Novel hypoxia- and lactate metabolism-related molecular subtyping and prognostic signature for colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38902737", "kind": "science-record", "revision": 4, "content_hash": "sha256:a74747a55339474d1d4da4e11ef324b1258e1c60e05bb0854effa594824307b5", "created_at": "2026-11-01T20:28:58Z", "updated_at": "2026-11-01T22:54:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Novel hypoxia- and lactate metabolism-related molecular subtyping and prognostic signature for colorectal cancer.", "doi": "10.1186/s12967-024-05391-5", "pmid": "38902737", "authors": ["An Huang", "Zhuang Sun", "Haopeng Hong", "Yong Yang", "Jiajia Chen", "Zhaoya Gao", "Jin Gu"], "journal": "Journal of translational medicine", "year": 2024, "abstract": "BACKGROUND: Colorectal cancer (CRC) is a serious global health burden because of its high morbidity and mortality rates. Hypoxia and massive lactate production are hallmarks of the CRC microenvironment. However, the effects of hypoxia and lactate metabolism on CRC have not been fully elucidated. This study aimed to develop a novel molecular subtyping based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs) and construct a signature to predict the prognosis of patients with CRC and treatment efficacy.\nMETHODS: Bulk and single-cell RNA-sequencing and clinical data of CRC were downloaded from the TCGA and GEO databases. HRGs and LMRGs were obtained from the Molecular Signatures Database. The R software package DESeq2 was used to perform differential expression analysis. Molecular subtyping was performed using unsupervised clustering. A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses. Finally, the sensitivity of tumor cells to chemotherapeutic agents before and after hypoxia was verified using in vitro experiments.\nRESULTS: We classified 575 patients with CRC into three molecular subtypes and were able to distinguish their prognoses clearly. The C1 subtype, which exhibits high levels of hypoxia, has a low proportion of CD8 + T cells and a high proportion of macrophages. The expression of immune checkpoint genes is generally elevated in C1 patients with severe immune dysfunction. Subsequently, we constructed a predictive model, the HLM score, which effectively predicts the prognosis of patients with CRC and the efficacy of immunotherapy. The HLM score was validated in GSE39582, GSE106584, GSE17536, and IMvigor210 datasets. Patients with high HLM scores exhibit high infiltration of CD8 + exhausted T cells (Tex), especially terminal Tex, and oxidative phosphorylation (OXPHOS)-Tex in the immune microenvironment. Finally, in vitro experiments confirmed that CRC cell lines were less sensitive to 5-fluorouracil, oxaliplatin, and irinotecan under hypoxic conditions.\nCONCLUSION: We constructed novel hypoxia- and lactate metabolism-related molecular subtypes and revealed their immunological and genetic characteristics. We also developed an HLM scoring system that could be used to predict the prognosis and efficacy of immunotherapy in patients with CRC.", "claims": [{"id": "claim-method", "statement": "A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38902737", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data can be found in the TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/), and IMvigor210CoreBiologies (http://research-pub.gene.com/IMvigor210CoreBiologies) databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38902737", "locator": "sections[35] \"Availability of data and materials\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. These data can be found in the TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/), and IMvigor210CoreBiologies (http://research-pub.gene.com/IMvigor210CoreBiologies) databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed hypoxia- and lactate metabolism-related molecular subtypes for colorectal cancer and developed an HLM scoring system to predict prognosis and immunotherapy efficacy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38902737", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We constructed novel hypoxia- and lactate metabolism-related molecular subtypes and revealed their immunological and genetic characteristics. We also developed an HLM scoring system that could be used to predict the prognosis and efficacy of immunotherapy in patients with CRC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed hypoxia- and lactate metabolism-related molecular subtypes for colorectal cancer and developed an HLM scoring system to predict prognosis and immunotherapy efficacy.", "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:38902737"], "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:GSE106584"], "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:GSE106584"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38902737", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-024-05391-5", "sha256": "sha256:cd799c7b405efce5536f48f88320e425c7440a861e008b11900bf700466004ec", "bytes": 76692, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12967-024-05391-5", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11191174", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE106584", "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=GSE106584", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE106584", "version": null, "note": "Publicly available datasets were analyzed in this study. 