Title:Mitochondrial Lipid Metabolism Genes as Diagnostic and Prognostic
Indicators in Hepatocellular Carcinoma
Volume: 24
Issue: 2
Author(s): Xuejing Li, Ying Tan, Bihan Liu, Houtian Guo, Yongjian Zhou, Jianhui Yuan*Feng Wang*
Affiliation:
- Department of Physiology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
- Research Center for Biomedical Photonics, Institute of Life Science, Guangxi Medical University, Nanning, China
- Research Center for Biomedical Photonics, Institute of Life Science, Guangxi Medical University, Nanning, China
- Key Laboratory of Biological Molecular Medicine Research, Guangxi Medical University, Education Department of
Guangxi Zhuang Autonomous Region, Nanning, China
- Department of Biochemistry and Molecular Biology, School
of Basic Medical Sciences, Guangxi Medical University, Nanning, China
Keywords:
Hepatocellular carcinoma, mitochondria, diagnosis, prognosis, immune, biomarker.
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.