Tumor prognosis evaluation gene set and applications thereof

Abstract

The present application relates to a tumor prognosis evaluation gene set and its application, the evaluation gene set is composed of 5 genes, the 5 genes are as follows: MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 , the tumor is selected from any one of colorectal cancer, lung cancer, pancreatic cancer.Compared with the prior art, the present application constructs a multi-type tumor prognosis prediction model MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 Synergistic effect is carried out from four core pathways of tumor proliferation, angiogenesis, invasion and metastasis, abnormal regulation, the detection accuracy of single index is significantly improved, and it is suitable for colorectal cancer, lung cancer and pancreatic cancer.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology and relates to a gene set for tumor prognosis assessment and its application. Background Technology

[0002] Tumor prognostic assessment is the core basis for guiding postoperative adjuvant therapy and developing follow-up plans. Current clinical prognostic assessment methods mainly rely on TNM staging, pathological differentiation degree, and single molecular marker detection, but they have the following significant drawbacks: TNM staging only reflects the anatomical characteristics of the tumor; single gene detection is easily affected by tumor heterogeneity, resulting in insufficient predictive sensitivity and specificity; traditional detection methods (such as immunohistochemistry IHC) are highly subjective, with large differences in result interpretation, making it difficult to standardize and promote.

[0003] In recent years, multi-gene combined detection has become a research hotspot for prognostic assessment. For example, CN113234829A disclosed a set of nine genes, including ATHL1, CD1A, CD1B, FES, LIME1, PLCH2, PMCH, S1PR5, and SIK1, as an assessment gene set for predicting the prognosis of colorectal cancer patients. However, this gene set mainly focuses on the assessment of colorectal cancer and depends on specific gene expression, which has limitations for the prognostic assessment of other types of tumors.

[0004] Because the occurrence and development of different types of tumors are specific, there are currently few multigenomic tumor diagnoses that are applicable to multiple types of tumors and can also achieve accurate predictions. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing methods that combine specific multi-gene predictions for the prognosis of various types of tumors, which cannot achieve both broad spectrum and high accuracy. In this way, we can provide a tumor prognosis assessment gene set and its application that is applicable to multiple tumor types such as colorectal cancer, lung cancer, and pancreatic cancer and has high predictive accuracy.

[0006] The objective of this invention can be achieved through the following technical solutions: One of the technical solutions of the present invention is to provide an assessment gene set for predicting the prognosis of cancer patients, wherein the assessment gene set consists of 5 genes, and the 5 genes are as follows: MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The tumor is selected from any one of colorectal cancer, lung cancer, and pancreatic cancer.

[0007] The second technical solution of the present invention is to provide the application of a substance for detecting the gene set as described in one of the above technical solutions in the preparation of a product for tumor prognosis assessment; The evaluation gene set is MKI67 , PCNA, VEGF-A , MMP9 , TRIM11 , The tumor is selected from any one of colorectal cancer, lung cancer, and pancreatic cancer.

[0008] In some specific embodiments, the substance includes substances that specifically recognize... MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The probe.

[0009] In some specific embodiments, the probe includes the ability to detect... MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The probe, among which, Detection MKI67 The probe is the MKI67 probe, and its nucleotide sequence is shown in SEQ ID NO.1; Detection PCNA The probe is a PCNA probe, and its nucleotide sequence is shown in SEQ ID NO.2; Detection VEGF-A The probe is a VEGF-A probe, and its nucleotide sequence is shown in SEQ ID NO.3; Detection MMP9 The probe is an MMP9 probe, and its nucleotide sequence is shown in SEQ ID NO.4; Detection TRIM11 The probe is a TRIM11 probe, and its nucleotide sequence is shown in SEQ DI NO.5.

[0010] In some specific embodiments, the 5' end or 3' end of the probe is connected to a fluorescent signal tag.

