Novel RNA biomarkers for diagnosing prostate cancer

Abstract

The present invention relates to an ex vivo diagnostic method of prostate cancer comprising the steps of: i) providing a sample of a patient suspected of having prostate cancer; ii) analysing the expression level of at least one newly identified prostate cancer biomarker in the sample wherein the sample is designated as prostate cancer positive if the expression level of said biomarker is above a threshold. Furthermore, the present invention relates to strict sample and method quality control criteria to ensure a reliable and specific method for diagnosing prostate cancer.

Description

Technical Field

[0001] The present invention belongs to the field of biology and chemistry. Specifically, the present invention belongs to the field of molecular biology. More specifically, the present invention relates to the analysis of RNA transcripts. More specifically, the present invention belongs to the field of prostate cancer diagnosis. Background Art

[0002] Prostate cancer (PCa) is the most common malignancy in men and the third leading cause of cancer-related death in the Western world. In 2020, Western Europe had the second highest number of newly diagnosed cases of prostate cancer each year. Despite widespread prostate cancer screening and major advances in the treatment of metastatic disease, PCa remained the third leading cause of cancer death in men in Europe in 2020.

[0003] Currently, prostate specific antigen (PSA) serum level detection and digital rectal examination represent the two main screening methods. Patients showing abnormal results are usually recommended to undergo a prostate biopsy. However, this can have serious consequences. PSA screening lacks specificity and can produce a large number of false positives, resulting in millions of men undergoing unnecessary prostate biopsies (overdiagnosis) every year worldwide. In addition, there is a considerable risk of infectious complications from performing a biopsy. Therefore, there is an urgent need for a more sensitive and specific early prostate cancer diagnostic test to improve prostate cancer screening and avoid a large number of unnecessary prostate biopsies. The present invention solves this problem by providing a group of biomarkers for prostate cancer screening and diagnosis.

[0004] Prostate-specific antigen

[0005] Currently, the most common prostate cancer screening test is to measure serum PSA concentration. However, there is no single threshold value of PSA that can reliably distinguish patients with prostate cancer from those without prostate cancer. The false positive rate is as high as 75%, indicating that PSA testing lacks specificity (Duffy, 2020). It is reported that the area under the ROC curve (AUC) value of PSA testing to distinguish PCa from non-cancer is 0.678 (Thompson, 2005). Although a large number of studies have been devoted to improving the performance of PSA itself, including measuring free PSA or truncated forms of PSA (Jansen, 2009), all problems cannot be solved. Based on this evidence, the U.S. Preventive Services Task Force recommends against PSA-based prostate cancer screening due to its high false positive rate and the risks associated with biopsy and overtreatment (Moyer, 2012).

[0006] PSA-derived biomarkers include the Prostate Health Index (PHI, Jansen, 2010) and the Four Kellick Group (4K score, Vickers, 2008). Both combine information on serum levels of different PSA protein variants, and the latter additionally measures kallikrein 2. Related studies have mainly focused on high-grade PCa (GS>7) to predict aggressive tumors (Duffy, 2020).

[0007] PCA3

[0008] Prostate cancer antigen 3 (PCA3) is a noncoding RNA whose expression is restricted to the prostate and is overexpressed in 95% of PCa samples compared with normal or benign prostatic hyperplasia tissue (Salagierski, 2012). The Progensa PCA3 test (Gen Probe Inc., San Diego, CA, USA) is a commercially available diagnostic test that can quantify PCA3 in urine and prostatic fluid. A PCA3 score of >35 in urine has been determined to have an average sensitivity and specificity of 66% and 76% for the diagnosis of PCa, respectively (Van Gils, 2007), and a reported AUC value of 0.693 (Aubin, 2010). It has been shown that an increased PCA3 score increases the probability of a positive re-biopsy in men with one or two previous negative biopsies (Haese, 2008). Although the specificity and sensitivity values ​​of the PCA3 test are slightly higher than those of the PSA serum test, they are still unsatisfactory, and therefore the PCA3 test has not been widely accepted in actual clinical applications.

[0009] Gleason Rating

[0010] The so-called Gleason scoring system is used in conjunction with other parameters to predict PCa prognosis and guide treatment. The Gleason score is assigned by pathologists based on microscopic features of prostate biopsies and reflects the differentiation state of prostate tumor cells observed in the biopsy material (Gleason, 1977). Prostate cancers with lower Gleason scores have a better prognosis, and most of them may not require treatment and should be actively monitored. However, its informative value is limited, and accurately locating aggressive and lethal tumors despite low Gleason scores remains a clinical challenge (Irshad, 2013).

[0011] Novel biomarkers

[0012] TMPRSS2:ERG gene fusion is the most common genetic aberration in PCa, present in approximately 40% to 70% of patients. Sensitive TMPRSS2-ERG testing is available for clinical use, but it does not cover TMPRSS2:ERG-negative PCa cases. In addition, the correlation of TMPRSS2:ERG with prognosis is controversial. In addition, in recent years, many novel biomarkers have been proposed as candidates for PCa diagnosis or prognosis, but have not yet been introduced into clinical routine. This includes genetic and RNA biomarkers. A recent study to externally confirm the accuracy of the Select MDx test showed that its performance was lower than previously reported (Lendinez-Cano, 2021). Mi-Prostate Score and ExoDx use expression level information of prostate-specific noncoding RNA PCA3 and fusion transcript TMPRSS2:ERG. SelectMDx represents a qPCR-based test that measures DLX1 and HOXC6 mRNA levels to detect PCa. The above three tests highlight their good clinical performance in predicting high-grade PCa (GS ≥ 7). In general, incorporation of clinical information such as age, digital rectal examination (DRE) results, and previous biopsies can further improve test performance (Cucchiara, 2017). Summary of the invention

[0013] The above tests focus on high-grade tumors, which improves test performance and may exclude low-risk PCa (equivalent to GS 6) from biopsy. However, it carries the risk of overlooking at least some aggressive tumors. GS 6 PCa is often described as non-threatening, but it is still cancer and may progress to GS 7 or higher and metastasize. In this case, a different treatment approach is needed. Active surveillance is often considered for patients with GS 6 PCa. Studies have shown that more men who undergo active surveillance have disease progression than those who undergo a radical treatment strategy. A test for early detection of PCa before biopsy should identify patients who would benefit from further diagnostics, so excluding GS 6 tumors in test development may make the test insensitive for the high-risk subgroup of GS 6 PCa patients. Whether patients with GS 6 PCa should be excluded a priori from further invasive diagnostics is controversial.

[0014] The purpose of this study is to identify novel RNA biomarkers for the early diagnosis of PCa (GS>6) to reduce unnecessary biopsies. This study also did not exclude patients with GS=6, so the method of the present invention can also detect the early stages of PCa. Specifically, the method of the present invention is intended to diagnostically distinguish PCa tumors from BPH (benign prostatic hyperplasia). The previously published application WO 2015 / 082418 A1 also proposed newly identified biomarkers for the diagnosis of PCa, but the present application uses different models and biomarker selections to provide a superior diagnostic method with improved reliability.

[0015] Brief Description

[0016] Transcripts differentially expressed in tumor and control tissues were identified by next-generation sequencing of 64 prostate cancer patient and control samples; and validated by microarray and qRT-PCR analysis of 203 and 338 samples, respectively. From these samples, we identified a selection of potential RNA biomarkers suitable for the diagnosis of prostate cancer. The identified biomarkers were evaluated and optimized to develop a diagnostic approach that represents an improved and more reliable diagnosis of PCa. In essence, this diagnostic approach involves a rigorous quality assessment of patient samples, followed by normalization of RNA biomarkers to a selected, optimized reference RNA panel. The normalized expression data of RNA biomarkers are then analyzed by algorithms to determine the diagnosis.

[0017] The present invention relates to a method for diagnosing prostate cancer, comprising the step of analyzing the expression level of at least one nucleic acid selected from the group consisting of SEQ ID NOs: 2 and 20, wherein if at least one of said nucleic acids is present and / or the expression level of at least one of said nucleic acids is above a threshold value, the sample is designated as positive for prostate cancer.

[0018] In a preferred embodiment, the present invention relates to a method for diagnosing prostate cancer, which comprises the step of analyzing the expression level of at least one nucleic acid according to SEQ ID NO: 2, SEQ ID NO: 19-20 and SEQ ID NO: 35-37 in a patient sample, wherein if the expression level of the nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer.

[0019] In an alternative and equally preferred embodiment of the present invention, the present invention relates to a method for diagnosing prostate cancer, which comprises the step of analyzing the expression level of at least one nucleic acid according to SEQ ID NO: 1-3, SEQ ID NO: 20-34 and SEQ ID NO: 38-50 in a patient sample, wherein if the expression level of the nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer.

[0020] In one embodiment, the present invention relates to a nucleic acid that hybridizes under stringent conditions to one of the nucleic acids having SEQ ID NO: 1-3 or SEQ ID NO: 19-50 or any part thereof, or a nucleic acid that shares preferably 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with a nucleic acid according to any of the nucleic acids according to SEQ ID NO: 1-3 or SEQ ID NO: 19-50.

[0021] The present invention also relates to the use of a nucleic acid that hybridizes under stringent conditions to one of the nucleic acids having SEQ ID NOs: 1-3 or SEQ ID NOs: 19-50 or any part thereof, or a nucleic acid that shares preferably 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with a nucleic acid according to any of the nucleic acids according to SEQ ID NOs: 1-3 or SEQ ID NOs: 19-50 for diagnosing prostate cancer.

[0022] The present invention relates to a probe or primer, wherein the probe or primer is specific to a sequence in the group of SEQ ID NOs: 1-131, and preferably, the specific sequence of the probe or primer is selected from the group including SEQ ID NOs: 136-174.

[0023] The present invention relates to the use of a nucleic acid having a sequence from the group of SEQ ID NOs: 136-174 for diagnosing prostate cancer.

[0024] The present invention relates to nucleic acids having a sequence from the group of SEQ ID NOs: 1-131 or their reverse complement, or nucleic acids sharing preferably 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with a nucleic acid according to any of the nucleic acids according to SEQ ID NOs: 1-131.

