Use of biomarker patj-dt in diagnosis or prognosis prediction of colorectal cancer

Abstract

The application discloses application of a biomarker PATJ-DT in diagnosis or prognosis prediction of colorectal cancer, and specifically provides application of a reagent for detecting the biomarker in preparation of a product for diagnosing or prognosis predicting colorectal cancer, and further provides a product, a system and a device for diagnosing or prognosis predicting colorectal cancer. It is found for the first time that the expression level of the biomarker PATJ-DT is reduced in patients with colorectal cancer, and through detection of the biomarker PATJ-DT, whether a subject is suffering from colorectal cancer or has a risk of suffering from colorectal cancer can be diagnosed, and the prognosis effect of a colorectal cancer patient can be predicted, so that the application has a high practical application value.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology and relates to the application of the biomarker PATJ-DT in the diagnosis or prognostic prediction of colorectal cancer. Background Technology

[0002] Colorectal cancer, also known as colon cancer, rectal cancer, colorectal cancer, or bowel cancer, is a cancer originating in the colon or rectum (part of the large intestine). Due to abnormal cell growth, it can invade or metastasize to other parts of the body. Symptoms may include blood in the stool, changes in bowel habits, weight loss, and fatigue. 75–95% of colorectal cancer cases have little or no genetic predisposition. Other risk factors include increasing age, being male, high intake of fats, alcohol or red meat, processed meats, obesity, smoking, and lack of physical activity. Approximately 10% of cases are related to lack of exercise. The harmful effects of alcohol consumption gradually increase after one drink per day.

[0003] Long noncoding RNAs (lncRNAs) are a class of RNA molecules longer than 200 nucleotides. Although they do not encode proteins, they exhibit cellular functions. Increasing evidence suggests that lncRNAs play a crucial role in tumor biology. Summary of the Invention

[0004] To address the shortcomings of existing technologies, a biomarker, PATJ-DT, is proposed, specifically for its application in the diagnosis or prognostic prediction of colorectal cancer.

[0005] Therefore, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides the use of a reagent for detecting a biomarker in the preparation of products for diagnosing or predicting the prognosis of colorectal cancer, wherein the biomarker is PATJ-DT.

[0007] In some specific embodiments, the biomarker PATJ-DT has the gene number 118732297. This includes genes and their encoded proteins, as well as their homologs, mutations, and isotypes. The term encompasses full-length, unprocessed biomarkers, as well as any form of biomarker derived from cell-processed sources. The term also encompasses naturally occurring variants of the biomarker (e.g., splice variants or allelic variants).

[0008] Furthermore, the product includes reagents, reagent kits, chips, or test strips.

[0009] Furthermore, the reagents include those used in the following methods: TaqMan probe method, sequencing method, microarray method, mass spectrometry of flight detection, restriction fragment length polymorphism method, single strand conformation polymorphism method, allele-specific PCR, SNaPshot method, SNPlex method, denaturing high performance liquid chromatography, and denaturing gradient gel electrophoresis.

[0010] Furthermore, the product determines whether a subject has colorectal cancer or is at risk of developing colorectal cancer by measuring the expression level of PATJ-DT in the sample.

[0011] In certain specific embodiments, the present invention can be used to assess the disease status of subjects who are not suspected or are predisposed to have colorectal cancer. Selected subjects may be any of the following: subjects who have not been diagnosed with colorectal cancer; subjects assessed by the system described in this invention who have not been diagnosed with colorectal cancer; subjects who have not undergone bowel examinations or have not recently (e.g., within the past year); subjects who have not been diagnosed with inflammatory diseases, lesions, or non-neoplastic polyposis; subjects who have not experienced rectal bleeding, abdominal pain, or abdominal masses; subjects who do not express clinically significant amounts of fibrosis biomarkers consisting of extracellular matrix synthesis and degradation products, and enzymes involved in these processes; and subjects whose ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), or transient elastography (TE) (FibroScan) results suggest the absence of colorectal disease.

[0012] Furthermore, the samples include: cell supernatant, cell lysate, platelets, serum, plasma, vitreous fluid, lymph, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, extracts from oral swabs, saliva, sputum, tears, sweat, mucus, tumor lysate, tissue culture medium, tissue extracts, homogenized tissue, tumor tissue, and cell extracts.