[0011] The third technical solution of the present invention is to provide a kit for predicting tumor prognosis, including separate detection of... MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The probe, among which, Detection MKI67 The probe is the MKI67 probe, and its nucleotide sequence is shown in SEQ ID NO.1; Detection PCNA The probe is a PCNA probe, and its nucleotide sequence is shown in SEQ ID NO.2; Detection VEGF-AThe probe is a VEGF-A probe, and its nucleotide sequence is shown in SEQ ID NO.3; Detection MMP9 The probe is an MMP9 probe, and its nucleotide sequence is shown in SEQ ID NO.4; Detection TRIM11 The probe is a TRIM11 probe, and its nucleotide sequence is shown in SEQ ID NO.5; The tumor is selected from any one of colorectal cancer, lung cancer, and pancreatic cancer.

[0012] In some specific embodiments, the kit includes a reaction buffer composed of 50% formamide, 2×SSC, 10% dextran sulfate, and 0.1% SDS.

[0013] The fourth technical solution of the present invention is to provide a system for tumor prognosis assessment, comprising the following modules: 1) Data collection module: Collects patient samples, detects the evaluation gene set as described in one of the above technical solutions, obtains the expression level of the evaluation gene set, and outputs the data. 2) Model Calculation Module: Receives data from the data collection module and calculates the patient's total expression level in the assessment gene set as described in one of the above technical solutions, i.e., the risk score. The formula for calculating the risk score is as follows: 0.22×OD MKI67 +0.26×OD PCNA +0.08×OD VEGF-A +0.02×OD MMP9 +0.42×OD TRIM11 , 3) Output module: Based on the risk score calculated by the model calculation module, the patient's prognosis is predicted. The lower the patient's risk score, the better the prognosis. The risk score is compared with the threshold value. If it is higher than the threshold value, the output is a poor prognosis. If it is lower than the threshold value, the output is a good prognosis.

[0014] In some specific implementations, the prognostic criteria are as follows: a risk score ≥0.75 indicates a high risk of recurrence; 0.35-0.74 indicates a medium risk of recurrence; and ≤0.34 indicates a low risk of recurrence.

[0015] In some specific embodiments, the detection and evaluation of the gene set includes hybridizing tissue sections using probes attached to fluorescent signal tags, and the expression level of the gene set is evaluated based on the fluorescence signal intensity of each gene.

[0016] Compared with the prior art, the present invention has the following advantages: (1) The present invention combinedMKI67 , PCNA , VEGF-A , MMP9 , TRIM11 A multi-type tumor prognosis prediction model was constructed, which synergistically works on four core pathways: tumor proliferation, angiogenesis, invasion and metastasis, and abnormal regulation. The accuracy rate is improved by an average of more than 30% compared with single-indicator detection. The AUC value of 5 genes combined to predict the prognosis of colorectal cancer reaches 0.89, which is higher than 0.51-0.66 for single genes, significantly improving the prediction accuracy.

[0017] (2) The multi-type tumor prognosis prediction model constructed by the present invention can quantify the high, medium and low risk stratification of the prognosis of lung cancer, pancreatic cancer and colorectal cancer with high accuracy and can be used to directly guide the decision of postoperative adjuvant therapy. Attached Figure Description

[0018] Figure 1 For 5 target genes ( MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 Box plot of expression levels in pan-cancer species.

[0019] Figure 2 For 5 target genes ( MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 Prognostic risk heatmap in pan-cancer types.

[0020] Figure 3 For 5 target genes ( MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 Immunohistochemical staining and H-score comparison in normal tissue and colorectal cancer tissue.

[0021] Figure 4 For the training set of 5 target genes ( MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The ROC curves of lung cancer, colorectal cancer, and pancreatic cancer were obtained by combining detection methods.

[0022] Figure 5 To validate the five target genes ( MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The ROC curves of lung cancer, colorectal cancer, and pancreatic cancer were obtained by combining detection methods. Detailed Implementation

[0023] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0024] 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 application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0025] Unless otherwise specified, the materials and processes described in the following embodiments or examples are conventional materials and processes used in the art to achieve the corresponding functions.

