Identification and validation of genes associated with prognosis of cisplatin-resistant ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39103807", "kind": "science-record", "revision": 4, "content_hash": "sha256:36a1bfc28179ee3d6e1843651d0d3559d9333b4180a6aff3c40eb5bb82b3ef6f", "created_at": "2026-11-01T20:29:11Z", "updated_at": "2026-11-01T22:54:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of genes associated with prognosis of cisplatin-resistant ovarian cancer.", "doi": "10.1186/s12885-024-12264-z", "pmid": "39103807", "authors": ["Dajiang Liu", "Ruiyun Li", "Yidan Wang", "Dan Li", "Leilei Li"], "journal": "BMC cancer", "year": 2024, "abstract": "PURPOSE: To investigate the role of prognostic genes related to cisplatin resistance in ovarian cancer during disease progression.\nMETHOD: The gene expression profile of the NCI-60 cell line was acquired through comprehensive analysis of the GEO database accession GSE116439. We performed a thorough analysis of gene expression differences in samples from seven individuals exposed to cisplatin concentrations of 0 nM compared to seven samples exposed to 15000 nM over a 24-h period. Key genes were initially identified through LASSO regression, followed by their enrichment through differential gene function analysis (GO) and pathway enrichment analysis (KEGG). Subsequently, a prognostic risk model was established for these key genes. The prognostic model's performance was assessed through K-M survival curves and ROC curves. To examine the variance in immune cell infiltration between the high and low-risk groups, CIBERSORTx analysis was employed. Finally, validation of prognostic gene expression in cisplatin-resistant ovarian cancer was carried out using clinical samples, employing RT-qPCR and Western Blot techniques.\nRESULTS: A total of 132 differential genes were found between cisplatin resistance and control group, and 8 key prognostic genes were selected by analysis, namely VPS13B, PLGRKT, CDKAL1, TBC1D22A, TAP1, PPP3CA, CUX1 and PPP1R15A. The efficacy of the risk assessment model derived from prognostic biomarkers, as indicated by favorable performance on both Kaplan-Meier survival curves and ROC curves. Significant variations in the abundance of Macrophages M1, T cells CD4 memory resting, T cells follicular helper, and T cells gamma delta were observed between the high and low-risk groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, confirming differential expression of the identified eight key genes between the two groups.\nCONCLUSION: VPS13B, TBC1D22A, PPP3CA, CUX1 and PPP1R15A were identified as poor prognostic genes of cisplatin resistance in ovarian cancer, while PLGRKT, CDKAL1 and TAP1 were identified as good prognostic genes. This offers a novel perspective for future advancements in ovarian cancer treatment, suggesting potential avenues for the development of new therapeutic targets.", "claims": [{"id": "claim-method", "statement": "A prognostic model was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, incorporating only genes with non-zero regression coefficients. Following this, Kaplan–Meier survival curve analysis and time-dependent ROC analysis were performed to evaluate the predictive accuracy of the model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39103807", "locator": "sections[7] \"Construct prognosis model based on TCGA data\" paragraph 2", "quote": "A prognostic model was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, incorporating only genes with non-zero regression coefficients."}, {"asset": "source:pmid:39103807", "locator": "sections[8] \"Evaluation of prognostic models\" paragraph 2", "quote": "Following this, Kaplan–Meier survival curve analysis and time-dependent ROC analysis were performed to evaluate the predictive accuracy of the model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analysed during the current study are available in the GEO repository, (https://www.ncbi.nlm.nih.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39103807", "locator": "sections[35] \"Availability of data and materials\" paragraph 2", "quote": "The datasets generated and analysed during the current study are available in the GEO repository, (https://www.ncbi.nlm.nih.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The LASSO-Cox regression algorithm established a prognostic risk model comprising eight cisplatin-resistance-related genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39103807", "locator": "sections[22] \"A prognostic model of cisplatin resistance related genes was constructed based on TCGA data\" paragraph 2", "quote": "The LASSO-Cox regression algorithm was employed to establish the prognostic model (Fig. 4A, B), resulting in the identification of a risk model comprising eight genes (Table S7)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The LASSO-Cox regression algorithm established a prognostic risk model comprising eight cisplatin-resistance-related genes.", "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:39103807"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE116439"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE116439"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39103807", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-024-12264-z", "sha256": 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RESULTS: A tota", "sk groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, con", "ied out using clinical samples, employing RT-qPCR and Western Blot techniques. RESULTS: A tota", "groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, con"], "order_basis": "paper_text_order:1152", "id": "assay:1"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["sing clinical samples, employing RT-qPCR and Western Blot techniques. RESULTS: A total of 132 differen", "o further validate our findings, RT-qPCR and Western Blot analyses were employed, confirming different"], "order_basis": "paper_text_order:1164", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["A prognostic model was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, incorporating only genes with non-zero regression coefficients. Following this, Kaplan–Meier survival curve analysis and time-dependent ROC analysis were performed to evaluate the predictive accuracy of the model."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}