Prognostic and immune predictive roles of a novel tricarboxylic acid cycle-based model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer. Since the tricarboxylic acid cycle is widely involved in tumor metabolic reprogramming and cuproptosis, investigating related genes may help to identify prognostic signature of patients with HCC. Data on patients with HCC wer...

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Published in:Scientific reports Vol. 14; no. 1; pp. 2333 - 15
Main Authors: Zeng, Yifan, Yu, Tao, Jiang, Shuwen, Wang, Jinzhi, Chen, Lin, Lou, Zhuoqi, Pan, Liya, Zhang, Yongtao, Ruan, Bing
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 28.01.2024
Nature Publishing Group
Nature Portfolio
Subjects:
631/114
692/4028
692/53
Algorithms
Cancer immunotherapy
Carcinoma, Hepatocellular - genetics
CD44 antigen
Cell cycle
Citric Acid Cycle - genetics
Hepatocellular carcinoma
Humanities and Social Sciences
Humans
Immune checkpoint
Immunotherapy
Inhibitor drugs
Liver cancer
Liver Neoplasms - genetics
Lymphocytes T
Machine learning
Macrophages
Metastases
multidisciplinary
Patients
Prognosis
Risk groups
Science
Science (multidisciplinary)
Tricarboxylic acid cycle
Tumor Microenvironment
Tumors
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer. Since the tricarboxylic acid cycle is widely involved in tumor metabolic reprogramming and cuproptosis, investigating related genes may help to identify prognostic signature of patients with HCC. Data on patients with HCC were sourced from public datasets, and were divided into train, test, and single-cell cohorts. A variety of machine learning algorithms were used to identify different molecular subtypes and determine the prognostic risk model. Our findings revealed that the risk score (TRscore), based on the genes OGDHL, CFHR4, and SPP1, showed excellent predictive performance in different datasets. Pathways related to cell cycle and immune inflammation were enriched in the high-risk group, whereas metabolism-related pathways were significantly enriched in the low-risk group. The high-risk group was associated with a greater number of mutations of detrimental biological behavior and higher levels of immune infiltration, immune checkpoint expression, and anti-cancer immunotherapy response. Low-risk patients demonstrated greater sensitivity to erlotinib and phenformin. SPP1 was mainly involved in the interaction among tumor-associated macrophages, T cells, and malignant cells via SPP1–CD44 and SPP1–(ITGA5 + ITGB1) ligand-receptor pairs. In summary, our study established a prognostic model, which may contribute to individualized treatment and clinical management of patients with HCC.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-52632-0