Open AccessA novel prognostic signature based on four glycolysis‐related genes predicts survival and clinical risk of hepatocellular carcinoma
Author(s) -
Chen Zhihong,
Zou Yiping,
Zhang Yuanpeng,
Chen Zhenrong,
Wu Fan,
Shi Ning,
Jin Haosheng
Publication year - 2021
Publication title -
journal of clinical laboratory analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 50
eISSN - 1098-2825
pISSN - 0887-8013
DOI - 10.1002/jcla.24005
Subject(s) - hepatocellular carcinoma , glycolysis , gene signature , signature (topology) , medicine , oncology , gene , cancer research , computational biology , biology , genetics , gene expression , metabolism , mathematics , geometry
Background Hepatocellular carcinoma (HCC) is the most common cancer with limited cure and poor survival. In our study, a bioinformatic analysis was conducted to investigate the role of glycolysis in the pathogenesis and progression of HCC. Methods Single‐sample gene set enrichment analysis (ssGESA) was used to calculate enrichment scores for each sample in TCGA‐LIHC and GEO14520 according to the glycolysis gene set. Weighted gene co‐expression network analysis identified a gene module closely related to glycolysis, and their function was investigated. Prognostic biomarkers were screened from these genes. Cox proportional hazard model and least absolute shrinkage and selection operator regression were used to construct the prognostic signature. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curve analyses evaluated the prediction performance of the prognostic signature in TCGA‐LIHC and ICGC‐LIRI‐JP. Combination analysis data of clinical features and prognostic signature constructed a nomogram. Area under ROC curves and decision curve analysis were used to compare the nomogram and its components. Results The glycolysis pathway was upregulated in HCC and was unfavorable for survival. The determined gene module was mainly enriched in cell proliferation. A prognostic signature (CDCA8, RAB5IF, SAP30, and UCK2) was developed and validated. KM and ROC curves showed a considerable predictive effect. The risk score derived from the signature was an independent prognostic factor. The nomogram increased prediction efficiency by combining risk signature and TNM stage and performed better than component factors in net benefit. Conclusion The gene signature may contribute to individual risk estimation, survival prognosis, and clinical management.