CN116206682B - Tumor typing method for remarkably changing co-expression gene module based on anti-vascular treatment - Google Patents

Tumor typing method for remarkably changing co-expression gene module based on anti-vascular treatment Download PDF

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CN116206682B
CN116206682B CN202310217562.0A CN202310217562A CN116206682B CN 116206682 B CN116206682 B CN 116206682B CN 202310217562 A CN202310217562 A CN 202310217562A CN 116206682 B CN116206682 B CN 116206682B
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白雪
于子航
张艳培
郭泽钦
董忠谊
吴德华
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Abstract

The invention discloses a tumor typing method for significantly changing a co-expression gene module based on anti-vascular treatment, which comprises the following steps: obtaining anti-vascular treatment queue data, screening genes with obviously changed anti-vascular treatment, carrying out weighted co-expression network analysis on the expression profile of the pan-cancer seed gene in the TCGA database, analyzing the biological functions of a gene module and the functions of the gene module in the anti-vascular treatment, and carrying out treatment effect and prognosis related typing on tumors according to the screened gene module; the invention provides the gene whose expression is obviously changed in the anti-vascular treatment of the tumor by integrating the anti-vascular treatment queue data and analyzing the anti-vascular treatment gene expression profile, constructs a co-expression gene module by weighting co-expression network analysis, screens the gene module, and has extremely strong correlation with the anti-vascular treatment reactivity and prognosis of the tumor of the patient, thereby being beneficial to the decision-making of the anti-vascular treatment of the tumor and improving the understanding of the micro-environmental change and treatment resistance of the anti-vascular treatment tumor.

Description

Tumor typing method for remarkably changing co-expression gene module based on anti-vascular treatment
Technical Field
The invention relates to the technical field of biological medicine, in particular to a tumor typing method for remarkably changing a co-expression gene module based on anti-vascular treatment.
Background
In the clinic, the treatment of tumor is mainly early operation treatment, the treatment method of middle and late radiotherapy and chemotherapy is mainly poor in prognosis, especially for the highly malignant tumor which is easy to generate metastasis, and particularly for the highly malignant tumor which is difficult to obtain good treatment effect, as early as seventies of the last century, scientists propose the theory of anti-angiogenesis treatment of tumor, then various researchers gradually recognize that the control of the angiogenesis speed and the generation range of tumor has good application effect on treating tumor, and in recent years, the anti-angiogenesis research on tumor has progressed from early non-specific embolism, cutting off tumor blood vessels to new height of specific and targeted blocking of tumor blood vessels.
In the existing tumor anti-vascular treatment process, tumors need to be typed in advance before treatment, most of the existing tumor typing methods have complicated flow and complex operation, have weak reactivity and prognosis correlation with tumor anti-vascular treatment, and do not have the characteristics of high objectivity, accuracy and repeatability, so that the decision of tumor anti-vascular treatment and the understanding of the change of microenvironment and treatment resistance of the anti-vascular treatment tumor are not facilitated, and therefore, the invention provides a tumor typing method for obviously changing a co-expression gene module based on anti-vascular treatment to solve the problems in the prior art.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a tumor typing method for obviously changing a co-expression gene module based on anti-vascular treatment, which solves the problems that the existing tumor typing method has weak relevance to the anti-vascular treatment reactivity and prognosis of tumors and does not have the characteristics of high objectivity, accuracy and repeatability, thereby being unfavorable for the decision of anti-vascular treatment of tumors and understanding the change of microenvironment and treatment resistance of anti-vascular treatment tumors.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme: a method for typing tumors by significantly altering the co-expressed gene module based on anti-vascular therapy, step one: firstly, obtaining anti-vascular treatment queue data from a public database, and then screening genes which are obviously changed by patients undergoing anti-vascular treatment from the obtained queue data;
step two: firstly, carrying out weighted co-expression network analysis on the gene expression profile of the pan-cancer seed gene of the patient which is screened in the first step and is subjected to anti-vascular treatment, and dividing the gene into 8 co-expression gene modules;
step three: firstly, analyzing the biological functions of the coexpression gene modules in the second step and the functions of the coexpression gene modules in anti-vascular treatment through correlation analysis and enrichment analysis, and then screening out 3 coexpression gene modules with influence on the curative effect of the anti-vascular treatment;
step four: firstly, typing the gene expression profile of a tumor patient based on the 3 co-expression gene modules screened in the step three, and then classifying the patient into an anti-vascular treatment beneficiary type and an anti-vascular treatment drug resistant type according to the tumor gene expression profile of the tumor patient.
