IncRNA combination for colorectal cancer diagnosis and prognosis prediction

Abstract

The invention discloses an lncRNA (long non-coding RNA) combination for diagnosis and prognosis prediction of colorectal cancer. An lncRNA biomarker for diagnosis and prognosis prediction of colorectal cancer comprises DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1 and LRP1-AS. The lncRNA biomarker can be used for diagnosis and prognosis prediction of colorectal cancer. Experiments prove that by detecting the biomarker combination provided by the invention, whether a subject suffers from the colorectal cancer or the risk of suffering from the colorectal cancer can be diagnosed, the prognosis risk of the patient suffering from the colorectal cancer can also be predicted, and the biomarker combination has relatively high accuracy, wide market prospect and high application value.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology and relates to a lncRNA combination for colorectal cancer diagnosis and prognosis prediction. Background Art

[0002] Colorectal cancer is a common malignant tumor in the gastrointestinal tract. The early symptoms are not obvious. As the tumor grows, symptoms such as changes in bowel habits, blood in the stool, diarrhea, alternating diarrhea and constipation, and local abdominal pain appear. In the late stage, systemic symptoms such as anemia and weight loss appear. Its morbidity and mortality rate are second only to gastric cancer, esophageal cancer, and primary liver cancer among malignant tumors of the digestive system.

[0003] Long non-coding RNA (lncRNA) is a non-coding RNA with a length greater than 200 nucleotides. Studies have shown that lncRNA plays an important role in many life activities such as dosage compensation effect, epigenetic regulation, cell cycle regulation and cell differentiation regulation, and has become a hot topic in genetic research. RNA not only plays an auxiliary role as an intermediate carrier of genetic information, but also undertakes various regulatory functions. LncRNA mainly relies on its secondary structure to exert its function. When it binds to proteins, it can cause chromatin remodeling and affect the function of transcription factors. The complex and precise regulatory functions of lncRNA in development and gene expression have greatly explained the difficulty of genome complexity, and also opened up new horizons for people to understand the complexity of life from the dimension of gene expression regulatory network. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides the following technical solutions:

[0005] The present invention provides the use of any two or more of the following lncRNAs as a biomarker combination in the preparation of a product for diagnosing or prognosing colorectal cancer, wherein the lncRNAs include at least two of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

[0006] Further, the lncRNA is selected from any two or more of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

[0007] Further, the lncRNA is selected from DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

[0008] Further, the product includes reagents for detecting the presence, absence and / or amount of at least one biomarker or a functional fragment thereof in a sample.

[0009] Furthermore, the reagents include primers for specifically detecting the aforementioned biomarkers.

[0010] Furthermore, the primers include primers for detecting DYNLRB2-AS1 as shown in SEQ ID NO:1 and SEQ ID NO:2.

[0011] Furthermore, the primers include primers for detecting EFCAB13-DT as shown in SEQ ID NO:3 and SEQ ID NO:4.

[0012] Furthermore, the primers include primers for detecting EWSAT1 as shown in SEQ ID NO:5 and SEQ ID NO:6.

[0013] Further, the primers include primers for detecting LINC00645 as shown in SEQ ID NO:7 and SEQ ID NO:8.

[0014] Furthermore, the primers include primers for detecting LINC00901 as shown in SEQ ID NO:9 and SEQ ID NO:10.

[0015] Furthermore, the primers include primers for detecting LINC01409 as shown in SEQ ID NO:11 and SEQ ID NO:12.

[0016] Furthermore, the primers include primers for detecting LINC01738 as shown in SEQ ID NO:13 and SEQ ID NO:14.

[0017] Furthermore, the primers include primers for detecting LINC02962 as shown in SEQ ID NO: 15 and SEQ ID NO: 16.

[0018] Furthermore, the primers include primers for detecting LRP1-AS as shown in SEQ ID NO:17 and SEQ ID NO:18.

[0019] Furthermore, the primers include primers for detecting PATJ-DT as shown in SEQ ID NO: 19 and SEQ ID NO: 20.

[0020] Furthermore, the reagents include reagents for detecting the presence, absence and / or amount of biomarkers or their functional fragments in a sample by PCR reaction, RT-PCR derivatization reaction, 3SR amplification, LCR, SDA, NASBA, TMA, SYBR Green, TaqMan probe, molecular beacon, double hybridization probe, composite probe, ISH, microarray, Southern blotting, Northern blotting.

[0021] Furthermore, the reagents for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample using PCR reaction, RT-PCR derivatization reaction, 3SR amplification, LCR, SDA, NASBA, TMA include primers that bind to the sequence of the biomarker or a functional fragment thereof.

[0022] Furthermore, the reagent for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample using SYBR Green includes a dye, and the dye is specific to the biomarker or a functional fragment thereof.

[0023] Furthermore, reagents for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample using TaqMan probes, molecular beacons, dual hybridization probes, composite probes, ISH, microarrays, Southern blots, and Northern blots include probes that are complementary to the sequence of the biomarker or a functional fragment thereof.

