Mitochondrial Lipid Metabolism Genes as Diagnostic and Prognostic Indicators in Hepatocellular Carcinoma

Xuejing Li; Ying Tan; Bihan Liu; Houtian Guo; Yongjian Zhou; Jianhui Yuan; Feng Wang

doi:10.2174/1389202924666230914110649

Mitochondrial Lipid Metabolism Genes as Diagnostic and Prognostic Indicators in Hepatocellular Carcinoma

Curr Genomics. 2023 Oct 27;24(2):110-127. doi: 10.2174/1389202924666230914110649.

Authors

Xuejing Li^{1

2}, Ying Tan^{1

2}, Bihan Liu², Houtian Guo³, Yongjian Zhou³, Jianhui Yuan^{1

2}, Feng Wang^{2

4

3}

Affiliations

¹ Department of Physiology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China.
² Research Center for Biomedical Photonics, Institute of Life Science, Guangxi Medical University, Nanning, China.
³ Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China.
⁴ Key Laboratory of Biological Molecular Medicine Research, Guangxi Medical University, Education Department of Guangxi Zhuang Autonomous Region, Nanning, China.

Abstract

Background: Due to the heterogeneity of Hepatocellular carcinoma (HCC), there is an urgent need for reliable diagnosis and prognosis. Mitochondria-mediated abnormal lipid metabolism affects the occurrence and progression of HCC.

Objective: This study aims to investigate the potential of mitochondrial lipid metabolism (MTLM) genes as diagnostic and independent prognostic biomarkers for HCC.

Methods: MTLM genes were screened from the Gene Expression Omnibus (GEO) and Gene Set Enrichment Analysis (GSEA) databases, followed by an evaluation of their diagnostic values in both The Cancer Genome Atlas Program (TCGA) and the Affiliated Cancer Hospital of Guangxi Medical University (GXMU) cohort. The TCGA dataset was utilized to construct a gene signature and investigate the prognostic significance, immune infiltration, and copy number alterations. The validity of the prognostic signature was confirmed through GEO, International Cancer Genome Consortium (ICGC), and GXMU cohorts.

Results: The diagnostic receiver operating characteristic (ROC) curve revealed that eight MTLM genes have excellent diagnostic of HCC. A prognostic signature comprising 5 MTLM genes with robust predictive value was constructed using the lasso regression algorithm based on TCGA data. The results of the Stepwise regression model showed that the combination of signature and routine clinical parameters had a higher area under the curve (AUC) compared to a single risk score. Further, a nomogram was constructed to predict the survival probability of HCC, and the calibration curves demonstrated a perfect predictive ability. Finally, the risk score also unveiled the different immune and mutation statuses between the two different risk groups.

Conclusion: MTLT-related genes may serve as diagnostic and prognostic biomarkers for HCC as well as novel therapeutic targets, which may be beneficial for facilitating further understanding the molecular pathogenesis and providing potential therapeutic strategies for HCC.

Keywords: Hepatocellular carcinoma; biomarker; diagnosis; immune; mitochondria; prognosis.