different set of informative sequences; (ii) determining a Sentinel Score that examines the aggregate expression profiles of all the sequences and interactions between the sequences included in a second Classification algorithm; (iii) comparing the Sentinel Score in (ii) for the informative sequences to a Sentinel Score obtained for training data sets from patients GG1 prostate cancer and GG2-GG-5 prostate cancer; and (iv) determining whether the subject has GG1 prostate cancer or GG2-GG-5 prostate cancer. 12. The method of form 11, wherein the method for screening a subject having prostate cancer is classified as having indolent or low grade (GG1) or intermediate or high grade (GG-2 - GG-5) prostate cancer further comprises performing a Sentinel™ High Grade (HG -test, wherein the CS test comprises the steps of: (i) interrogating sncRNAs of SEQ. ID. NOs: 561-840 in a third round on an Open Array platform for a different set of informative sequences; (ii) determining a Sentinel Score that examines the aggregate expression profile of all the sequences and interactions between the sequences included in a third Classification algorithm; (iii) comparing the Sentinel Score in (ii) for the informative sequences to a Sentinel Score obtained for training data sets from patients low / intermediate grade (GG1-GG2) prostate cancer and high grade (GG3-GG-5) prostate cancer; and (iv) determining whether the subject has low / intermediate grade (GG1-GG2) prostate cancer or high grade (GG3-GG-5) prostate cancer. 13. The method of form 1, wherein the step of detecting comprises a micro-array method, a reverse transcription polymerase chain reaction, a polymerase chain reaction, a nucleic acid hybridization, or a combination thereof. 2026234022   16 Sep 2026 14. The method of form 1, wherein the aggregate expression profile of a signature collection of sncRNAs in a subject at risk for prostate cancer is a combination of higher or lower aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 1280) than the aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 1-280) in a subject with no prostate cancer. 15. The method of form 1, wherein the aggregate expression profile of a signature collection of sncRNAs in a subject with intermediate / high grade (GG2-GG5) prostate cancer is a combination of higher or lower aggregate expression profile of the signature collection of sncRNAs I (SEQ ID NOs: 281-560) than the aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 281-560) in a subject with indolent or low grade (GG1) prostate cancer. 16. A method for diagnosing a subject for prostate cancer, the method comprising: (i) obtaining a biological sample from a subject; (ii) detecting the aggregate expression profiles of a signature collection of small non-coding RNAs (sncRNAs) from the biological sample, wherein detecting aggregate expression profiles of the collection of signature sncRNAs comprises hybridizing probes specific for each of the cDNA derived from sncRNAs obtained from the biological sample, wherein the hybridizing probes selected from the group consisting of SEQ. ID. NOs: 1-280; (iii) correlating the expression of SEQ ID NOs: 1-280 from the subject by comparing the expression of SEQ ID NOs: 1-280 in a training data set from a target population having no prostate cancer or having prostate cancer; and (iv) determining the likelihood of a subject at risk for prostate based on the results obtained from (iii) above. 17. The method of form 16, wherein the subject determined to be at risk for prostate cancer is re-analysed and compared to the expression of SEQ ID NOs: 281-560 in a training data set from a target population with indolent or aggressive prostate cancer to further classify the 2026234022   16 Sep 2026 subject as having either indolent (low grade, GG1) or -intermediate or high grade (GG2-GG5) prostate cancer. 18. The method of form 17, wherein the subject determined to be at risk for intermediate or high grade (GG2-GG5) prostate cancer is re-analysed and compared to the expression of SEQ ID NOs: 561-840 in a training data set from a target population with low / intermediate grade (GG1-GG2) prostate cancer to further classify the subject as having either indolent (low / intermediate grade, GG1-GG2) or aggressive or high grade (GG3-GG5) prostate cancer. 