Identification of biomarkers for Barcelona Clinic Liver Cancer staging and overall survival of patients with hepatocellular carcinoma

Abstract

The aim of the current study was to identify biomarkers that correlate with the Barcelona Clinic Liver Cancer (BCLC) staging system and prognosis of patients with hepatocellular carcinoma (HCC). We downloaded 4 gene expression datasets from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo), and screened for genes that were differentially expressed between HCC and normal liver tissues, using significance analysis of the microarray algorithm. We used a weighted gene co-expression network analysis (WGCNA) to identify hub genes that correlate with BCLC staging, functional enrichment analysis to associate hub genes with their functions, protein-protein interaction network analysis to identify interactions among hub genes, UALCAN analysis to assess gene expression levels based on tumour stage, and survival analyses to clarify the effects of hub genes on patients’ overall survival (OS). We identified 50 relevant hub genes using WGCNA; among them, 13 genes (including TIGD5, C8ORF33, NUDCD1, INSB8, and STIP1) correlated with OS and BCLC staging. Significantly enriched gene ontology biological process terms included RNA processing, non-coding RNA processing and phosphodiester bond hydrolysis, and 6 genes were found to interact with 10 or more hub genes. We identified several candidate biomarkers that correlate with BCLC staging and OS of HCC. These genes might be used for prognostic assessment and selection of HCC patients for surgery, especially those with intermediate or advanced disease.

CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis

“…The weighted gene coexpression network analysis (WGCNA), an algorithm based on large-scale datasets and modules of highly correlated genes, was used to explore associations between gene sets and clinical features and to identify candidate biomarkers [6,7]. This approach has been successfully applied in multiple tumors, such as clear cell renal cell carcinoma [8], glioblastoma [6], pancreatic carcinoma [9], adrenocortical carcinoma [10], breast cancer [11], and HCC [12,13]. We mined prognostic markers by constructing a coexpression network and performing differential gene analysis and survival analysis to verify their prognostic values.…”

Serum STIP1, a Novel Indicator for Microvascular Invasion, Predicts Outcomes and Treatment Response in Hepatocellular Carcinoma

“…STIP1 was preferentially expressed in cancerous tissues of various solid tumors, including HCC, and high STIP1 expression was closely associated with dismal outcomes (11,18,21,29). Moreover, functional assays confirmed STIP1 as a vital pro-oncogene during carcinogenesis process (30,31). Interestingly, recent studies demonstrated STIP1 could be secreted by tumor cells and act as a critical cytokine to regulate malignant phenotype (17).…”

Tumor Suppressive Maspin-Sensitized Prostate Cancer to Drug Treatment Through Negative Regulating Androgen Receptor Expression

“…The above two sets of microarray data were analyzed by using Affymetrix Human Genome U133A Array. All the raw array data were pre-processed and analyzed as described (Xu et al, 2018;Mazzini et al, 2019). Log2 conversion and quantile normalization were applied to data if appropriate.…”