Evidence map›Paper›PMID 41088247›Full record

ArticleCancer cell international2025

Construction of a lipid metabolism-based prognostic gene signature in cervical squamous cell carcinoma and validation of LIPG's oncogenic role.

Gaigai Bai, Fanghua Chen, Junjun Qiu, Keqin Hua

Abstract read
In one paragraph

Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Gaigai Bai *Obstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai, 200433, China.
Fanghua Chen *Obstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai, 200433, China.
Junjun QiuObstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai, 200433, China. qiu_junjun@fudan.edu.cn.
Keqin HuaObstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai, 200433, China. huakeqin@fudan.edu.cn.

Funding

Medical Innovation Research of Shanghai Science and Technology 21Y11906900National Natural Science Foundation of China 82173188National Natural Science Foundation of China 82472993
6 · The paper itself

Abstract

backgroundCervical cancer, in which cervical squamous cell carcinoma (CSCC) accounts for 60-70% of cases, has a poor prognosis and poses a significant health threat to global patients. Lipid metabolism reprogramming is a key driver of tumor progression and tumor microenvironment (TME) regulation, making it a promising target for improving the efficacy of immunotherapy. This study aimed to construct a lipid metabolism prognostic signature (LMPS) in CSCC and identify key genes involved in tumor progression.

methodsThrough RNA-sequencing and clinical data from TCGA and GTEx databases, we identified differentially expressed lipid metabolism-related genes (DLMGs) and constructed the LMPS using machine learning algorithms. Next, the value of the LMPS was validated using the HTMCP database and the GEO database. Furthermore, the relationship between the LMPS and the TME was analyzed, including immune cell infiltration, immune checkpoint gene expression, and drug sensitivity. The key gene lipase G (LIPG) was identified through machine learning methods and validated through cellular and molecular biology experiments.

resultsA total of 60 DLMGs were identified, with 9 DLMGs showing prognostic value. The LMPS was constructed using 6 genes (ACOT4, PLA2G2D, GAL3ST1, ALOX12B, PLA2G3, and LIPG), which effectively predicted patients' survival (AUC: 0.76, 0.75, 0.68 at 1, 3, 5 years, respectively). High LMPS was correlated with an immune-suppressive TME, reduced immune cell infiltration, lower human leukocyte antigen (HLA) and immune checkpoint gene expression, and higher IC50 values for common chemotherapy drugs. LIPG was identified as a key gene, showing higher expression in advanced cancer stages. As revealed by functional experiments, LIPG promoted lipid accumulation and phosphatidylcholine (PC) hydrolysis in CSCC cells. Additionally, LIPG facilitated tumor progression through activation of the MAPK-p38 signaling pathway.

conclusionsThe LMPS was a valuable prognostic tool and was correlated with the TME and drug sensitivity. LIPG was a key regulator of lipid metabolism and facilitated CSCC development by hydrolyzing PC into lysophosphatidylcholine (LPC) and activating the MAPK-p38 signaling pathway. These findings may highlight the potential of targeting lipid metabolism for therapeutic intervention in CSCC.

Indexed as

Cervical squamous cell carcinomaLIPGLipid metabolismMachine learningPrognostic signatureTumor microenvironment

Identifiers

PMID41088247
PMCPMC12522510

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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.