Research Paper Volume 13, Issue 13 pp 17847—17863
Prognostic value of fatty acid metabolism-related genes in patients with hepatocellular carcinoma
- 1 Department of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian 351100, Fujian Province, China
Received: February 18, 2021 Accepted: June 2, 2021 Published: July 13, 2021https://doi.org/10.18632/aging.203288
How to Cite
Copyright: © 2021 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The deregulation of fatty acid metabolism plays a crucial role in cancer. However, the prognostic value of genes involved in the metabolism in hepatocellular carcinoma (HCC) remains largely unknown. We first constructed a multi-fatty acid metabolic gene prognostic model of HCC based on The Cancer Genome Atlas (TCGA) and further validated it using the International Cancer Genome Consortium (ICGC) database. The model was integrated with the clinical parameters, and a nomogram was built and weighted. Moreover, immune cell infiltration of the tumor microenvironment was investigated. A prognostic model was constructed using 6 selected fatty acid metabolism-related genes, and HCC patients were divided into high- and low-risk groups. Receiver operating characteristic curve (ROC) analysis, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) analysis showed the optimal performance of the model. The concordance index (C-index), ROC curve, calibration plot and decision curve analysis (DCA) all confirmed the satisfactory predictive capacity of the nomogram. The analysis of immune cell infiltration in HCC patients revealed a correlation with different risk levels. Our findings indicate that a prognostic model based on fatty acid metabolism-related genes has superior predictive capacities, which provides the possibility for further improving the individualized treatment of patients with HCC.
HCC: Hepatocellular carcinoma; TCGA: The Cancer Genome Atlas; ICGC: International Cancer Genome Consortium; MSigDB: Molecular Signature Database; DEGs: Differentially expressed genes; FDR: False discovery rate; OS: Overall survival; LASSO: Least absolute shrinkage and selection operator; ROC: Receiver operating characteristic curve; AUCs: Areas under the curve; K-M: Kaplan-Meier; PCA: Principal component analysis; t-SNE: t-distributed stochastic neighbor embedding; C-index: Concordance index; DCA: Decision curve analysis; TMB: Tumor mutation burden; SNV: Simple nucleotide variation; CIBERSORT: Cell type Identification By Estimating Relative Subsets Of RNA Transcripts; TILs: Tumor-infiltrating lymphocytes; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology.