Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38023180", "kind": "science-record", "revision": 2, "content_hash": "sha256:3871a9a0ef2ffa96043f3bbbaa5c51b9930443e28d2d81a793f960f2c36077e4", "created_at": "2026-11-01T19:02:56Z", "updated_at": "2026-11-01T21:32:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data.", "doi": "10.3389/fonc.2023.1277084", "pmid": "38023180", "authors": ["Ruoyang Chai", "Yajie Zhao", "Zhengjia Su", "Wei Liang"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Background: Colon cancer (CC) ranks as one of the leading causes of cancer-related mortality globally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene expression data for distinct cell types. This study aimed to utilize scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data from CC samples to develop a novel prognostic model.\nMethods: scRNA-seq data was downloaded from the GSE161277 database. R packages including \"Seurat\", \"Harmony\", and \"singleR\" were employed to categorize eight major cell types within normal and tumor tissues. By comparing tumor and normal samples, differentially expressed genes (DEGs) across these major cell types were identified. Gene Ontology (GO) enrichment analyses of DEGs for each cell type were conducted using \"Metascape\". DEGs-based signature construction involved Cox regression and least absolute shrinkage operator (LASSO) analyses, performed on The Cancer Genome Atlas (TCGA) training cohort. Validation occurred in the GSE39582 and GSE33382 datasets. The expression pattern of prognostic genes was verified using spatial transcriptome sequencing (ST-seq) data. Ultimately, an established prognostic nomogram based on the gene signature and age was established and calibrated. Sensitivity to chemotherapeutic drugs was predicted with the \"oncoPredict\" R package.\nResults: Using scRNA-Seq data, we examined 33,213 cells, categorizing them into eight cell types within normal and tumor samples. GO enrichment analysis revealed various cancer-related pathways across DEGs in these cell types. Among the 55 DEGs identified via univariate Cox regression, four independent prognostic genes emerged: PTPN6, CXCL13, SPINK4, and NPDC1. Expression validation through ST-seq confirmed PTPN6 and CXCL13 predominance in immune cells, while SPINK4 and NPDC1 were relatively epithelial cell-specific. Creating a four-gene prognostic signature, Kaplan-Meier survival analyses emphasized higher risk scores correlating with unfavorable prognoses, confirmed across training and validation cohorts. The risk score emerged as an independent prognostic factor, supported by a reliable nomogram. Intriguingly, drug sensitivity analysis unveiled contrasting anti-cancer drug responses in the two risk groups, suggesting significant clinical implications.\nConclusion: We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.", "claims": [{"id": "claim-method", "statement": "Initially, we executed univariate Cox regression analysis to explore their prognostic relevance. This analysis revealed a subset of 55 DEGs that displayed a statistically significant correlation with the prognosis of CC patients, evidenced by P values < 0.05 ( Figure 3A ). We next employed LASSO regression to circumvent potential model overfitting (See Methods, Figures 3B, C ). Subsequently, we undertook multivariate Cox regression analysis on the identified 24 candidate genes, aiming to ascertain their roles as independent prognostic factors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38023180", "locator": "sections[14] \"Prognostic genes identification\" paragraph 2", "quote": "Initially, we executed univariate Cox regression analysis to explore their prognostic relevance. This analysis revealed a subset of 55 DEGs that displayed a statistically significant correlation with the prognosis of CC patients, evidenced by P values < 0.05 ( Figure 3A ). We next employed LASSO regression to circumvent potential model overfitting (See Methods, Figures 3B, C ). Subsequently, we undertook multivariate Cox regression analysis on the identified 24 candidate genes, aiming to ascertain their roles as independent prognostic factors."}], "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:38023180", "locator": "sections[23] \"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": "We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38023180", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.", "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:38023180"], "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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molecular and survival characteristics [Expression]", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE161277 not downloaded", "asset:data:geo:GSE161277 licence not read", "asset:data:geo:GSE33382 not downloaded", "asset:data:geo:GSE33382 licence not read", "asset:data:geo:GSE33882 not downloaded", "asset:data:geo:GSE33882 licence not read", "asset:data:geo:GSE39582 not downloaded", "asset:data:geo:GSE39582 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Initially, we executed univariate Cox regression analysis to explore their prognostic relevance. This analysis revealed a subset of 55 DEGs that displayed a statistically significant correlation with the prognosis of CC patients, evidenced by P values < 0.05 ( Figure 3A ). We next employed LASSO regression to circumvent potential model overfitting (See Methods, Figures 3B, C ). Subsequently, we undertook multivariate Cox regression analysis on the identified 24 candidate genes, aiming to ascertain their roles as independent prognostic factors."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-4", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "PCA"}}, {"id": "method-5", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "GO enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE161277", "GSE39582", "GSE33382", "GSE33882"], "resolved_accessions": ["GSE161277", "GSE39582", "GSE33382", "GSE33882"], "statements": ["The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author."], "basis": "The paper names GSE161277, GSE39582, GSE33382, GSE33882 in GSE161277 database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:38023180", "data:geo:GSE161277"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE161277"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:00:34Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15525, "completion_tokens": 2846, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38023180", "type": "paper", "label": "pmid:38023180"}, {"id": "source:pmid:38023180", "type": "manuscript", "label": "PMID 38023180 full text"}, {"id": "data:geo:GSE161277", "type": "dataset", "label": "GEO GSE161277"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38023180", "type": "paper", "label": "pmid:38023180"}, {"id": "source:pmid:38023180", "type": "manuscript", "label": "PMID 38023180 full text"}, {"id": "data:geo:GSE161277", "type": "dataset", "label": "GEO GSE161277"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38023180", "type": "extraction", "used": ["source:pmid:38023180"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE161277", "type": "accession_resolution", "used": ["data:geo:GSE161277"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38023180", "parent_hash": "sha256:58490e06edc340255407ed985bffc355ace18f2395a56e28ec3beff23bae6753", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE161277 licence not read", "asset:data:geo:GSE161277 not downloaded", "asset:data:geo:GSE33382 licence not read", "asset:data:geo:GSE33382 not downloaded", "asset:data:geo:GSE33882 licence not read", "asset:data:geo:GSE33882 not downloaded", "asset:data:geo:GSE39582 licence not read", "asset:data:geo:GSE39582 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["onse in colon cancer patients using bulk and single-cell RNA-seq data.", "bally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene expression data for dis", "inct cell types. This study aimed to utilize scRNA-seq and bulk transcriptome sequencing (bulk RNA-", "o develop a novel prognostic model. Methods: scRNA-seq data was downloaded from the GSE161277 datab"], "order_basis": "paper_text_order:97", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["n cancer patients using bulk and single-cell RNA-seq data.", "-seq and bulk transcriptome sequencing (bulk RNA-seq) data from CC samples to develop a novel pro", "ation and Projection; scRNA-seq, single-cell RNA sequencing.Click here for additional data file. Supplem", "ncer-related mortality globally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene e"], "order_basis": "paper_text_order:109", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Initially, we executed univariate Cox regression analysis to explore their prognostic relevance. This analysis revealed a subset of 55 DEGs that displayed a statistically significant correlation with the prognosis of CC patients, evidenced by P values < 0.05 ( Figure 3A ). We next employed LASSO regression to circumvent potential model overfitting (See Methods, Figures 3B, C ). Subsequently, we undertook multivariate Cox regression analysis on the identified 24 candidate genes, aiming to ascertain their roles as independent prognostic factors."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}