[0025] The present invention also relates to a kit for diagnosing prostate cancer, which comprises a nucleic acid that hybridizes with a nucleic acid according to SEQ ID NO: 1-131 under stringent conditions, or a nucleic acid that shares preferably 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with a nucleic acid according to any one of the nucleic acids according to SEQ ID NO: 1-131; and reagents for nucleic acid amplification and / or quantification and / or detection.

[0026] definition

[0027] The following provides definitions for certain terms used in the text of this application.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this technology belongs. Although all methods and materials similar or equivalent to those described herein can be used in the practice or testing of this technology, the preferred methods, devices and materials are now described.

[0029] When an indefinite or definite article is used to refer to a singular noun, such as "a" or "an" or "the", this includes the plural form of that noun unless otherwise stated. Vice versa, when a plural form of a noun is used, it also refers to the singular form. For example, when referring to a biomarker, this should also be understood to refer to a single biomarker.

[0030] As used herein, "nucleic acid" or "nucleic acid molecule" generally refers to any ribonucleic acid or deoxyribonucleic acid, which may be unmodified or modified. "Nucleic acid" includes, but is not limited to, single-stranded and double-stranded nucleic acids. As used herein, the term "nucleic acid" also includes nucleic acids containing one or more modified bases as described above. Therefore, nucleic acids with one or more backbone modifications for stability or other reasons are "nucleic acids". The term "nucleic acid" as used herein encompasses such chemically, enzymatically or metabolically modified forms of nucleic acids, as well as nucleic acid chemical forms specific to viruses and cells (including, for example, simple and complex cells).

[0031] The term "transcript" refers to a nucleic acid produced by replicating a template nucleic acid. This definition includes nucleic acids obtained by polymerase transcription of a template nucleic acid (e.g., a locus) to produce a new nucleic acid having a sequence complementary to the template. The term "transcript" also includes any nucleic acid that is further processed after transcription. Such further processing includes, but is not limited to, splicing, polyadenylation, editing, partial digestion, connection, and labeling. The term also includes nucleic acids derived from the transcript by, for example, further transcription, reverse transcription, amplification, or extension. The transcript may correspond to any portion of the template nucleic acid, preferably at least 20 nucleotides. The transcript also has at least 90%, preferably at least 95%, and most preferably at least 99% sequence identity to the reverse complement sequence of the template nucleotide or a portion of the template nucleotide or the template nucleic acid.

[0032] The term "locus (hg38)" and the abbreviation "hg38" are used to provide reference to a specific locus in the hg38 assembly of the human genome. References are expressed in the following format, for example: "Chr2:1,550,437-1,629,191", which refers to a sequence interval on human chromosome 2 in the genome assembly.

[0033] As used herein, "sample molecule" or "single sample template" refers to any type of nucleic acid molecule contained in the sample to be analyzed, such as single-stranded or double-stranded DNA and / or RNA.

[0034] The term "biomarker" in the present invention refers to any gene, gene transcript, gene transcript variant, or non-coding genomic sequence that can predict the diagnosis of PCa.

[0035] The term "level" or "expression level" in the context of the present invention relates to the presence level of a biomarker in a patient sample. The expression level of a biomarker is typically measured by comparing its expression level with the expression level of one or more reference or housekeeping genes in a sample for standardization. If the expression level of a biomarker exceeds the expression level of the same biomarker in an appropriate control (e.g., healthy tissue) and exceeds a set threshold, the patient sample is designated as prostate cancer positive. The expression level is further calibrated using the reference genes measured in the calibration sample for batch-to-batch comparison.

[0036] As used herein, the terms "analyzing the presence and / or level of nucleic acid in a sample" or "specifically estimating nucleic acid levels" relate to means and methods for assessing and quantifying nucleic acid levels. A useful method is quantitative reverse transcription PCR. Similarly, RNA levels may also be analyzed by Northern blotting, next generation sequencing, or after amplification using spectroscopic techniques including measuring absorbance at 260 and 280 nm.

[0037] "Amplification" of a single sample template refers to any type of nucleic acid amplification method that results in the production of multiples of the original template.

[0038] Analysis or inspection herein refers to identifying whether amplification occurs, identifying whether the target sequence is located between the primer regions and optionally, whether it has the correct length, identifying the amount of amplification product with the correct target sequence. Preferably, in the method of the present invention, the amplification product needs to be accurately quantified.

[0039] As used herein, the term "amplification" when applied to a nucleic acid sequence refers to the process of generating one or more copies of a specific nucleic acid sequence from a nucleic acid template sequence, preferably by a polymerase chain reaction (PCR) method. Other amplification methods include, but are not limited to, ligase chain reaction (LCR), polynucleotide-specific amplification (NSBA), loop-mediated isothermal amplification (LAMP), quantitative reverse transcription real-time PCR (qRT-PCR), quantitative (real-time) PCR (qPCR), droplet digital PCR, digital PCR, or any other amplification method known in the art.

[0040] As used herein, the term "correlating" with respect to the use of diagnostic and prognostic markers refers to comparing the presence or amount of a marker in a patient sample with the presence or expression level of the marker in a sample of a person known to have or be at risk for a given condition. The marker expression level in a patient sample can be compared to a level known to be associated with a particular diagnosis.

[0041] As used herein, the term "diagnosis" refers to the identification of a disease (in this case, prostate cancer) at any stage in the development of the disease, and also includes the determination of a subject's predisposition to develop the disease.

[0042] "Labeling" of a DNA or RNA strand can be achieved by associating or binding to any kind of marker detectable using conventional imaging techniques (eg, a fluorescent marker).

[0043] As used herein, the term "fluorescent dye" refers to any chemical substance that absorbs light energy at a specific wavelength and re-emits the light at a different wavelength.

[0044] The application of fluorescent dyes for target detection means either using an intercalating dye in a PCR reaction to detect the formation of a double-stranded DNA molecule, or labeling a primer or probe with a suitable fluorophore molecule to observe the presence of the formation of a nucleic acid molecule with a complementary target sequence based on the fluorescence signal of the detected fluorophore molecule. By using different fluorophores with different emission and excitation spectra, multiple detection systems can be combined into one PCR reaction (multiplex PCR) and the fluorescence signals are detected separately. Potential labeling molecules include, but are not limited to, fluorescent dyes or chemiluminescent dyes, particularly cyanine dyes. In the context of the present invention, fluorescence-based assays include the use of dyes, which can be selected from the group comprising, for example, FAM (5- or 6-carboxyfluorescein), VIC, NED, fluorescein, fluorescein isothiocyanate (FITC), IRD-700 / 800, cyanine dyes (such as CY3, CY5, CY3.5, CY5.5, Cy7), xanthan gum, 6-carboxy-2′, 4′, 7′, 4,7-hexachlorofluorescein (H EX), TET, 6-carboxy-4′,5′-dichloro-2′,7′-dimethoxyfluorescein (JOE), N,N,N′,N′-tetramethyl-6-carboxyrhodamine (TAMRA), 6-carboxy-X-rhodamine (ROX), 5-carboxyrhodamine-6G (R6G5), 6-carboxyrhodamine-6G (RG6), rhodamine, rhodamine green, rhodamine red, rhodamine 110, BODIPY dyes (such as BODIPY TMR, Oregon Green), coumarins (such as umbelliferone), benzimides (such as Hoechst 33258); phenanthridines (such as Texas Red, Yakima Yellow, Alexa Fluor, PET, ethidium bromide), acridine dyes, carbazole dyes, phenoxazine dyes, porphyrin dyes, polymethine dyes, etc. In the context of the present invention, chemiluminescent assays include the use of dyes based on the physical principles of chemiluminescent materials described in Kirk-Othmer, Encyclopedia of chemical technology, 4th edition, JI Kroschwitz, executive editor; M. Howe-Grant, John Wiley & Sons, 1993, Vol. 15, pp. 518-562, incorporated herein by reference (including citations on pp. 551-562). Preferred chemiluminescent dyes are acridinium esters.

[0045] As used herein, "isolation" when used to refer to nucleic acids refers to naturally occurring sequences that have been removed from their normal cell (e.g., chromosome) environment or synthesized (e.g., artificially) in a non-natural environment. Thus, an "isolated" sequence can be in a cell-free solution or placed in a different cell environment.

[0046] As used herein, a "kit" is a packaged combination, optionally including instructions for use of the combination and / or other reactions and components for such use. If the kit contains nucleic acids, the kit may also contain synthetic or non-natural variants of the nucleic acids. Synthetic or non-natural nucleic acids are understood to include any chemical, biochemical or biologically modified nucleic acids such that the nucleic acids do not occur in this form in nature. Such modifications include, but are not limited to, labeling with fluorescent dyes or quencher moieties, biotin tags, and modifications in the nucleic acid backbone, or any other modification that distinguishes the nucleic acid from its natural counterpart. The same applies to other natural compounds, such as proteins, lipids, etc.

[0047] As used herein, the term "patient" refers to a living human or non-human organism who is receiving medical care or should receive medical care due to a disease or is suspected of having a disease. This includes people who have not been diagnosed with a disease but are being investigated for signs of pathology. Therefore, the methods and assays described herein are applicable to both human and veterinary diseases.

[0048] The term "primer" as used herein refers to a nucleic acid, whether naturally occurring (such as a purified restriction digest) or synthetically produced, which can serve as a starting point for synthesis when placed under conditions that induce the synthesis of primer extension products complementary to a nucleic acid chain, i.e., in the presence of nucleotides and an inducing agent (such as a DNA polymerase) and at a suitable temperature and pH. A primer can be single-stranded or double-stranded and must be long enough to initiate the synthesis of a desired extension product in the presence of an inducing agent. The exact length of a primer depends on many factors, including temperature, primer source, and the method used. Preferably, the length of a primer is about 15-100 bases, more preferably about 20-50 bases, and most preferably about 20-40 bases. One of ordinary skill in the art will readily appreciate the factors involved in determining an appropriate primer length. Alternatively, a primer can be a synthetic element, i.e., it comprises chemical, biochemical, or biological modifications. Such modifications include, but are not limited to, labeling with a fluorescent dye or quencher moiety, or modifications in the nucleic acid backbone, or any other modifications that distinguish a primer from its natural nucleic acid counterpart.

