Development of lactate-related gene signature and prediction of overall survival and chemosensitivity in patients with colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36776001", "kind": "science-record", "revision": 3, "content_hash": "sha256:61a1953b38f1a5eea38fbe26240a696ef0676b44baa3f24fb55c382d9a76fe37", "created_at": "2026-11-01T19:01:21Z", "updated_at": "2026-11-01T22:08:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of lactate-related gene signature and prediction of overall survival and chemosensitivity in patients with colorectal cancer.", "doi": "10.1002/cam4.5682", "pmid": "36776001", "authors": ["Zhi Tong", "Xinyu Wang", "Sanbao Shi", "Tiewei Hou", "Guangrong Gao", "Da Li", "Yongqi Shan", "Cheng Zhang"], "journal": "Cancer medicine", "year": 2023, "abstract": "BACKGROUND: Colorectal cancer (CRC) is a malignant tumor of the digestive system that contains high levels of immune cells. Lactic acid, a major metabolite, plays a crucial role in tumor development, maintenance, and therapeutic response. However, the prognostic potential and therapeutic biomarker potential of lactate-related genes (LRGs) in CRC patients remain to be elucidated.\nMETHODS: We collected the mRNA expression profile and clinical data of CRC patients from the Cancer Genome Atlas (TCGA) database and the GSE59382 cohort. Univariate Cox regression, Lasso regression and multivariate Cox regression analysis were used to construct the prognosis model. Combined with the risk score and important clinicopathological features, the nomogram was established. In addition, the relationship between risk score and immune infiltration, immune checkpoint gene expression, and drug sensitivity was investigated.\nRESULTS: We constructed lactate-related gene signatures (LRGS) based on four LRGs, which independently predicted the prognosis of CRC. Patients with different risk scores are found to have distinct immune status, tumor mutation load, immune response, and drug sensitivity. In addition, nomogram results determined by risk scores and clinical factors have higher predictive performance.\nCONCLUSION: We found that LRGS is a reliable biomarker for predicting clinical outcomes, evaluating immune infiltration and efficacy, and predicting the sensitivity to drugs in patients with CRC.", "claims": [{"id": "claim-method", "statement": "According to the combined univariate and multivariate Cox regression analysis, the LRGS model predicted the OS independently (HR 1.550, 95% CI 1.363–1.761, P 2.44 E‐06) (Figure 4A,B); the result was confirmed by analyzing the GEO cohort (Figure 4C,D).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36776001", "locator": "sections[18] \"Prognostic significance of LRGS\" paragraph 4", "quote": "According to the combined univariate and multivariate Cox regression analysis, the LRGS model predicted the OS independently (HR 1.550, 95% CI 1.363–1.761, P 2.44 E‐06) (Figure 4A,B); the result was confirmed by analyzing the GEO cohort (Figure 4C,D)."}], "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 authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36776001", "locator": "sections[31] \"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 authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The lactate-related gene signature independently predicted overall survival in colorectal cancer (HR 1.550, 95% CI 1.363–1.761, P 2.44 E−06).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "According to the combined univariate and multivariate Cox regression analysis, the LRGS model predicted the OS independently (HR 1.550, 95% CI 1.363–1.761, P 2.44 E‐06) (Figure 4A,B); the result was confirmed by analyzing the GEO cohort (Figure 4C,D)."}, "evidence": [{"asset": "source:pmid:36776001", "locator": "sections[18] \"Prognostic significance of LRGS\" paragraph 4", "quote": "According to the combined univariate and multivariate Cox regression analysis, the LRGS model predicted the OS independently (HR 1.550, 95% CI 1.363–1.761, P 2.44 E‐06) (Figure 4A,B); the result was confirmed by analyzing the GEO cohort (Figure 4C,D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The lactate-related gene signature independently predicted overall survival in colorectal cancer (HR 1.550, 95% CI 1.363–1.761, P 2.44 E−06).", "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:36776001"], "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:GSE39582"], "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:GSE39582"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36776001", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/cam4.5682", "sha256": "sha256:938f206af01efa3928d0176843b2e2293aa38e045f86ed57ecb1de9f7d2c4e17", "bytes": 41004, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1002/cam4.5682", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10166923", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE39582", "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=GSE39582", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE39582", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary Material. 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No tolerance is published because the paper states none."}], "gaps": ["method.reader_differs_from_extractor"]}, "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. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:36776001", "data:geo:GSE39582"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE39582"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:27:06Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11003, "completion_tokens": 2022, "cached_tokens": 128}}, "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:36776001", "type": "paper", "label": "pmid:36776001"}, {"id": "source:pmid:36776001", "type": "manuscript", "label": "PMID 36776001 full text"}, {"id": "data:geo:GSE39582", "type": "dataset", "label": "GEO GSE39582"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36776001", "type": "paper", "label": "pmid:36776001"}, {"id": "source:pmid:36776001", "type": "manuscript", "label": "PMID 36776001 full text"}, {"id": "data:geo:GSE39582", "type": "dataset", "label": "GEO GSE39582"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36776001", "type": "extraction", "used": ["source:pmid:36776001"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE39582", "type": "accession_resolution", "used": ["data:geo:GSE39582"], "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:36776001", "parent_hash": "sha256:0376012d97f7fd11a9c3356361a26143f72a3c8e42ae32ff7aa6ed848a1eb956", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE39582 licence not read", "asset:data:geo:GSE39582 not downloaded", "asset:data:geo:GSE59382 licence not read", "asset:data:geo:GSE59382 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "methylation_array", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["Iseq, McP‐counter, xCELL, ESTIMATE, IPS, and EPIC algorithms to compare differences in immune"], "order_basis": "paper_text_order:3960", "id": "assay:1"}, {"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": ["According to the combined univariate and multivariate Cox regression analysis, the LRGS model predicted the OS independently (HR 1.550, 95% CI 1.363–1.761, P 2.44 E‐06) (Figure 4A,B); the result was confirmed by analyzing the GEO cohort (Figure 4C,D)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}