Evidence map›Paper›PMID 41310900›Full record

ArticleEuropean journal of medical research2025

HGF knockdown suppresses ovarian cancer malignancy: insights from a transient receptor potential-related gene model.

Mengyi Hu, Wenwei Zhou, Lin Wang, Tingsu Zhang

Abstract read
In one paragraph

Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mengyi HuDepartment of Oncology, Ningbo Municipal Hospital of Traditional Chinese Medicine (TCM), Affiliated Hospital of Zhejiang Chinese Medical University, Ningbo, 315010, China.
Wenwei ZhouDepartment of Traditional Chinese Medicine Internal Medicine, Ningbo Haishu Traditional Chinese Medicine Hospital, Ningbo, 315010, China.
Lin WangDepartment of Oncology, Ningbo Municipal Hospital of Traditional Chinese Medicine (TCM), Affiliated Hospital of Zhejiang Chinese Medical University, Ningbo, 315010, China.
Tingsu ZhangDepartment of Oncology, Ningbo Municipal Hospital of Traditional Chinese Medicine (TCM), Affiliated Hospital of Zhejiang Chinese Medical University, Ningbo, 315010, China. zts20070901@163.com.ORCID http://orcid.org/0009-0006-3440-1728

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOvarian carcinoma (OV) is a prevalent gynecologic malignancy. While transient receptor potential (TRP) channels are substantially correlated with tumor growth, their role in OV remains unclear. This study therefore aims to construct a comprehensive TRP-related prognostic model and analyze its association with the cell infiltration and checkpoint expression.

methodsBased on transcriptomic data from public OV databases, TRP activity scores were calculated using single-sample gene set enrichment analysis (ssGSEA), and correlated gene modules were identified through Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis was performed to elucidate underlying biological pathways. A prognostic signature was developed via machine learning algorithms, and its clinical utility was validated through construction of a nomogram integrating key clinicopathological parameters. Comprehensive immune characterization was conducted to compare microenvironmental features between risk subgroups. Finally, functional assays including cell counting kit-8 (CCK-8), wound healing, and Transwell were employed to experimentally validate the role of a candidate gene in OV progression.

resultsOf the 21 co-expression modules identified, the brown module demonstrated the strongest positive correlation with TRP scores. Functional enrichment analysis revealed that TRP-related genes were predominantly involved in immune system pathways. Using the nine identified genes, a risk model was developed, and Riskscore was employed to categorize patients into high- and low-risk groups. Overall survival (OS) was notably higher for patients in the OV low-risk group than for those in the high-risk group. The nomogram's findings demonstrated that the Riskscore had an independent impact on prognosis. According to immunological characterization, the low-risk group had higher levels of cellular infiltration, including activated B cells and activated CD4 T cells. High-risk group had low expressions of immune checkpoint genes, including LAG3, CD274, and CD27. In vitro tests showed that HGF knockdown markedly reduced the viability, motility, and invasion of OV cells.

conclusionThe TRP-related gene signature constructed in this study predicts the prognosis and immune microenvironment status of OV patients, providing a new perspective for prognosis assessment and targeted therapy in OV.

Indexed as

Ovarian NeoplasmsBiomarkers, TumorCell Line, TumorFemaleGene Expression Regulation, NeoplasticGene Knockdown TechniquesGene Regulatory NetworksHumansNomogramsPrognosisTumor MicroenvironmentBiomarkers, TumorImmune signatureNomogramOvarian cancerPrognostic modelTransient receptor potential

Identifiers

PMID41310900
PMCPMC12752000

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.