[0013] The biomarkers described in this invention can be selectively extracted from and measured from a sample. For example, methods and steps may include contacting a sample with an extraction medium, extracting the biomarker from the sample, and measuring the extracted lipids (e.g., measuring the biomarker by mass spectrometry after separation by chromatography).

[0014] This invention may optionally include an internal standard for measuring one or more (e.g., each) biomarkers. The internal standard can be mixed with the sample prior to measurement. An initial level of the internal standard known before sample preparation can be used to standardize the measurement signal of the corresponding biomarker.

[0015] In some respects, the label is attached to one or more probes and has one or more of the following properties: (i) providing a detectable signal; (ii) interacting with a second label to modify the detectable signal provided by the second label, e.g., FRET (fluorescence resonance energy transfer); (iii) stabilizing hybridization, e.g., forming a double strand; and (iv) providing a member of the binding complex or affinity group, e.g., affinity, antibody-antigen, ionic complex, hapten-ligand (e.g., biotin-avidin). In other respects, the use of the label can be achieved using any of a large number of known techniques employing known labels, bonds, linking groups, reagents, reaction conditions, and analytical and purification methods.

[0016] The term "probe" refers to any molecule capable of selectively binding to a specific, intended target biomolecule. In some embodiments, the term "probe" herein refers to any molecule or molecule associated with, which can bind indirectly or directly, covalently or nonvalently, to any substrate and / or reaction product and / or protease disclosed herein, and whose association or binding can be detected using the methods disclosed herein. In some embodiments, the probe is a fluorescent probe, an antibody, or an absorbance-based probe. If it is an absorbance-based probe, the chromophore pNA (p-nitroaniline) may be used as a probe for detecting and / or quantifying the target nucleic acid sequence disclosed herein. In some embodiments, the probe may be a nucleic acid sequence comprising a fluorescent molecule or substrate that becomes fluorescent upon exposure to an enzyme, and the nucleic acid sequence is complementary to a fragment of a nucleic acid sequence.

[0017] In specific embodiments of the present invention, the following situation may occur: the sample preparation process sometimes leads to a significant loss of biomarkers before measurement. Using the same instrument (e.g., the same LC-MS) for sample preparation, separation, and measurement for disease prediction improves the accuracy and precision of the measurement; using different instruments (e.g., different laboratories) for sample preparation, separation, and measurement for disease prediction can lead to unpredictable changes in biomarker detection signals. Therefore, in the present invention, an internal standard is preferably used to correct for the loss and / or signal level changes of the corresponding biomarkers during sample preparation and measurement.

[0018] Furthermore, the subjects include humans or non-human mammals.

[0019] Furthermore, the subjects mentioned are human.

[0020] Furthermore, the reagents include specific primers, probes, antisense oligonucleotides, aptamers, or antibodies for detecting PATJ-DT.

[0021] Furthermore, the specific primers mentioned are primers for detecting PATJ-DT lncRNA.

[0022] Furthermore, the primers for specifically detecting PATJ-DT lncRNA are shown in SEQ ID NO:1 and SEQ ID NO:2.

[0023] Furthermore, the products include those that detect PATJ-DT gene expression levels using reverse transcription PCR, real-time quantitative PCR, immunoassay, in situ hybridization, microarray, or high-throughput sequencing platforms to predict the prognosis of colorectal cancer patients.

[0024] Furthermore, the product for detecting PATJ-DT gene expression levels using reverse transcription PCR, real-time quantitative PCR, immunoassay, in situ hybridization, microarray, or high-throughput sequencing platforms to predict the prognosis of colorectal cancer patients includes at least one pair of primers that specifically amplify PATJ-DT.

[0025] In some embodiments, quantitative PCR can be used to determine the expression level of lncRNA. Quantitative or real-time PCR is a technique well-known and readily available to those skilled in the art and does not require precise description. In certain embodiments, which should not be considered as limiting the scope of the invention, the determination of expression profiles using quantitative PCR can be performed as follows.