[0024] Furthermore, the probe is a labeled probe.

[0025] Furthermore, the sample includes blood, tissue, urine, serum, plasma, amniotic fluid, cerebrospinal fluid, placental cells or tissue, endothelial cells, leukocytes, monocytes, tumor cells, tumor tissue, tissue extract, and cell extract.

[0026] The present invention also provides a product for diagnosing or prognosing colorectal cancer, the product comprising a reagent for detecting biomarkers, the biomarkers comprising at least two of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

[0027] Further, the biomarkers are selected from any two or more of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

[0028] Further, the biomarker is selected from DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

[0029] Furthermore, the products include primers, probes, chips, and kits.

[0030] Furthermore, the kit includes reagents for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample by PCR reaction, RT-PCR derivatization reaction, 3SR amplification, LCR, SDA, NASBA, TMA, SYBR Green, TaqMan probe, molecular beacon, double hybridization probe, composite probe, ISH, microarray, Southern blotting, Northern blotting.

[0031] The present invention also provides a system comprising:

[0032] sample;

[0033] one or more probes and / or stains that bind to at least one biomarker and / or a homologous sequence thereof; and

[0034] One or more devices capable of quantifying the presence, absence and / or amount of at least one probe or stain that binds to the biomarker and / or its homologous sequence.

[0035] The present invention also provides a system / device for diagnosing whether a subject has colorectal cancer or is at risk of developing colorectal cancer, comprising:

[0036] processor;

[0037] An input unit, for inputting the expression level of a biomarker in a biological sample, wherein the biomarker is selected from: DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1 and / or LRP1-AS;

[0038] a processing unit, wherein the instructions stored in the computer-readable medium, when executed by the processor, perform an algorithm on input levels of at least two genes and / or their expression products; and

[0039] An output unit that provides one or more markers based on the input levels of the at least two genes and / or their expression products, wherein the one or more markers indicate the presence of colorectal cancer in the subject.

[0040] Furthermore, the system / device also includes a reagent for detecting the biomarker.

[0041] Further, the biomarkers are selected from any two or more of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

[0042] Further, the biomarker is selected from DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS;

[0043] Furthermore, the processing unit also includes an analysis module for analyzing the prognosis and survival prediction effect of colorectal cancer in the subject according to the expression level of the aforementioned biomarker;

[0044] Further, the analysis formula used by the analysis module is riskscore=(-0.003109)×DYNLRB2-AS1 expression level+(-0.005306)×EFCAB13-DT expression level+(-0.004038)×EWSAT1 expression level+0.003939×LINC00645 expression level+0.008362×LINC00901 expression level+(-0.007397)×LINC01409 expression level+(-0.002291)×LINC01738 expression level+0.003238×LINC02962 expression level+(-0.002573)×LRP1-AS expression level+(-0.003843)×PATJ-DT expression level.

[0045] The present invention also provides a computer model, on which a computer program is stored, and when the computer program is executed by a processor, the system / device described above is implemented.

[0046] The present invention also provides a method for predicting the prognosis and recurrence risk of colorectal cancer patients, the method detecting the expression level of a biomarker indicating colorectal cancer in a sample separated from a patient, the method comprising:

[0047] 1) contacting the sample with a reagent capable of detecting the expression level of a biomarker, wherein the biomarker includes the biomarker described above;

[0048] 2) obtaining the detection results of the expression levels of the aforementioned biomarkers;

[0049] 3) Predict the risk of recurrence in patients with colorectal cancer based on the expression levels of the biomarkers mentioned above in the test results.

[0050] The present invention also provides the use of biomarkers in preparing a pharmaceutical composition for treating colorectal cancer, wherein the biomarkers include any two or more of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

[0051] Furthermore, the pharmaceutical composition includes a promoter or inhibitor that promotes or inhibits the expression level of the biomarker.

[0052] Furthermore, the promoter promotes the expression level of a biomarker whose expression is downregulated in colorectal cancer, and the inhibitor inhibits the expression level of a biomarker whose expression is upregulated in colorectal cancer.

[0053] Furthermore, the downregulated biomarkers were selected from LINC01409 and PATJ-DT.

[0054] definition

[0055] The term "biomarker" refers to a measurable indicator of some biological state or disease. In some cases, a biomarker can be a substance found in a subject, the amount of that substance, or some other indicator. For example, a biomarker can be the amount of a protein and / or other gene expression product in a sample.

[0056] The terms "sample", "biological sample", "test sample", "specimen", "sample from a subject", and "patient sample" are used interchangeably and can be a sample of blood, tissue, urine, serum, plasma, amniotic fluid, cerebrospinal fluid, placental cells or tissue, endothelial cells, leukocytes, or monocytes. The sample can be obtained directly from the patient in some manner discussed herein or otherwise known in the art, or the sample can be pre-treated (e.g., by filtration, distillation, extraction, concentration, centrifugation, inactivation of interfering components, addition of reagents, etc.) to change the characteristics of the sample.