19.     The method of forms 16 and 17, where the subject classified as having intermediate or high grade or aggressive prostate cancer is treated with one or more of radical prostatectomy, brachytherapy of the prostate, radiotherapy of the prostate, neoadjuvant hormone therapy and adjuvant hormone therapy. 20.    The method of form 16, wherein the biological sample is cell-free urine. 21.    The method of form 16, wherein the biological sample is a urinary exosome. 22.    The method of form 16, wherein the biological sample is a sncRNA extracted from the urinary exosome. 23. The method of form 16, the sncRNA comprises miRNA, C / D box snoRNA, H / ACA box snoRNA, scaRNA, piRNA, and lncRNA. 24.   The method of form 16, wherein the sncRNA is miRNA, and snoRNA. 25.    The method of form 16, further comprising analyzing whether the aggregate expression profile of the collection of signature sncRNAs is higher or lower than the expression level of the collection of signature sncRNAs in an indolent prostate biological sample. 26. The method of form 16, wherein the method for diagnosing a subject at risk for prostate cancer comprises performing an Sentinel™ Prostate Cancer (PCa) Test. 27. The method of form 17, wherein the method for diagnosing a subject as having indolent / low grade (GG-1) or intermediate / high risk (GG2-GG5) prostate cancer comprises performing a Sentinel™ Clinical Significance (CS) test. 2026234022   16 Sep 2026 28. The method of form 18, wherein the method for diagnosing a subject as having low / intermediate grade (GG-1-GG2) or high risk (GG3-GG5) prostate cancer comprises performing a Sentinel™ High Grade (HG) test. 29. The method of form 16, wherein the step of diagnosing comprises a micro-array method, a reverse transcription polymerase chain reaction, a polymerase chain reaction, a nucleic acid hybridization, or a combination thereof. 30. A method for treating prostate cancer in a subject, the method comprising: (i) obtaining a biological sample from a patient; (ii) detecting the aggregate expression profile of a signature collection of small noncoding RNAs (sncRNAs) from the biological sample, wherein detecting aggregate expression profiles of the collection of signature sncRNAs comprises hybridizing probes specific for each of the cDNA derived from sncRNAs obtained from the biological sample, wherein the hybridizing probes selected from the group consisting of SEQ. ID. NOs: 1-280; (iii) correlating the expression of SEQ ID NOs: 1-280 from the subject by comparing (a) the expression of SEQ ID NOs: 1-280 in a training data set from a target population having no prostate cancer or having prostate cancer; and (b) determining the likelihood of having cancer in a subject having prostate cancer based on the results obtained from (a) above; (iv) re-analyzing the aggregate expression profile of SEQ ID NOs: 281-560 from the subject with prostate cancer by comparing the aggregate expression profile of SEQ ID NOs: 281-560 in a training data set from a target population with indolent or low risk (GG1) prostate cancer to further classify the subject as having either indolent or low risk (GG1) or intermediate / high grade (GG2-GG5) prostate cancer; and 2026234022   16 Sep 2026 (v) treating the subject having intermediate / high grade (GG2-GG5) prostate cancer with one or more of (a) radical prostatectomy, (b) brachytherapy of the prostate, (c) radiotherapy of the prostate, (d) neoadjuvant hormone therapy, (e) adjuvant hormone therapy and (f) a combination thereof. 31.    The method of form 30, wherein the biological sample is cell free urine. 32.    The method according to form 30, wherein the biological sample is a urinary exosome. 33.    The method of form 30, wherein the biological sample is a sncRNA extracted from the urinary exosome. 34. The method of form 30, wherein the sncRNA comprises miRNA, C / D box snoRNA, H / ACA box snoRNA, scaRNA, piRNA, and lncRNA. 35.   