Evidence map›Paper›PMID 39686988›Full record

ArticlePeerJ2024

Identification of immunogenic cell death gene-related subtypes and risk model predicts prognosis and response to immunotherapy in ovarian cancer.

Wenjing Pan, Zhaoyang Jia, Xibo Zhao, Kexin Chang, Wei Liu, Wenhua Tan

Abstract read
In one paragraph

Article in PeerJ, 2024. 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

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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

6 authors.

Wenjing PanDepartment of Gynecology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Zhaoyang JiaDepartment of Gynecology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Xibo ZhaoDepartment of Gynecological Oncology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Sun Yat-Sen University of Medical Sciences, Guangzhou, China.
Kexin ChangDepartment of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Wei LiuDepartment of Gynecology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Wenhua TanDepartment of Gynecology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Immunogenic cell death (ICD) has been associated with enhanced anti-tumor immunotherapy by stimulating adaptive immune responses and remodeling the immune microenvironment in tumors. Nevertheless, the role of ICD-related genes in ovarian cancer (OC) and tumor microenvironment remains unexplored. Methods: In this study, high-throughput transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases as training and validation sets separately were obtained and proceeded to explore ICD-related clusters, and an ICD-related risk signature was conducted based on the least absolute shrinkage and selection operator (LASSO) Cox regression model by iteration. Multiple tools including CIBERSORT, ESTIMATE, GSEA, TIDE, and immunohistochemistry were further applied to illustrate the biological roles of ICD-related genes as well as the prognostic capacity of ICD risk signature in OC. Results: Two ICD-related subtypes were identified, with the ICD-high subtype showing more intense immune cell infiltration and higher activities of immune response signaling, along with a favorable prognosis. Additionally, four candidate ICD genes (IFNG, NLRP3, FOXP3, and IL1B) were determined to potentially impact OC prognosis, with an upregulated expression of NLRP3 in OC and metastatic omental tissues. A prognostic model based on these genes was established, which could predict overall survival (OS) and response to immunotherapy for OC patients, with lower-risk patients benefiting more from immunotherapy. Conclusion: Our research conducted a prognostic and prediction of immunotherapy response model based on ICD genes, which could be instrumental in assessing prognosis and assigning immunotherapeutic strategies for OC patients. NLRP3 is a promising target for prognosis in OC.

Indexed as

Immunogenic Cell DeathImmunotherapyOvarian NeoplasmsTumor MicroenvironmentBiomarkers, TumorFemaleForkhead Transcription FactorsGene Expression Regulation, NeoplasticHumansInterleukin-1betaMiddle AgedNLR Family, Pyrin Domain-Containing 3 ProteinPrognosisTranscriptomeBiomarkers, TumorForkhead Transcription FactorsFOXP3 protein, humanIL1B protein, humanInterleukin-1betaNLR Family, Pyrin Domain-Containing 3 ProteinNLRP3 protein, humanImmunogenic cell deathImmunotherapyOvarian cancerRisk signatureTumor microenvironment

Identifiers

PMID39686988
PMCPMC11648682

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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.