The further improvement is that: in the first step, the public database comprises a GEO database and an ArrayExpress database, and the anti-vascular treatment queue data comprises a gene expression profile matrix and treatment information of the patient.
The further improvement is that: in the first step, the specific steps of screening the significantly altered genes are: differential genes of patients receiving anti-vascular treatment and patients receiving contrast control treatment are calculated from the obtained queue data, and are subjected to integration treatment, and the differential genes after the integration treatment are regarded as genes with significantly changed anti-vascular treatment.
The further improvement is that: in the differential gene calculation process, differential analysis is carried out on the expression profile of the anti-vascular treatment sample and the expression profile of the control treatment sample in each queue data set, and the differential gene is judged when the P value is smaller than 0.05 and the logFC absolute value is larger than 0.5.
The further improvement is that: the differential genes of patients receiving anti-vascular treatment were subjected to an integration treatment by the R language RobustRankAggreg software package, and genes with Score value less than 1 were selected to remove the influence due to individual differences, whereby the screened differential genes were regarded as genes significantly altered in anti-vascular treatment.
The further improvement is that: in the second step, 10 co-expression genomes are obtained through weighted co-expression network analysis, and 2 unstable co-expression modules are removed based on intra-module correlation, so that 8 co-expression gene modules are obtained.
The further improvement is that: in the second step, the anti-vascular treatment of 8 co-expression gene modules is obviously changed, and the genes with intra-module connectivity larger than 0.85 are judged to be gene module key genes, and the single sample gene set enrichment analysis scores of the key genes are used as gene module expression quantity.
The further improvement is that: in the third step, the screening process is to perform correlation analysis of gene modules and cells and gene sets which possibly influence the curative effect of anti-vascular treatment on TCGA database pan-cancer seeds and enrichment analysis of gene sets of the gene modules in patients benefiting anti-vascular treatment.
The further improvement is that: in the fourth step, the tumor is classified into an anti-vascular treatment beneficiary type and an anti-vascular treatment drug resistant type according to the expression conditions of a red gene module, a green gene module and a pink gene module of a tumor of a patient during the tumor typing, and the typing operation is performed by adopting an NTP module of a GenePattern website.
The beneficial effects of the invention are as follows: the invention provides a gene for expressing significant change in anti-vascular treatment of tumor by acquiring anti-vascular treatment queue data and integrating analysis of anti-vascular treatment gene expression profile, constructs a co-expression gene module by weighting co-expression network analysis, and screens out a gene module which has influence on anti-vascular treatment curative effect, so as to realize parting of tumor of a patient, thereby ensuring that the parting method has extremely strong correlation with anti-vascular treatment reactivity and prognosis of tumor, has the characteristics of high objectivity, accuracy and repeatability, is beneficial to decision of anti-vascular treatment of tumor and improves understanding on micro-environmental change and treatment resistance of anti-vascular treatment tumor, and is worthy of wide popularization and application.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of a tumor typing method of the present invention;
FIG. 2 is a schematic representation of the differential genes of patients receiving anti-vascular treatment versus control treatment in an embodiment of the present invention;
FIG. 3 is a schematic diagram of significantly altered gene integration in an embodiment of the invention;
FIG. 4 is a thermal diagram of the correlation of gene modules in an embodiment of the invention;
FIG. 5 is a heat map of the correlation of gene modules in an embodiment of the invention with single sample gene set enrichment analysis (ssGSEA) score;
FIG. 6 is a graph of a selected gene module Gene Set Enrichment Analysis (GSEA) in an embodiment of the invention;
FIG. 7 is a thermal diagram of genotyping of a selected gene module tumor in an embodiment of the invention;
FIG. 8 is a schematic representation of the prognostic relationship of tumor typing and anti-tumor vascular treatment of the selected gene modules in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The data sources in this embodiment are:
GEO database: GSE72951, GSE81465, GSE37956, GSE114403, GSE87455, GSE60331, GSE98973, GSE109211, GSE94550, GSE31060, GSE160825, GSE54323, GSE51305, GSE84048, GSE114686, GSE26644, GSE149901;
arrayexpress database: E-MTAB-4439;
article Constructionandoptimizationofgeneexpression signaturesforpredictionofsurvivalintwo-armclinical trials.
