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Establishing and validating a pathway prognostic signature in pancreatic cancer based on miRNA and mRNA sets using GSVA.

ABSTRACT: Pancreatic cancer (PC) is a severe disease with the highest mortality rate among various cancers. It is urgent to find an effective and accurate way to predict the survival of PC patients. Gene set variation analysis (GSVA) was used to establish and validate a miRNA set-based pathway prognostic signature for PC (miPPSPC) and a mRNA set-based pathway prognostic signature for PC (mPPSPC) in independent datasets. An optimized miPPSPC was constructed by combining clinical parameters. The miPPSPC, optimized miPPSPC and mPPSPC were established and validated to predict the survival of PC patients and showed excellent predictive ability. Four metabolic pathways and one oxidative stress pathway were identified in the miPPSPC, whereas linoleic acid metabolism and the pentose phosphate pathway were identified in the mPPSPC. Key factors of the pentose phosphate pathway and linoleic acid metabolism, G6PD and CYP2C8/9/18/19, respectively, are related to the survival of PC patients according to our tissue microarray. Thus, the miPPSPC, optimized miPPSPC and mPPSPC can predict the survival of PC patients efficiently and precisely. The metabolic and oxidative stress pathways may participate in PC progression.

SUBMITTER: Zhang J

PROVIDER: S-EPMC7746356 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Establishing and validating a pathway prognostic signature in pancreatic cancer based on miRNA and mRNA sets using GSVA.

Zhang Junfeng J Gu Jianyou J Guo Shixiang S Huang Wenjie W Zheng Yao Y Wang Xianxing X Zhang Tao T Zhao Weibo W Ni Bing B Fan Yingfang Y Wang Huaizhi H

Aging 20201110 22

Pancreatic cancer (PC) is a severe disease with the highest mortality rate among various cancers. It is urgent to find an effective and accurate way to predict the survival of PC patients. Gene set variation analysis (GSVA) was used to establish and validate a miRNA set-based pathway prognostic signature for PC (miPPSPC) and a mRNA set-based pathway prognostic signature for PC (mPPSPC) in independent datasets. An optimized miPPSPC was constructed by combining clinical parameters. The miPPSPC, op ...[more]

PMID: 33197892

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Project description:BackgroundWith the progress of precision medicine treatment in pancreatic ductal adenocarcinoma (PDAC), individualized cancer-related medical examination and prediction are of great importance in this high malignant tumor and tumor-immune microenvironment with changed pathways highly enrolled in the carcinogenesis of PDAC.MethodsHigh-throughput data of pancreatic ductal adenocarcinoma were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. After batch normalization, the enrichment pathway and relevant scores were identified by the enrichment of immune-related pathway signature using gene set variation analysis (GSVA). Then, cancerous subtype in TCGA and GEO samples was defined through the NMF methods by cancertypes packages in R software, respectively. Subsequently, the significance between the characteristics of each TCGA sample and cancer type and the significant prognosis-related pathway with risk score formula is calculated through t-test and univariate Cox analysis. Next, the prognostic value of gained risk score formula and each significant prognosis-related pathway were validated in TCGA and GEO samples by survival analysis. The pivotal hub genes in the enriched significant prognosis-related pathway are identified and validated, and the TIMER database was used to identify the potential role of hub genes in the PDAC immune environment. The potential role of hub genes is promoting the transdifferentiation of cancer-associated fibroblasts.ResultsThe enrichment pathway and relevant scores were identified by GSVA, and 3 subtypes of pancreatic ductal adenocarcinoma were defined in TCGA and GEO samples. The clinical stage, tumor node metastasis classification, and tumor grade are strongly relative to the subtype above in TCGA samples. A risk formula about GSVA significant pathway "GSE45365_WT_VS_IFNAR_KO_CD11B_DC_MCMV_INFECTION_DN ∗ 0.80 + HALLMARK_GLYCOLYSIS ∗ 16.8 + GSE19888_CTRL_VS_T_CELL_MEMBRANES_ACT_MAST_CELL_DN ∗ 14.4" was identified and validated in TCGA and GEO samples through survival analysis with significance. DCN, VCAN, B4GALT7, SDC1, SDC2, B3GALT6, B3GAT3, SDC3, GPC1, and XYLT2 were identified as hub genes in these GSVA significant pathways and validated in silico.ConclusionsThree pancreatic ductal adenocarcinoma subtypes are identified, and an individualized GSVA immune pathway score-related prognostic risk score formula with 10 hub genes is identified and validated. The predicted function of the 10 upregulated hub genes in tumor-immune microenvironment was promoting the infiltration of cancer-associated fibroblasts. These findings will contribute to the precision medicine of pancreatic ductal adenocarcinoma treatment and tumor immune-related basic research.

Project description:BackgroundCervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients.MethodsWe used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network.ResultsWe identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP∗ 2.617 + N_HALLMARK_ANGIOGENESIS∗ 4.860. Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH.ConclusionsThree novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways.

Project description:BackgroundIn this study, miRNAs and their critical target genes related to the prognosis of pancreatic cancer were screened based on bioinformatics analysis to provide targets for the prognosis and treatment of pancreatic cancer.MethodsR software was used to screen differentially expressed miRNAs (DEMs) and genes (DEGs) downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. A miRNA Cox proportional hazards regression model was constructed based on the miRNAs, and a miRNA prognostic model was generated. The target genes of the prognostic miRNAs were predicted using TargetScan and miRDB and then intersected with the DEGs to obtain common genes. The functions of the common genes were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. A protein-protein interaction (PPI) network of the common genes was constructed with the STRING database and visualized with Cytoscape software. Key genes were also screened with the MCODE and cytoHubba plug-ins of Cytoscape. Finally, a prognostic model formed by the key gene was also established to help evaluate the reliability of this screening process.ResultsA prognostic model containing four downregulated miRNAs (hsa-mir-424, hsa-mir-3613, hsa-mir-4772 and hsa-mir-126) related to the prognosis of pancreatic cancer was constructed. A total of 118 common genes were enriched in two KEGG pathways and 33 GO functional annotations, including extracellular matrix (ECM)-receptor interaction and cell adhesion. Nine key genes related to pancreatic cancer were also obtained: MMP14, ITGA2, THBS2, COL1A1, COL3A1, COL11A1, COL6A3, COL12A1 and COL5A2. The prognostic model formed by nine key genes also possessed good prognostic ability.ConclusionsThe prognostic model consisting of four miRNAs can reliably predict the prognosis of patients with pancreatic cancer. In addition, the screened nine key genes, which can also form a reliable prognostic model, are significantly related to the occurrence and development of pancreatic cancer. Among them, one novel miRNA (hsa-mir-4772) and two novel genes (COL12A1 and COL5A2) associated with pancreatic cancer have great potential to be used as prognostic factors and therapeutic targets for this tumor.

Dataset Information

Establishing and validating a pathway prognostic signature in pancreatic cancer based on miRNA and mRNA sets using GSVA.

Publications

Establishing and validating a pathway prognostic signature in pancreatic cancer based on miRNA and mRNA sets using GSVA.

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