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Research Paper|Volume 15, Issue 5|pp 1496—1523

Identification of fatty acid metabolism-related clusters and immune infiltration features in hepatocellular carcinoma

Zhixuan Ren1,2, Duan Gao3, Yue Luo1,2, Zhenghui Song1,2, Guojing Wu1,2, Na Qi1,3, Aimin Li1,2, Xinhui Liu1,2
  • 1Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou 510315, China
  • 2Cancer Center, Southern Medical University, Guangzhou 510315, China
  • 3Department of Pharmacy, Guilin Medical University, Guilin 541004, China
* Equal contribution
Received: September 12, 2022Accepted: February 15, 2023Published: March 6, 2023
https://doi.org/10.18632/aging.204557

Copyright: © 2023 Ren 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.

Abstract

Hepatocellular Carcinoma (HCC) is a type of liver cancer which is characterized by inflammation-associated tumor. The unique characteristics of tumor immune microenvironment in HCC contribute to hepatocarcinogenesis. It was also clarified that aberrant fatty acid metabolism (FAM) might accelerate tumor growth and metastasis of HCC. In this study, we aimed to identify fatty acid metabolism-related clusters and establish a novel prognostic risk model in HCC. Gene expression and corresponding clinical data were searched from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) portal. From the TCGA database, by unsupervised clustering method, we determined three FAM clusters and two gene clusters with distinct clinicopathological and immune characteristics. Based on 79 prognostic genes identified from 190 differentially expressed genes (DEGs) among three FAM clusters, five prognostic DEGs (CCDC112, TRNP1, CFL1, CYB5D2, and SLC22A1) were determined to construct risk model by least absolute shrinkage and selection operator (LASSO) and multivariate cox regression analysis. Furthermore, the ICGC dataset was used to validate the model. In conclusion, the prognostic risk model constructed in this study exhibited excellent indicator performance of overall survival, clinical feature, and immune cell infiltration, which has the potential to be an effective biomarker for HCC immunotherapy.