[0026] The term "level" as used in this invention with respect to biomarkers can be any quantitative or qualitative measure of the biomarker in a sample, such as amount (e.g., mass) or concentration (e.g., w / w, w / v, or molar concentration). When the level is a concentration, the level is relative to the original volume (weight) of the sample prior to sample preparation (e.g., extraction).

[0027] The method of the present invention includes detecting one or more biomarkers in a biological sample by measuring the level of one or more biomarkers in the biological sample. The measurement method can be any effective method for measuring biomarkers. For example, it can be any in vitro measurement method for the level of biomarkers in a biological sample (e.g., blood, plasma, or serum).

[0028] A second aspect of the present invention provides a system for predicting colorectal cancer using the biomarker PATJ-DT, comprising: a result determination unit for comparing the critical value of the biomarker obtained by the data processing unit with a set diagnostic value.

[0029] Furthermore, the system includes a nucleic acid sample separation unit for separating nucleic acid samples from samples provided by the test subject.

[0030] Furthermore, the system includes a sequencing unit for sequencing nucleic acid samples to obtain sequencing results.

[0031] Furthermore, the system includes a data processing unit for detecting the expression level of biomarkers based on sequencing results, analyzing the obtained expression levels, and determining the critical value of the biomarkers.

[0032] The levels (e.g., test results) or scores (e.g., model calculation results) of biomarkers are compared with comparable levels or scores, using criteria such as reference ranges, discrimination ranges, diagnostic or classification thresholds or cutoff values, and abnormal values ​​to determine whether a subject has colorectal cancer or the risk of developing colorectal cancer. The comparable levels of the aforementioned biomarkers can be the biomarker levels or scores of a subject whose colorectal cancer status is unknown.

[0033] A third aspect of the present invention provides a product for diagnosing or predicting the prognosis of colorectal cancer, the product comprising a reagent for detecting the expression level of PATJ-DT.

[0034] Furthermore, the reagents include specific primers, probes, antisense oligonucleotides, aptamers, or antibodies for detecting the PATJ-DT.

[0035] Furthermore, the specific primers are primers capable of detecting the PATJ-DT lncRNA.

[0036] Furthermore, the primers for specifically detecting PATJ-DT lncRNA are shown in SEQ ID NO:1 and SEQ ID NO:2.

[0037] In some embodiments, kits for diagnosing and / or predicting the prognosis of colorectal cancer comprise one or more oligonucleotide probes specific to a target lncRNA, and reagents for purifying the probe-target nucleic acid complex. The oligonucleotide probe contains a sequence complementary to a region of the target lncRNA. The oligonucleotide probe can be DNA or RNA. DNA is preferred. The length of the lncRNA-specific oligonucleotide probe can be 30 to 80 nucleotides, for example 40 to 70 nucleotides, 40 to 60 nucleotides, or approximately 50 nucleotides.

[0038] The fourth aspect of the present invention provides a system for predicting the prognostic risk of colorectal cancer patients using the biomarker PATJ-DT. The system includes a result prediction module for comparing the expression level of the biomarker PATJ-DT with a set prediction value to obtain a prognostic risk assessment of colorectal cancer patients.

[0039] Furthermore, the system also includes an input module for inputting the PATJ-DT expression level detected in colorectal cancer patient samples.

[0040] Furthermore, the system also includes an output module for marking and outputting the prognostic risks of colorectal cancer patients in the processed results.

[0041] In some embodiments, sequencing (e.g., next-generation sequencing) is used to determine the expression level of lncRNA. Sequencing can be performed after the extracted RNA has been converted to cDNA using reverse transcriptase, or the RNA molecule can be sequenced directly. In certain embodiments, which should not be considered as limiting the scope of the invention, the measurement of expression levels using next-generation sequencing can be performed as follows: Briefly, RNA is extracted from a sample (e.g., a blood sample). After removing the rRNA, the RNA sample is then reverse transcribed into cDNA. The library can be sequenced using any next-generation sequencing technology known to those skilled in the art.

