Multi-omics decipher the immune microenvironment and unveil therapeutic strategies for postoperative ovarian cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39697734", "kind": "science-record", "revision": 3, "content_hash": "sha256:1ed231c317706f5b5c1d4cf99ec63234dc1d402633552daf07d2c19d798182d1", "created_at": "2026-11-01T20:30:00Z", "updated_at": "2026-11-01T22:13:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Multi-omics decipher the immune microenvironment and unveil therapeutic strategies for postoperative ovarian cancer patients.", "doi": "10.21037/tcr-24-656", "pmid": "39697734", "authors": ["Zhibing Liu", "Fei Wang", "Weiwei Chen", "Yujie Zhai", "Jinbo Jian", "Xiaole Wang", "Yingjiang Xu", "Jiajia An", "Lei Han"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Ovarian cancer (OC) is a highly aggressive and often fatal disease that frequently goes undetected until it has already metastasized. The classic treatment for OC involves surgery followed by chemotherapy. However, despite the effectiveness of surgery, relapse is still a common occurrence. Unfortunately, there is currently no ideal predictive model for the progression and drug sensitivity of postoperative OC patients. Cell death patterns play an important role in tumor progression. So we aimed to investigate their potential to be used as indicators of postoperative OC prognosis and drug sensitivity.\nMethods: A total of 12 programmed cell death (PCD) patterns were employed to construct novel classification and prognosis model. Bulk transcriptome, genomics, and clinical information were collected from The Cancer Genome Atlas (TCGA) Program-OV, GSE9891, GSE26712, GSE49997 and GSE63885. In addition, single-cell transcriptome data GSE210347 were procured from the Gene Expression Omnibus (GEO) database for subsequent analysis.\nResults: In this study, a novel PCD classification has been employed to phenotype postoperative OC patients, revealing that patients in cluster 1 exhibited heightened sensitivity to immune-based therapies combined with high expression of chemokines, interleukins, interferons, and checkpoints. Meanwhile, a programmed cell death index (PCDI) was established using an 8-gene signature with the help of a machine learning algorithm. The patients with high-PCDI had a worse prognosis after surgery in OC. In addition, we also found that patients with low PCDI patients may exhibit sensitivity to immunotherapy, while those with high PCDI patients may display increased responsiveness to tyrosine kinase inhibitors.\nConclusions: This study provides a novel PCD model and nomogram that can effectively predict the clinical prognosis and drug sensitivity of OC patients post-surgery.", "claims": [{"id": "claim-method", "statement": "To examine the prognosis of PCD-related mRNAs, we created a PCDI using least absolute shrinkage and selection operator (LASSO) and multiple Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39697734", "locator": "sections[17] \"Establishment and evaluation of a PCDI in OC\" paragraph 2", "quote": "To examine the prognosis of PCD-related mRNAs, we created a PCDI using least absolute shrinkage and selection operator (LASSO) and multiple Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Uterine Corpus Endometrial Carcinoma (UCEC) transcriptome datasets combined with clinical data (e.g., stage T stage, overall survival) of OC patients were downloaded from The Cancer Genome Atlas (TCGA) Program data portal (https://portal.gdc.cancer.gov/) (12).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39697734", "locator": "sections[4] \"Transcriptome and single cell datasets collection\" paragraph 2", "quote": "Uterine Corpus Endometrial Carcinoma (UCEC) transcriptome datasets combined with clinical data (e.g., stage T stage, overall survival) of OC patients were downloaded from The Cancer Genome Atlas (TCGA) Program data portal (https://portal.gdc.cancer.gov/) (12)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:39697734"], "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:GSE210347"], "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:GSE210347"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39697734", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-24-656", "sha256": "sha256:3fcdd66dd50880885abba177629a1d529e8e0b8821304f01e848c329390d2a0f", "bytes": 52616, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-24-656", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11651737", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE210347", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE210347", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE210347", "version": null, "note": "Uterine Corpus Endometrial Carcinoma (UCEC) transcriptome datasets combined with clinical data (e.g., stage T stage, overall survival) 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