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, *, ,

  • 1 Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou 510315, China
  • 2 Cancer Center, Southern Medical University, Guangzhou 510315, China
  • 3 Department of Pharmacy, Guilin Medical University, Guilin 541004, China
* Equal contribution

Received: September 12, 2022       Accepted: February 15, 2023       Published: March 6, 2023
How to Cite

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.


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.


HCC: Hepatocellular Carcinoma; FAM: fatty acid metabolism; TCGA: The cancer genome atlas database; ICGC: International Cancer Genome Consortium; DEGs: differentially expressed genes; LASSO: Least absolute shrinkage and selection operator; TKI: Tyrosine kinase inhibitors; TME: tumor microenvironment; FAMs: fatty acid metabolism-related genes; LIHC: liver hepatocellular carcinoma; FPKM: fragments per kilobase million; TPM: transcripts per kilobase million; MSigDB: molecular signatures database; GSVA: gene set variation analysis; ssGSEA: single-sample gene set enrichment analysis; K-M curve: Kaplan-Meier curve; ROC curve: receiver operating characteristic curve; CNVs: copy number alterations; ssGSEA: single-sample gene-set enrichment analysis; PCA: principal component analysis; GO: Gene ontology; KEGG: The Kyoto Encyclopedia of Genes and Genomes; OS: overall survival; EMT: epithelial-mesenchymal transition; APC: antigen presenting cell; MHC: major histocompatibility complex; HLA: human leukocyte antigen; AUC: Area under curve; ICGC-JP: ICGC-Japan; ICIs: immune checkpoint inhibitors; FDA: Food and Drug Administration.