[0057] The term "primer" or "probe" encompasses an oligonucleotide having a specific sequence or an oligonucleotide having a specific sequence. In other embodiments, the nucleic acid is detected by indirect detection methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a histogram of the differential expression of the test set riskscore model in normal tissues and colorectal cancer tissues;

[0059] Figure 2 It is a histogram of the differential expression of the validation set riskscore model in normal tissues and colorectal cancer tissues;

[0060] Figure 3 The ROC curves for colorectal cancer diagnosis using the riskscore model, where A is the ROC curve drawn using GTEx-TCGA database data for samples, and B is the ROC curve drawn using clinical sample data;

[0061] Figure 4 This is a ROC curve diagram for colorectal cancer prognosis prediction using the riskscore model, where T represents the growth of the cancer tumor itself, including the size of the tumor and its infiltration range; N represents the degree of metastasis to the regional lymph nodes; M represents whether the distant organs have blood circulation;

[0062] Figure 5 This is the survival curve obtained after using the riskscore model to predict the prognosis of colorectal cancer. DETAILED DESCRIPTION

[0063] In a specific embodiment of the present invention, the biomarkers include DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1 and / or LRP1-AS.

[0064] In the present invention, biomarkers such as DYNLRB2-AS1 (Gene ID: 102724084), EFCAB13-DT (Gene ID: 102724508), LINC00645 (Gene ID: 100505967), LINC00901 (Gene ID: 100506724), LINC01409 (Gene ID: 105378580), LINC01738 (Gene ID: 107984953), LINC02962 (Gene ID: 339400), PATJ-DT (Gene ID: 118732297), EWSAT1 (Gene ID: 283673), LRP1-AS (Gene ID: 105751187) include genes and proteins encoded by them and homologs, mutations, and isoforms thereof. The term encompasses full-length, unprocessed biomarkers, as well as any form of the biomarker that results from processing in the cell. The term encompasses naturally occurring variants of the biomarker (e.g., splice variants or allelic variants).

[0065] The methods, products and systems / devices disclosed herein can be used to classify one or more samples from one or more subjects. The sample can be any material containing tissues, cells, nucleic acids, genes, gene fragments, expression products, proteins, polypeptides, exosomes, gene expression products or gene expression product fragments of the object to be tested. The sample can include, but is not limited to, tissues, cells, plasma, serum or any other biological material from cells or derived from individual cells. The sample can be a heterogeneous or homogeneous population of cells or tissues. The sample can be a cell-free or cell-poor fluid (e.g., plasma or serum). In some cases, the sample is from a single patient.

[0066] In some embodiments, the sample is obtained from the subject by the same party (e.g., a testing laboratory) that subsequently obtains biomarker data from the sample. In some embodiments, the sample is received from another entity (e.g., by a testing laboratory), and the other entity collects the sample from the subject (e.g., a physician, a nurse, a phlebotomist, or a medical caregiver). In some embodiments, the sample is obtained from the subject by a medical professional under the guidance of a separate entity (e.g., a testing laboratory), and then provided to the entity (e.g., a testing laboratory). In some embodiments, the sample is collected at home by a subject or a caregiver of the subject, and is subsequently provided to a party (e.g., a testing laboratory) that obtains biomarker data from the sample.

[0067] In some embodiments, the methods, products, devices / systems described herein utilize any reliable method to measure the level or quantity in a sample. Generally, various methods known for mRNA can be used to detect and quantify from a sample (including its fractions) (such as a sample of isolated RNA), various known methods include, for example, amplification-based methods (e.g., polymerase chain reaction (PCR), real-time polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), rolling circle amplification, etc.), hybridization-based methods (e.g., hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched DNA (bDNA) signal amplification, in situ hybridization, etc.), and sequencing-based methods (e.g., next-generation sequencing methods, e.g., using Illumina or IonTorrent platforms). Other exemplary techniques include ribonuclease protection assays (RPA) and mass spectrometry.

[0068] In some embodiments, the present disclosure provides an analysis kit for analyzing and detecting any group of biomarkers for colorectal cancer. In some specific cases, the assay kit includes one or more detection reagents. The detection reagents include but are not limited to probes, primers, and antibodies.

[0069] The probe or primer can include standard (A, T or U, G and C) bases, or modified bases. Modified bases include, but are not limited to, AEGIS bases. In some aspects, the bases are connected by natural phosphodiester bonds or different chemical bonds. Different chemical bonds include, but are not limited to, peptide bonds or locked nucleic acid (LNA) bonds.

[0070] In some embodiments, provided assay products such as kits are suitable for multiple homogeneous biomarker assays, suitable for detecting all analytes in a single reaction (e.g., in the same solution chamber). In some embodiments, provided assay products such as kits are suitable for multiple heterogeneous biomarker assays, which are suitable for detecting all analytes in separate reactions (e.g., in separate solution chambers).

[0071] The products and systems / devices disclosed herein may include algorithms or uses thereof. One or more algorithms may be used to classify one or more samples from one or more subjects. One or more algorithms may be applied to data from one or more samples. The data may include biomarker expression data.

[0072] The products and systems / devices disclosed herein may include assigning a classification to one or more samples from one or more subjects. Assigning a classification to a sample may include applying an algorithm to an expression level. In some cases, the gene expression level is input to a data analysis system, which includes a trained algorithm. For distinguishing a sample as a normal control and colorectal cancer; in some embodiments, the algorithm may, as part of its execution, calculate an index for the sample and compare the index of the sample to a threshold; the predetermined relationship may indicate the likelihood that the sample belongs to a particular classification.

[0073] The data analysis system can be a trained algorithm. The algorithm can include a linear classifier. In some cases, the linear classifier includes one or more of linear discriminant analysis, Fisher linear discriminant, naive Bayes classifier, logistic regression, perceptron, support vector machine or a combination thereof. The linear classifier can be a support vector machine (SVM) algorithm. The algorithm can include a bidirectional classifier. The bidirectional classifier can include one or more decision trees, random forests, Bayesian networks, support vector machines, neural networks or logistic regression algorithms.

[0074] Further disclosed herein is a system for classifying (or excluding classification) one or more samples and its use. The system may include: (a) a digital processing device including an operating system and a storage device configured to execute executable instructions; (b) a computer program including instructions executable by the digital processing device to classify samples from a subject, including: (i) a first software module configured to receive a biomarker expression profile of one or more biomarkers from a sample from a subject; (ii) a second software module configured to analyze the biomarker expression profile from a subject; (iii) a third software module configured to classify samples from a subject based on a classification system. In some embodiments, the classification system includes two categories. In other embodiments, the classification system includes two or more categories. At least two categories may be selected from colorectal cancer, normal control. Analyzing the biomarker expression profile from a subject may include applying an algorithm. Analyzing the biomarker expression profile may include normalizing the biomarker expression profile from a subject.

[0075] The products, systems or devices disclosed herein for identifying, classifying (or excluding classification) or characterizing samples can be characterized by using the methods disclosed herein with a specificity of at least about 60%. In some embodiments, the specificity of the method is at least about 70%. In some embodiments, the specificity of the method is at least about 80%. In some embodiments, the specificity is at least about 90%. In some embodiments, the specificity is at least about 95%.

[0076] In some embodiments, the invention provides methods for identifying, classifying (or excluding classification) or characterizing samples using methods disclosed herein, which methods give a sensitivity of at least about 60%. In some embodiments, the sensitivity of the method is at least 70%. In some embodiments, the sensitivity of the method is at least 80%. In some embodiments, the sensitivity of the method is at least 90%. In some embodiments, the sensitivity of the method is at least 95%.

[0077] The methods, kits, and systems disclosed herein can improve the AUC of current methods for monitoring or predicting the status or outcome of colorectal cancer or determining or ruling out the classification of a sample. The AUC of the methods, products, systems, or devices disclosed herein can be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any range between these values. The specific implementation of the present application is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present application, and is not used to limit the present application.

[0078] Example 1 lncRNA associated with colorectal cancer

[0079] 1. Experimental subjects

[0080] The lncRNA sequencing data and clinical information of the TCGA colorectal cancer cohort used for testing in this experiment 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 de-batch processing of the TCGA database and the GTEx database, a total of 741 samples were used for differential expression and colorectal cancer diagnosis analysis, including 359 normal tissues and 382 colorectal cancer tissues; in addition, after removing normal, duplicated, and deleted samples in the TCGA database, a total of 619 samples were used for prognostic analysis.

[0081] The samples used for verification in this experiment were from real-world samples, all of which were 29 patients with colorectal cancer, including 29 normal tissues and 29 colorectal cancer tissues, which were used to verify the accuracy of the riskscore model in diagnosing colorectal cancer.

[0082] The inclusion and exclusion criteria for colorectal cancer patients were as follows:

[0083] 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's Digestive System Tumors (Fourth Edition) classification and the American Joint Committee on Cancer staging system, and the films were independently read and diagnosed by two pathologists; ② All patients underwent radical resection of colorectal cancer, with no less than 10 lymph nodes removed; ③ No anti-tumor treatment was performed before surgical treatment, including neoadjuvant chemotherapy, radiotherapy, targeted therapy or biological therapy.

[0084] Exclusion criteria: ① incomplete clinical case data; ② no systematic lymph node dissection or palliative surgery; ③ previous history of tumor or concurrent tumors in other parts of the body; ④ death of patients not due to tumor or within 1 month after surgery; ⑤ severe perioperative heart, lung, liver, or kidney dysfunction.

[0085] Detailed sampling object information is shown in Table 1.