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"taxon": "Homo sapiens", "title": "Gene expression Classification of Colon Cancer defines six molecular subtypes with distinct clinical, 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:GSE106584 not downloaded", "asset:data:geo:GSE106584 licence not read", "asset:data:geo:GSE132465 not downloaded", "asset:data:geo:GSE132465 licence not read", "asset:data:geo:GSE17536 not downloaded", "asset:data:geo:GSE17536 licence not read", "asset:data:geo:GSE39582 not downloaded", "asset:data:geo:GSE39582 licence not read"]}, "analysis": {"state": "partial", "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": "A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "ANOVA"}}, {"id": "method-4", "family": "differential_expression", "specific": "deseq2", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.7, "band": "medium", "quote": "DESeq2"}}, {"id": "method-5", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}, {"id": "method-6", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "chi-square"}}], "targets": [], "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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE39582", "GSE106584", "GSE17536", "GSE132465"], "resolved_accessions": ["GSE39582", "GSE106584", "GSE17536", "GSE132465"], "statements": ["Publicly available datasets were analyzed in this study. These data can be found in the TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/), and IMvigor210CoreBiologies (http://research-pub.gene.com/IMvigor210CoreBiologies) databases."], "basis": "The paper names GSE39582, GSE106584, GSE17536, GSE132465 in Gene Expression Omnibus (GEO) 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:38902737", "data:geo:GSE106584"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE106584"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:22:00Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 21768, "completion_tokens": 1900, "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:38902737", "type": "paper", "label": "pmid:38902737"}, {"id": "source:pmid:38902737", "type": "manuscript", "label": "PMID 38902737 full text"}, {"id": "data:geo:GSE106584", "type": "dataset", "label": "GEO GSE106584"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38902737", "type": "paper", "label": "pmid:38902737"}, {"id": "source:pmid:38902737", "type": "manuscript", "label": "PMID 38902737 full text"}, {"id": "data:geo:GSE106584", "type": "dataset", "label": "GEO GSE106584"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38902737", "type": "extraction", "used": ["source:pmid:38902737"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE106584", "type": "accession_resolution", "used": ["data:geo:GSE106584"], "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:38902737", "parent_hash": "sha256:bf748ef3d5237707d5b902d9658e139729e6b79292ec24690dcc70b6eb1d2aa9", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE106584 licence not read", "asset:data:geo:GSE106584 not downloaded", "asset:data:geo:GSE132465 licence not read", "asset:data:geo:GSE132465 not downloaded", "asset:data:geo:GSE17536 licence not read", "asset:data:geo:GSE17536 not downloaded", "asset:data:geo:GSE39582 licence not read", "asset:data:geo:GSE39582 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": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ment efficacy. METHODS: Bulk and single-cell RNA-sequencing and clinical data of CRC were downloaded fro", "R package GSVA (version 1.40.1). Single-cell RNA sequencing (scRNA-seq) data processing and analysis The", "132465. This dataset contains single-cell 3'-RNA sequencing data from 23 patients with primary CRC [46]."], "order_basis": "paper_text_order:631", "id": "assay:1"}, {"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": ["t using the R package GSVA (version 1.40.1). Single-cell RNA sequencing (scRNA-seq) data processing and analysis The", "version 1.40.1). Single-cell RNA sequencing (scRNA-seq) data processing and analysis The scRNA-seq", "(scRNA-seq) data processing and analysis The scRNA-seq data used in this study were downloaded from", "R package (version 5.0) was used to analyze scRNA-seq data according to standard analysis procedur"], "order_basis": "paper_text_order:3570", "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": ["A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}