[0042] In some implementations, the expression level of lncRNA can be determined using a nucleic acid microarray. A nucleic acid microarray consists of different nucleic acid probes attached to a matrix, which can be a microchip, a glass slide, or microsphere-sized beads. The microchip can be composed of polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glass, or nitrocellulose. The probes can be nucleic acids, such as cDNA (“cDNA microarray”) or oligonucleotides (“oligonucleotide microarray”). To determine the expression profile of a target nucleic acid sample, the sample is labeled and brought into contact with the microarray under hybridization conditions, resulting in the formation of a complex between target nucleic acids complementary to the probe sequences attached to the surface of the microarray. The presence of the labeled hybridization complex is then detected. Many variations of microarray hybridization techniques are available to those skilled in the art.

[0043] The fifth aspect of this invention provides the application of the biomarker PATJ-DT in constructing computational models for the diagnosis or prognostic prediction of colorectal cancer.

[0044] The sixth aspect of this invention relates to the use of the biomarker PATJ-DT in the preparation of pharmaceutical compositions for treating colorectal cancer.

[0045] According to the present invention, the term "pharmaceutical composition" refers to a composition administered to a patient, preferably a human patient. The pharmaceutical compositions of the present invention comprise the compounds described above. Optionally, they also include molecules capable of altering the characteristics of the compounds of the present invention, thereby, for example, stabilizing, modulating, and / or activating their function. The compositions may be in solid, liquid, or gaseous form, particularly in one or more powder, tablet, solution, or aerosol forms. Optionally, the pharmaceutical compositions of the present invention further comprise a pharmaceutically acceptable carrier or excipient. Examples of suitable pharmaceutical carriers and excipients are well known in the art and include phosphate-buffered saline solutions, water, emulsions such as oil / water emulsions, various types of wetting agents, sterile solutions, organic solvents including DMSO, etc. Compositions containing such carriers or excipients can be formulated using well-known conventional methods. These pharmaceutical compositions can be administered to a subject at an appropriate dose. Dosing regimens are determined by the attending physician and clinical factors. As is well known in the medical field, the dose for any patient depends on many factors, including patient size, body surface area, age, the specific compound to be administered, sex, time and route of administration, overall health condition, and other concurrently administered medications. The effective therapeutic dose for a given situation can be easily determined through routine laboratory tests and is within the skill and judgment of a general clinician or physician.

[0046] As used herein, the term "ncRNA" or "non-coding RNA" refers to a functional RNA molecule that is not translated into a protein. The DNA sequence from which non-coding RNA is transcribed is commonly referred to in the art as an RNA gene. The term "lncRNA" or "long non-coding RNA" is commonly used in the art and refers to ncRNAs containing more than 200 nucleotides.

[0047] The term "nucleic acid sequence" or "nucleotide sequence" includes DNA such as cDNA, or, in a preferred embodiment, genomic DNA and RNA. It should be understood that the term "RNA" as used herein includes all forms of RNA, including lncRNA or ncRNA in a preferred embodiment. The term "nucleic acid sequence" is used interchangeably with the term "polynucleotide" according to the present invention.

[0048] Expression levels between the two groups were analyzed using the two-tailed Welch t-test and / or the Wilcoxon-Mann-Whitney test. Significant differential expression was identified as p < 0.05. Fold change and AUC (area under the curve) were calculated for each lncRNA and for each test condition. Attached Figure Description

[0049] Figure 1 This is a bar chart showing the differential expression of the test set PATJ-DT in normal tissues and colorectal cancer tissues;

[0050] Figure 2This is a bar chart showing the differential expression of the validation set PATJ-DT in normal tissues and colorectal cancer tissues;

[0051] Figure 3 The chart shows the ROC curves for colorectal cancer diagnosis using PATJ-DT. In the chart, A is the ROC curve plotted using data from the GTEx-TCGA database, and B is the ROC curve plotted using clinical sample data.

[0052] Figure 4 This is an ROC curve plot for predicting the prognosis of colorectal cancer using PATJ-DT. In it, T represents the growth of the cancer tumor itself, including the size of the tumor and its extent of invasion; N represents the degree of regional lymph node metastasis; and M represents whether there is blood supply to distant organs.

[0053] Figure 5 This is a survival curve obtained after using PATJ-DT for colorectal cancer prognosis prediction. Detailed Implementation

[0054] The following examples detail the accuracy of the biomarker PATJ-DT in diagnosing colorectal cancer and predicting the prognosis of colorectal cancer patients.

