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CN-116230217-B - M6A cluster differential gene set for evaluating prognosis survival risk of non-small cell lung cancer, screening method and prognosis survival risk scoring model

CN116230217BCN 116230217 BCN116230217 BCN 116230217BCN-116230217-B

Abstract

The invention provides a group of m6A cluster difference gene sets for evaluating the prognosis survival risk of non-small cell lung cancer, a screening method thereof and a prognosis risk evaluation model, which belong to the technical field of cancer prognosis risk evaluation, wherein the m6A cluster difference gene sets for evaluating the prognosis survival risk of non-small cell lung cancer comprise the following genes ：S100A10、IGFBP1、SLC52A1、KREMEN2、SCPEP1、COL4A3、GSTM2、STRAP、PCDH7、PAK2、CYP4B1、MTUS1、ESPL1、PRRX2、TTK、COL4A4、CCR7、NPC2 and SKA1. The invention constructs a survival risk scoring model according to the m6A cluster difference gene set, and provides a new insight for predicting NSCLC prognosis.

Inventors

  • QU YIQING
  • LI RUI
  • LIU XIAO
  • Jiang Yingxiao
  • QU JIAJIA

Assignees

  • 山东大学
  • 山东大学齐鲁医院

Dates

Publication Date
20260505
Application Date
20220608

Claims (7)

  1. 1. A set of m6A cluster differential gene sets for assessing the risk of survival for prognosis of non-small cell lung cancer, comprising the following genes ：S100A10、IGFBP1、SLC52A1、KREMEN2、SCPEP1、COL4A3、GSTM2、STRAP、PCDH7、PAK2、CYP4B1、MTUS1、ESPL1、PRRX2、TTK、COL4A4、CCR7、NPC2 and SKA1.
  2. 2. A non-small cell lung cancer prognosis survival risk score model constructed using the m6A cluster differential gene set for assessing the prognosis survival risk of non-small cell lung cancer according to claim 1, characterized in that the model is expression + (0.1543) x S100a10 expression + (0.0926) x IGFBP1 expression level+ (-0.1585) x SLC52A1 expression level+ (-0.0512) x KREMEN2 expression level+ (-0.0787) x SCPEP1 expression level+ (0.1195) x COL4A3 expression level+ (-0.0915) x GSTM2 expression level+ (0.1032) x STRAP expression level+ (0.0693) x PCDH7 expression level+ (0.1389) x PAK2 expression level+ (0.0437) x CYP4B1 expression level+ (-0.0950) x MTUS1 expression level+ (0.1204) x PRRX) x (PRRX) 2 expression level+ (-0.0726) x 6) x 4 x (6) x co-3 x 6 x (6) x co-3) x co-expression level (6) x 3 x p-3 x 6 x co-4B 1 expression level (6) x 3) x co-4 x p1 expression level (3).
  3. 3. Use of a kit for detecting expression of m6A cluster differential gene set for assessing risk of prognosis survival of non-small cell lung cancer according to claim 1 in the preparation of a product for diagnosis or auxiliary diagnosis of overall survival of a patient with non-small cell lung cancer.
  4. 4. The method for screening m6A cluster differential gene set for assessing the risk of survival in prognosis of non-small cell lung cancer according to claim 1, comprising the steps of: 1) Downloading clinical data, somatic mutation data and CNV data of a non-small cell lung cancer patient, and RNA-Seq transcriptome data, somatic mutation data and CNV data of a cancer tissue and a paracancer normal tissue in a database; 2) Extracting m6A related regulatory factors from transcriptome data obtained in the step (1), and carrying out non-monitored cluster analysis on the extracted m6A related regulatory factors to obtain the typing of the m6A related regulatory factors; 3) Analyzing the expression difference of m6A related regulatory factors of different genotypes in cancer tissues and paracancerous normal tissues of a non-small cell lung cancer patient; 4) And 3) carrying out univariate and multivariate Cox regression analysis on the expression difference of the m6A related regulatory factors with different types in the cancer tissues and paracancerous normal tissues of the non-small cell lung cancer patient and the complete survival information data of the patient according to the step 3) to obtain an m6A cluster difference gene set for evaluating the prognosis survival risk of the non-small cell lung cancer.
  5. 5. The method of claim 4, wherein the databases in step 1) are GEO database, TCGA and UCSC-Xena, and the collection yields 2 NSCLC queues (GSE 50081, GSE 68465) and (TCGA-LUSC, TCGA-LUAD).
  6. 6. The screening method of claim 5, wherein the unsupervised clustering analysis of step 2) is performed using ConsensuClusterPlus packages.
  7. 7. The method according to claim 4, wherein the difference in expression of the m 6A-related regulatory factors of the different genotypes in step 3) is performed using the "limma" package.