[0026] Example 1 This embodiment analyzes candidate genes with particularly significant differential expression in lung cancer, colorectal cancer, and pancreatic cancer using pan-cancer transcriptome data from the TCGA database. The results are as follows: Figure 1 As shown, MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The mRNA expression levels in various tumor tissues were significantly higher than those in paired normal tissues (P<0.001), with particularly significant differences observed in lung cancer, colorectal cancer, and pancreatic cancer.

[0027] Subsequently, a pan-cancer prognostic analysis was performed to determine the prognostic value of these five candidate genes, such as... Figure 2 The results showed that MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 High expression in lung cancer, colorectal cancer, and pancreatic cancer was significantly associated with shortened overall survival (HR>1, P<0.05), especially TRIM11 It also showed a highly significant poor prognostic association in tumors such as glioblastoma (HR>100, P<0.05), indicating that the gene combination of the present invention has a certain effectiveness in prognostic assessment in multiple cancer types.

[0028] Taking colorectal cancer tissue as an example, this invention further verified these 5 target genes through immunohistochemical staining. MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The expression of [a certain substance] differs between normal tissues and colorectal cancer tissues. For example... Figure 3The results showed that MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The H-score in tumor tissue was significantly higher than that in normal tissue (P<0.001), confirming that these genes are specifically highly expressed in tumor tissue.

[0029] Example 2 Training set: Gene expression data and clinical follow-up information of 2015 patients with lung cancer (LUAD / LUSC), colorectal cancer (COAD / READ), and pancreatic cancer (PAAD) from the TCGA database (including 589 cases of lung adenocarcinoma LUAD, 552 cases of lung squamous cell carcinoma LUSC, 514 cases of colonic adenocarcinoma COAD, 177 cases of rectal adenocarcinoma READ, and 183 cases of pancreatic ductal adenocarcinoma PAAD). After excluding samples with missing follow-up time or survival status, 1996 valid samples were analyzed. Number of deaths: 696 (event incidence rate 34.87%). Number of truncated samples: 1300.

[0030] Validation set: Independent cohort, from the GEO database (GSE30219: 293 cases of lung cancer, GSE39582: 566 cases of colorectal cancer, GSE62452: 69 cases of pancreatic cancer), totaling 928 cases.

[0031] All of the above samples had "death" as the primary endpoint and a follow-up period of ≥12 months.

[0032] Feature extraction: Extracting 5 target genes from database data ( MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The expression level of ) was calculated and then standardized (Z-score standardization).

[0033] Model construction: A Cox proportional hazards regression model was adopted, with the standardized expression levels of five genes as independent variables and "time of death" as the dependent variable, and multivariate regression analysis was performed.

[0034] Weight calculation: The regression coefficients (β values) of each gene were obtained from the regression analysis and normalized to obtain the final weights, as shown in Table 1.

[0035] Table 1. Cox Regression Analysis Data Prognostic risk score = TRIM11 Expression level × 0.42+ PCNA Expression level × 0.26+ MKI67 Expression level × 0.22+ VEGFAExpression level × 0.08+ MMP9 Expression level × 0.02.

[0036] The prognostic risk scores of 1996 patients in the training set were sorted, ROC curves were plotted, and the point with the largest Youden index (sensitivity + specificity - 1) was calculated as the optimal cutoff value (0.75).

[0037] Patients were divided into high-risk and low-risk groups based on the optimal cutoff value (0.75). Threshold optimization was then performed. High-risk threshold: The maximum Youden score corresponds to a total score of 0.75 or higher.

[0038] Low-risk threshold: 0.34 points, determined based on the 25th percentile of the total score in the training set, to ensure a reasonable distribution of the three sample sizes (high risk ≈ 25%, medium risk ≈ 50%, low risk ≈ 25%).

[0039] The event rate in the high-risk group (39.86%) was higher than that in the low-risk group (24.07%). The specificity of the low-risk group reached 87.23%, which can effectively exclude most low-risk patients.

