Stemness signature and targeted therapeutic drugs identification for Triple Negative Breast Cancer.

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This study aimed to analyze cancer stemness-related gene signature to determine patients' risk stratification and prognosis feature with TNBC. Here one-class logistic regression (OCLR) algorithm was applied to compute the stemness index of TNBC patients. Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort. We diagnosed the expression level of prognostic genes signature in the tumor immune microenvironment, analyzed the TNBC scRNA-seq GSE176078 dataset, and further validated the expression level of prognostic genes using the HPA database. Finally, the small molecular compounds targeted at the anti-tumor effect of predictive genes were screened by molecular docking; this novel stemness-based prognostic genes signature study could facilitate the prognosis of patients with TNBC and thus provide a feasible therapeutic target for TNBC.", "claims": [{"id": "claim-method", "statement": "Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37985782", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the current work are available from GDC TCGA Breast Cancer (BRCA) (https://gdc.xenahubs.net) with dataset ID TCGA-BRCA.htseq_counts.tsv with a total of 1217 samples and extracted 127 samples of TNBC subtype of breast cancer for our analysis. The data used for validation in this article were obtained from METABRIC (Breast Cancer, Nature 2012 & Nat Commun 2016) (http://www.cbioportal.org/datasets) with a total of 2509 samples and we extracted 320 samples of TNBC subtype of breast cancer for our analysis and merged 320 samples with 127 TNBC sample obtained from TCGA-BRCA and used 447 patient data with TNBC as validation cohort, The data we have generated through this study can be found on figshare53. The stem cell expression profiles (syn2701943) were downloaded from the 91 Progenitor Cell Biology Consortium database (https://www.synapse.org)54 and the scRNA-seq dataset obtained from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE176078 (nTNBC = 10)55.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37985782", "locator": "sections[26] \"Data availability\" paragraph 2", "quote": "The data that support the current work are available from GDC TCGA Breast Cancer (BRCA) (https://gdc.xenahubs.net) with dataset ID TCGA-BRCA.htseq_counts.tsv with a total of 1217 samples and extracted 127 samples of TNBC subtype of breast cancer for our analysis. The data used for validation in this article were obtained from METABRIC (Breast Cancer, Nature 2012 & Nat Commun 2016) (http://www.cbioportal.org/datasets) with a total of 2509 samples and we extracted 320 samples of TNBC subtype of breast cancer for our analysis and merged 320 samples with 127 TNBC sample obtained from TCGA-BRCA and used 447 patient data with TNBC as validation cohort, The data we have generated through this study can be found on figshare53. The stem cell expression profiles (syn2701943) were downloaded from the 91 Progenitor Cell Biology Consortium database (https://www.synapse.org)54 and the scRNA-seq dataset obtained from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE176078 (nTNBC = 10)55."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37985782", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort.", "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:37985782"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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