Machine learning developed a PI3K/Akt pathway-related signature for predicting prognosis and drug sensitivity in ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37851341", "kind": "science-record", "revision": 3, "content_hash": "sha256:1100adb8b2caf8fb961b3d4b9892b9272d80d786c2e051620eabf920c1c263f0", "created_at": "2026-11-01T19:02:42Z", "updated_at": "2026-11-01T22:10:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning developed a PI3K/Akt pathway-related signature for predicting prognosis and drug sensitivity in ovarian cancer.", "doi": "10.18632/aging.205119", "pmid": "37851341", "authors": ["Xiaofang Han", "Liu Yang", "Hui Tian", "Yuanyuan Ji"], "journal": "Aging", "year": 2023, "abstract": "BACKGROUND: Ovarian cancer is one of the deadliest malignancies among females, generally having a poor prognosis. The PI3K/Akt pathway plays a vital role in the oncogenesis and progression of many types of cancer. Limited studies have fully clarified the role of PI3K/Akt pathway in the prognosis of ovarian cancer and its correlation with drug sensitivity.\nMETHODS: A prognostic PI3K/Akt pathway related signature (PRS) was constructed with 10 machine learning algorithms using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 datasets. Gaussian mixture and logistic regression were performed to identify the optimal models for classifying lymphatic and venous invasion.\nRESULTS: The optimal prognostic PRS developed by Lasso + survivalSVM algorithm acted as an independent risk factor for overall survival (OS) of ovarian cancer patients and had a good performance in evaluating OS rate of ovarian cancer patients. Significant correlation was obtained between PRS-based risk score and Immune score, ESTIMATE score, immune cells and cancer-related hallmarks. Low risk score indicated a lower immune escape score, TIDE score, and higher PD1&CTLA4 immunophenoscore in ovarian cancer. Moreover, PRS-based risk score acted as an indicator for drug sensitivity in the immunotherapy and chemotherapy of ovarian cancer patients.\nCONCLUSIONS: All in all, our study developed a prognostic PRS showing powerful and good performance in predicting clinical outcome of ovarian cancer patients. PRS could serve as an indicator for drug sensitivity in the chemotherapy and immunotherapy.", "claims": [{"id": "claim-method", "statement": "Harrell’s concordance index (C-index) was calculated in all cohorts. Consistent with a previous study [12] and the link to the R scripts available on the Github website (https://github.com/Zaoqu-Liu/IRLS), we set the TCGA cohort as the training cohort and GEO cohorts as the testing cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37851341", "locator": "sections[4] \"Integrative machine learning algorithms constructed an optimal PRS\" paragraph 2", "quote": "Harrell’s concordance index (C-index) was calculated in all cohorts."}, {"asset": "source:pmid:37851341", "locator": "sections[4] \"Integrative machine learning algorithms constructed an optimal PRS\" paragraph 2", "quote": "Consistent with a previous study [12] and the link to the R scripts available on the Github website (https://github.com/Zaoqu-Liu/IRLS), we set the TCGA cohort as the training cohort and GEO cohorts as the testing cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The analyzed data sets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov/repository) and GEO database (https://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37851341", "locator": "sections[13] \"Availability of data and materials\" paragraph 2", "quote": "The analyzed data sets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov/repository) and GEO database (https://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We could see that the model constructed by Lasso + survivalSVM method was considered as the optimal model and they had a highest average C-index of 0.6 (Figure 1B).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37851341", "locator": "sections[16] \"Integrative machine learning algorithms developed an optimal prognostic PRS\" paragraph 2", "quote": "We could see that the model constructed by Lasso + survivalSVM method was considered as the optimal model and they had a highest average C-index of 0.6 (Figure 1B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We could see that the model constructed by Lasso + survivalSVM method was considered as the optimal model and they had a highest average C-index of 0.6 (Figure 1B).", "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:37851341"], "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": "blocked", "blocked_on": "method:student_t", "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": ["step:run_analysis blocked on method:student_t"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37851341", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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