[0086] Table 1 Information of 29 patients with colorectal cancer

[0087]

[0088]

[0089] 2. Experimental methods

[0090] 1) Riskscore model construction method

[0091] ①Preliminary processing of TCGA database data

[0092] A. Remove samples without clinical follow-up information;

[0093] B. Remove samples with unknown survival time, less than 0 days, and no survival status;

[0094] C. Remove samples without N stages;

[0095] D. Convert the probe to Gene Symbol;

[0096] E. A probe corresponds to multiple genes, so remove the probe;

[0097] F. The average value of the expression of multiple Gene Symbols is taken.

[0098] ② Analysis of coagulation-related lncRNA in colorectal cancer: Coagulation genes were obtained in the MSigDB database, and the coagulation-related lncRNA in colorectal cancer was obtained through Spearman correlation analysis (|r|>0.3, p<0.05).

[0099] ③ Based on the screened colorectal cancer coagulation-related lncRNA, the stepwise regression dimensionality reduction method was used to screen the variables in the TCGA data to reduce the number of genes in the risk model. Finally, a multi-factor Cox regression model was used to construct a risk score model for colorectal cancer-related genes. The genes used to construct the risk score were defined as characteristic genes, and then the median of the risk score was used as the dividing point to divide the patients into high-level group and low-level group. Next, the survival curve of the risk score was analyzed by Kaplan-Meier, and the time-dependent ROC curve was used to verify the accuracy.

[0100] ④ In the experimental group data set, the screened characteristic genes were applied to the stepwise regression model to construct a prediction model for the overall survival of colorectal cancer patients in the TCGA data set. Finally, 10 characteristic genes (DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, LRP1-AS) were obtained to construct a risk score model. The final formula is as follows:

[0101] Riskscore=(-0.003109)×DYNLRB2-AS1 expression+(-0.005306)×EFCAB13-DT expression+(-0.004038)×EWSAT1 expression+0.003939×LINC00645 expression+

[0102] 0.008362×LINC00901 expression level + (-0.007397) × LINC01409 expression level + (-0.002291) × LINC01738 expression level + 0.003238 × LINC02962 expression level + (-0.002573) × LRP1-AS expression level + (-0.003843) × PATJ-DT expression level.

[0103] 2) Differential expression analysis

[0104] Test set: SPSS software was used to perform differential expression analysis on the sequencing data of the characteristic genes in the screening set, and to compare the differential expression of genes between colorectal cancer tissues and adjacent tissues in the screening set.

[0105] Validation set:

[0106] ① RNA extraction

[0107] Total RNA was extracted from tissues using the RNeasy kit (Beyotime, Shanghai, 456 China, R0027). The details are as follows:

[0108] a. Sample preparation: Take 20 mg of animal tissue, place it in a 1.5 ml centrifuge tube, add about 6 zirconium oxide beads, quickly add 600 μl of ice-cooled lysis solution, and homogenize with a micro-electric homogenizer. After grinding or homogenization, gently blow the homogenate 8-10 times and leave it at room temperature for 3-5 minutes. Then centrifuge at about 14,000g for 2 minutes and transfer the supernatant to a new centrifuge tube.

[0109] b. Add an equal volume of binding buffer to the lysate and gently invert 3-5 times to mix.

[0110] c. Transfer the mixture of lysate and binding solution into the purification column, centrifuge at 16,000g for 1 minute, and discard the lower layer of liquid.

[0111] d. Add 600 μL of washing solution I to the purification column, centrifuge at 16,000 g for 1 minute, and discard the lower layer of liquid.

[0112] Add 600 μL of washing solution II to the purification column, centrifuge at 16,000 g for 1 min, and discard the lower layer.

[0113] This step was repeated twice.

[0114] e. Centrifuge at 16,000 g for 2 minutes to remove the residual liquid on the purification column.

[0115] f. Discard the collection tube, place the RNA purification column in the RNA elution tube, add 50 μL of elution solution to the center of the purification column, leave at room temperature for 3 minutes, and centrifuge at 16,000g for 30 seconds. Add the obtained eluate to the center of the purification column again and repeat the elution, centrifuge at 16,000g for 30 seconds to obtain purified RNA.

[0116] ②Reverse transcription:

[0117] a. Determine the concentration and quality of sample RNA:

[0118] Reverse transcription was performed on sample RNA with a concentration higher than 143 ng / μL and OD230 / 260 and OD280 / 260 meeting the standards.

[0119] b. Removal of DNA from RNA:

[0120] 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.

[0121]

[0122] The above reaction system was heated at 42°C for two minutes to remove DNA.

[0123] c. Reverse transcription reaction:

[0124] First, prepare a mixed solution except the first step reaction solution on ice, and then add the first step reaction solution.

[0125]

[0126]

[0127] The above reaction system was heated at 37°C for 15 minutes and then at 85°C for 30 seconds. 180 μL RNase Freed HO was added to each tube. 2 O dilution.