The method of form 30, wherein the sncRNA comprises a miRNA and a snoRNA. 36.    The method of form 30, wherein the method for treating a subject at risk for prostate cancer comprises performing a Sentinel™ PCa (PCa) Test. 37. The method of form 30, wherein the method for treating a subject to classify the subject as having indolent / low grade (GG1) or intermediate / high grade (GG2-GG5) prostate cancer comprises performing a Sentinel™ Clinical Significance (CS) test. 38. The method of form 30, wherein the step of detecting comprises a micro-array method, a reverse transcription polymerase chain reaction, a polymerase chain reaction, a nucleic acid hybridization, or a combination thereof. 39. The method of form 30, wherein the aggregate expression profiles of a signature collection of sncRNAs in a subject diagnosed of having prostate cancer is a combination of higher or lower aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 1-280) than the aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 1-280) in a subject with no prostate cancer. The method of form 30, wherein the aggregate expression profiles of a signature collection of sncRNAs in a subject with intermediate / high grade (GG2-GG-5) prostate cancer is a combination of higher or lower aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 281-840) 2026234022   16 Sep 2026 than the aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 281-840) in a subject with indolent or low grade (GG1) prostate cancer. 40. A system for determining if a subject has cancer or no cancer and classifying the subject with cancer as having (i) indolent (low grade, GG1), (ii) intermediate or high grade (GG2-GG5), (iii) low / intermediate risk (GG1-G2) or (iv) aggressive (high grade, GG3-GG5) prostate cancer comprising: (i) a first processor configured to: (a) interrogate sncRNA sequences of SEQ ID NOs: 1-280 on first Open Array platform for informative sequences; (b) determine a Sentinel Score that examine the aggregate expression profile of all the sequences of SEQ ID NOs: 1-280 and interaction between the sequences included in a first Classification algorithm; (c) compare the Sentinel Score in (b) for the informative sequences to a Sentinel Score obtained in training data sets from patients known to have prostate cancer or no prostate cancer; and (d) determine whether the subject has prostate cancer or no prostate cancer; wherein the subject determined to have prostate cancer is classified as having indolent (low grade, GG1) or intermediate or high grade (GG2-GG5) prostate cancer; (ii) a second processor configured to: (a) interrogate sncRNA sequences of SEQ ID NOs: 281-560 on a second Open Array platform for informative sequences; (b) determine a Sentinel Score that examine the aggregate expression profile of all the sequences of SEQ ID NOs: 281-560 and interaction between the sequences included in a second Classification algorithm; 2026234022   16 Sep 2026 (c) compare the Sentinel Score in (f) for the informative sequences to a Sentinel Score obtained in training data sets from patients known to have indolent (low grade, GG1) or high grade (GG2-GG5) prostate cancer; and (d) determine whether the subject has indolent (low grade, GG1) or high grade (GG2-GG5) prostate cancer; wherein the subject determined to have high grade (GG2-GG5) prostate cancer is further classified as having low / intermediate risk (GG1-GG2) or aggressive (high grade, GG3-GG5) prostate cancer; (iii) a third processor configured to (a) interrogate sncRNAs sequences of SEQ ID NOs: 561-840 on a third Open Array platform for informative sequences; (b) determine a Sentinel Score that examine the aggregate expression profile of all the sequences of SEQ ID NOs: 561-840 and interaction between the sequences included in a third Classification algorithm; (c) compare the Sentinel Score in (j) for the informative sequences to a Sentinel Score obtained in training data sets from patients known to have low / intermediate risk (GG1-GG2) or aggressive (high grade, GG3-GG5) prostate cancer; and (d) determine whether the subject has low / intermediate risk (GG1-GG2) or aggressive (high grade, GG3-GG5).