[0049] The term "probe" refers to any element that can be used to specifically detect a biological entity (such as a nucleic acid, protein, or lipid). In addition to the portion that allows the probe to specifically bind to the biological entity, the probe also includes at least one modification that allows detection in the assay. Such modifications include, but are not limited to, labels such as fluorescent dyes, quenchers, specifically introduced radioactive elements, or biotin tags. The probe may also include modifications in its structure, such as locked nucleic acids.

[0050] The term "sample" as used herein refers to a bodily fluid or tissue sample obtained for the purpose of diagnosis, prognosis or assessment of a subject of interest (e.g., a patient). In addition, those skilled in the art will recognize that some test samples will be more easily analyzed after a fractionation or purification procedure, such as separating whole blood into serum or plasma components.

[0051] Therefore, in a preferred embodiment of the present invention, the sample is selected from the following group: prostate tissue, biopsy material, lymph nodes, urine, semen, blood, serum, plasma, circulating tumor cells in blood or lymph, any tissue suspected of containing metastasis, and any source that may contain prostate tumor cells or parts thereof, including vesicles (such as exosomes, microvesicles, etc.) and free or protein-bound RNA molecules derived from prostate tumor cells. Preferably, the sample is a blood sample, most preferably a serum sample or a plasma sample. Importantly, urine (especially after a rectal examination) and semen are the most preferred samples. Tissue samples can also be biopsy materials or tissue samples obtained during surgery. The sample of the method of the present invention is provided from the patient's body by an appropriate extraction method, so the sample is an in vitro sample, that is, a sample taken from the patient's body. Subsequent analysis of the sample is performed by the in vitro diagnostic method of the present invention.

[0052] The term "area under the curve (AUC)" as used herein describes the area under the curve of a receiver operating characteristic (ROC) or ROC curve. AUC is related to the specificity and sensitivity of a biomarker. A perfect marker (AUC = 1.0) will produce a point in the upper left corner of the ROC space or coordinates (0, 1), representing 100% sensitivity (no false negatives) and 100% specificity (no false positives).

[0053] The term "p-value" relates to the probability of obtaining the observed sample results (or a more extreme result) when the null hypothesis is actually true, i.e., there is no difference between the group means. The smaller the p-value, the higher the probability that the alternative hypothesis explains the observed results better than the null hypothesis.

[0054] The term “adjusted p-value” refers to p-values ​​adjusted for multiple comparisons according to Benjamini & Hochberg. The methods used are described in detail in the Experimental section.

[0055] Description of the drawings

[0056] Figure 1 : Processing queue for developing ProstaCheck

[0057] Figure 2: Boxplot of RNA-seq data for transcript PCA3. RNA sequencing results for the retrospective PCa cohort included eight prostate tissue samples (from benign prostatic hyperplasia) as controls (C), eight PCa tumor samples each from group V (very low risk; Gleason score <7, pN0), group L (low risk; Gleason score = 7, pN0), and group M (intermediate risk; Gleason score <= 7, pN+), and 16 pairs of tumor and non-tumor tissue samples from group H (high risk including GS>7; Gleason score >7).

[0058] Figure 3 : Transcripts per million (TPM) expression of known diagnostic markers (PCA3, HOXC6, DLX1) compared to genes tested by ProstaCheck in urine sediment (RNA sequencing of 6 BPH and 5 PCa urine sediments). Genes included in ProstaCheck were expressed on average at higher levels in urine sediment than known markers. DETAILED DESCRIPTION

[0059] The present invention describes a method for diagnosing prostate cancer. The method includes analyzing a sample collected from a patient and specifically determining the level of a biomarker or combination of biomarkers in the patient sample. The result is then associated with a threshold, and if the result is above the threshold, the patient sample is designated as positive for prostate cancer. The calculated scores and thresholds for the biomarkers are obtained based on a machine learning model.

[0060] The experimental data of the present invention show that the newly identified biomarker (ProstaCheck; including 2 different detection models) has impressive sensitivity and specificity, which is significantly better than the existing detection methods on the market, as can be seen from the increase in the AUC values ​​of the ProstaCheck models (Progensa PCA3, SelectMDx; see Table 1). Figure 3 A comparison of the newly identified biomarkers with the commercially available SelectMDx and PCA3 assays is also shown, which also clearly demonstrates that the newly identified biomarkers outperform the state of the art assays.

[0061] Therefore, diagnostic tests based on these new biomarkers reduce the high false positive rate of current tests, thereby helping to avoid unnecessary invasive prostate biopsies. In addition, the method of the present invention does not exclude men with GS=6, who are also included in the experimental data set, so early diagnosis of prostate cancer can be performed by the method of the present invention. In addition, the method of the present invention specifically includes standards for strict assessment of sample quality before applying the diagnostic assay and necessary standardization and calibration procedures for the detected biomarkers. Sample quality assessment, standardization and calibration are important elements of the method of the present invention, which can ensure the reliability and repeatability of the results.

[0062] Table 1: Direct comparison of ProstaCheck with existing PCa assays in urine sediment samples using RT-qPCR Progensa PCA3 and SelectMDx

[0063]

[0064] (*) AUC evaluated on the same patients in an independent validation cohort (n=87). (**) AUC taken from Cui et al. "Evaluation of prostate cancer antigen 3 for detecting prostate cancer: a systematic review and meta-analysis", Scientific Reports, May 2016

[0065] In addition to prostate cells, other cells may also be present in urine or urine sediment samples. In order to ensure that the biomarker detection of the present invention will not be negatively affected by the presence of different numbers of non-prostate cells, the expression of the promising biomarker candidate genes detected is related to the expression of cell-specific reference genes. Usually, urine or urine sediment samples may contain leukocytes, kidney cells or bladder cells in addition to prostate cells. Therefore, the cell-specific reference genes of these cell types, i.e. CD45 (leukocytes), UMOD (kidney cells), NPHS2 (kidney cells) and UPK2 (bladder cells), are selected. Only when the expression of the biomarker candidate is not correlated with the expression of the above-mentioned cell-specific reference genes, these candidates will be further considered. Therefore, the biomarker of the present invention represents the expression in prostate cells.

[0066] The term "urine sediment" refers to cells, cell debris, and other precipitates obtained by centrifugation of urine.

[0067] The calculation of the combination and threshold of the biomarkers and the calculation of the diagnostic sample analysis are realized by a multivariate statistical model, which is obtained by a machine learning method using a training set sample of different PCa stages compared with BPH (benign prostatic hyperplasia). The newly identified biomarkers of the present invention were found to be located in the genomic regions of the following genes: CPNE4 (biomarker: PCaD2 [SEQ ID NO: 1]; PCaD4 [SEQ ID NO: 2]; PCaD52 [SEQ ID NO: 21-34]), TAPIR novel ncRNA (biomarker PCaD11 [SEQ ID NO: 3]), HOXB13 (biomarker PCaD42 [SEQ ID NO: 19]), PCAT14 (biomarker PCaD50 [SEQ ID NO: 20]), MSMB (biomarker PCaD54 [SEQ ID NO: 35-37]) and FOLH1 (biomarker PCaD58 [SEQ ID NO: 38-50]). According to current annotations, some of the listed biomarkers are located in exons of known transcript variants of their respective genes, while other biomarkers are located in introns and may therefore be associated with novel exons of as yet unknown transcript variants or novel genes encoding noncoding RNAs. As mentioned previously, the commonly used PCa biomarker PCA3 was also measured using custom designed primers or the commercially available Progensa PCA3 test (biomarker PCaD30 [SEQ ID NO: 132-135]). Table 2 summarizes the newly identified biomarkers for the diagnosis of prostate cancer and the statistical evaluation of these biomarkers. The performance of these biomarkers is summarized in Tables 2 and Figure 3 shown.

[0068] These biomarkers were identified in the “discovery cohort” (see Figure 1 ), using a negative binomial log-linear model, the model was fitted to the observed data for each transcript or gene, and the regression coefficients were determined by the likelihood ratio test. As shown in Table 1, the identified new biomarkers were incorporated into the diagnostic test in two different logistic models. These models were obtained by machine learning. The scheme for biomarker identification, evaluation, validation, and machine learning is shown in Figure 1As shown. These models are logistic regression models, which are fitted to the observed data of each biomarker and regression coefficients are obtained. The first logistic model combines the biomarkers PCaD4, PCaD42, PCaD50 and PCaD54. The regression coefficients of these biomarkers in this model are: -0.03104, 0.04972, -0.13662 and -0.14546, respectively. The second logistic model combines the biomarkers PCaD2, PCaD4, PCaD11, PCaD50, PCaD52 and PCaD58. In the preferred form of the second logistic model, PCaD2 represents a combination of marker measurement regions of the CPNE4 gene (PCaD2, PCaD4 and PCaD52). It represents the minimum c measured after standardization and calibration of PCaD2, PCaD4 or PCaD52. q Value, that is, the highest expression level of these markers observed in the patient. The regression coefficients of these biomarkers in the model are: -0.06262, -0.01419, -0.03030, -0.05290, -0.03138 and -0.04455, respectively. Therefore, both models contain PCaD4 and PCaD50, which represent the basic minimum components of the two models. In addition, as an alternative embodiment of the present invention, these biomarkers can also be combined into another model separately. However, the addition of additional biomarkers to the two models can improve the predictive value of diagnostic testing.

[0069] The statistical analysis was also performed on the necessary minimal component biomarkers of both models (i.e. PCaD4 and PCaD50) in combination with each other, and excluding any other markers of either model of the invention. This means that the data used to analyze the combination of PCaD4 and PCaD50 was considered as data for each model of the invention, including the quality control of the invention and the normalization using the reference biomarkers of the invention. Subsequently, the regression model was calculated using the training set, as Figure 1 Finally, the combination of PCaD4 and PCaD50 was tested in the validation cohort, just like Models 1 and 2, to determine the test parameters for the combination of PCaD4 and PCaD50 compared to prior art tests. These parameters showed that the combination of PCaD4 and PCaD50 already had predictive and diagnostic advantages compared to prior art tests.

[0070] All applied models also involve the use of reference biomarkers. These reference biomarkers are used for quality control of samples and standardization of biomarker measurements. In the detection model of the present invention, the following reference markers are used: KLK2 (biomarker PCaD91 [SEQ ID NO: 51-73]), KLK3 (biomarker PCaD32 [SEQ ID NO: 4-18]), EMC7 (biomarker PCaD92 [SEQ ID NO: 74-77]), RAB7A (biomarker PCaD93 [SEQ ID NO: 78-95]) and VCP (biomarker PCaD94 [SEQ ID NO: 96-131]).

