Evidence map›Paper›PMID 41035672›Full record

ArticleFrontiers in oncology2025

Identification of amino acid metabolism-related gene Leucyl-tRNA synthetase 1 (LARS1) as a potential prognostic and therapeutic target in hepatocellular carcinoma.

Qiyu Shi, Yirong Chi, Ziyi Peng, Chao Li, Jingwen Zhao, Jie Zhang

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qiyu Shi *Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin, China.
Yirong Chi *Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China.
Ziyi Peng *Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China.
Chao LiDepartment of General Surgery, Cangzhou People's Hospital, Hebei, China.
Jingwen ZhaoDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China.
Jie ZhangDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Amino acid metabolism plays a critical role in tumorigenesis in hepatocellular carcinoma (HCC). Thus, we explore the amino acid metabolic profile in HCC to construct effective prognosis model and identify novel potential therapeutic target for HCC. Methods: The transcriptomic data and clinical information of HCC patients were directly obtained from The Cancer Genome Atlas (TCGA). Then we classified them into two subtypes based on selected amino acid metabolism-related genes (SARGs) and explored the differences between them. Besides, risk models were constructed based on SARGs through LASSO regression, and we further validated and evaluated the predictive effect of the model. Subsequently, we validated the key gene of LARS1 in the model. We analyzed the discrepancy of LARS1 in tumor and adjacent non-tumor tissues in both TCGA and the Gene Expression Omnibus (GEO) database and the results were verified in HCC patients undergoing hepatectomy from our hospital via PCR and Immunohistochemistry (IHC). Finally, we explored the biological function of LARS1 Results: We classified HCC patients into Cluster A and B subtypes based on 81 SARGs. And patients in Cluster B exhibited significantly poorer prognosis, higher tumor malignancy levels, higher TIDE scores and T cell exhaustion or dysfunction. Then 15 genes were included to construct the risk model. The risk score was positively associated with poor prognosis. We further extracted LARS1 as the key gene of the model and found that high LARS1 tended to have poorer prognosis with higher expression in tumor tissues than in adjacent non-tumor ones in both TCGA and GEO. PCR and IHC were conducted for verification. Suppression of LARS1 markedly inhibited the growth of HCC cells. Additionally, LARS1 knockdown significantly impeded cellular migration and invasion Conclusion: We have successfully developed a prognostic model based on 15 genes associated with amino acid metabolism. We also verified that knockdown of LARS1 significantly inhibited the proliferation, invasion and migration of HCC

Indexed as

amino acid metabolismautophagyHCCLARS1prognosis model

Identifiers

PMID41035672
PMCPMC12479282

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