Description

M6A cluster differential gene set for evaluating prognosis survival risk of non-small cell lung cancer, screening method and prognosis survival risk scoring model Technical Field The invention belongs to the technical field of tumor prognosis evaluation, and particularly relates to an m6A cluster difference gene set for evaluating the prognosis survival risk of non-small cell lung cancer, a screening method thereof and a prognosis survival risk scoring model. Background Lung cancer is a major cause of cancer-related death worldwide. Non-small cell lung cancer (NSCLC) accounts for about 80-85% of the total incidence of lung cancer. Although molecular immunotherapy and targeted therapies have been common in cancer research. But five-year survival in NSCLC patients is still low (18%). Thus, there is an urgent need to improve prognosis and immunotherapeutic effect prediction of NSCLC. Techniques exploring RNA modification have shown that some of these modifications are present in certain messenger RNAs (mRNAs). The m6A modification is involved in a variety of functions including tumorigenesis. Among the RNA modifications, the m6A modification is most common and is involved in various biological processes such as the formation of cancer stem cells, tumorigenesis and cell death. However, no model related to prognosis evaluation of non-small cell lung cancer for m6A modification is reported. Disclosure of Invention In view of the above, the present invention aims to provide a m6A cluster differential gene set for assessing the prognosis survival risk of non-small cell lung cancer, a screening method thereof and a prognosis survival risk scoring model. The invention provides a m6A cluster difference gene set for evaluating the prognosis survival risk of non-small cell lung cancer, which is characterized by comprising the following genes ：S100A10、IGFBP1、SLC52A1、KREMEN2、SCPEP1、COL4A3、GSTM2、STRAP、PCDH7、PAK2、CYP4B1、MTUS1、ESPL1、PRRX2、TTK、COL4A4、CCR7、NPC2 and SKA1. The invention provides a non-small cell lung cancer prognosis survival risk scoring model constructed by using the m6A cluster difference gene set for evaluating the non-small cell lung cancer prognosis survival risk, the model is survival risk score = (0.1543) x S100a10 expression + (0.0926) x IGFBP1 expression level + (-0.1585) x SLC52A1 expression level + (-0.0787) x SCPEP1 expression level + (0.1195) x COL4A3 expression level + (-0.0915) x GSTM2 expression level+ (0.1032) x STRAP expression level + (0.0693) x PCDH7 expression level+ (0.1389) x PAK2 expression level+ (0.0437) x CYP4B1 expression level + (-0.0950) x MTUS1 expression level+ (0.1204) x ESPL1 expression level+ (0.0690) x PRRX2 expression level+ (-0.1307) x TTK expression level+ (-0.1181) x COL4A4 expression level+ (-0.097) x sk2 expression level (346) x 080) x np6. The invention provides application of a kit for detecting m6A cluster differential gene set expression for evaluating non-small cell lung cancer prognosis survival risk in preparation of a product for diagnosing or assisting in diagnosing overall survival rate of a non-small cell lung cancer patient. The invention also provides a screening method of the m6A cluster difference gene set for evaluating the prognosis survival risk of the non-small cell lung cancer, which comprises the following steps: 1) Downloading clinical data, somatic mutation data and CNV data of a non-small cell lung cancer patient, and RNA-Seq transcriptome data, somatic mutation data and CNV data of a cancer tissue and a paracancer normal tissue in a database; 2) Extracting m6A related regulatory factors from transcriptome data obtained in the step (1), and carrying out non-monitored cluster analysis on the extracted m6A related regulatory factors to obtain the typing of the m6A related regulatory factors; 3) Analyzing the expression difference of m6A related regulatory factors of different types in cancer tissues and paracancerous normal tissues of a non-small cell lung cancer patient; 4) And 3) carrying out univariate and multivariate Cox regression analysis on the expression difference of the m6A related regulatory factors with different types in the cancer tissues and paracancerous normal tissues of the non-small cell lung cancer patient and the complete survival information data of the patient according to the step 3) to obtain an m6A cluster difference gene set for evaluating the prognosis survival risk of the non-small cell lung cancer. Preferably, the databases in step 1) are GEO database, TCGA and UCSC-Xena, and collecting to obtain 2 NSCLC queues (GSE 50081, GSE 68465) and (TCGA-LUSC, TCGA-LUAD). Preferably, the unsupervised cluster analysis described in step 2) uses ConsensuClusterPlus packages for execution. Preferably, the difference in expression of the m 6A-related regulatory factors of the different genotypes in step 3) is performed using the "limma" package. Compared with the prior art, the invention has the beneficial effects that a group of m6A