Referring to fig. 1, 2, 3, 4, 5, 6, 7, 8, the present embodiment provides a tumor typing method for significantly altering a co-expressed gene module based on anti-vascular therapy, comprising the steps of:
step one: firstly, obtaining anti-vascular treatment queue data from a public database, wherein the anti-vascular treatment queue data comprises a gene expression profile matrix and treatment information of patients, and then screening genes which are obviously changed by the patients subjected to anti-vascular treatment from the obtained queue data, wherein the specific steps of screening the genes which are obviously changed are as follows: firstly, performing differential analysis on an anti-vascular treatment sample expression spectrum and a control treatment sample expression spectrum from the obtained queue data, judging as differential genes when the P value is smaller than 0.05 and the logFC absolute value is larger than 0.5, as shown in figure 2, integrating the differential genes of patients receiving the anti-vascular treatment by using a R language RobustRankAggreg software package, and selecting genes with Score value smaller than 1 to remove the influence caused by individual differences, wherein the screened differential genes are regarded as genes with obviously changed anti-vascular treatment, as shown in figure 3;
the anti-vascular treatment queue data obtained therein were derived from the GEO database, the ArrayExpress database and the article Constructionandoptimizationof geneexpressionsignaturesforpredictionofsurvivalin two-armclinicaltrias.
Step two: firstly, carrying out weighted co-expression network analysis (WGCNA) on the gene expression profile of the pan-cancer seed gene of the patient which is screened out in the first step and is subjected to anti-vascular treatment, obtaining 10 co-expression genomes, removing 2 unstable co-expression modules based on intra-module correlation, dividing the genes into 8 co-expression gene modules, wherein the anti-vascular treatment of the 8 co-expression gene modules is obviously changed, judging that genes with intra-module connectivity of more than 0.85 are gene module key genes, and taking a single sample gene set enrichment analysis (ssSGEA) score of the key genes as a gene module expression quantity, as shown in figure 4;
step three: the biological function of each co-expressed gene module in step two and its role in anti-vascular therapy were analyzed by correlation of gene modules with anti-vascular therapy related gene set (geneset) TCGA database pan cancer single sample gene set enrichment analysis (ssGSEA) score, and 3 co-expressed gene modules were screened out which may have an effect on anti-vascular therapy efficacy, wherein red module was related to endothelial cell (endothelial) Angiogenesis (angenesis), green module was related to myogenic action (Myogenesis) of endothelial (endothelial) and Pericytes (Pericytes) in Angiogenesis (angenesis), pink module was related to myeoid inflammation (myeoid inflammation) and myeoid suppressor cells (MDSC), as shown in fig. 5;
based on the 3 gene module biological process, this example considers that it may affect anti-vascular therapy efficacy, and Gene Set Enrichment Analysis (GSEA) was performed in the renal clear cell carcinoma data set E-MTAB-3267 that received anti-vascular therapy, and found that red and green modules were significantly enriched in CR/PR patients (p < 0.001), and pink modules were significantly enriched in PD/SD patients (p < 0.001), so this example considered that red and green modules were associated with renal clear cell carcinoma anti-vascular therapy efficacy, while pink modules were associated with therapy failure, as shown in fig. 6;
step four: the method comprises the steps of firstly typing the gene expression profile of a tumor patient based on 3 co-expression gene modules screened in the step three, and then classifying the tumor into an anti-vascular treatment beneficiary type and an anti-vascular treatment drug resistant type according to the expression conditions of a red gene module, a green gene module and a pink gene module of the tumor of the patient during the tumor typing, wherein the typing operation is carried out by adopting an NTP module of a GenePattern website.
