Integrating TCGA and single-cell sequencing data for colorectal cancer: a 10-gene prognostic risk assessment model.

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This study sought to address this gap by using the Seurat package to analyze a single-cell sequencing dataset (GSE178318) of colorectal cancer, thereby identifying distinctive marker genes characterizing various cell subpopulations. Through CIBERSORT analysis of colorectal cancer data within The Cancer Genome Atlas (TCGA) database, significant differences existed in both cell subpopulations and prognostic values. Employing WGCNA, we pinpointed modules exhibiting strong correlations with these subpopulations, subsequently utilizing the survival package coxph to isolate genes within these modules. Further stratification of TCGA dataset based on these selected genes brought to light notable variations between subtypes. The prognostic relevance of these differentially expressed genes was rigorously assessed through survival analysis, with LASSO regression employed for modeling prognostic factors. Our resulting model, anchored by a 10-gene signature originating from these differentially expressed genes and LASSO regression, proved adept at accurately predicting clinical prognoses, even when tested against external datasets. Specifically, natural killer cells from the C7 subpopulation were found to bear significant associations with colorectal cancer survival and prognosis, as observed within the TCGA database. These findings underscore the promise of an integrated 10-gene signature prognostic risk assessment model, harmonizing single-cell sequencing insights with TCGA data, for effectively estimating the risk associated with colorectal cancer.", "claims": [{"id": "claim-method", "statement": "LASSO Cox regression analysis was conducted using the R software package glmnet.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37702857", "locator": "sections[14] \"Construction of clinical prognosis molecular model\" paragraph 4", "quote": "LASSO Cox regression analysis was conducted using the R software package glmnet."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available within the manuscript and its supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37702857", "locator": "sections[21] \"Data availability\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available within the manuscript and its supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our resulting model, anchored by a 10-gene signature originating from these differentially expressed genes and LASSO regression, proved adept at accurately predicting clinical prognoses, even when tested against external datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37702857", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Our resulting model, anchored by a 10-gene signature originating from these differentially expressed genes and LASSO regression, proved adept at accurately predicting clinical prognoses, even when tested against external datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our resulting model, anchored by a 10-gene signature originating from these differentially expressed genes and LASSO regression, proved adept at accurately predicting clinical prognoses, even when tested against external 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:37702857"], "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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