[0055] The following examples are provided to help better understand the present invention, but are not intended to limit the invention.

[0056] Unless otherwise specified, the experimental methods described in the following examples are conventional methods.

[0057] Unless otherwise specified, all experimental materials used in the following examples were purchased from regular biochemical reagent stores.

[0058] The logarithmic function used to associate the biomarker combination with the disease preferably employs an algorithm developed and obtained through the application of statistical methods. Suitable statistical methods include discriminant analysis (DA) (i.e., linear, quadratic, regular DA), kernel methods (i.e., SVM), nonparametric methods (i.e., k-nearest neighbor classifier), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest), generalized linear models (i.e., logistic regression), principal component analysis (i.e., SIMCA), generalized superposition models, fuzzy logic-based methods, and methods based on neural networks and genetic algorithms. Those skilled in the art will have no problem selecting suitable statistical methods to evaluate the biomarker combination of the present invention and thereby obtaining suitable mathematical algorithms.

[0059] The receiver operating characteristic (ROC) curve is a curve plotted using a series of different binary classification methods (cutoff values ​​or decision thresholds), with sensitivity (true positive rate) on the ordinate and 1-specificity (false positive rate) on the abscissa. The area under the ROC curve is an important indicator of test accuracy; the larger the area under the ROC curve, the greater the diagnostic value of the test.

[0060] Example: Biomarker PATJ-DT is associated with the diagnosis and prognostic prediction of colorectal cancer.

[0061] 1. Experimental subjects

[0062] In this experiment, the lncRNA sequencing data and clinical information of the TCGA colorectal cancer cohort used for testing were downloaded from the GDC website, and the gene expression data of the normal colon cohort in the GTEx database were downloaded from the UCSC xene website. After batch processing of the TCGA and GTEx databases, a total of 741 samples were used for differential expression and colorectal cancer diagnosis analysis, including 359 normal tissue samples and 382 colorectal cancer tissue samples. In addition, after removing normal, duplicate, and missing samples from the TCGA database, a total of 619 samples were used for prognostic analysis.

[0063] The samples used for validation in this experiment were real-world samples, consisting of 29 patients with colorectal cancer. 29 samples were normal tissue and 29 samples were colorectal cancer tissue, used to validate the accuracy of the biomarker PATJ-DT in diagnosing colorectal cancer.

[0064] The inclusion and exclusion criteria for colorectal cancer patients are as follows:

[0065] Inclusion criteria: ① All patients were diagnosed with colorectal cancer for the first time, and the pathological diagnosis and staging of colorectal cancer were completed according to the World Health Organization Classification of Digestive System Tumors (4th Edition) and the American Joint Committee on Cancer staging system, and the diagnosis was made independently by two pathologists; ② All patients underwent radical resection of colorectal cancer, and the number of lymph nodes removed was no less than 10; ③ All patients had not received anti-tumor treatment before surgery, including neoadjuvant chemotherapy, radiotherapy, targeted therapy or biological therapy.

[0066] Exclusion criteria: ① Incomplete clinical case data; ② Failure to perform systematic lymph node dissection or conversion to palliative surgery; ③ History of cancer or concurrent cancer in other sites; ④ Patient died from non-tumor causes or within 1 month postoperatively; ⑤ Perioperative complications of severe cardiac, pulmonary, hepatic, or renal insufficiency.

[0067] Detailed information on the sampled objects is shown in Table 1.

[0068] Table 1 Information on 29 patients with colorectal cancer

[0069]

[0070]

[0071] 2. Experimental Methods

[0072] 1) Differential expression analysis

[0073] Test set: Differential expression analysis was performed on the sequencing data of PATJ-DT in the screening set using SPSS software to compare the differential gene expression between colorectal cancer tissues and adjacent normal tissues in the screening set.

[0074] Validation set:

[0075] ①RNA extraction

[0076] Total RNA was extracted from tissues using the RNeasy kit (Beyotime, Shanghai, 456 China, R0027). Details are as follows:

[0077] a. Sample preparation: Take 20 mg of animal tissue and place it in a 1.5 ml centrifuge tube. Add about 6 zirconia beads and quickly add 600 μl of lysis buffer pre-chilled in an ice bath. Homogenize using a micro electric homogenizer. After grinding or homogenizing, gently pipette the homogenate 8-10 times and let it stand at room temperature for 3-5 minutes. Then centrifuge at about 14,000 g for 2 minutes and transfer the supernatant to a new centrifuge tube.

