Evidence map›Paper›PMID 40533802›Full record

ArticleJournal of translational medicine2025

The impact of de novo lipogenesis on predicting survival and clinical therapy: an exploration based on a multigene prognostic model in hepatocellular carcinoma.

Xin Zhou, Guangzu Cui, Erya Hu, Xinwen Wang, Diya Tang, Xiangyang Zhang, Jiayao Ma, Yin Li, Haicong Liu, Qingping Peng and 5 more

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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  5. The Role of CD4Cells · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Xin ZhouDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Guangzu CuiDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Erya HuDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Xinwen WangDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Diya TangDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Xiangyang ZhangDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Jiayao MaDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Yin LiDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Haicong LiuDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Qingping PengDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Ying HanDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Yihong ChenDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Shan ZengDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Yan ZhangDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China. yanzhang2021@hunnu.edu.cn.
Hong ShenDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China. hongshen2000@csu.edu.cn.ORCID 0000-0002-6456-8231

Funding

National Natural Science Foundation of China 82173342National Natural Science Foundation of China 82203015National Natural Science Foundation of China 82373275Natural Science Foundation of Changsha 73201Natural Science Foundation of Hunan Province 2022JJ40458Natural Science Foundation of Hunan Province 2023JJ40942Scientific Research Program of Hunan Provincial Health Commission 202203105261
6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) ranks among the most aggressive malignancies worldwide, with poor outcomes attributed to delayed diagnosis and therapeutic limitations. Emerging evidence suggests that de novo lipogenesis (DNL) plays a crucial role in HCC progression and its interaction with the immune microenvironment.

methodsWe systematically analyzed DNL-related gene expression profiles from TCGA, GEO, ICGC-LIRI datasets, and our Xiangya HCC cohort (n = 106) to construct a prognostic risk model. Through LASSO-Cox regression analysis, we identified six signature genes (G6PD, LCAT, SERPINE1, SOAT2, CYP2C9, and UGT1A10) that effectively stratified patients into distinct risk groups. We evaluated clinical characteristics, immune cell infiltration patterns, and differential therapeutic responses between high-risk and low-risk groups. Comprehensive validation included immunohistochemical analysis and Western blotting to assess expression levels of key model genes, along with multiplex immunofluorescence staining and single-cell RNA sequencing(scRNA-seq) to characterize immune microenvironmental differences between risk groups.

resultsWe successfully established a robust six-gene prognostic signature (G6PD, LCAT, SERPINE1, SOAT2, CYP2C9, and UGT1A10) based on de novo lipogenesis pathways, which demonstrated excellent predictive performance (AUC: 0.78-0.82). The model revealed significant differences in immune infiltration patterns between risk groups, with the high-risk group exhibiting immunosuppressive characteristics characterized by increased Treg cell infiltration, while the low-risk group showed greater NK cell retention. Integrated scRNA-seq and our cohort validation further demonstrated that high-risk scores were associated with poorer response to immunotherapy but greater sensitivity to targeted therapies. These findings suggest that de novo lipogenesis-mediated immune evasion contributes to therapy resistance and worse prognosis in high-risk HCC patients, whereas low-risk HCC patients maintain an immunologically active microenvironment more amenable to immunotherapy.

conclusionsThis study provided a novel prognostic model for HCC, incorporating 6 representative DNLs. The model demonstrated the potential for predicting HCC prognosis and highlighted the involvement of immune cell infiltration and the association between risk scores and clinical therapy. Validation of model genes further supported the association between de novo lipogenesis and HCC development.

Indexed as

Carcinoma, HepatocellularLipogenesisLiver NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisReproducibility of ResultsRisk FactorsSurvival AnalysisTumor MicroenvironmentClinical therapyDe novo lipogenesisMultigene prognostic modelRNA-seqSingle-cell RNA-seq

Identifiers

PMID40533802
PMCPMC12178006

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.