[0040] Therefore, the grading criteria are determined as follows: Total score ≥ 0.75: High risk of recurrence; 0.34-0.75 points: Moderate risk of recurrence; ≤0.34 points: Low risk of recurrence.

[0041] These 5 target genes ( MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The predictive efficacy of the combined assays on lung cancer, colorectal cancer, and pancreatic cancer patients in the training and validation sets is shown in Table 2 below. Figure 4 , Figure 5 : Table 2 These 5 target genes ( MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The predictive power of individual tests on lung cancer, colorectal cancer, and pancreatic cancer patients in the training and validation sets is shown in Table 3 below: Table 3 Since tumor recurrence and progression are the result of the synergistic effects of four core pathways: proliferation, angiogenesis, invasion and metastasis, and abnormal regulation, MKI67 , PCNAFocusing on the "cell proliferation" pathway, MMP9 Covering the "invasion and transfer" pathway, VEGF-A Dominant "angiogenesis" TRIM11 Dominate "abnormal regulation". If only one or a few combinations are selected (such as...) MKI67 + PCNA + MMP9 This inevitably leads to incomplete coverage of molecular mechanisms and an inability to capture prognostic differences caused by tumor heterogeneity. In particular, in highly invasive solid tumors such as pancreatic cancer and lung cancer, a large number of "false low risk" or "false high risk" misjudgments will occur.

[0042] These genes, as key regulators of tumor progression, can cross-regulate with proliferation and invasion pathways by modulating apoptosis and EMT pathways. While individual detection has some prognostic value (AUC=0.51-0.66), it cannot reflect their synergistic effects with genes from other pathways.

[0043] Combinations of any few genes cannot form stable risk grading thresholds: the score distribution of single / partial gene combinations has high dispersion, making it impossible to determine uniform high, medium, and low risk cutoff values. However, the overall combination of 5 genes, through full pathway coverage and weight optimization, has achieved stable thresholds (≥0.75 points, 0.35-0.74 points, ≤0.34 points) in the dataset, ensuring standardization in clinical application.

[0044] Example 3 Based on 5 target genes in the NCBI database ( MKI67 : NM_002417.4, PCNA: NM_002592.2, VEGF- A : NM_001025366.2、 MMP9 : NM_004994.3、 TRIM11 Using the reference sequence (NM_015371.4), specific probe sequences were designed using Primer3 software. The probe length was 45-55 nt, avoiding homologous gene regions to ensure binding specificity.

[0045] Detection MKI67 The probe is the MKI67 probe, and its nucleotide sequence is shown in SEQ ID NO.1: GCTGCCCTGACCTCCAGTCTCTACGACATGCTGGAGAACATCAAGGAG Detection PCNA The probe is a PCNA probe, and its nucleotide sequence is shown in SEQ ID NO.2: CGACGAAAACCTGCCTCATGCTGGAGTGGCTGAAGGAGTTCACCATC Detection VEGF-A The probe is a VEGF-A probe, and its nucleotide sequence is shown in SEQ ID NO. 3: CTTCCTCCCTCCCTCCCCTCTGCTGCTGCTGCTGCTGCTGCTGCTG Detection MMP9 The probe is an MMP9 probe, and its nucleotide sequence is shown in SEQ ID NO.4: GACGACGTGCTGGACACCTACACCAACTGCTACCTGGTGACCTAC Detection TRIM11 The probe is a TRIM11 probe, and its nucleotide sequence is shown in SEQ ID NO.5; CTGCAGCTGGAGCTGCAGCTGGAGAACATCGAGCTGCTGAAGCTGC The 5' ends of the probes were labeled with fluorescent dyes: MKI67 probe labeled with FITC at an excitation wavelength of 488 nm; PCNA probe labeled with Cy3 at an excitation wavelength of 543 nm; VEGF-A probe labeled with Cy5 at an excitation wavelength of 633 nm; MMP9 probe labeled with Alexa Fluor 555 at an excitation wavelength of 555 nm; and TRIM11 probe labeled with Texas Red at an excitation wavelength of 596 nm. The labeled probes were purified, aliquoted, and stored at -20°C.