[0071] Therefore, the present invention relates to a group of nucleic acids whose sequences include SEQ ID NOs 1 to 131. The relevant features of these sequences are listed in Table 2 below, with further information on statistical analysis of individual biomarkers. The sequences are provided separately in the sequence table. These sequences represent selected biomarkers and selected reference markers (reference genes) for the present diagnostic method. Performing the present diagnostic method involves detecting at least one listed biomarker and at least one listed reference marker, and using the detected expression levels to determine the patient's diagnosis of PCa.

[0072] Table 2: List of biomarkers and reference genes: Each biomarker and reference gene is listed together with the gene annotation and the statistical evaluation of each biomarker. For several biomarkers and reference genes, multiple transcript variants are known. Each transcript variant corresponds to the SEQ ID NO listed for each biomarker and reference gene, and the sequence and Ensembl transcript ID of each SEQ ID NO are shown in the sequence listing and Table 6.

[0073]

[0074]

[0075] PPV: positive predictive value; NPV: negative predictive value; AUC: area under the curve; IVC: independent validation cohort (see Figure 1); *: The sequence and transcript ID of each transcript variant are provided in the sequence listing and Table 6, as well as the corresponding SEQ ID NOs for biomarkers and reference genes. If no known transcript variant is given, the sequence of the corresponding gene ID is entered into the corresponding SEQ ID NO; **: Sequence is protein coding or non-coding; ***: Statistical evaluation is only applicable to biomarkers, not reference markers; #: Prostacheck PCA3 / Progensa PCA3 assay (including QC filter) / ProgensaPCA3 assay (excluding QC filter)

[0076] In order to detect the biomarkers in Table 2, specific primers and probes are designed. As known to those skilled in the art, there are usually many possible design options that can be used to obtain specific and reliable primers and probes for sufficiently long target sequences. Therefore, any primer or probe suitable for reliable and specific detection of the biomarkers and reference genes listed in Table 2 is a part of the present invention. The inventors have specially designed and verified the primers and probes listed in Table 3. As mentioned above, these primers and probes should be understood as possible examples, other primers and probes are also available, and those skilled in the art know how to design and verify these primers and probes. Therefore, Table 3 does not represent a comprehensive list of all primers and probes of the present invention. It is a preferred embodiment of the present invention to detect biomarkers and reference genes using the primers and probes of Table 3.

[0077] Table 3: List of primers (FW, RE), probes (PR), and amplicon positions used for amplification and detection of target biomarkers and reference genes in Table 2.

[0078]

[0079]

[0080]

[0081]

[0082] FW: forward primer; RE: reverse primer; RP: probe; *: amplicon position defined by forward and reverse primers on the reference human genome assembly hg38

[0083] The biomarker PCA3 is commonly used in the diagnosis of prostate cancer (PCa). Therefore, as expected, the PCA3 expression level in the subjects tested by the inventors by next generation sequencing indicated PCa ( Figure 2). However, the study found that the biomarker was expressed at the highest levels in very low-risk tumors (V) and decreased as tumor risk factors increased. This finding makes PCA3 an unreliable marker for intermediate- and high-risk tumors and suggests the need for better prostate cancer biomarkers.

[0084] Therefore, the present invention relates to a method for diagnosing prostate cancer, which comprises the step of analyzing the expression levels of nucleic acids according to SEQ ID NOs: 1-3 and 19-50, wherein the sample is designated as positive for prostate cancer if at least one of said nucleic acids is present and / or the expression level of at least one of said nucleic acids is above a threshold value.

[0085] In alternative embodiments, analyzing the expression level of nucleic acid means analyzing the reverse complement sequence or cDNA of the nucleic acid. Experimental results show that the new biomarker has high specificity and sensitivity for detecting PCa, such as Figure 3 shown.

[0086] In a preferred embodiment, the sample is selected from the group consisting of prostate tissue, biopsy material, lymph nodes, urine, semen, blood, serum, plasma, circulating tumor cells in blood or lymph, any tissue suspected of containing metastases, and any source that may contain prostate tumor cells or parts thereof, including vesicles (such as exosomes, microvesicles, etc.) and free or protein-bound RNA molecules from prostate tumor cells or parts thereof. More preferably, the sample is urine, even more preferably, the sample is urine sediment, still more preferably, the sample is urine obtained from a patient after a digital rectal examination, and most preferably, the sample is urine sediment obtained from a patient after a digital rectal examination.

[0087] The measurement of RNA biomarkers of the present invention can be measured by any method suitable for specifically estimating RNA levels, such as PCR-based methods such as qRT-PCR. The detection can be used for early diagnosis (screening) of PCa, prediction of tumor aggressiveness (prognosis) and / or auxiliary selection of treatment methods.

[0088] In one aspect of the invention, the expression levels of the biomarker nucleic acid transcripts according to SEQ.ID NOs: 1-3 and 19-50 are compared with the expression levels of one or more other gene transcripts used as reference genes in the sample. Thus, the expression levels of the biomarker genes can be normalized according to the expression levels of the reference genes.

[0089] Examples of suitable reference genes are shown in Table 4 below, however, this list is not exhaustive and those skilled in the art will be aware of further possible candidate genes.

[0090] Table 4: Potential reference genes

[0091]

[0092]

[0093] In another aspect of the invention, the method of the invention involves subjecting the subject's sample to strict quality control standards for diagnostic testing. The inventors of the present invention unexpectedly discovered that a three-tier quality control scheme can ensure the reliability and accuracy of the diagnostic test. The first tier involves careful inspection of the amplification curve in terms of appropriate S-shaped curve shape, sufficient amplification, and sufficient amplification efficiency.

[0094] Only amplifications of sufficient quality and reliability can be validly included in diagnostic assays. This includes confirmation that the amplification curve is S-shaped, with an asymptote difference of at least 1.5 and a minimum qPCR amplification efficiency of at least 1.5.

[0095] In addition, the quality control (QC) of the test sample involves a first QC filter. The first filter detects a sufficient minimum amount of cells and a sufficient minimum amount of prostate cells in the sample (urine sediment sample). The first QC filter includes determining the expression of a selected generally applicable reference gene and at least one prostate-specific reference gene. The inventors tested different possible reference genes to determine their suitability in the method of the present invention. It is known to those skilled in the art that, in general, any gene with consistent and reliable detectable expression, which is stable under at least most cell conditions and culture conditions, is a potential candidate gene for a reference gene. Although it is always necessary to test and appropriately determine the suitability of candidate genes for specific applications. Suitable methods for selecting candidate genes and testing suitability belong to the common sense of those skilled in the art. Suitable candidate reference genes can be selected from the group of genes listed in Table 4, but are not limited thereto. Many genes were tested as candidate reference genes, of which the following genes were tested as general reference genes: EMC7, GAPDH, GPI, HMBS, HPRT1, RAB7A, TBP and VCP, and the following genes were tested as prostate-specific reference genes: ACPP, KLK2, KLK3 and RDH11 (Table 4). All of these reference genes can be used in alternative embodiments of the present invention. In a preferred embodiment of the present invention, the reference genes of the first QC filter are selected from the group consisting of: general reference genes EMC7, RAB7A and VCP and prostate-specific reference gene KLK2. More relevant information about these reference markers is provided in Tables 2 and 3.

[0096] In order for a sample to pass the first QC filter, all four reference genes tested (EMC7, RAB7A, VCP, KLK2) must show expression levels above the threshold level. The inventors determined the following threshold levels: for EIVIC7, if the expression level is at least less than a cq value of 28, the sample passes the threshold (all cq values ​​of the present invention are calculated according to the Cy0 method-Guescini, 2008; however, alternative methods for calculating cq values ​​are well known in the art and are equally valid for the present invention), for RAB7A, if the expression level is at least less than a cq value of 30, it is the threshold, for VCP, if the expression level is at least less than a cq value of 28, it is the threshold, and for KLK2, if the expression level is at least less than a cq value of 28, it is the threshold.

[0097] To further improve the reliability of the first QC filter, expression levels were determined in triplicate amplification reactions. Each test was suitable for analysis only if the maximum difference in cq values ​​between a single triple reaction did not exceed 0.5 or the maximum difference in cq values ​​between two triple reactions did not exceed 0.25. If a sample did not pass the first QC filter, the sample was invalid and excluded from further analysis. The first QC filter was then combined with the second QC filter.

[0098] After the observed diagnostic results are standardized and calibrated, a second QC filter is applied. This aspect of the quality control of the present invention ensures that only reliable and reproducible data are included in the analysis without large differences. Those skilled in the art will appreciate that this aspect of the present invention depends on the methods and devices used to quantify the expression levels of biomarkers, and those skilled in the art will be able to adjust these quality control methods accordingly to methods other than RT-qPCR. In addition, minimal changes in the number of replicates (4, 5 or 6 replicates instead of 3) or slight changes in the cutoff value will only have a minimal impact on the quality control test and are therefore also included in the present invention.

[0099] Similarly, like the reference genes, the selected biomarkers are also determined in triplicate amplification reactions for each sample to ensure a reliable diagnosis. If the cq value of at least two triplicate samples of the sample is at least less than the selected threshold, the corresponding test is only applicable to the analysis of the maximum difference in cq values ​​between single triplicate samples of up to 0.5 or the maximum difference in cq values ​​between two triplicate samples of up to 0.25. If the sample fails the test, the sample is invalid and is not further analyzed. Alternatively, if the cq value of at least two triplicates of each test is not at least less than the selected threshold or the threshold is not pre-selected, the corresponding test can still be analyzed using only the valid cq value. The valid cq value in the sense of the present invention refers to the cq value of the sample that has passed all previous QC checks. However, in this alternative case, it can be expected that the variance of the results will increase. As described above, those skilled in the art know that this quality control can be adapted to different methods and slightly changed, which is also included in the present invention.

[0100] The measured cq values ​​of valid replicates (i.e., replicates that passed all previous QC steps) of each sample were used for the diagnostic analysis. The diagnostic analysis consisted of calculating the arithmetic mean of each set of technical replicate measurements (Equation 1).

