Abstract
The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.
Highlights
Hepatocellular carcinoma (HCC) is a fatal tumor with a poor prognosis due to the broad range of its underlying systemic symptoms
A total of 276 Differentially expressed genes (DEGs) were recognized in hepatocellular carcinoma (HCC) tissues compared with non-cancerous tissues
Gene co-expression modules for the expression of DEGs were established in the International Cancer Genome Consortium (ICGC) cohort using Weighted gene co-expression network analysis (WGCNA)
Summary
Hepatocellular carcinoma (HCC) is a fatal tumor with a poor prognosis due to the broad range of its underlying systemic symptoms. Elevated expression of TXNDC12 has been correlated with elevated expression of nuclear β-catenin and with OS and disease-free survival (Yuan et al, 2019) These studies indicated that next-generation sequencing could be performed to distinguish the biomarkers of HCC. We screened differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) datasets. The top 10 genes were screened out using univariate Cox regression analysis These genes were verified in the Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) databases. The 10 hub genes identified by bioinformatics were upregulated in HCC and able to predict prognosis, providing highly reliable analytic results. Kaplan–Meier survival curve and the time-dependent receiver operating characteristic (ROC) curve were constructed to assess the predictive potential of these genes using the “survival” and “survivalROC” functions
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