The 3 gene modules in this example include the co-expressed RNA markers from weighted co-expression network analysis (WGCNA) represented by red, green, GALNT15, FGD5, HEDH 2, NRP1, FRZB, KCNJ8, RARB, SSPN, APLNR, GUCY A3, RGL1, PDE2A, CCDC102B, GPC, PCAT19, PODXL, PDE3A, SH BP5, LEPR, GA9, WBP1L, SLC A1, GEM, TSC22D3, EPHA3, PCDH12, HEYL, PCDH17, GALNT15, FGD5, HEDH 2, NRP1, FRZB, KCNJ8, RARB, SSPN, APLNR, GUCY A3, RGL1, PDE2A, CCDC102B, GPC, PCAT19, PODXL, PDE3A, SH BP5, LEPR, GA9, WBP1L, SLC A1, GEM, TSC22D3, EPH 2, EK 1, GA 2, PLGA 17, GLITOD 10, GLITOD 2, GLYPD 2, GLITY 2, green gene modules (RNA names SEZ6L, EID1, NCAM1, LIFR, MAP1B, RANBP3L, TCEAL1, BBIP1, DIXDC1, GPM6A, GABRA1, TSPAN7, RGS7, PRKACB, MERTK, FAT3, TMEM231, ZNF302, PYGO1, SMG6, LRRC49, SPOCK1, AC018647.3, SMARCA1, FRY, FAM171B, PRSS35, PPM1E, ABCG2, ZBTB4, BEX1, DUSP27, MXI1, IGSF1, GPR146, NTRK2, NRCAM, SNAP 25) FGF14, TP53INP2, CDH20, FGF13, SORBS1, RORB, F8, PHLDB1, GRM6, RASL12, DENND2A, RIMS1, ABCA8, PLCXD3, FRMD3, C22orf23, UBE2E2, B3GALT1, GLCCI1, KIAA1191, EFHD1, DGKK, AMFR, FBXL2, SRSF8, ERBB4, ZCCHC12, YPEL1, KLHL3, TMEM130, HYDIN, B3GALT2, HIGD1B, CLDN, XL16, NES, SEMA 6A), pink gene modules (each RNA name KRT80, EPPK1, CEACAM6, SFN, MMP10, C15orf48, DSP, FAM83G, FUT, KRT4, KRT19, C3orf52, MAL2, GJB2, S100P, FXYD3, DSC3, UCA1, IL1RN, LY6D, PGLYRP4, TACSTD2, IER3, MMP1, PAWR, GPRC5A, KRT, PI3, C6orf141, LGALS9B, CORO2A, ESRP1, PROM2, SLC10A3, MMP3, SLC28A3, AQP3, TSKU, ROS1, CDH3, CXCL1, GPX2, RP11-757F18.5, CGB5, CASP7, PCSK9, CDH1, TFCP2L1, PIM3, COX6B2, SERPINB2, S100A2, EOV, ERR 1B10, CGB8, BAX 2, CCL 20).
The coexpression gene module in the embodiment is applied to the prediction of the curative effect and prognosis of tumor anti-vascular treatment.
This example groups another renal clear cell carcinoma dataset (Avelumabplusaxitinibversussunitinibin advancedrenalcellcarcinoma: biomarker analysis of the phase3javelin real 101 three) that received anti-vascular treatment into two groups of patients based on red, green and pink modules, including clusteria (anti-vascular treatment benefit) that highly expressed the red green module and clusterib (anti-vascular treatment tolerance) that highly expressed the pink module, as shown in fig. 7;
finally, the patient benefit rate after treatment in ClusterA in renal clear cell carcinoma anti-vascular treatment was found to be significantly higher than that of ClusterB, as shown in FIG. 8.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.

