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Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma.

ABSTRACT: In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN, USHBP1 and ZNF532 as the top five mRNAsi-related genes. We constructed our survival model with these five genes and tested its performance using 243 HCC and 202 normal liver samples from the ICGC database. Kaplan-Meier survival curve and receive operating characteristic curve analyses showed that the survival model accurately predicted the prognosis and survival of high- and low-risk HCC patients with high sensitivity and specificity. The expression of these five genes was significantly higher in the HCC tissues from the TCGA, ICGC, and GEO datasets (GSE25097 and GSE14520) than in normal liver tissues. These findings demonstrate that a new survival model derived from five strongly correlating mRNAsi-related genes provides highly accurate prognoses for HCC patients.

SUBMITTER: Zhang Q

PROVIDER: S-EPMC7377834 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma.

Zhang Qiujing Q Wang Jia J Liu Menghan M Zhu Qingqing Q Li Qiang Q Xie Chao C Han Congcong C Wang Yali Y Gao Min M Liu Jie J

Aging 20200709 13

In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN ...[more]

PMID: 32644941

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Project description:Background:With characteristic self-renewal and multipotent differentiation, cancer stem cells (CSCs) have a crucial influence on the metastasis, relapse and drug resistance of gastric cancer (GC). However, the genes that participates in the stemness of GC stem cells have not been identified. Methods:The mRNA expression-based stemness index (mRNAsi) was analyzed with differential expressions in GC. The weighted gene co-expression network analysis (WGCNA) was utilized to build a co-expression network targeting differentially expressed genes (DEG) and discover mRNAsi-related modules and genes. We assessed the association between the key genes at both the transcription and protein level. Gene Expression Omnibus (GEO) database was used to validate the expression levels of the key genes. The risk model was established according to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Furthermore, we determined the prognostic value of the model by employing Kaplan-Meier (KM) plus multivariate Cox analysis. Results:GC tissues exhibited a substantially higher mRNAsi relative to the healthy non-tumor tissues. Based on WGCNA, 17 key genes (ARHGAP11A, BUB1, BUB1B, C1orf112, CENPF, KIF14, KIF15, KIF18B, KIF4A, NCAPH, PLK4, RACGAP1, RAD54L, SGO2, TPX2, TTK, and XRCC2) were identified. These key genes were clearly overexpressed in GC and validated in the GEO database. The protein-protein interaction (PPI) network as assessed by STRING indicated that the key genes were tightly connected. After LASSO analysis, a nine-gene risk model (BUB1B, NCAPH, KIF15, RAD54L, KIF18B, KIF4A, TTK, SGO2, C1orf112) was constructed. The overall survival in the high-risk group was relatively poor. The area under curve (AUC) of risk score was higher compared to that of clinicopathological characteristics. According to the multivariate Cox analysis, the nine-gene risk model was a predictor of disease outcomes in GC patients (HR, 7.606; 95% CI, 3.037-19.051; P < 0.001). We constructed a prognostic nomogram with well-fitted calibration curve based on risk score and clinical data. Conclusion:The 17 mRNAsi-related key genes identified in this study could be potential treatment targets in GC treatment, considering that they can inhibit the stemness properties. The nine-gene risk model can be employed to predict the disease outcomes of the patients.

Dataset Information

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma.

Publications

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma.

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