[0078] b. Add an equal volume of binding solution to the lysis buffer and gently invert to mix 3-5 times.

[0079] c. Transfer the mixture of lysis buffer and binding buffer into a purification column, centrifuge at 16,000g for 1 minute, and discard the lower layer.

[0080] d. Add 600 μL of wash buffer I to the purification column, centrifuge at 16,000 g for 1 minute, and discard the lower layer. Add 600 μL of wash buffer II to the purification column, centrifuge at 16,000 g for 1 minute, and discard the lower layer. Repeat this step twice.

[0081] Centrifuge at 16,000g for 2 minutes to remove residual liquid from the purification column.

[0082] f. Discard the collection tube. Place the RNA purification column in the RNA elution tube, add 50 μL of elution buffer to the center of the purification column, incubate at room temperature for 3 minutes, and centrifuge at 16,000 g for 30 seconds. Add the resulting elution buffer back to the center of the purification column and repeat the elution process, centrifuging at 16,000 g for 30 seconds to obtain purified RNA.

[0083] ②Reverse transcription:

[0084] a. Determine the concentration and quality of the sample RNA:

[0085] Reverse transcription was performed on RNA samples with concentrations higher than 143 ng / μL and OD230 / 260 and OD280 / 260 meeting the standards.

[0086] b. Removing DNA from RNA:

[0087] First, prepare a mixed solution of gDNA Eraser and 5×gDNA Eraser Buffer on ice and dispense it into each reaction tube. Then, add 1 μg of total RNA and enzyme-free water.

[0088]

[0089]

[0090] The above reaction system was heated at 42°C for two minutes to remove DNA.

[0091] c. Reverse transcription reaction:

[0092] First, prepare the mixed solution (excluding the solution from the first step) on ice, and then add the solution from the first step.

[0093]

[0094] The above reaction system was heated at 37°C for 15 minutes and then at 85°C for 30 seconds. 180 μL of Nase FreedH2O was added to each tube for dilution.

[0095] ③PCR

[0096] a. Primer design

[0097] The primers were synthesized by the company, and the relevant primer sequences for PATJ-DT are as follows:

[0098] F: 5'-CTGCGTGTAGAGCGAGACC-3' (SEQ ID NO: 1);

[0099] R: 5'-GTTAAGCGGATTGACCCAACG-3' (SEQ ID NO: 2).

[0100] b. Preparation of the reaction system:

[0101] Use TB Green Premix Ex Taq II reagent in the dark, and mix the PCR forward and reverse primers in advance.

[0102] First, prepare a mixed solution excluding cDNA on ice, aliquot it into octet arrays, and then add the cDNA.

[0103]

[0104] c. Centrifuge in an eight-unit configuration and run it on the machine.

[0105] Real-time quantitative PCR detection and analysis uses a two-step PCR standard amplification program. The reaction conditions are as follows: qRT-PCR is performed using cDNA as a template, and amplification is performed using GAPDH as an internal reference. The reaction conditions are: first step pre-denaturation (98℃ for 30 seconds) and second step PCR amplification (95℃ for 5 seconds, 60℃ for 30 seconds, 40 cycles).

[0106] The test data results are recorded and analyzed using a specific software program on a qRT-PCR instrument, according to the formula fold = 2. -ΔΔCt Calculate the relative expression levels of each target gene.

[0107] 2) Diagnostic efficacy analysis

[0108] The receiver operating characteristic (ROC) curve was plotted using the R package "pROC". The AUC values, sensitivity, and specificity of lncRNAs that showed significant differential expression between colorectal cancer tissues and normal tissues were analyzed in the screening and validation sets to determine their diagnostic efficacy for colorectal cancer.

[0109] The expression level of the lncRNA (Log2 expression level) was used for evaluation and analysis. The point corresponding to the largest Youden index was selected as its cutoff value. That is, the optimal cutoff threshold was determined by the point with the largest Youden index.