Claims (6)

1. A method for typing a tumor with a significant change in co-expressed gene modules based on anti-vascular therapy, comprising the steps of:
step one: firstly, obtaining anti-vascular treatment queue data from a public database, and then screening genes which are obviously changed by patients undergoing anti-vascular treatment from the obtained queue data;
step two: firstly, carrying out weighted co-expression network analysis on the gene expression profile of the pan-cancer seed gene of the patient which is screened in the first step and is subjected to anti-vascular treatment, and dividing the gene into 8 co-expression gene modules;
step three: firstly, analyzing the biological functions of the coexpression gene modules in the second step and the functions of the coexpression gene modules in anti-vascular treatment through correlation analysis and enrichment analysis, and then screening out 3 coexpression gene modules with influence on the curative effect of the anti-vascular treatment;
step four: firstly, typing the gene expression profile of a tumor patient based on the 3 co-expression gene modules screened in the step three, and then classifying the patient into an anti-vascular treatment beneficiary type and an anti-vascular treatment drug resistant type according to the tumor gene expression profile of the tumor patient;
in the first step, the specific steps of screening the significantly altered genes are: firstly, calculating differential genes of patients receiving anti-vascular treatment and patients receiving contrast control treatment from the obtained queue data, and carrying out integration treatment, wherein the differential genes after the integration treatment are regarded as genes with obviously changed anti-vascular treatment; in the differential gene calculation process, carrying out differential analysis on the expression profile of the anti-vascular treatment sample and the expression profile of the control treatment sample in each queue data set, and judging as a differential gene when the P value is smaller than 0.05 and the logFC absolute value is larger than 0.5;
in the second step, the anti-vascular treatment of 8 co-expression gene modules is obviously changed, and the genes with intra-module connectivity larger than 0.85 are judged to be gene module key genes, and the single sample gene set enrichment analysis scores of the key genes are used as gene module expression quantity.
2. The method for tumor typing based on significant changes in co-expressed gene modules for anti-vascular therapy according to claim 1, wherein: in the first step, the public database comprises a GEO database and an ArrayExpress database, and the anti-vascular treatment queue data comprises a gene expression profile matrix and treatment information of the patient.
3. The method for tumor typing based on significant changes in co-expressed gene modules for anti-vascular therapy according to claim 1, wherein: the differential genes of patients receiving anti-vascular treatment were subjected to an integration treatment by the R language RobustRankAggreg software package, and genes with Score value less than 1 were selected to remove the influence due to individual differences, whereby the screened differential genes were regarded as genes significantly altered in anti-vascular treatment.
4. The method for tumor typing based on significant changes in co-expressed gene modules for anti-vascular therapy according to claim 1, wherein: in the second step, 10 co-expression genomes are obtained through weighted co-expression network analysis, and 2 unstable co-expression modules are removed based on intra-module correlation, so that 8 co-expression gene modules are obtained.
5. The method for tumor typing based on significant changes in co-expressed gene modules for anti-vascular therapy according to claim 1, wherein: in the third step, the screening process is to perform correlation analysis of gene modules and cells and gene sets which possibly influence the curative effect of anti-vascular treatment on TCGA database pan-cancer seeds and enrichment analysis of gene sets of the gene modules in patients benefiting anti-vascular treatment.
6. The method for tumor typing based on significant changes in co-expressed gene modules for anti-vascular therapy according to claim 1, wherein: in the fourth step, the tumor is classified into an anti-vascular treatment beneficiary type and an anti-vascular treatment drug resistant type according to the expression conditions of a red gene module, a green gene module and a pink gene module of a tumor of a patient during the tumor typing, and the typing operation is carried out by adopting an NTP module of a GenePat term website.
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