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  • Title: Identification of Biomarkers Associated with Hepatocellular Carcinoma Stem Cell Characteristics Based on Co-Expression Network Analysis of Transcriptome Data and Stemness Index.
    Author: Zhao Z, Mu H, Feng S, Liu Y, Zou J, Zhu Y.
    Journal: Crit Rev Eukaryot Gene Expr; 2022; 32(2):47-60. PubMed ID: 35381131.
    Abstract:
    Mounting evidence has revealed the key role of cancer stem cells in hepatocellular carcinoma (HCC) metastasis and therapy resistance, yet the genes maintaining HCC stem cell features remain to be explored. This study aimed to identify and validate the key biomarkers associated with HCC stemness. mRNA expression-based stemness index (mRNAsi) was calculating using one-class logistic regression algorithm. RNA-sequencing data and clinical information of HCC samples were downloaded from the cancer genome atlas (TCGA) and merged with the corresponding mRNAsi. We investigated the correlation between mRNAsi and HCC clinical characteristics, including tumor grades, pathologic stages, vascular invasion, and survival outcomes. Significant genes associated HCC stemness features were screened through weighted gene co-expression network analysis (WGCNA) and were functionally annotated using enrichment analysis. Protein-protein interaction network was constructed among significant genes and the key biomarkers were finally identified based on the maximal clique centrality (MCC) method. The expression of key biomarkers and its correlation with HCC clinical outcomes were validated using oncomine and gene expression omnibus (GEO) database. mR-NAsi was significantly higher in HCC tissues and gradually increased according to tumor grades and pathologic stages. Patients with vascular invasion or poor survival exhibited higher mRNAsi. Forty-four highly-correlated significant gens were screened through WGCNA and functionally related to cell cycle, cellular senescence, p53 signaling pathway, DNA replication, and mismatch repair. Four different GEO datasets confirmed that the expression levels of these 44 genes were notably higher in HCC tissues. We finally identified 15 key biomarkers (KIF4A, TTK, CCNB1, CDC20, NCAPG, CCNB2, CDC45, UBE2C, CENPA, AURKB, RRM2, CDCA8, BIRC5, TPX2, and KIF2C) through MCC method. The expression of these biomarkers was up-regulated in multiple types of cancers and showed a gradually increasing trend with HCC tumor grades. Furthermore, high expression levels of these biomarkers were also correlated with HCC metastasis, recurrence, sorafenib resistance, and poor overall survival. We identified 15 key biomarkers associated with HCC stemness features and these genes might serve as promising therapeutic targets for HCC.
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