Model constructions of chemosensitivity and prognosis of high grade serous ovarian cancer based on evaluation of immune microenvironment and immune response.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34736480", "kind": "science-record", "revision": 4, "content_hash": "sha256:7a62414969bfa71c085c32908bddc92aa01905a08e183025be2ae9aea98793c5", "created_at": "2026-11-01T17:36:25Z", "updated_at": "2026-11-01T22:05:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Model constructions of chemosensitivity and prognosis of high grade serous ovarian cancer based on evaluation of immune microenvironment and immune response.", "doi": "10.1186/s12935-021-02295-y", "pmid": "34736480", "authors": ["Han Zhang", "Yijun Wu", "Hao Li", "Liping Sun", "Xiangkai Meng"], "journal": "Cancer cell international", "year": 2021, "abstract": "BACKGROUND: The prognosis of high grade serous ovarian cancer (HGSOC) patients is closely related to the immune microenvironment and immune response. Based on this, the purpose of this study was to construct a model to predict chemosensitivity and prognosis, and provide novel biomarkers for immunotherapy and prognosis evaluation of HGSOC.\nMETHODS: GSE40595 (38 samples), GSE18520 (63 samples), GSE26712 (195 samples), TCGA (321 samples) and GTEx (88 samples) were integrated to screen differential expressed genes (DEGs) of HGSOC. The prognosis related DEGs (DEPGs) were screened through overall survival analysis. The DEGs-encoded protein-protein interaction network was constructed and hub genes of DEPGs (DEPHGs) were generated by STRING. Immune characteristics of the samples were judged by ssGSEA, ESTIMATE and CYBERSORT. TIMER was used to analyze the relationship between DEPHGs and tumor-infiltrating immunocytes, as well as the immune checkpoint genes, finally immune-related DEPHGs (IDEPHGs) were determined, and whose expression in 12 pairs of HGSOC tissues and tumor-adjacent tissues were analyzed by histological verification. Furthermore, the chemosensitivity genes in IDEPHGs were screened according to GSE15622 (n = 65). Finally, two prediction models of paclitaxel sensitivity score (PTX score) and carboplatin sensitivity score (CBP score) were constructed by lasso algorithm. The area under curve was calculated to estimate the accuracy of candidate gene models in evaluating chemotherapy sensitivity.\nRESULTS: 491 DEGs were screened and 37 DEGs were identified as DEPGs, and 11 DEPHGs were further identified. Among them, CXCL13, IDO1, PI3, SPP1 and TRIM22 were screened as IDEPHGs and verified in the human tissues. Further analysis showed that IDO1, PI3 and TRIM22 could independently affect the chemotherapy sensitivity of HGSOC patients. The PTX score was significantly better than TRIM22, PI3, SPP1, IDO1 and CXCL13 in predicting paclitaxel sensitivity, so was CBP score in predicting carboplatin sensitivity. What's more, both of the HGSOC patients with high PTX score or high CBP score had longer survival time.\nCONCLUSIONS: Five IDEPHGs identified through comprehensive bioinformatics analysis were closely related with the prognosis, immune microenvironment and chemotherapy sensitivity of HGSOC. Two prediction models based on IDEPHGs might have potential application of chemotherapy sensitivity and prognosis for patients with HGSOC.", "claims": [{"id": "claim-method", "statement": "We chose GSE15622 dataset to screen genes incorporated in models from CXCL13, IDO1, PI3, SPP1 and TRIM22 using lasso algorithm by “glmnet” package, and calculated the corresponding coefficients", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34736480", "locator": "sections[13] \"Construction of PTX score model and CBP score model\" paragraph 2", "quote": "We chose GSE15622 dataset to screen genes incorporated in models from CXCL13, IDO1, PI3, SPP1 and TRIM22 using lasso algorithm by “glmnet” package, and calculated the corresponding coefficients"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/), UCSC (http://xenabrowser.net/hub/) repository.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34736480", "locator": "sections[35] \"Availability of data and material\" paragraph 2", "quote": "The datasets generated and analyzed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/), UCSC (http://xenabrowser.net/hub/) repository."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The PTX score was significantly better than TRIM22, PI3, SPP1, IDO1 and CXCL13 in predicting paclitaxel sensitivity, so was CBP score in predicting carboplatin sensitivity.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34736480", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The PTX score was significantly better than TRIM22, PI3, SPP1, IDO1 and CXCL13 in predicting paclitaxel sensitivity, so was CBP score in predicting carboplatin sensitivity."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The PTX score was significantly better than TRIM22, PI3, SPP1, IDO1 and CXCL13 in predicting paclitaxel sensitivity, so was CBP score in predicting carboplatin sensitivity.", "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:34736480"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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