Construction of a new prognostic model for colorectal cancer based on bulk RNA-seq combined with The Cancer Genome Atlas data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38988915", "kind": "science-record", "revision": 2, "content_hash": "sha256:0447931d1664e02435439b3f89092739098683331894b1fe1c3c1ad0d61a4768", "created_at": "2026-11-01T20:29:03Z", "updated_at": "2026-11-01T21:33:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a new prognostic model for colorectal cancer based on bulk RNA-seq combined with The Cancer Genome Atlas data.", "doi": "10.21037/tcr-23-2281", "pmid": "38988915", "authors": ["Yu Ye", "Gang Xu"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths, and improving the prognosis of CRC patients is an urgent concern. The aim of this study was to explore new immunotherapy targets to improve survival in CRC patients.\nMethods: We analyzed CRC-related single-cell data GSE201348 from the Gene Expression Omnibus (GEO) database, and identified differentially expressed genes (DEGs). Subsequently, we performed differential analysis on the rectum adenocarcinoma (READ) and colon adenocarcinoma (COAD) transcriptome sequencing data [The Cancer Genome Atlas (TCGA)-CRC queue] and clinical data downloaded from TCGA database. Subgroup analysis was performed using CIBERSORTx and cluster analysis. Finally, biomarkers were identified by one-way cox regression as well as least absolute shrinkage and selection operator (LASSO) analysis.\nResults: In this study, we analyzed CRC-related single-cell data GSE201348, and identified 5,210 DEGs. Subsequently, we performed differential analysis on the TCGA-CRC queue database, and obtained 4,408 DEGs. Then, we categorized the cancer samples in the sequencing data into three groups (k1, k2, and k3), with significant differences observed between the k1 and k2 groups via survival analysis. Further differential analysis on the samples in the k1 and k2 groups identified 1,899 DEGs. A total of 77 DEGs were selected among those DEGs obtained from three differential analyses. Through subsequent Cox univariate analysis and LASSO analysis, seven biomarkers (RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1) were identified and selected to establish a risk score (RS).\nConclusions: To sum up, this study demonstrates the potential of the seven-gene prognostic risk model as instrumental variables for predicting the prognosis of CRC.", "claims": [{"id": "claim-data-access", "statement": "The CRC-related single-cell RNA sequencing (scRNA-seq) data were obtained from the GSE201348 dataset in the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Two cancer samples (GSM6061645 and GSM6061686) and two normal samples (GSM6061709 and GSM6061713) were selected for subsequent analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38988915", "locator": "sections[4] \"Data acquisition and processing\" paragraph 2", "quote": "The CRC-related single-cell RNA sequencing (scRNA-seq) data were obtained from the GSE201348 dataset in the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Two cancer samples (GSM6061645 and GSM6061686) and two normal samples (GSM6061709 and GSM6061713) were selected for subsequent analysis."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified a seven-gene prognostic risk model for colorectal cancer comprising RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38988915", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "To sum up, this study demonstrates the potential of the seven-gene prognostic risk model as instrumental variables for predicting the prognosis of CRC."}, {"asset": "source:pmid:38988915", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 77 DEGs were selected among those DEGs obtained from three differential analyses."}, {"asset": "source:pmid:38988915", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Through subsequent Cox univariate analysis and LASSO analysis, seven biomarkers (RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1) were identified and selected to establish a risk score (RS)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified a seven-gene prognostic risk model for colorectal cancer comprising RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1.", "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:38988915"], "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:GSE12945"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "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:GSE12945"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38988915", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-2281", "sha256": "sha256:81fdafa1b72c7465885a4ace6b8d10cb988901be79ebadc5dab247a799e1ed19", "bytes": 44389, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-23-2281", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11231782", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE12945", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12945", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE12945", "version": null, "note": "The CRC-related single-cell RNA sequencing (scRNA-seq) data were obtained from the GSE201348 dataset in the GEO database (https://www.ncbi.nlm.nih.gov/geo/). 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