Machine learning-assisted analysis of epithelial mesenchymal transition pathway for prognostic stratification and immune infiltration assessment in ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37404304", "kind": "science-record", "revision": 2, "content_hash": "sha256:4ae06f389ee1715467a497151df2149145b91f7c31fb21af0838908cd95ed2cb", "created_at": "2026-11-01T19:02:10Z", "updated_at": "2026-11-01T21:31:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning-assisted analysis of epithelial mesenchymal transition pathway for prognostic stratification and immune infiltration assessment in ovarian cancer.", "doi": "10.3389/fendo.2023.1196094", "pmid": "37404304", "authors": ["Qian Li", "Xiyun Xiao", "Jing Feng", "Ruixue Yan", "Jie Xi"], "journal": "Frontiers in endocrinology", "year": 2023, "abstract": "Background: Ovarian cancer is the most lethal gynaecological malignancy, and serous ovarian cancer (SOC) is one of the more important pathological subtypes. Previous studies have reported a significant association of epithelial tomesenchymal transition (EMT) with invasive metastasis and immune modulation of SOC, however, there is a lack of prognostic and immune infiltration biomarkers reported for SOC based on EMT.\nMethods: Gene expression data for ovarian cancer and corresponding patient clinical data were collected from the TCGA database and the GEO database, and cell type annotation and spatial expression analysis were performed on single cell sequencing data from the GEO database. To understand the cell type distribution of EMT-related genes in SOC single-cell data and the enrichment relationships of biological pathways and tumour functions. In addition, GO functional annotation analysis and KEGG pathway enrichment analysis were performed on mRNAs predominantly expressed with EMT to predict the biological function of EMT in ovarian cancer. The major differential genes of EMT were screened to construct a prognostic risk prediction model for SOC patients. Data from 173 SOC patient samples obtained from the GSE53963 database were used to validate the prognostic risk prediction model for ovarian cancer. Here we also analysed the direct association between SOC immune infiltration and immune cell modulation and EMT risk score. and calculate drug sensitivity scores in the GDSC database.In addition, we assessed the specific relationship between GAS1 gene and SOC cell lines.\nResults: Single cell transcriptome analysis in the GEO database annotated the major cell types of SOC samples, including: T cell, Myeloid, Epithelial cell, Fibroblast, Endothelial cell, and Bcell. cellchat revealed several cell type interactions that were shown to be associated with EMT-mediated SOC invasion and metastasis. A prognostic stratification model for SOC was constructed based on EMT-related differential genes, and the Kapan-Meier test showed that this biomarker had significant prognostic stratification value for several independent SOC databases. The EMT risk score has good stratification and identification properties for drug sensitivity in the GDSC database.\nConclusions: This study constructed a prognostic stratification biomarker based on EMT-related risk genes for immune infiltration mechanisms and drug sensitivity analysis studies in SOC. This lays the foundation for in-depth clinical studies on the role of EMT in immune regulation and related pathway alterations in SOC. It is also hoped to provide effective potential solutions for early diagnosis and clinical treatment of ovarian cancer.", "claims": [{"id": "claim-method", "statement": "Multi-factorial Cox regression and LASSO analysis were performed on 50 prognosis-related EMT risk genes ( Figures 3A , B ), and 7 EMT risk genes with significant correlation with prognosis of ovarian cancer patients were obtained (P < 0.01), which constituted a prognostic prediction model for ovarian cancer.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37404304", "locator": "sections[17] \"Construction of SOC prognostic biomarkers for EMT genetic risk model and functional assessment\" paragraph 2", "quote": "Multi-factorial Cox regression and LASSO analysis were performed on 50 prognosis-related EMT risk genes ( Figures 3A , B ), and 7 EMT risk genes with significant correlation with prognosis of ovarian cancer patients were obtained (P < 0.01), which constituted a prognostic prediction model for ovarian cancer."}], "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:37404304", "locator": "sections[21] \"Data 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": "The grouping curves based on EMT risk marker expression demonstrated significant prognostic stratification and survival predictive power.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37404304", "locator": "sections[17] \"Construction of SOC prognostic biomarkers for EMT genetic risk model and functional assessment\" paragraph 2", "quote": "Using the TCGA-OV cohort as the training group, the cohort selected 7 genes of prognostic significance from 22 genes according to Lasso regression and constructed prognostic models."}, {"asset": "source:pmid:37404304", "locator": "sections[17] \"Construction of SOC prognostic biomarkers for EMT genetic risk model and functional assessment\" paragraph 2", "quote": "The grouping curves based on EMT risk marker expression demonstrated significant prognostic stratification and survival predictive power."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The grouping curves based on EMT risk marker expression demonstrated significant prognostic stratification and survival predictive power.", "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:37404304"], "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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Results: Single cell transcriptome analysis in the GEO database annotated the m", "tion analysis, an analysis of microarray and RNA-seq data gene sets under parameter-free and unsu"], "order_basis": "paper_text_order:1737", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["l pre-processing and downscaling analysis by scRNA-seq of GSE154600, we sought to understand the in", "s of single cell samples from GSE154600. (A) scRNA-seq analysis of 4 soc samples from GSE154600, t-"], "order_basis": "paper_text_order:3016", "id": "assay:2"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["gene set variation analysis, an analysis of microarray and RNA-seq data gene sets under parameter-f", "ix (GSVA converts a gene-sample data matrix (microarray data, FPKM, RPKM, etc.) into a gene-set-samp"], "order_basis": "paper_text_order:4300", "id": "assay:3"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ur samples and 10 paracancerous tissues. The qPCR technique was used to obtain the transcript", "1 gene in tumour tissues and normal tissues. qPCR experiments were performed in a similar way", "samples from ovarian cancer and paracancer, qPCR showed that GAS1 was significantly highly ex", "hymal transition at the cellular level. (A). qPCR analysis of GAS1 expression histograms in 10"], "order_basis": "paper_text_order:6556", "id": "assay:4"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["or lentivirus generation, HEK293T cells were transfected with 4.05 µg of target plasmid, 0.45 µg of p", "ere set up as follows. Cell culture and gas1 knockdown validation SKOV3 as well as 3AO cell lines w", "bator at 37°C containing 5% CO2. GAS1 stable knockdown clones of both cell lines were obtained by u", "lines were obtained by using Mission shRNA (knockdown [KD]1: TRCN0000084044, KD2: TRCN0000294078,"], "order_basis": "paper_text_order:6844", "id": "assay:5"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ere obtained from the ATCC repository. All 2 cell lines were cultured in McCoy’s 5a medium supplemented with 10% f", "m the ATCC repository. 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