[0128] ③PCR

[0129] a. Primer design

[0130] The mRNA sequencing data of the TCGA colorectal cancer cohort was downloaded from the GDC website. The primers were synthesized by the company, and the relevant primer sequences for the riskscore model are as follows:

[0131] DYNLRB2-AS1

[0132] F: 5'-CTGTAAGGAGCCTGGACATGG-3' (SEQ ID NO: 1);

[0133] R: 5'-CCAAAGCGGTCAGAAGAAGTG-3' (SEQ ID NO: 2);

[0134] EFCAB13-DT

[0135] F: 5'-GGGATAAGACTTTGGGGCCT-3' (SEQ ID NO: 3);

[0136] R: 5'-TCGATCGCTTGCTTCCCTTT-3' (SEQ ID NO: 4);

[0137] EWSAT1

[0138] F: 5'-CTGAAACTCCCACCGAGACC-3' (SEQ ID NO: 5);

[0139] R: 5'-ATGGGCTTAAGGGTGGGGTA-3' (SEQ ID NO: 6);

[0140] LINC00645

[0141] F: 5'-AGCAGCTGTTAAGGTCACTCC-3' (SEQ ID NO: 7);

[0142] R: 5'-TGCACAGTTCCATCACCAGA-3' (SEQ ID NO: 8);

[0143] LINC00901

[0144] <h2 style=";text-align:left;direction:ltr">F:5'-GGGGGAGCCTGTGTATATGCG-3'(SEQ ID NO:9);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0145] <h2 style=";text-align:left;direction:ltr"> R:5'-CAGGTTATCTTCCTGCCGCT-3'(SEQ ID NO:10);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0146] <h2 style=";text-align:left;direction:ltr"> LINC01409<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0147] <h2 style=";text-align:left;direction:ltr"> F:5'-CCCATCCTGCCACTAACAGA-3'(SEQ ID NO:11);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0148] <h2 style=";text-align:left;direction:ltr"> R:5'-GGAGGAAGCCCCTTGGTAGAAG-3'(SEQ ID NO:12);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0149] <h2 style=";text-align:left;direction:ltr"> LINC01738<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0150] <h2 style=";text-align:left;direction:ltr"> F:5'-AGGCCAGCAAAGTCTTTGTCA-3'(SEQ ID NO:13);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0151] <h2 style=";text-align:left;direction:ltr"> R:5'-TGTCTGAACGTGGCCTTCTC-3'(SEQ ID NO:14);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0152] <h2 style=";text-align:left;direction:ltr"> LINC02962<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0153] <h2 style=";text-align:left;direction:ltr"> F:5'-GCAGTTCCTGATACTGGTTTTTCT-3'(SEQ ID NO:15);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0154] <h2 style=";text-align:left;direction:ltr"> R:5'-CAGCCATGCAATATTCATAGACTTC-3'(SEQ ID NO:16);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0155] <h2 style=";text-align:left;direction:ltr"> LRP1-AS<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0156] <h2 style=";text-align:left;direction:ltr"> F:5'-CGTCTGTGTTGGTGCTATAGC-3'(SEQ ID NO:17);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0157] <h2 style=";text-align:left;direction:ltr"> R:5'-CACTTCGTTGCCCTTGTCAC-3'(SEQ ID NO:18);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0158] <h2 style=";text-align:left;direction:ltr"> PATJ-DT<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0159] <h2 style=";text-align:left;direction:ltr"> F:5'-CTGCGTGTAGAGCGAGACC-3'(SEQ ID NO:19);<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0160] <h2 style=";text-align:left;direction:ltr"> R:5'-GTTAAGCGGATTGACCCAACG-3'(SEQ ID NO:20);<h2 style=";text-align:left;direction:ltr">

[0161] (10 lncRNAs).

[0162] b. Prepare the reaction system:

[0163] TB Green Premix Ex Taq II reagent was kept away from light, and the PCR forward and reverse primers were mixed in advance.

[0164] First, prepare the mixed solution except cDNA on ice, dispense it into eight strips, and then add cDNA.

[0165]

[0166] c. Centrifuge eight rows and load onto the machine.

[0167] Real-time quantitative PCR detection and analysis used a two-step PCR standard amplification procedure. The reaction conditions were: cDNA as a template for qRT-PCR, and GAPDH as an internal reference for amplification. The reaction conditions were: first step pre-denaturation (98°C for 30 seconds) and second step PCR amplification (95°C for 5 seconds, 60°C for 30 seconds, 40 cycles).

[0168] The test data results were recorded and analyzed by the specific software program of the qRT-PCR instrument. According to the formula, multiple = 2 -ΔΔCt Calculate the relative expression level of each target gene.

[0169] 3) Diagnostic efficacy analysis

[0170] The R package "pROC" was used to draw the receiver operating curve (ROC) and analyze the AUC values, sensitivity, and specificity of the lncRNAs that were screened and showed significant differential expression between colorectal cancer tissues and normal tissues in the screening set and validation set, respectively, to determine their diagnostic efficacy for colorectal cancer.

