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pmid:40055390

Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer.

Scientific reports · 2025 doi:10.1038/s41598-025-91761-y

Paper

partial

Experiment

partial

Data

partial

Analysis

absent

Execution

absent

Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
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Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer.
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10.1038/s41598-025-91761-y
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Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes.
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Need identify single numerical result headline main finding. Source abstract says prognostic model consisting of 9 genes. Likely target headline numerical result = 9 genes? Need parse. User asks \"single numerical result that a replication of the registered target has to reproduce. Report headline number only: value paper itself states for registered target, not calculated...\" Registered target headline numerical result for main finding. In source, developed prognostic model consisting of 9 genes. Other numbers: 19 clusters, 11 cell types, 5 drugs low-risk, 1 high-risk. Main finding likely 9-gene prognostic model. Need quote exact contiguous clause. Evidence exact quote perhaps \"Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes.\" Unit \"genes\". parent_value 9. target_result perhaps \"The paper states that its prognostic model consists of 9 genes.\" number_stated true.\n\nNeed be cautious: Could main headline result be C-index/AUC? Source omitted results section and no performance numerical metric. Abstract states prognostic model of 9 genes. Registered target \"headline numerical result the paper states for its main finding\" likely 9 genes. They ask \"single numerical result replication ... reproduce.\" So yes.\n\nNeed exact evidence contiguous. Unit paper prints? It prints \"9 genes\", unit genes. Evidence quote whole sentence clause. target_result likely maybe \"A prognostic model consisting of 9 genes.\" Ensure JSON valid. No markdown.</think>\n\n{\"parent_value\":9,\"unit\":\"genes\",\"evidence\":\"Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes.\",\"target_result\":\"The paper states that it developed a prognostic model consisting of 9 genes.\",\"number_stated\":true}", "reading": {"parent_value": 9, "unit": "genes", "evidence": "Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes.", "target_result": "The paper states that it developed a prognostic model consisting of 9 genes.", "number_stated": true, "source_locator": "chars 998-1154 of source sha256 ea69bd3d22c1b3e75e867ec05732b901d0782fdc498931f74caf78e749a89f1d"}, "recorded_at": "2026-10-31T15:32:49.522476+00:00"}], "agreement": false, "disagreements": [], "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "blocked", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "reason": "a repository quotation is absent from the supplied text", "raw_response": "{\n  \"target_result\": \"A prognostic model consisting of 9 genes for colorectal cancer.\",\n  \"work_type\": \"analysis_only_for_target\",\n  \"physical_methods\": [],\n  \"analysis_methods\": [\n    \"univariate Cox regression\",\n    \"LASSO (Least Absolute Shrinkage and Selection Operator)\",\n    \"Kaplan-Meier method\",\n    \"log-rank test\",\n    \"multivariate Cox proportional hazards model\"\n  ],\n  \"statistical_method\": \"univariate Cox regression and LASSO\",\n  \"method_evidence\": \"Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes.\",\n  \"repositories\": [\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE221575\",\n      \"data_description\": \"The scRNA-seq and RNA-seq data behind the study were obtained from GEO; GSE221575 is the single-cell RNA-sequencing dataset.\",\n      \"evidence\": \"We downloaded data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, we performed single-cell analysis and developed a prognostic model associated with colorectal cancer. We divided the scRNA-seq dataset (GSE221575) into 19 cell clusters and classified these clusters into 11 distinct cell types using marker genes. scRNA-seq and RNA-seq data can be obtained from the GEO and TCGA databases. (GSE221575 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE221575).\",\n      \"publicly_downloadable\": true\n    },\n    {\n      \"repository\": \"The Cancer Genome Atlas (TCGA)\",\n      \"accession\": \"unclear\",\n      \"data_description\": \"RNA-seq data behind the study were obtained from TCGA; no specific TCGA accession is printed in the supplied text.\",\n      \"evidence\": \"We downloaded data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, we performed single-cell analysis and developed a prognostic model associated with colorectal cancer. scRNA-seq and RNA-seq data can be obtained from the GEO and TCGA databases. (GSE221575 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE221575).\",\n      \"publicly_downloadable\": true\n    }\n  ],\n  \"availability_statement\": \"scRNA-seq and RNA-seq data can be obtained from the GEO and TCGA databases. (GSE221575 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE221575).\",\n  \"data_on_request\": false,\n  \"source_claims\": [\n    \"Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes.\",\n    \"Based on our 9-gene model, we divided patients into high-risk and low-risk groups using the median risk score.\",\n    \"Survival curves were generated using the Kaplan-Meier method and were compared using the log-rank test.\",\n    \"Multivariate analysis was conducted using the Cox proportional hazards model.\",\n    \"All statistical analyses were conducted using R (version 4.3.0), with p < 0.05 considered statistically significant.\"\n  ],\n  \"missing_information\": [\n    \"The accession or dataset identifiers for the TCGA data are not stated in the supplied text.\",\n    \"The text does not explicitly assign the GEO and TCGA accession identifiers to separate datasets; only GSE221575 is printed.\"\n  ],\n  \"evidence_spans\": [\n    \"Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes.\",\n    \"Based on our 9-gene model, we divided patients into high-risk and low-risk groups using the median risk score.\",\n    \"We downloaded data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases.\",\n    \"We divided the scRNA-seq dataset (GSE221575) into 19 cell clusters and classified these clusters into 11 distinct cell types using marker genes.\",\n    \"Survival curves were generated using the Kaplan-Meier method and were compared using the log-rank test.\",\n    \"Multivariate analysis was conducted using the Cox proportional hazards model.\",\n    \"All statistical analyses were conducted using R (version 4.3.0), with p < 0.05 considered statistically significant.\",\n    \"scRNA-seq and RNA-seq data can be obtained from the GEO and TCGA databases. 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