[0110] 3) Survival Analysis

[0111] This embodiment evaluates the prognostic impact of the described lncRNA on overall survival (OS) in colorectal cancer patients using Kaplan-Meier analysis on the TCGA dataset. Patients were divided into two groups based on the optimal cleavage point of the described lncRNA expression.

[0112] 4) Prognostic efficacy analysis

[0113] The receiver operating function (ROC) curve was plotted using the R package "pROC" to analyze the prognostic efficacy of the lncRNA in colorectal cancer in the TCGA dataset.

[0114] 5) Statistical analysis methods

[0115] The expression differences of the described lncRNAs were assessed using the Wilcoxon signed-rank test. Survival analysis was performed using the Log-rank test. Other data were analyzed using Student's t-test (for normally distributed variables) or the Wilcoxon rank-sum test (for non-normally distributed variables). All statistical tests were performed using R (version 3.6.3), with a significance threshold of 0.05.

[0116] 3. Experimental Results

[0117] 1) Differential expression analysis

[0118] Test set: Analysis results of differential expression in the test set are as follows Figure 1 As shown, the expression level of PATJ-DT was significantly downregulated in colorectal cancer tissues compared with normal tissues.

[0119] Validation set: The results of differential expression analysis in the validation set are as follows Figure 2 As shown, the expression level of PATJ-DT was significantly downregulated in colorectal cancer tissues compared with normal tissues.

[0120] 2) ROC curve analysis of the biomarker PATJ-DT in the diagnosis of colorectal cancer

[0121] Using the biomarker PATJ-DT as a variable, and employing samples from the GTEx-TCGA database and real-world collected samples, ROC curves were plotted for normal tissue and colorectal cancer tissue. The results are as follows: Figure 3 As shown, where Figure 3 The results in A show that the AUC value is 0.816 in the ROC curve plotted for the samples in the GTEx-TCGA database. Figure 3 The results in B showed that the AUC value was 0.824 in the ROC curves plotted from 29 clinically collected samples, indicating that the biomarker PATJ-DT can be used as a biomarker for the diagnosis of colorectal cancer.

[0122] 3) ROC curve and survival curve analysis of the biomarker PATJ-DT in predicting the prognosis of colorectal cancer

[0123] Using the biomarker PATJ-DT as a variable and sample data from the TCGA database, prognostic ROC curves for colorectal cancer patients were plotted, and the results are as follows: Figure 4 As shown, the results indicate that, compared with the conventional prognostic prediction method using M+N+T (AUC value 0.703), the prediction method using M+N+T+PATJ-DT for colorectal cancer patients has an AUC value of 0.732, which is higher than the former. This suggests that the biomarker PATJ-DT can improve the accuracy of prognostic prediction for colorectal cancer patients.

[0124] Using the biomarker PATJ-DT as a variable, and sample data from the TCGA database, high- and low-level survival curves for colorectal cancer prognosis were plotted. The results are as follows: Figure 5 As shown, the overall survival (OS) of patients in the high-level group was significantly higher than that of patients in the low-level group.

Claims

1. The application of a reagent for detecting the expression level of biomarkers in the preparation of products for diagnosing colorectal cancer, characterized in that, The biomarker is PATJ-DT.

2. The application according to claim 1, characterized in that, The product includes reagents.

3. The application according to claim 2, characterized in that, The reagents include those used in the following methods: TaqMan probe method.

4. The application according to claim 1, characterized in that, The product includes a reagent kit.

5. The application according to claim 1, characterized in that, The product determines whether a subject has colorectal cancer by measuring the expression level of PATJ-DT in a sample.

6. The application according to claim 5, characterized in that, The sample was: tumor tissue; The subjects were humans.

7. The application according to claim 5, characterized in that, The reagents include specific primers and probes for detecting the expression level of PATJ-DT.

8. The application according to claim 7, characterized in that, The specific primers mentioned are primers for detecting PATJ-DT lncRNA.

9. The application according to claim 8, characterized in that, The nucleotide sequences of the primers for detecting PATJ-DT lncRNA are shown in SEQ ID NO:1 and SEQ ID NO:2.

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