[0101]

[0102] Formula 1: Average cq value calculation; N: number of technical repetitions; i: technical repetition count; j: gene count; k: sample count

[0103] For relative quantification, the average expression level of all samples on the same plate was calculated for each gene (Equation 2).

[0104] This calculation does not include reference samples for inter-batch calibration.

[0105]

[0106] Formula 2: Calculation of the average CQ value of each gene in all samples; s: number of samples; k: sample count; j: gene count

[0107] Calculate the average Cq value (Formula 1) and the average expression of the corresponding gene (Cq 参考，j , Formula 2), which represents the relative quantification of the expression level of the biomarker or reference gene in the corresponding sample (Formula 3).

[0108] RQ jk =ΔCq jk =Cq jk -Cq 参考，j

[0109] Formula 3: Calculation of relative quantification: RQ; j: gene count; k: sample count

[0110] These relative quantifications were normalized according to standard practice in the field by first calculating a normalization factor. The normalization factor was calculated as the arithmetic mean of the relative quantifications of the reference genes (Equation 4).

[0111]

[0112] Formula 4: Calculation of normalization factor: NF; f: number of reference genes; p: reference gene count; k: sample count

[0113] In a preferred embodiment, the reference gene used is the general reference gene of the first QC filter. Nevertheless, those skilled in the art know how to select and verify possible alternative reference gene sets. The standardized relative quantification (Formula 5) of the selected biomarker is calculated by the difference of the relative quantification of the biomarker and the standardized factor using the standardized factor thus obtained. This standardization ensures the comparability of the test sample in operation (standardization in operation).

[0114] NRQ jk =RQ jk -NF k

[0115] Formula 5: Normalization of relative quantification: N RQ; j: gene count; k: sample count

[0116] In order to perform reproducible diagnostic analysis, batch calibration is required. The calculation method of batch calibration is similar to standardization. First, the calibration factor is determined, where the calibration factor is the arithmetic mean of the standardized relative quantification of the batch calibrators. For batch calibration, each batch / test plate contains three PCa tissue sample pools and three BPH patient tissue sample pools. For each gene, the average expression in these two calibration samples is calculated (Formula 6).

[0117]

[0118] Formula 6: Calculation of calibration factor: CF; c: number of batch calibrators; m: batch calibrator count; j: gene count; I: batch / test plate count

[0119] At least one calibration sample must be tested effectively. Subsequently, the calibration factor is used to calibrate the standardized relative quantification of the selected biomarker by calculating the difference between the standardized relative quantification of the biomarker and the calibration factor (Formula 7). This calibration ensures the comparability of test samples between different batches (inter-batch calibration).

[0120] CN R jkl =N RQ jkl -CF jl

[0121] Formula 7: Calibration of standardized relative quantification: CN RQ; j: gene count; k: sample count; I: batch / test plate count

[0122] Those skilled in the art are aware of alternative mathematical methods for calculating gene normalization and calibration, which are also encompassed by the present invention.

[0123] The results of standardization and calibration are then checked by applying a second QC filter. The second QC filter determines whether the subject's sample contains a minimum amount of prostate cells sufficient to detect a selected biomarker for prostate cancer. As with the first QC filter, those skilled in the art know how to select and verify suitable reference genes. For prostate-specific reference genes, the inventors considered and tested several possible candidate genes. Suitable reference genes can be selected, but are not limited to the following groups: ACPP, KLK2, KLK3 and RDH11 (Table 4). In a preferred embodiment of the present invention, the reference genes of the second QC filter are selected from the group of prostate-specific reference genes KLK2 and KLK3.

[0124] In order for a sample to pass the second QC filter, the reference gene tested must show an expression level above a threshold level. The inventors determined the following threshold levels: for KLK2, if the expression level is at least less than the calibrated relative quantification 9.6676667, the sample passes the threshold; for KLK3, if the expression level is at least less than the calibrated relative quantification 11.113, the sample passes the threshold. If the sample does not pass the second QC filter, the sample is invalid and is excluded from further analysis.

[0125] The calibrated relative quantification of the selected biomarkers is then combined into a mathematical prediction model for patient diagnosis. The inventors have found two applicable and predictable models using different biomarker groups. Surprisingly, the diagnostic prediction capabilities achieved by these two models are superior to the diagnostic methods of the prior art (Table 1). The identified models have two common biomarkers, which are crucial to both models. The common biomarkers are PCaD4 and PCaD50. Therefore, in one aspect of the invention, a method for diagnosing PCa includes detecting at least biomarkers PCaD4 and PCaD50. The determined expression levels are then used in the mathematical model to obtain a diagnosis of the patient.

[0126] Alternatively, the inventors determined a first diagnostic model. The model combines calibration and standardized relative quantification of the following biomarkers: PCaD4, PCaD54, PCaD50, and PCaD42. The AUC of the model was found to be 0.817 and the specificity at 90% sensitivity was 0.512. The logistic regression model was fitted to the observed data for each transcript or gene to obtain the regression coefficients.

[0127] Alternatively, the inventors determined a second diagnostic model. The model combines the calibration and standardized relative quantification of the following biomarkers: PCaD4, PCaDll, PCaD50, PCaD52, PCaD58 and PCaD2. The AUC of the model was found to be 0.829 and the specificity at 90% sensitivity was 0.605. The logistic regression model was fitted to the observed data of each transcript or gene to obtain the regression coefficient. The threshold is the minimum score value calculated based on the application model, the calibration and standardized relative expression of the selected biomarkers between the test sample and the control sample, and each sample reaching this score value is designated as cancer positive.

[0128] For the essential minimum set of two models containing the biomarkers PCaD4 and PcaD50, the statistical analysis was similar to the above regression models, and the respective test parameters were also determined in the validation cohort. For this essential minimum set of PCaD4 and PcaD50, an AUC of 0.832 with a 95% CI of 0.74-0.92 was determined. The respective specificity of the essential minimum set at 90% sensitivity was 0.721. These parameters indicate that even the essential minimum set has achieved superior predictive values ​​compared to the reference tests SelectMDx and Progensa PCA3 (see Table 1).

[0129] The logistic regression model can predict the tumor status of the test sample by combining the measured expression value of the sample with the regression coefficient determined by the model. For an estimated tumor probability ≥ 50%, the sample is classified as tumor positive for the corresponding model (model 1 or model 2). The present invention relates to the quantification of RNA biomarker expression levels. After amplification, quantification is simple and can be accomplished by a variety of methods. When at least one of the primers used has a fluorescent dye attached to it, the fluorescent signal from the dye can be used for quantification. Various primer systems and dyes can be used, such as SYBR green, multiplex probes, TaqMan probes, molecular beacons, and Scorpion primers. These are suitable for performing PCR-based methods, such as quantitative reverse transcription PCR (qRT-PCR). Other possible quantification methods are, for example, northern blotting, next-generation sequencing, or measuring absorbance at 260 and 280 nm.

[0130] Any suitable nucleic acid quantification method can be used to analyze the expression level of nucleic acid. In one embodiment of the invention, the analysis in the method is performed by a fluorescence-based assay. In a preferred embodiment, the analysis is performed by measuring the fluorescence of a labeled primer, a labeled probe, or a fluorescent detector (such as SYBR Green). More preferably, the expression level analysis is performed by RT-qPCR. In this method, after reverse transcription, the sample is mixed with forward and reverse primers for at least one nucleic acid selected from the group of SEQ ID NOs: 1 to 131, preferably selected from the primer and probe sets listed in Table 2 (SEQ ID NOs: 136-174), and then amplified. The probes or primers are designed so that they hybridize with their respective target sequences under stringent conditions.

[0131] In one embodiment, analysis of expression levels is performed by next generation sequencing.

[0132] The present invention also relates to a nucleic acid which hybridizes under stringent conditions to one of the nucleic acids according to SEQ ID NOs: 1 to 131 or any part thereof, wherein the nucleic acid is a primer or a probe, preferably a labeled probe.

[0133] In a preferred embodiment of the invention, the length of the nucleic acid that hybridizes under stringent conditions to one of the nucleic acids according to SEQ ID NOs: 1-131 is about 5 to 500 nt, more preferably 10 to 200 nt, even more preferably 10 to 100 nt. In a most preferred embodiment, the length of the nucleic acid is 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 nt.

[0134] In one embodiment of the invention, the nucleic acid that hybridizes under stringent conditions to one of the nucleic acids according to SEQ ID NO: 1-131 comprises a detectable label. In an even more preferred embodiment, the nucleic acid that hybridizes under stringent conditions to one of the nucleic acids according to SEQ ID NO: 1-131 further comprises a quencher moiety.

[0135] The invention also relates to the use of a nucleic acid which hybridizes under stringent conditions to one of the nucleic acids according to SEQ ID NO: 1-131 for the diagnosis of prostate cancer.

[0136] The present invention also relates to a nucleic acid having a sequence from the group of SEQ ID NOs: 1 to 131, or its reverse complement or any part thereof, or a nucleic acid having preferably at least 85%, 90%, 95% or 99% sequence identity with a nucleic acid according to any of the nucleic acids according to SEQ ID NOs: 1 to 131, or its reverse complement or any part thereof, preferably, the nucleic acid is selected from the group comprising SEQ ID NOs: 136-174.

[0137] The present invention also relates to the use of a nucleic acid having a sequence from the group of SEQ ID NOs: 1 to 131 for diagnosing prostate cancer.

[0138] The invention also relates to a kit for diagnosing prostate cancer, comprising at least one nucleic acid that hybridizes under stringent conditions to one of the nucleic acids according to SEQ ID NO: 1-131. The kit may comprise more than one nucleic acid. In a preferred embodiment, the kit further comprises reagents for nucleic acid amplification and / or quantification and / or detection. In another embodiment, the kit comprises a control sample.

[0139] In an alternative embodiment, the present invention is directed to a method for treating and diagnosing prostate cancer, comprising the steps of analyzing the expression levels of nucleic acids according to SEQ ID NOs: 1-3 and 19-50 in a patient sample, wherein if the expression level of the nucleic acids is above a threshold, the sample is designated as positive for prostate cancer; and administering one or more prostate cancer therapeutic agents to the patient.

