Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma

Ai Zhengdong; Xing Xiaoying; Fu Shuhui; Liang Rui; Tang Zehui; Song Guanbin; Yang Li; Tang Xi; Liu Wanqian

doi:10.1186/s12935-024-03306-4

Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma

Cancer Cell Int. 2024 Apr 7;24(1):130. doi: 10.1186/s12935-024-03306-4.

Authors

Ai Zhengdong¹, Xing Xiaoying¹, Fu Shuhui¹, Liang Rui¹, Tang Zehui¹, Song Guanbin¹, Yang Li¹, Tang Xi², Liu Wanqian³

Affiliations

¹ Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China.
² Gastrointestinal Cancer Center, Chongqing University Cancer Hospital, Chongqing, 400000, People's Republic of China. tangxi@cqu.edu.cn.
³ Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China. wqliu@cqu.edu.cn.

Abstract

Background: Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive.

Methods: The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein-protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments.

Results: Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells.

Conclusions: This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.

Keywords: Acetyl-CoA carboxylase1; Cancer stemness; Fatty acids; Hepatocellular carcinoma; Immune microenvironment.

Abstract

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