Claims

1. A method for screening a human male subject at risk for prostate cancer, the methodcomprising:(i) obtaining a urinary sample from the subject;(ii) detecting an aggregate expression profile of cDNAs derived from a signature collection of small non-coding RNAs (sncRNAs) obtained from the urinary sample in a first microarray assay, using hybridizing probes specific for the cDNA wherein the hybridizing probes comprise SEQ ID NOs: 1-280;(iii) correlating the aggregate expression profile of SEQ ID NOs: 1-280 from the sample by comparing the aggregate expression profile of SEQ ID NOs: 1-280 in a training data set from a target population having no prostate cancer or having prostate cancer; and(iv) determining the likelihood that the subject is at risk for prostate cancer when the aggregate expression profile of SEQ ID NOs: 1-280 of the sample from the subject is statistically greater than or equal to the aggregate expression profile of SEQ ID NOs: 1-280 in the training data set from a target population having prostate cancer, oris not at risk for prostate cancer when the aggregate expression profile of SEQ ID NOs: 1-280 of the sample from the subject is statistically less than or equal to the aggregate expression profile of SEQ ID NOs: 1-280 in the training data set from a target population having no prostate cancer.

2. A method for diagnosing a human male subject with prostate cancer, the methodcomprising:(i) obtaining a urinary sample from a subject;(ii) detecting an aggregate expression profile of cDNAs derived from a signature collection of small non-coding RNAs (sncRNAs) obtained from the urinary sample in a first microarray assay, using hybridizing probes specific for the cDNA wherein the hybridizing probes comprise SEQ ID. NOs: 1-280;2026234022   16 Sep 2026(iii) correlating the aggregate expression profile of SEQ ID NOs: 1-280 from the sample by comparing the aggregate expression profile of SEQ ID NOs: 1-280 in a training data set from a target population having no prostate cancer or having prostate cancer; and(iv) determining the likelihood that the subject is at risk for prostate cancer based on the results obtained from (iii) above, wherein the subject is at risk for prostate cancer when the aggregate expression profile of SEQ ID NOs: 1-280 of the sample from the subject is statistically greater than or equal to the aggregate expression profile of SEQ ID NOs: 1-280 in the training data set from a target population having prostate cancer, oris not at risk for prostate cancer when the aggregate expression profile of SEQ ID NOs: 1-280 of the sample from the subject is statistically less than or equal to the aggregate expression profile of SEQ ID NOs: 1-280 in the training data set from a target population having no prostate cancer.

3. The method of claim 1 or claim 2, wherein the aggregate expression profile of sncRNAsin the subject determined to be at risk for prostate cancer is re-analysed in a second microarray assay using a second set of hybridizing probes comprising SEQ ID NOs: 281-560, and correlating the aggregate expression profile of SEQ ID NOs: 281-560 from the subject by comparing the aggregate expression profile of SEQ ID NOs: 281 -560 in a training data set from a target population having indolent (low grade, GG1) prostate cancer or intermediate or high grade (GG2-GG5) prostate cancer to further classify the subject as having indolent (low grade, GG1) prostate cancer or intermediate or high grade (GG2-GG5) prostate cancer.

4. The method of claim 3, wherein the aggregate expression profile of sncRNAs in thesubject determined to have intermediate or high grade (GG2-GG5) prostate cancer is reanalysed in a third microarray assay using a third set of hybridizing probes comprising SEQ ID NOs: 561-840, and correlating the aggregate expression profile of SEQ ID NOs: 561-840 by comparing the aggregate expression profile of SEQ ID NOs: 561-840 in a training set from a target population having low / intermediate risk (GG1-GG2) prostate cancer or aggressive (high grade, GG3-GG5) prostate cancer to further classify the subject as having low / intermediate risk (GG1-GG2) prostate cancer or aggressive (high grade, GG3-GG5) prostate cancer.2026234022   16 Sep 20265.     The method of claim 4, wherein the subject classified as having aggressive (high grade,GG3-GG5) prostate cancer is treated with one or more of radical prostatectomy, brachytherapy of the prostate, radiotherapy of the prostate, neoadjuvant hormone therapy, and adjuvant hormone therapy.

6. The method of claim 1 or claim 2, wherein the urinary sample is cell free urine.

7. The method of claim 1 or claim 2, wherein the urinary sample are urinary exosomes.

8. The method of claim 1 or claim 2, wherein the urinary sample is intracellular sncRNAextracted from urinary exosomes.

9. The method of claim 1 or claim 2, wherein the sncRNA comprises miRNA, C / D boxsnoRNA, H / ACA box snoRNA, scaRNA, piRNA, and lncRNA.