[0140] In one embodiment, the prostate cancer therapeutic agent comprises: docetaxel Cabazitaxel Mitoxantrone Estramustine Adriamycin Etoposide (VP-16); Vinblastine Paclitaxel Carboplatin Abiraterone acetate, bicalutamide, casoteramide, degarelix, enzalutamide, goserelin acetate, leuprolide acetate, prednisone, sipuleucel-T, radium 223 dichloride, and / or vinorelbine

[0141] One aspect of the present invention is a method for in vitro diagnosis of prostate cancer comprising the following steps:

[0142] a) provide samples from patients suspected of having prostate cancer,

[0143] b) analyzing the expression level of at least the nucleic acid according to SEQ ID NO: 2 and SEQ ID NO: 20 in the sample,

[0144] Wherein, if the expression level of the nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer.

[0145] Another aspect of the present invention relates to an ex vivo method for diagnosing prostate cancer, comprising the following steps:

[0146] a) provide samples from patients suspected of having prostate cancer,

[0147] b) analyzing the expression level of at least the nucleic acids according to SEQ ID NO: 2, SEQ ID NO: 19-20 and SEQ ID NO: 35-37 in the sample,

[0148] Wherein, if the expression level of the nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer.

[0149] Another aspect of the present invention relates to an ex vivo method for diagnosing prostate cancer, comprising the following steps:

[0150] a) provide samples from patients suspected of having prostate cancer,

[0151] b) analyzing the expression level of at least the nucleic acids according to SEQ ID NOs: 1-3, SEQ ID NOs: 20-34 and SEQ ID NOs: 38-50 in the sample,

[0152] Wherein, if the expression level of the nucleic acid is above a threshold, the sample is designated as positive for prostate cancer.

[0153] In one embodiment of the present invention, the sample used in the method of the present invention is selected from the group comprising: prostate tissue, biopsy material, lymph nodes, urine, semen, blood, serum, plasma, circulating tumor cells in the blood or lymph, any tissue suspected of containing metastasis, and any source that may contain prostate tumor cells or parts thereof, including vesicles (such as exosomes, microvesicles, etc.) and free or protein-bound RNA molecules derived from prostate tumor cells.

[0154] In a preferred embodiment of the present invention, the sample is a urine sample, and in a more preferred embodiment, the sample is a urine sediment. In a more preferred embodiment, the sample is a urine sample obtained after a digital rectal examination (DRE): DRE-urine, and in the most preferred embodiment, the sample is a urine sediment obtained after DRE: DRE urine sediment. DRE is a routine examination method that can obtain a urine sample containing a certain amount of prostate cells. The obtained DRE urine sample is centrifuged and washed twice with PBS to separate the cells. Subsequently, according to well-known and mature techniques, the separated cells are lysed and the RNA contained therein is isolated. Those skilled in the art are familiar with the different applicable methods for obtaining purified RNA from cell samples.

[0155] In another embodiment of the present invention, the method involves analyzing the expression level by measuring the fluorescence of a labeled primer, a labeled probe, or a fluorescent detection agent.

[0156] In another embodiment of the present invention, the method involves analyzing expression levels by qRT-PCR.

[0157] In another embodiment of the present invention, the method involves normalizing the expression levels of the biomarkers to general and prostate cell specific reference genes.

[0158] In a preferred embodiment of the present invention, the general and prostate cell specific reference genes are selected from the group comprising: EMC7, RAB7A, VCP, KLK2 and KLK3.

[0159] In another embodiment of the method of the present invention, the method involves using general and prostate cell-specific reference genes to assess sample quality. The expression levels of general and prostate cell-specific reference genes are used to provide at least one quality control filter, and only samples that pass at least one quality control filter are included in the analysis.

[0160] In another aspect of the invention, the present invention relates to a nucleic acid that hybridizes under stringent conditions to one of the nucleic acids of SEQ ID NOs: 1-3 and 19-50. This means that the present invention relates to a nucleic acid that has at least 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity to one of the sequences of SEQ ID NOs: 1-3 and 19-50 or their reverse complement or any partial sequence thereof. Preferably, the nucleic acid is selected from the group consisting of SEQ ID Nos: 136-144 and 148-162, wherein these nucleic acids can be used as primers or probes to detect the sequences of SEQ ID NOs: 1-3 and 19-50 or sequences that have at least 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity thereto.

[0161] The nucleic acid that can be used as a primer or probe has a length of about 10 to 100 nucleotides, more preferably about 10-50 nucleotides, even more preferably about 15-40 nucleotides, even more preferably about 15-35 nucleotides, and most preferably about 15-25 nucleotides.

[0162] The nucleic acid may also include a detectable label. In a preferred embodiment, the detectable label is a fluorescent label covalently attached to the nucleic acid. The fluorescent label may also be combined with at least one quencher molecule, which is also covalently attached to the nucleic acid and can quench the fluorescent signal of the fluorescent label.

[0163] The above nucleic acid can be used for the diagnosis of prostate cancer.

[0164] A nucleic acid having one of the sequences of SEQ ID NOs: 1-131 or its reverse complement or any part thereof, or a nucleic acid sharing preferably at least 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with one of the sequences of SEQ ID NOs: 1-131 or its reverse complement or any part thereof, preferably, the nucleic acid is selected from the group comprising SEQ ID NOs: 136-174, wherein these nucleic acids can be used as primers or probes to detect sequences of SEQ ID NOs: 1-131 or sequences sharing at least 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity therewith.

[0165] The above-mentioned nucleic acid can also include a detectable label. In a preferred embodiment, the detectable label is a fluorescent label covalently attached to the nucleic acid. The fluorescent label can also be combined with at least one quencher molecule, which is also covalently attached to the nucleic acid and can quench the fluorescent signal of the fluorescent label. The labeled primer or probe can be used for the diagnosis of prostate cancer.

[0166] In another aspect of the present invention, the present invention also relates to a kit for diagnosing prostate cancer, which comprises the above-mentioned nucleic acid according to any one of SEQ ID NOs: 1-3 and 19-50, or SEQ ID NOs: 1-131, or SEQ ID NOs: 136-144 and 148-162, or SEQ ID NOs: 136-174, and optionally also comprises reagents for nucleic acid amplification and / or quantification and / or detection.

[0167] In another aspect of the present invention, the present invention also relates to a method for treating and diagnosing prostate cancer, comprising the following steps:

[0168] a) analyzing the expression level of a nucleic acid according to any one of SEQ ID NOs: 2 and 20, or SEQ ID NOs: 2, 19-20 and 35-37, or SEQ ID NOs: 1-3, 20-34 and 38-50 in a sample from a patient,

[0169] b) wherein if the expression level of said nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer; and

[0170] c) administering one or more prostate cancer therapeutic agents to the patient.

[0171] Possible known prostate cancer therapeutic agents include, but are not limited to, the following group: docetaxel Cabazitaxel Mitoxantrone Estramustine Adriamycin Etoposide (VP-16); Vinblastine Paclitaxel Carboplatin Abiraterone acetate, bicalutamide, casoteramide, degarelix, enzalutamide, goserelin acetate, leuprolide acetate, prednisone, sipuleucel-T, radium 223 dichloride, and / or vinorelbine

[0172] In another aspect of the present invention, the present invention relates to a method for diagnosing prostate cancer, comprising the following steps:

[0173] a) analyzing the expression level of at least the nucleic acids according to SEQ ID NOs: 2 and 20, or SEQ ID NOs: 2, 19-20 and 35-37, or SEQ ID NOs: 1-3, 20-34 and 38-50 in a sample from a patient,

[0174] b) wherein if the expression level of the nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer.

[0175] The following examples describe in detail methods for identifying and evaluating novel biomarkers and reference genes for diagnosing PCa. The newly identified biomarkers and the diagnostic methods based on these biomarkers can diagnose the disease more accurately and sensitively than current biomarkers.

[0176] Example

[0177] Materials and methods

[0178] Clinical cohort

[0179] Methods: Prostate cancer (PCa) patients who underwent radical prostatectomy (RPE) or surgery to remove benign prostatic hyperplasia (BPH) were included in a retrospective clinical cohort aimed at identifying novel biomarkers for PCa. Approval from the local ethics committee and informed consent from the patients were obtained in accordance with legal regulations. Clinical follow-up data of PCa patients for at least five years were collected.

[0180] Prostate tissue samples were collected from 40 PCa patients and 8 BPH patients by whole genome RNA sequencing to identify diagnostically relevant biomarkers. Four PCa groups were defined based on the Gleason staging (The Veteran's Administration Cooperative Urologic Research Group: histologic grading and clinical staging of prostatic carcinoma; in Tannenbaum, M. Urologic Pathology: The Prostate, Philadelphia: Lea and Febiger. Pages 171-198) and the presence or absence of metastases in adjacent lymph nodes on the RPE (see Table 5). The composition of the test cohort is also shown in Figure 1 middle.

[0181] Selected biomarker candidates were further validated by custom microarray and quantitative reverse transcription real-time PCR (qRT-PCR) in a cohort of 203 patients (39 control BPH, 164 tumor samples) and 338 patients (126 control BPH, 212 tumor samples), respectively.

[0182] Table 5: PCa cohorts used for whole-genome RNA sequencing screening: the control group (C) consisted of BPH samples. The very low-risk (V) and low-risk (L) groups included samples from patients with Gleason scores (GS) <7 and =7, respectively, and all without lymph node metastasis (pN0). The intermediate-risk (M) group included cases with GS <=7 and lymph node metastasis (pN+); the high-risk (H) group consisted of tissues with GS>7. For the latter, pairs of tumor and tumor-free tissue samples obtained from the same patient were analyzed.

[0183]

[0184] Prostate tissue sample

[0185] Prostate tissue samples were obtained from surgery and stored in liquid nitrogen. Prostate tissue samples obtained from radical prostatectomy (RPE) in patients with prostate cancer (PCa) were divided into tumor and non-tumor samples. Prostate tissue samples from patients with benign prostatic hyperplasia (BPH) were used as controls. Patient consent was always obtained.

[0186] In order to verify the status of the sample and its tumor cell content, all samples were divided into a series of frozen sections. To this end, the frozen tissue samples were embedded in Tissue-Tek OCT compound (Sakura Finetek GmbH) and fixed on the metal press head by freezing. Frozen sections were prepared using a cryostat (Leica) equipped with a slicer blade C35 (FEATHER) cooled to -28°C. Each sample was cut into a total of 208 frozen sections, of which 4 sections were HE stained and evaluated by a pathologist for their tumor cell content. This produced 3 stacks of continuous frozen sections, with HE stained sections on both sides of each stack. Only the slice stacks containing at least 60% or up to 5% tumor cells on either side of the slice were used as tumor or tumor-free samples, respectively. RNA was then prepared for 50 stacks of frozen sections in the selected slice stack.