[0046] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. An assessment gene set for predicting the prognosis of cancer patients, characterized in that, The evaluation gene set consists of 5 genes, which are as follows: MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The tumor is selected from any one of colorectal cancer, lung cancer, and pancreatic cancer.

2. The use of the substance for evaluating gene sets as described in claim 1 in the preparation of a product for tumor prognostic assessment, characterized in that, The evaluation gene set is MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 , The tumor is selected from any one of colorectal cancer, lung cancer, and pancreatic cancer.

3. The application according to claim 2, characterized in that, The substances include those that specifically recognize... MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The probe.

4. The application according to claim 3, characterized in that, The probes include those for detecting... MKI67 , PCNA , VEGF- A , MMP9 , TRIM11 The probe, among which, Detection MKI67 The probe is the MKI67 probe, and its nucleotide sequence is shown in SEQ ID NO.1; Detection PCNA The probe is a PCNA probe, and its nucleotide sequence is shown in SEQ ID NO.2; Detection VEGF-A The probe is a VEGF-A probe, and its nucleotide sequence is shown in SEQ ID NO.3; Detection MMP9 The probe is an MMP9 probe, and its nucleotide sequence is shown in SEQ ID NO.4; Detection TRIM11 The probe is a TRIM11 probe, and its nucleotide sequence is shown in SEQ DI NO.

5.

5. The application according to claim 3, characterized in that, A fluorescent signal tag is attached to the 5' or 3' end of the probe.

6. A kit for predicting tumor prognosis, characterized in that, Including separate testing MKI67 , PCNA , VEGF-A , MMP9 , TRIM11 The probe, among which, Detection MKI67 The probe is the MKI67 probe, and its nucleotide sequence is shown in SEQ ID NO.1; Detection PCNA The probe is a PCNA probe, and its nucleotide sequence is shown in SEQ ID NO.2; Detection VEGF-A The probe is a VEGF-A probe, and its nucleotide sequence is shown in SEQ ID NO.3; Detection MMP9 The probe is an MMP9 probe, and its nucleotide sequence is shown in SEQ ID NO.4; Detection TRIM11 The probe is a TRIM11 probe, and its nucleotide sequence is shown in SEQ ID NO.5; The tumor is selected from any one of colorectal cancer, lung cancer, and pancreatic cancer.

7. The reagent kit according to claim 6, characterized in that, The kit includes a reaction buffer consisting of 50% formamide, 2×SSC, 10% dextran sulfate, and 0.1% SDS.

8. A system for predicting tumor prognosis, characterized in that, Includes the following modules: 1) Data collection module: Collects patient samples, detects the evaluation gene set as described in claim 1, obtains the expression level of the evaluation gene set, and outputs the results; 2) Model Calculation Module: Receives data from the data collection module and calculates the patient's total expression level in the assessment gene set as described in claim 1, i.e., the risk score. The formula for calculating the risk score is as follows: 0.22×OD MKI67 +0.26×OD PCNA +0.08×OD VEGF-A +0.02×OD MMP9 +0.42×OD TRIM11 , 3) Output module: Based on the risk score calculated by the model calculation module, the patient's prognosis is predicted. The lower the patient's risk score, the better the prognosis. The risk score is compared with the threshold value. If it is higher than the threshold value, the output is a poor prognosis. If it is lower than the threshold value, the output is a good prognosis.

9. The system according to claim 8, characterized in that, Prognostic criteria: A risk score ≥0.75 indicates a high risk of recurrence; 0.35-0.74 indicates a medium risk of recurrence; and ≤0.34 indicates a low risk of recurrence.

10. The system according to claim 8, characterized in that, The detection and evaluation of the gene set includes hybridization of tissue sections using probes attached to fluorescent signal tags, and the expression level of the gene set is evaluated based on the fluorescence signal intensity of each gene.

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