10. The method of claim 1 or claim 2, wherein the sncRNA comprises miRNA and snoRNA.

11. The method of claim 1 or claim 2, wherein the method comprises performing a first test (PCa test or Prostate Cancer test), wherein the first test comprises the steps of:(i) interrogating sncRNAs sequences SEQ ID NOs: 1-280 for a first set of informative sequences, using a first panel of hybridization probes in a first microarray platform;(ii) determining a score that examines the aggregate expression profile of all the sequences and interaction between the sequences of SEQ ID NOs: 1-280 included in a first classification algorithm, wherein the first classification algorithm is trained on a cohort of participants whose core-needle biopsy is positive, and on a cohort of age matched participants seen at urology clinics for issues unrelated to urological oncology, and from men with a suspicion of prostate cancer, whose core-needle biopsy is negative;(iii) comparing the score determined in step (ii) for the informative sequences to a score obtained for training data sets from a target population having prostate cancer or having no prostate cancer; and2026234022   16 Sep 2026(iv) determining whether the subject is at risk for prostate cancer when the score from the subject is statistically greater than or equal to the score in the training data set from a target population having prostate cancer, oris not at risk for prostate cancer when the score from the subject is statistically less than or equal to the score in the training data set from a target population having no prostate cancer.

12. The method of claim 11, wherein the method for screening a human male subject at risk for prostate cancer to classify the subject as having indolent (low grade, GG1) prostate cancer or intermediate or high grade (GG2-GG5) prostate cancer comprises performing a second test (CS test or Clinical Significance test), wherein the second test comprises the steps of:(i) interrogating sncRNAs of SEQ ID NOs: 281-560 for a second set of informative sequences, using a second panel of hybridization probes in a second microarray platform;(ii) determining a score that examines the aggregate expression profile of all the sequences and interactions between the sequences SEQ ID NOs: 281-560 included in a second classification algorithm, wherein the second classification algorithm is trained on a cohort of participants labeled as low grade (GG1) and a second cohort of participants labeled as intermediate or high grade prostate cancer (GG2-GG5);(iii) comparing the score in step (ii) for the informative sequences to a score obtained for training data sets from a target population having indolent or low grade (GG1) prostate cancer or a target population having intermediate or high grade (GG2-GG5) prostate cancer; and(iv) determining whether the subject has indolent (low grade, GG1) prostate cancer when the score from the subject is statistically less than or equal to the score in the training data set from a target population having indolent (low grade, GG1) prostate cancer, orhas intermediate or high grade (GG2-GG5) prostate cancer when the score from the subject is statistically greater than or equal to the score in a training data set2026234022   16 Sep 2026from a target population having intermediate or high grade (GG2-GG5) prostate cancer.

13. The method of claim 12, wherein the method for screening a human male subj ect having intermediate or high grade (GG2-GG5) prostate cancer to classify the subject as having low / intermediate risk (GG1-GG2) prostate cancer or aggressive (high grade, GG3-GG5) prostate cancer further comprises performing a third test (High Grade test or HG test), wherein the third test comprises the steps of:(i) interrogating sncRNAs of SEQ ID NOs: 561-840 for a third set of informative sequences, using a third panel of hybridization probes in a third microarray platform;(ii) determining a score that examines the aggregate expression profile of all the sequences and interactions between the sequences SEQ ID NOs: 561-840 included in a third classification algorithm, wherein the third Classification algorithm is trained on a cohort of participants from a target population known to have aggressive (high grade, GG3-GG5) prostate cancer or low / intermediate-risk (GG1-GG2) prostate cancer;(iii) comparing the score in step (ii) for the informative sequences to a score obtained for training data sets from a target population having low / intermediate risk (GG1-GG2) prostate cancer and a target population having aggressive (high grade, GG3-GG5) prostate cancer; and(iv) determining whether the subject has low / intermediate risk (GG1-GG2) prostate cancer when the score from the subject is statistically less than or equal to the score in the training data set from a target population having low / intermediate risk (GG1-GG2) prostate cancer, orhas aggressive (high grade, GG3-GG5) prostate cancer when the score from the subject is statistically greater than or equal to the score in the training data set from the target population having aggressive (high grade, GG3-GG5) prostate cancer.2026234022   16 Sep 202614. The method of claim 1, wherein the aggregate expression profile of a signature collection of sncRNAs in the subject at risk for prostate cancer is a combination of higher or lower aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 1280) than the aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 1-280) in a subject having no prostate cancer.