[0187] RNA Isolation

[0188] Total RNA was isolated from cryopreserved tissues using Qiazol and miRNeasy Mini kits on QIAcube (both from Qiagen), followed by manual DNase I digestion. RNA concentration was determined using Nanodrop 1000 (Peqlab). RNA integrity was verified on an Agilent Bioanalyzer 2100 (Agilent Technologies, Palo Alto, CA), and only RNA samples with an RNA integrity number (RIN) of at least 6 were further processed.

[0189] Whole-genome long RNA next-generation sequencing

[0190] Whole-genome long RNA sequencing was performed using a subset of the retrospective PCa cohort, which included 8 prostate tissue samples from benign prostatic hyperplasia (BPH) as controls and 56 samples from prostate cancer patients (including tumor and tumor-free tissue pairs from samples with Gleason score >7). Ribosomal RNA was depleted from 1 μg of total RNA using the Ribo-Zero rRNA Depletion Kit (Epicentre). Sequencing libraries were prepared from 50 ng of rRNA-depleted RNA using the ScriptSeq v2 RNA-Seq Library Preparation Kit (Epicentre). Dual-labeled cDNA was purified using the Agencourt AMPure XP System Kit (Beckman Coulter). PCR was performed for 10 cycles to integrate index barcodes for sample multiplexing and to amplify cDNA libraries. The quality and concentration of the amplified libraries were determined using the DNA High Sensitivity Kit on an Agilent Bioanalyzer (Agilent Technologies). 4 ng of each of the 8 samples were pooled and size-selected on a 2% agarose gel using agarose gel electrophoresis. According to the manufacturer's instructions, the sample range between 150bp and 600bp was gel excised and purified using the MinElute gel extraction kit (Qiagen). The purified library was quantified on an Agilent bioanalyzer using a DNA high sensitivity chip (Agilent Technologies). Each purified and size-selected library pool was then loaded into an Illumina HiSeq2000 flow cell and distributed to all channels. Cluster generation was performed in an Illumina cBOT instrument using TruSeq PE Cluster Kits v3 according to the manufacturer's protocol. Sequencing was performed on an Illumina HiSeq2000 sequencer (Illumina, Inc.). The details of the sequencing run are as follows: paired end sequencing strategy; Read1 101 cycles, index sequence 7 cycles, Read2 101 cycles.

[0191] Sequencing Data Analysis: Raw Data Preparation

[0192] The raw sequencing data, including base call files (BCL files), were processed using CASAVA v1.8.1 (Illumina) to generate FASTQ files. The FASTQ files contain all sequenced RNA fragments for each clinical sample, hereinafter referred to as "reads". Specific adapter sequences were removed using cutadapt v1.6 (http: / / code.google.eom / p / cutadapt / ).

[0193] Sequencing data analysis: genome mapping and transcript assembly:

[0194] Reads were mapped to the human genome (assembly GRCh37 / hgl9) using Segemehl v0.1.4-382 and TopHat v2.0.87. New transcripts (i.e., transcripts not annotated in Gencode v17) were assembled using Cufflinks v2.1.18 and Cuffmerge v2.1.1. All new transcripts and all known Gencode v17 transcripts were combined into a comprehensive annotation set.

[0195] Sequencing data analysis: statistical analysis

[0196] Htseq - count v0.6.0 (http: / / www-huber.embl.de / users / anders / HTSeq / doc / count.html) was used to calculate read counts for each transcript and gene contained in a comprehensive annotation set of novel and known transcripts. Differentially expressed transcripts and genes were identified using R and the Bioconductor library DeSeq2 v1.81. The raw read counts were normalized and the variances were stabilized (vst transformation). A negative binomial log-linear model was fitted to the read counts for each transcript or gene, and the coefficients different from zero were determined by the likelihood ratio test. The false discovery rate was controlled by the Benjanimi-Hochberg adjustment.

[0197] Validation by custom microarray

[0198] Based on the sequencing results, a custom microarray with 180,000 probes (Agilent SurePrint G3Custom Exon Array, 4x180K, design ID 058029) was designed, which included mRNA, long noncoding RNA (gencode v15), novel transcripts, and all transcripts found to be differentially expressed between tumor and control tissue samples by RNA sequencing. Probe design was completed using Agilent's custom design tool eArray.

[0199] Microarray screening was performed using a retrospective PCa cohort consisting of 40 prostate tissue samples and 8 samples from patients with benign prostatic hyperplasia (BPH) as controls, as well as 164 tumor and 52 tumor-free tissue samples from patients with prostate cancer after radical prostatectomy. cRNA was synthesized from 200 ng of total RNA using the Rapid Amp Labeling Kit (Agilent), and 1650 ng of cRNA was hybridized to the array (Agilent Gene Expression Hybridization Kit).

[0200] RNA Custom Microarray Data Analysis:

[0201] Differential expression analysis in the validation cohort was performed only for patients not included in the exploration cohort (164 PCa patients and 39 BPH patients). Differentially expressed probes were identified by using R v3.2.2 and Bioconductor packagelimma v3.24.15. Quality control of the arrays was performed by examining the distribution of “bright corner”, “dark corner” probes and the relationship between the relative spike-in concentration and the normalized signal. To retrieve a set of probes that mapped to unique genomic locations in hgl9 BLAT v35, the parameter -minldentity=93 was used. All probes that mapped to more than one different genomic region were discarded. Normalization between arrays was done by using quantile normalization. To reduce the number of tests, nonspecific filtering was applied as follows: the expression of the probe must be greater than the background expression in at least 20 arrays. Background expression was defined as the mean intensity plus three times the standard deviation of the negative control spot (3xSLv spot from Agilent). In addition, the probe must show a nonspecific expression change of at least IQR greater than 0.5. Finally, linear models were fitted using the R package limma, and robust variance estimates were obtained using empirical Bayes adjusted t statistics. The false discovery rate was controlled by the Benjamini-Hochberg adjustment.

[0202] Validation by quantitative real-time PCR

[0203] In order to verify the results obtained by next generation sequencing and microarray screening, quantitative real-time PCR was used for analysis. According to the manufacturer's instructions, cDNA was synthesized from 100ng total RNA using a high capacity reverse transcription kit (Applied Biosystems) and random primers. 4 μl of diluted cDNA was subsequently used for PCR detection. On the Applied Biosystems 7900HT real-time PCR system, custom and pre-designed TaqMan gene expression detection (Applied Biosystems) was used to perform quantitative real-time PCR on reference and biomarker transcripts. The biomarkers and reference genes tested are listed in Table 2, and the primers and probes used are listed in Table 3.

[0204] All samples were measured in triplicate and the average of these measurements was used for further calculations. All samples were checked for adequate quality using the QC filters described above. Only samples that passed the QC filters were valid and suitable for inclusion in the final analysis. The first QC filter tested whether there were adequate numbers of cells in the sample, while the second QC filter confirmed whether there were adequate numbers of prostate cells in the sample. This was further combined with the amplification quality assessment to ensure that repeated measurements of the sample were reproducible and consistent and that the amplification met the minimum standards of the MIQE guidelines.

[0205] DRE urine sample validation: DRE urine sample collection and RNA isolation

[0206] Urine samples (DRE urine) were collected after a digital rectal examination (DRE) of the prostate. This routine examination method can obtain urine samples containing a certain amount of prostate cells. The DRE urine samples were centrifuged and washed twice with PBS. The resulting cell pellet was resuspended in 700 μl Qiazol. Total RNA was isolated using the miRNeasy Mini kit (all from Qiagen) on a QIAcube and DNase I digestion was performed manually. RNA concentration was determined using a Nanodrop 1000 (Peqlab). RNA integrity was verified on an Agilent Bioanalyzer 2100 (Agilent Technologies, Palo Alto, CA).

[0207] Quantitative real-time PCR screening of DRE urine samples

[0208] According to the manufacturer's instructions, cDNA was synthesized from 2x50ng total RNA using Superscript III reverse transcriptase (Applied Biosystems) and random primers. PCR detection was subsequently run using 4 μl cDNA. Quantitative real-time PCR was performed on the Applied Biosystems 7900HT real-time PCR system for housekeeping (PSA) and target transcripts. All samples were measured in duplicate, and the mean values ​​of these measurements were used for further calculations.

[0209] Whole-genome long RNA next-generation sequencing of DRE urine samples

[0210] For whole-genome long RNA sequencing, total RNA from 11 DRE urine samples was ethanol precipitated to concentrate the RNA amount and resuspended in 10 μl RNase-free water. 4 ng of total RNA was rRNA-depleted using the Low Input Ribo-Zero rRNA Removal Kit (Epicentre, modified by Clontech) to obtain 10 μl rRNA-depleted RNA. Sequencing libraries were prepared from 8 μl rRNA-depleted RNA using the SMARTER stranded RNAseq kit (Clontech). Dual-labeled cDNA was purified using the Agencourt AMPure XP System Kit (Beckman Coulter). PCR was performed for 18 cycles to integrate index barcodes for sample multiplexing and amplify cDNA libraries. The quality and concentration of the amplified libraries were determined using the DNA High Sensitivity Kit on an Agilent Bioanalyzer (Agilent Technologies). Samples were pooled and clustered using 15 pmol / 1 pooled libraries and the TruSeq PE Cluster Kit v4 (Illumina Inc.) in an Illumina cBOT instrument according to the manufacturer's protocol. Sequencing was performed using HiSeq SBS v4 sequencing reagents (250 cycles) on an Illumina HiSeq2500 sequencer (Illumina, Inc.). The details of the sequencing run are as follows: paired-end sequencing strategy; Read1 126 cycles, index sequence 7 cycles, Read2 126 cycles.

[0211] Statistical analysis of DRE urine qRT-PCR results

[0212] For relative quantification, the change in gene expression of each sample was analyzed relative to the average expression of the gene in the same run. Data normalization was performed for the general reference genes listed in Table 4: EMC7 (SEQ ID NO: 74-77), RAB7A (SEQ ID NO: 78-95) and VCP (for SEQ ID NO: 96-131). Calibration samples from pooled positive control samples of patients diagnosed with benign prostatic hyperplasia (BPH) or diagnosed with prostate cancer were used to calibrate the measurements between different runs.