[0171] The expression level of the lncRNA (Log 2 The expression level was evaluated and analyzed, and the point corresponding to the maximum Youden index was selected as its cutoff value, that is, the optimal division threshold was determined by the point with the largest Youden index.

[0172] 4) Survival analysis

[0173] In this example, the prognostic effect of the lncRNA on the overall survival (OS) of colorectal cancer patients was evaluated by Kaplan-Meier analysis in the TCGA dataset. The patients were divided into two groups according to the optimal cut-off point of the lncRNA expression.

[0174] 5) Prognostic prediction efficiency analysis

[0175] The R package “pROC” was used to draw the receiver operating curve (ROC) to analyze the efficacy of the lncRNA in predicting the prognosis of colorectal cancer in the TCGA dataset.

[0176] 6) Statistical analysis methods

[0177] The Wilcoxon signed rank test was used to evaluate the differential expression of the lncRNAs. The log-rank test was used for survival analysis. The remaining data were analyzed using the Student's t test (normally distributed variables) or the Wilcoxon rank sum test (non-normally distributed variables). All statistical tests were performed using R (version: 3.6.3), and the significance threshold was set at 0.05.

[0178] 3. Experimental results

[0179] 1) Differential expression analysis

[0180] Test set: The analysis results of differential expression in the test set are as follows Figure 1 As shown, the expression level of the riskscore model in colorectal cancer tissues was significantly upregulated compared with normal tissues.

[0181] Validation set: The analysis results of differential expression in the validation set are as follows Figure 2 As shown, the expression level of the riskscore model in colorectal cancer tissues was significantly upregulated compared with normal tissues.

[0182] 2) ROC curve analysis of lncRNA in diagnosing colorectal cancer

[0183] The data of 10 lncRNAs (DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS) in 29 pairs of samples collected from real-world clinical settings were used to construct a risk score model, and the ROC curves of the risk score model for normal tissues and colorectal cancer tissues were plotted. The results are shown in Figure 2. Figure 3 As shown, the ROC curve of the test set riskscore model drawn with the GTEx-TCGA database data showed an AUC value of 0.817, and the ROC curve of the validation set riskscore model drawn with 29 pairs of clinically collected sample data showed an AUC value of 0.943, indicating that the riskscore model can be used as a biomarker combination for diagnosing colorectal cancer.

[0184] 2) ROC curve and survival curve analysis of lncRNA predicting the prognosis of colorectal cancer

[0185] Using TCGA database data, the ROC curve of the prognostic risk score model was drawn ( Figure 4 ), the results showed that compared with the conventional prognosis prediction method using M+N+T, the AUC value was 0.703, and the prognosis prediction method using M+N+T+riskscore had a higher AUC value (0.791), indicating that the riskscore model composed of 10 lncRNAs can improve the accuracy of colorectal cancer prognosis prediction.

[0186] Using the TCGA database data, we plotted the survival curves of high and low levels of the prognostic risk score model. The results are as follows: Figure 5 As shown, the overall survival (OS) of patients in the low-level group was significantly higher than that in the high-level group.

[0187] The description of the above embodiments is only used to understand the method and core idea of ​​the present invention. It should be pointed out that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications will also fall within the scope of protection of the claims of the present invention.

Claims

1. Use of any two or more of the following lncRNAs as a combination of biomarkers in the preparation of a product for diagnosis or prognosis prediction of colorectal cancer, wherein the lncRNAs include at least two of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS; Preferably, the lncRNA is selected from any two or more of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS; Preferably, the lncRNA is selected from DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS.

2. The use according to claim 1, wherein the product comprises a reagent for detecting the presence, absence and / or amount of at least one biomarker or a functional fragment thereof in a sample; Preferably, the reagent comprises primers for specifically detecting the biomarker of claim 1; Preferably, the primers include primers for detecting DYNLRB2-AS1 as shown in SEQ ID NO: 1 and SEQ ID NO: 2; Preferably, the primers include primers for detecting EFCAB13-DT as shown in SEQ ID NO: 3 and SEQ ID NO: 4; Preferably, the primers include primers for detecting EWSAT1 as shown in SEQ ID NO:5 and SEQ ID NO:6; Preferably, the primers include primers for detecting LINC00645 as shown in SEQ ID NO:7 and SEQ ID NO:8; Preferably, the primers include primers for detecting LINC00901 as shown in SEQ ID NO:9 and SEQ ID NO:10; Preferably, the primers include primers for detecting LINC01409 as shown in SEQ ID NO: 11 and SEQ ID NO: 12; Preferably, the primers include primers for detecting LINC01738 as shown in SEQ ID NO: 13 and SEQ ID NO: 14; Preferably, the primers include primers for detecting LINC02962 as shown in SEQ ID NO: 15 and SEQ ID NO: 16; Preferably, the primers include primers for detecting LRP1-AS as shown in SEQ ID NO: 17 and SEQ ID NO: 18; Preferably, the primers include primers for detecting PATJ-DT as shown in SEQ ID NO: 19 and SEQ ID NO: 20; Preferably, the reagents include reagents for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample by PCR reaction, RT-PCR derivatization reaction, 3SR amplification, LCR, SDA, NASBA, TMA, SYBR Green, TaqMan probe, molecular beacon, double hybridization probe, composite probe, ISH, microarray, Southern blot, Northern blot; Preferably, the reagents for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample using PCR reaction, RT-PCR derivatization reaction, 3SR amplification, LCR, SDA, NASBA, TMA include primers that bind to the sequence of the biomarker or a functional fragment thereof; Preferably, the reagent for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample using SYBR Green comprises a dye that is specific to the biomarker or a functional fragment thereof; Preferably, the reagents for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample using TaqMan probes, molecular beacons, dual hybridization probes, composite probes, ISH, microarrays, Southern blots, Northern blots include probes that are complementary to the sequence of the biomarker or a functional fragment thereof; Preferably, the probe is a labeled probe.

