Identification of immune related molecular subtypes and prognosis model for predicting prognosis, drug resistance in cervical squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37007965", "kind": "science-record", "revision": 3, "content_hash": "sha256:1ad55fb973129bf52ffdd7d649006ad1528d2dbd882cb6186ff85a01d9e3134c", "created_at": "2026-11-01T19:01:40Z", "updated_at": "2026-11-01T22:09:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune related molecular subtypes and prognosis model for predicting prognosis, drug resistance in cervical squamous cell carcinoma.", "doi": "10.3389/fgene.2023.1137995", "pmid": "37007965", "authors": ["Dongzhi Hu", "Zijian Zhang", "Yongjing Zhang", "Kangni Huang", "Xiaoxue Li"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Background: One of the features of tumor immunity is the immunosuppressive tumor microenvironment (TME). In this study, TME gene signatures were used to define the characteristics of Cervical squamous cell carcinoma (CESC) immune subtypes and construct a new prognostic model. Methods: Single sample gene set enrichment analysis (ssGSEA) was used to quantify pathway activity. RNA-seq of 291 CESC were obtained from the Cancer Genome Atlas (TCGA) database as a training set. Microarray-based data of 400 cases of CESC were obtained from the Gene Expression Compilation (GEO) database as an independent validation set. 29 TME related gene signatures were consulted from previous study. Consensus Cluster Plus was employed to identify molecular subtype. Univariate cox regression analysis and random survival forest (RSF) were used to establish the immune-related gene risk model based on the TCGA data set of CESC, and the accuracy of prognosis prediction was verified by GEO data set. ESTIMATE algorithm was used to perform immune and matrix scores on the data set. Results: three molecular subtypes (C1, C2, C3) were screened in TCGA-CESC on account of 29 TME gene signatures. Among, C3 with better survival outcome had higher immune related gene signatures, while C1 with worse prognosis time had enhanced matrix related features. Increased immune infiltration, inhibition of tumor related pathways, widespread genomic mutations and prone immunotherapy were observed in C3. Furthermore, a five immune genes signature was constructed and predicted overall survival for CESC, which successfully validated in GSE44001 dataset. A positive phenomenon was observed between five hub genes expressions and methylation. Similarly, high group enriched in matrix related features, while immune related gene signatures were enriched in low group. Immune cell, immune checkpoints genes expression levels were negatively, while most TME gene signatures were positively correlated with Risk Score. In addition, high group was more sensitive to drug resistance. Conclusion: This work identified three distinct immune subtypes and a five genes signature for predicting prognosis in CESC patients, which provided a promising treatment strategy for CESC.", "claims": [{"id": "claim-method", "statement": "Univariate cox regression analysis and random survival forest (RSF) were used to establish the immune-related gene risk model based on the TCGA data set of CESC, and the accuracy of prognosis prediction was verified by GEO data set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37007965", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate cox regression analysis and random survival forest (RSF) were used to establish the immune-related gene risk model based on the TCGA data set of CESC, and the accuracy of prognosis prediction was verified by GEO data set."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37007965", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified three distinct immune subtypes and a five-gene signature for predicting prognosis in cervical squamous cell carcinoma patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37007965", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This work identified three distinct immune subtypes and a five genes signature for predicting prognosis in CESC patients, which provided a promising treatment strategy for CESC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified three distinct immune subtypes and a five-gene signature for predicting prognosis in cervical squamous cell carcinoma patients.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37007965"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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