[0213] After standardization and calibration, the relative expression levels of biomarkers between tumor and control samples were compared using the Mann-Whitney U test. The package pROC was used to calculate the receiver operating characteristic (ROC) curve, which represents the area under the curve (AUC) of the diagnostic ability of each marker. Variable selection was performed by variable importance using random forest and stepwise model selection (logistic regression: top-down using AIC). The final model was trained using logistic regression and ridge logistic regression. All statistical analyses were performed using R statistical software.

[0214] result

[0215] The transcriptomes of 40 PCa tumor samples and 16 tumor-free samples were analyzed using strand-specific paired-end long RNA next-generation sequencing (NGS), with RPE and 8 BPH prostate tissue samples as benign non-tumor controls. At least three fragments were prepared for each sample, totaling approximately 150 frozen sections, aiming to achieve optimal data quality and analytical robustness. After pathological evaluation, only fragments that met the maximum and minimum tumor cell counts of 60% and 5% in tumor and tumor-free samples, respectively, were retained for further analysis. The transcriptome sequencing (RNAseq) approach aims to comprehensively identify and quantify RNAs expressed in normal or cancer prostate tissue. All classes of coding and long non-coding transcripts (irrespective of polyadenylation status) were sequenced. A large amount of RNA input was used to ensure high library complexity. In addition, an average of 200M 2x100nt paired-end reads were sequenced per library, allowing the assembly of new low-expressed transcripts due to the high coverage. This approach outperforms most similar published studies analyzing a large number of samples. In total, approximately 3000 novel transcripts were assembled that had no exon overlap with transcripts annotated in Gencode v17. At a false discovery rate of 0.01, 6442 differentially expressed genes were observed across all comparisons.

[0216] The results successfully reproduced the majority of transcripts previously reported to be differentially expressed between prostate tumors and normal tissues. In addition, many novel PCa-associated transcripts were identified that could be used to develop diagnostic assays for PCa. The most promising transcripts were selected by qRT-PCR for validation in a test cohort of PCa tumors and BPH control samples.

[0217] Several of these novel biomarker candidates significantly exceeded the specificity and sensitivity of PCA3, a biomarker already used for PCa diagnosis. In the sequencing cohort, PCA3 was shown to be significantly associated with PCa, but showed a strong downward trend in high-risk groups ( Figure 2 ).

[0218] The experimental results showed that the new biomarker has high specificity and sensitivity for detecting PCa. Figure 3 As shown, this figure compares the differences in expression levels of various PCa biomarkers between PCa samples and BPH control samples, showing the results for newly discovered biomarkers (ProstaCheck) and previously established biomarkers (SelectMDx and PCA3). Figure 3 It is clearly shown that for the newly identified biomarkers, more pronounced differences in expression levels were observed between PCa and control samples than for previously known biomarkers. Therefore, assays can be established for the diagnosis of PCa based on the measurement of these newly discovered biomarkers alone or in combination (or in combination with other markers) in all sources that may contain prostate tumor cells or their fractions, including vesicles such as exosomes, microvesicles, and free or protein-bound RNA molecules derived from prostate tumor cells. As shown in Table 1, these new assays provide excellent diagnostic reliability.

[0219] The advantages of diagnostic tests based on these biomarkers are that they have significantly lower false-positive rates than current tests, and measuring expression levels in urine samples avoids the need for unnecessary invasive prostate biopsies.

[0220] Sequence Listing

[0221] The sequences of the sequence listing are filed separately. Table 6 below discloses the Ensembl transcript IDs or Ensembl gene IDs (when no transcript variants are known), which are represented by individual SEQ ID NOs.

[0222] Table 6: Correlation of SEQ ID NOs of the present invention with Ensembl transcript variants / gene IDs

[0223]

[0224]

[0225]

[0226]

[0227] Citations

[0228] Aubin SMJ et al. J Urol. 2010;184:1947-52.

[0229] Yoav Benjamini and Yosef Hochberg. Controlling false discovery rate:a practical and powerful approach to multiple testing:Journal of the Royal Statistical Society 1995.

[0230] Cucchiara, V, Cooperberg, M.R., Dall′Era, M., Lin, D.W., Montorsi, F., Schalken, J.A. u. Evans, C.P.: Genomic Markers in Prostate Cancer Decision Making. European urology(2017)

[0231] Duffy, Michael J.″Biomarkers for prostate cancer:prostate - specific antigen and beyond″Clinical Chemistry and Laboratory Medicine(CCLM),(2020)

[0232] Gleason, D.F.(1977).″The Veteran′s Administration Cooperative Urologic Research Group:histologic grading and clinical staging of prostatic carcinoma". In Tannenbaum, M. Urologic Pathology:The Prostate. Philadelphia:Lea and Febiger. pp. 171 - 198.

[0233] Guescini M, Sisti D, Rocchi MB, Stocchi L, Stocchi V. A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition. BMC Bioinformatics. July 30, 2008; 9:326. doi:10.1186 / 1471-2105-9-326

[0234] Haese A et al. Eur Urol. 2008; 54:1081-8.

[0235] Irshad S et al. Sci Transl Med 2013; 5:202ral22.

[0236] Jansen FH et al. Eur Urol. 2009; 55:563-74.

[0237] Jansen FH, van Schaik RH, Kurstjens J, Horninger W, K1ocker H, Bektic J, et al. Prostate-specific antigen(PSA) isoform p2PSA in combination with total PSA and free PSA improves diagnostic accuracy in prostate cancer detection. Eur Urol 2010; 57:921-7.

[0238] Lendi′nez-Cano G. Prospective study of diagnostic accuracy in the detection of high-grade prostate cancer in biopsy-naive patients with clinical suspicion of prostate cancer who underwent the Select MDx test. Prostate. September 2021; 81(12):857-865

[0239] Moyer VA et al. Ann Intern Med. 2012; 157:120-134.

[0240] Salagierski M et al. J Urol 2012; 187: 795 - 801.

[0241] Thompson IM et al. J Am Med Assoc. 2005; 294: 66 - 70.

[0242] Van Gils MP et al. Clin Cancer Res. 2007; 13: 939 - 43.

[0243] Vickers AJ, Cronin AM, Aus G, Pihl CG, Becker C, Pettersson K, et al. A panel of kallikrein markers can reduce unnecessary biopsy for prostate cancer: data from the European Randomized Study of Prostate Cancer Screening in Goteborg, Sweden. BMC Med 2008; 6: 19.

Claims

1. An in vitro diagnostic method for prostate cancer, comprising the following steps: a) provide samples from patients suspected of having prostate cancer, b) analyzing the expression level of at least the nucleic acids according to SEQ ID NO: 2 and SEQ ID NO: 20 in the sample, Wherein, if the expression level of the nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer.

2. An in vitro diagnostic method for prostate cancer, comprising the following steps: a) provide samples from patients suspected of having prostate cancer, b) analyzing the expression level of at least the nucleic acids according to SEQ ID NO: 2, SEQ ID NO: 19-20 and SEQ ID NO: 35-37 in the sample, Wherein, if the expression level of the nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer.

3. An in vitro diagnostic method for prostate cancer, comprising the following steps: a) provide samples from patients suspected of having prostate cancer, b) analyzing the expression level of at least the nucleic acids according to SEQ ID NOs: 1-3, SEQ ID NOs: 20-34 and SEQ ID NOs: 38-50 in the sample, Wherein, if the expression level of the nucleic acid is above a threshold value, the sample is designated as positive for prostate cancer.

4. A method according to any one of the preceding claims, wherein: The sample is selected from the group consisting of: prostate tissue, biopsy material, lymph nodes, urine, semen, blood, serum, plasma, circulating tumor cells in the blood or lymph, any tissue suspected of containing metastases, and any source that may contain prostate tumor cells or parts thereof, including vesicles such as exosomes, microvesicles, and free or protein-bound RNA molecules derived from prostate tumor cells.

5. A method as claimed in any one of the preceding claims, wherein: The analysis of the expression level is performed by measuring the fluorescence of a labeled primer, a labeled probe or a fluorescent detection agent, and preferably the analysis of the expression level is performed by qRT-PCR.

6. A method as claimed in any one of the preceding claims, wherein: The expression levels were normalized to general and prostate cell-specific reference genes.

7. A method as claimed in any one of the preceding claims, wherein: The general and prostate cell-specific reference genes are used to assess sample quality, wherein the expression levels of the general and prostate cell-specific reference genes are used to provide at least one quality control filter, wherein only samples that pass the at least one quality control filter are included in the analysis.

8. A nucleic acid which hybridizes under stringent conditions to one of the nucleic acids of claim 1, 2 or 3.

9. The nucleic acid according to claim 8, wherein The nucleic acid is about 10 to 100 nucleotides in length.

10. The nucleic acid according to claim 8 or 9, wherein The nucleic acid comprises a detectable label.

11. Use of the nucleic acid of any one of claims 8 to 10 for diagnosing prostate cancer by the method of any one of claims 1 to 7.

12. A nucleic acid having one of the sequences of SEQ ID NOs: 1-131 or its reverse complementary sequence or any part thereof, or a nucleic acid having preferably at least 85%, 90%, 95% or 99% sequence identity with one of the sequences of SEQ ID NOs: 1-131 or its reverse complementary sequence or any part thereof, preferably, the nucleic acid is selected from the group comprising SEQ ID Nos: 136-174.

13. Use of the nucleic acid of claim 12 for diagnosing prostate cancer by the method of any one of claims 1 to 7.

14. A kit for diagnosing prostate cancer, comprising the nucleic acid according to any one of claims 8 to 10 or the nucleic acid according to claim 12 and a reagent for nucleic acid amplification and / or quantification and / or detection.

15. A method for treating and diagnosing prostate cancer, comprising the following steps: a) analyzing the expression level of a nucleic acid according to any one of claims 1, 2 or 3 in a patient sample, b) Among them, If the expression level of the nucleic acid is above a threshold, the sample is designated as positive for prostate cancer; and c) administering one or more prostate cancer therapeutic agents to the patient.

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