15. The method of claim 3, wherein the aggregate expression profile of a signature collection of sncRNAs in a subject having intermediate or high grade (GG2-GG5) prostate cancer is a combination of higher or lower aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 281-560) than the aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 281-560) in a subject having indolent (low grade, GG1) prostate cancer.

16. The method of claim 2, further comprising analyzing whether the aggregate expression profile of the collection of signature sncRNAs is higher or lower than the aggregate expression profile of the collection of signature sncRNAs in a target population having indolent (low grade, GG1) prostate cancer.

17. The method of claim 1 or claim 2, wherein the step of detecting comprises a microarray method, a reverse transcription polymerase chain reaction, a polymerase chain reaction, a nucleic acid hybridization, or a combination thereof.

18. A method for treating prostate cancer in a human male subject, the method comprising:(i) obtaining a urinary sample from a subject;(ii) detecting an aggregate expression profile of cDNAs derived from a signature collection of small non-coding RNAs (sncRNAs) obtained from the urinary sample in a first microarray assay, usinghybridizing probes specific for the cDNA,wherein the hybridizing probes comprise SEQ ID NOs: 1-280;(iii) correlating the aggregate expression profile of SEQ ID NOs: 1-280 from the sample by comparing the aggregate expression profile of SEQ ID NOs: 1-280 in a training data set from a target population having no prostate cancer or having prostate cancer; and2026234022   16 Sep 2026(iv) determining the likelihood that the subject is at risk for prostate cancer when the aggregate expression profile of SEQ ID NOs: 1-280 of the sample from the subject is statistically greater than or equal to the aggregate expression profile of SEQ ID NOs: 1-280 in the training data set from a target population having prostate cancer, or is not at risk for prostate cancer when the aggregate expression profile of SEQ ID NOs: 1-280 of the sample from the subject is statistically less than or equal to the aggregate expression profile of SEQ ID NOs: 1-280 in the training data set from a target population having no prostate cancer;(v) re-analyzing the aggregate expression profile of SEQ ID NOs: 281-560 from the sample at risk for prostate cancer by comparing the aggregate expression profile of the sample with the aggregate expression profile of SEQ ID NOs: 281-560 in a training data set from a target population having indolent (low grade, GG1) prostate cancer to further classify the subject as having indolent (low grade, GG1) prostate cancer or intermediate or high grade (GG2-GG5) prostate cancer; and(vi)   treating the subject having intermediate or high grade (GG2-GG5) prostatecancer with one or more of (a) radical prostatectomy, (b) brachytherapy of the prostate, (c) radiotherapy of the prostate, (d) neoadjuvant hormone therapy, (e) adjuvant hormone therapy, and (f) a combination thereof.

19. The method of claim 18, wherein the urinary sample is cell free urine.

20. The method of claim 18, wherein the urinary sample are urinary exosomes.

21. The method of claim 18, wherein the urinary sample is intracellular sncRNAs extractedfrom urinary exosomes.

22. The method of claim 18, wherein the sncRNAs comprise miRNA, C / D box snoRNA, H / ACA box snoRNA, scaRNA, piRNA, and lncRNA.

23. The method of claim 18, wherein the sncRNAs comprise miRNA and snoRNA.2026234022   16 Sep 202624. The method of claim 18, wherein the step of detecting comprises a micro-array method, a reverse transcription polymerase chain reaction, a polymerase chain reaction, a nucleic acid hybridization, or a combination thereof.

25. The method of claim 18, wherein the aggregate expression profile of a signature collection of sncRNAs in the subject having prostate cancer is a combination of higher or lower aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 1-280) than the aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 1-280) in a subject having no prostate cancer.

26. The method of claim 18, wherein the aggregate expression profile of a sncRNAs in the subject having intermediate or high grade (GG2-GG5) prostate cancer is a combination of higher or lower aggregate expression profile of the sncRNAs (SEQ ID NOs: 281-560) than the aggregate expression profile of the signature collection of sncRNAs (SEQ ID NOs: 281-560) in a subject having indolent (low grade, GG1) prostate cancer.

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