Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39697754", "kind": "science-record", "revision": 3, "content_hash": "sha256:639e7ec22d715dacf85dc5f9f62beaef5c1eff91737ac680c6f7f445018aa417", "created_at": "2026-11-01T20:30:01Z", "updated_at": "2026-11-01T22:13:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer.", "doi": "10.21037/tcr-24-539", "pmid": "39697754", "authors": ["Wenzhi Jiao", "Shasha Yang", "Yue Li", "Yu Li", "Shanshan Liu", "Jianwei Shi", "Minmin Yu"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Ovarian cancer (OC) is a globally prevalent malignancy with significant morbidity and mortality, yet its heterogeneity poses challenges in treatment and prognosis. Recognizing the crucial role of the tumor microenvironment (TME) in OC progression, this study leverages integrative multi-omics and machine learning to uncover TME-associated prognostic biomarkers, paving the way for more personalized therapeutic interventions.\nMethods: Employing a rigorous multi-omics approach, this study analyzed single-cell RNA sequencing (scRNA-seq) data from OC and normal tissue samples, including high-grade serous OC (HGSOC) from the Gene Expression Omnibus (GEO: GSE184880) and The Cancer Genome Atlas (TCGA) OC cohort, utilizing the Seurat package to annotate 700 TME-related genes. A prognostic model was developed using the least absolute shrinkage and selection operator (LASSO) regression and independently validated against similarly composed HGSOC datasets. Comprehensive gene expression and immune cell infiltration analyses were conducted, employing advanced algorithms like xCell to delineate the immune landscape of HGSOC.\nResults: Our investigation unveiled distinctive immune cell infiltration patterns and gene expression profiles within the TME of HGSOC. Notably, the prevalence of exhausted CD8+ T cells in high-risk patient samples emerged as a critical finding, underscoring the dualistic nature of the immune response in OC. The developed prognostic model, incorporating immune cell markers, exhibited robust predictive accuracy for patient outcomes, showing significant correlations with immunotherapy responses and drug sensitivities.\nConclusions: This study presents a groundbreaking exploration of the OC TME, offering vital insights into its molecular intricacies. By systematically deciphering the TME-associated gene signatures, the research illuminates the potential of these biomarkers in refining patient prognosis and guiding treatment strategies. Our findings underscore the necessity for personalized medicine in OC treatment, potentially enhancing patient survival rates and quality of life. This study marks a significant stride in understanding and combatting the complexities of OC.", "claims": [{"id": "claim-method", "statement": "To identify key genes influencing OC patient prognosis, we utilized the LASSO algorithm based on 700 TME-related genes in the training set. Subsequently, using stepwise multivariate COX regression analysis, we constructed a prognostic model for OC patients based on the key genes identified by LASSO.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39697754", "locator": "sections[10] \"Construction of TME-related genes prognostic signature\" paragraph 2", "quote": "To identify key genes influencing OC patient prognosis, we utilized the LASSO algorithm based on 700 TME-related genes in the training set. Subsequently, using stepwise multivariate COX regression analysis, we constructed a prognostic model for OC patients based on the key genes identified by LASSO."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All processed and analyzed data, along with corresponding R code, will be made publicly available in a permanent repository, adhering to scientific standards of transparency and reproducibility.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39697754", "locator": "sections[15] \"Data availability\" paragraph 2", "quote": "All processed and analyzed data, along with corresponding R code, will be made publicly available in a permanent repository, adhering to scientific standards of transparency and reproducibility."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic model demonstrated an AUC of 0.57 at 1 year, 0.70 at 2 years, and 0.69 at 3 years.", "kind": "quantitative", "target": {"value": 0.57, "unit": "auc", "tolerance": null, "source": "The prognostic model demonstrated an AUC of 0.57 at 1 year, 0.70 at 2 years, and 0.69 at 3 years."}, "evidence": [{"asset": "source:pmid:39697754", "locator": "sections[20] \"Development of a prognostic model based on key TME genes\" paragraph 5", "quote": "The prognostic model demonstrated an AUC of 0.57 at 1 year, 0.70 at 2 years, and 0.69 at 3 years."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The prognostic model demonstrated an AUC of 0.57 at 1 year, 0.70 at 2 years, and 0.69 at 3 years.", "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:39697754"], "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:GSE140082"], "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:GSE140082"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39697754", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-24-539", "sha256": 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