3. The use according to claim 2, wherein the sample comprises blood, tissue, urine, serum, plasma, amniotic fluid, cerebrospinal fluid, placental cells or tissue, endothelial cells, leukocytes, monocytes, tumor cells, tumor tissue, tissue extract, or cell extract.

4. A product for diagnosing or predicting colorectal cancer, the product comprising a reagent for detecting a biomarker, the biomarker comprising at least two of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS; Preferably, the biomarker is selected from any two or more of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS; Preferably, the biomarker is selected from DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, LRP1-AS.

5. The product according to claim 4, comprising primers, probes, chips, and kits; Preferably, the kit comprises reagents for detecting the presence, absence and / or amount of a biomarker or a functional fragment thereof in a sample by PCR reaction, RT-PCR derivatization reaction, 3SR amplification, LCR, SDA, NASBA, TMA, SYBR Green, TaqMan probe, molecular beacon, double hybridization probe, composite probe, ISH, microarray, Southern blot, Northern blot.

6. A system comprising: sample; one or more probes and / or stains that bind to at least one biomarker and / or a homologous sequence thereof; and One or more devices capable of quantifying the presence, absence and / or amount of at least one probe or stain that binds to the biomarker and / or its homologous sequence.

7. A system / device for diagnosing whether a subject has colorectal cancer or is at risk of developing colorectal cancer, comprising: processor; An input unit, for inputting the expression level of a biomarker in a biological sample, wherein the biomarker is selected from: DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1 and / or LRP1-AS; a processing unit, wherein the instructions stored in the computer-readable medium, when executed by the processor, perform an algorithm on input levels of at least two genes and / or their expression products; and an output unit that provides one or more markers based on the input levels of the at least two genes and / or their expression products, wherein the one or more markers indicate the presence of colorectal cancer in the subject; Preferably, the system / device further comprises a reagent for detecting the biomarker; Preferably, the biomarker is selected from any two or more of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS; Preferably, the biomarker is selected from DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, LRP1-AS; Preferably, the processing unit further comprises an analysis module for analyzing the prognosis and survival prediction effect of colorectal cancer in the subject according to the expression level of the aforementioned biomarker; Preferably, the analysis formula used by the analysis module is riskscore=-0.003109×DYNLRB2-AS1 expression amount-0.005306×EFCAB13-DT expression amount-0.004038×EWSAT1 expression amount+0.003939×LINC00645 expression amount+0.008362×LINC00901 expression amount-0.007397×LINC01409 expression amount-0.002291×LINC01738 expression amount+0.003238×LINC02962 expression amount-0.002573×LRP1-AS expression amount-0.003843×PATJ-DT expression amount.

8. A computer model having a computer program stored thereon, wherein the computer program implements the system / device of claim 7 when executed by a processor.

9. A method for predicting the risk of recurrence of colorectal cancer patients, the method detecting the expression level of a biomarker indicative of colorectal cancer in a sample isolated from the patient, the method comprising: 1) contacting the sample with a reagent capable of detecting the expression level of a biomarker, wherein the biomarker comprises the biomarker of claim 1; 2) obtaining the detection result of the expression level of the biomarker according to claim 1; 3) Predicting the prognosis and recurrence risk of colorectal cancer patients based on the expression level of the biomarker described in claim 1 in the test results.

10. Use of a biomarker in the preparation of a pharmaceutical composition for treating colorectal cancer, wherein the biomarker comprises any two or more of DYNLRB2-AS1, EFCAB13-DT, LINC00645, LINC00901, LINC01409, LINC01738, LINC02962, PATJ-DT, EWSAT1, and LRP1-AS; Preferably, the pharmaceutical composition comprises a promoter or inhibitor that promotes or inhibits the expression level of the biomarker; Preferably, the promoter promotes the expression level of a biomarker whose expression is downregulated in colorectal cancer, and the inhibitor inhibits the expression level of a biomarker whose expression is upregulated in colorectal cancer; Preferably, the down-regulated biomarker is selected from LINC01409 and PATJ-DT.

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