Exploring tumor microenvironment in molecular subtyping and prognostic signatures in ovarian cancer and identification of SH2D1A as a key regulator of ovarian cancer carcinogenesis.

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Addressing the tumor microenvironment (TME) in solid tumors has shown promise since it is crucial in promoting cancer progression.\nMethods: We obtained bulk RNA-seq data from TCGA-OV, GSE26712, GSE102073, and ICGC cohorts, as well as scRNA-seq data from EMTAB8107, GSE118828, GSE130000, and GSE154600 cohorts using the TISCH2 database. The ConsensusClusterPlus package was used to cluster the OV tumor tissues hierarchically to determine two molecularly different groups (C1 and C2). A total of ten different types of machine learning techniques with 101 combinations were used for prognostic model construction. Using eight TME algorithms integrated into the IOBR R package, the bulk RNA-seq dataset was analyzed. For in vitro experiments, OVCAR3 and SKOV3, two OV cell lines, were used. The migratory potential of the ovarian cancer cells was assessed using Transwell assay, while proliferation was assessed using CCK8 assay.\nResults: Based on TME-related gene set expression, two distinct molecular subgroups (C1 and C2) were identified through consensus clustering, with C1 showing higher TME activity. Further analysis indicated that C1 had increased cancer-associated fibroblasts (CAFs), M1 macrophages, and CD8+ T cells, suggesting a more activated and pro-inflammatory TME. Drug sensitivity analysis revealed that 5-Fluorouracil might be beneficial to C1 patients. Functional differences between C1 and C2 were identified, including cell adhesion, mononuclear cell differentiation, and leukocyte migration. A machine learning model was developed to create a TME-related prognostic signature, demonstrating strong prognostic capabilities across multiple datasets. High-risk patients showed a more immune-suppressive TME and higher tumor stemness. ScRNA-seq disclosed a highly activated TME-related signature in OV. Cancer cell lines had significantly higher SH2D1A mRNA expression than normal ovarian epithelial cells. We observed that SH2D1A knockdown in 2 ovarian cancer cell lines (OVCAR3 and SKOV3) reduced migration and proliferation through a series of in-vitro experiments.\nConclusion: TME-associated genes were efficient in ovarian cancer molecular subtyping. A TME-based prognosis model was constructed for vigorous prognostic stratification efficacy across multiple datasets. Moreover, we identified a pivotal role of SH2D1A in promoting proliferation and migration in ovarian cancer.", "claims": [{"id": "claim-method", "statement": "The study utilized the survival package to conduct Cox and Kaplan-Meier regression analyses to evaluate the association between predictor variables and survival outcomes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39347397", "locator": "sections[13] \"Statistical analysis\" paragraph 2", "quote": "The study utilized the survival package to conduct Cox and Kaplan-Meier regression analyses to evaluate the association between predictor variables and survival outcomes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39347397", "locator": "sections[28] \"Data and code availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A machine-learning prognostic signature based on tumor-microenvironment-related genes was constructed and demonstrated strong prognostic capabilities across multiple ovarian cancer datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39347397", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A machine learning model was developed to create a TME-related prognostic signature, demonstrating strong prognostic capabilities across multiple datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A machine-learning prognostic signature based on tumor-microenvironment-related genes was constructed and demonstrated strong prognostic capabilities across multiple ovarian cancer datasets.", "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:39347397"], "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. 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