Multi-Algorithm-Integrated Tertiary Lymphoid Structure Gene Signature for Immune Landscape Characterization and Prognosis in Colorectal Cancer Patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39595209", "kind": "science-record", "revision": 4, "content_hash": "sha256:4ef118e019f36f9d10c126c3a0af1666123f755cf45ec5af9b153afebe52e191", "created_at": "2026-11-01T20:29:51Z", "updated_at": "2026-11-01T22:55:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Multi-Algorithm-Integrated Tertiary Lymphoid Structure Gene Signature for Immune Landscape Characterization and Prognosis in Colorectal Cancer Patients.", "doi": "10.3390/biomedicines12112644", "pmid": "39595209", "authors": ["Xianqiang Liu", "Dingchang Li", "Yue Zhang", "Hao Liu", "Peng Chen", "Yingjie Zhao", "Guanchao Sun", "Wen Zhao", "Guanglong Dong"], "journal": "Biomedicines", "year": 2024, "abstract": "PURPOSE: Colorectal cancer (CRC) is a common malignancy with a low survival rate as well as a low response rate to immunotherapy. This study aims to develop a risk model based on tertiary lymphoid structure (TLS)-associated gene signatures to enhance predictions of prognosis and immunotherapy response.\nMETHODS: TLS-associated gene data were obtained from TCGA-CRC and GEO cohorts. A comprehensive analysis using univariate Cox regression identified TLS-associated genes with significant prognostic implications. Subsequently, multiple algorithms were employed to select the most influential genes, and a stepwise Cox regression model was constructed. The model's predictive performance was validated using independent datasets (GSE39582, GSE17536, and GSE38832). To further investigate the immune microenvironment, immune cell infiltration in high-risk (HRG) and low-risk (LRG) groups was assessed using the CIBERSORT and ssGSEA algorithms. Additionally, we evaluated the model's potential to predict immune checkpoint blockade therapy response using data from The Cancer Imaging Archive, the TIDE algorithm, and external immunotherapy cohorts (GSE35640, GSE78200, and PRJEB23709). Immunohistochemistry (IHC) was employed to characterize TLS presence and CCL2 gene expression.\nRESULTS: A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes and immunotherapy efficacy. Notably, patients in the low-risk group (LRG) had a higher overall survival rate as well as a higher re-response rate to immunotherapy compared to the high-risk group (HRG). Finally, IHC results confirmed significantly elevated CCL2 expression in the TLS regions.\nCONCLUSIONS: The multi-algorithm-integrated model demonstrated robust performance in predicting patient prognosis and immunotherapy response, offering a novel perspective for assessing immunotherapy efficacy. CCL2 may function as a TLS modulator and holds potential as a therapeutic target in CRC.", "claims": [{"id": "claim-method", "statement": "Next, we performed least absolute shrinkage and selection operator (LASSO) regression analysis to look for genes highly relevant to prognosis by tenfold cross-validation [29]. Then, we further refined the set of feature genes screened by LASSO using support vector machine (SVM)-RFE to identify the genes that contributed most to the model through a recursive process [30]. Finally, we identified and calculated the coefficients of key genes by stepwise multivariate Cox regression analysis and constructed a risk model based on the three TLS-related genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39595209", "locator": "sections[5] \"2.3. Model Construction and Validation\" paragraph 2", "quote": "Next, we performed least absolute shrinkage and selection operator (LASSO) regression analysis to look for genes highly relevant to prognosis by tenfold cross-validation [29]. Then, we further refined the set of feature genes screened by LASSO using support vector machine (SVM)-RFE to identify the genes that contributed most to the model through a recursive process [30]. Finally, we identified and calculated the coefficients of key genes by stepwise multivariate Cox regression analysis and constructed a risk model based on the three TLS-related genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets utilized and analyzed in this study are accessible through GEO (https://www.ncbi.nlm.nih.gov/geo/; accessed on 20 May 2024) and TCGA (https://portal.gdc.cancer.gov/; accessed on 20 May 2024). For additional information, please feel free to contact the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39595209", "locator": "sections[28] \"Data Availability Statement\" paragraph 2", "quote": "The datasets utilized and analyzed in this study are accessible through GEO (https://www.ncbi.nlm.nih.gov/geo/; accessed on 20 May 2024) and TCGA (https://portal.gdc.cancer.gov/; accessed on 20 May 2024). For additional information, please feel free to contact the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes and immunotherapy efficacy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39595209", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes and immunotherapy efficacy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes 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:39595209"], "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:GSE17536"], "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:GSE17536"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39595209", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/biomedicines12112644", "sha256": "sha256:e9e024805e5ae81b7f9f4032eb4d31fd670097175dd7a1f45aa5f9dc93069bdf", "bytes": 50871, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/biomedicines12112644", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11592260", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17536", "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=GSE17536", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17536", "version": null, "note": "The datasets utilized and analyzed in this study are accessible through GEO (https://www.ncbi.nlm.nih.gov/geo/; accessed on 20 May 2024) and TCGA (https://portal.gdc.cancer.gov/; accessed on 20 May 2024). 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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:GSE17536 not downloaded", "asset:data:geo:GSE17536 licence not read", "asset:data:geo:GSE38832 not downloaded", "asset:data:geo:GSE38832 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": "Next, we performed least absolute shrinkage and selection operator (LASSO) regression analysis to look for genes highly relevant to prognosis by tenfold cross-validation [29]. Then, we further refined the set of feature genes screened by LASSO using support vector machine (SVM)-RFE to identify the genes that contributed most to the model through a recursive process [30]. Finally, we identified and calculated the coefficients of key genes by stepwise multivariate Cox regression analysis and constructed a risk model based on the three TLS-related genes."}}, {"id": "method-2", "family": "survival_analysis", "specific": "restricted_mean_survival", "label": "Survival analysis", "dsl_operations": ["restricted_mean_survival"], "missing_operations": [], "expressible": true, "parameters": {"hazard_ratio": "and genders within the HRG and LRG"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "RMST"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "support vector machine"}}, {"id": "method-4", "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": 0.8, "band": "high", "quote": "pathway 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": ["GSE39582", "GSE17536", "GSE38832"], "resolved_accessions": ["GSE39582", "GSE17536", "GSE38832"], "statements": ["The datasets utilized and analyzed in this study are accessible through GEO (https://www.ncbi.nlm.nih.gov/geo/; accessed on 20 May 2024) and TCGA (https://portal.gdc.cancer.gov/; accessed on 20 May 2024). For additional information, please feel free to contact the corresponding authors."], "basis": "The paper names GSE39582, GSE17536, GSE38832 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:39595209", "data:geo:GSE17536"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE17536"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:39:43Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14911, "completion_tokens": 1978, "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:39595209", "type": "paper", "label": "pmid:39595209"}, {"id": "source:pmid:39595209", "type": "manuscript", "label": "PMID 39595209 full text"}, {"id": "data:geo:GSE17536", "type": "dataset", "label": "GEO GSE17536"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39595209", "type": "paper", "label": "pmid:39595209"}, {"id": "source:pmid:39595209", "type": "manuscript", "label": "PMID 39595209 full text"}, {"id": "data:geo:GSE17536", "type": "dataset", "label": "GEO GSE17536"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39595209", "type": "extraction", "used": ["source:pmid:39595209"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE17536", "type": "accession_resolution", "used": ["data:geo:GSE17536"], "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:39595209", "parent_hash": "sha256:0d45fbc10e5bc2da8f0d813bbd86d671d8961d31ae716ebcfc9547f7e472aa7a", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE17536 licence not read", "asset:data:geo:GSE17536 not downloaded", "asset:data:geo:GSE38832 licence not read", "asset:data:geo:GSE38832 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": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ohorts (GSE35640, GSE78200, and PRJEB23709). Immunohistochemistry (IHC) was employed to characterize TLS prese", "8200, and PRJEB23709). Immunohistochemistry (IHC) was employed to characterize TLS presence a", "pared to the high-risk group (HRG). Finally, IHC results confirmed significantly elevated CCL", "osin (H&E) staining along with CD20 antibody IHC staining (GB14030, Servicebio, Wuhan, Hubei,"], "order_basis": "paper_text_order:1292", "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": ["Next, we performed least absolute shrinkage and selection operator (LASSO) regression analysis to look for genes highly relevant to prognosis by tenfold cross-validation [29]. Then, we further refined the set of feature genes screened by LASSO using support vector machine (SVM)-RFE to identify the genes that contributed most to the model through a recursive process [30]. Finally, we identified and calculated the coefficients of key genes by stepwise multivariate Cox regression analysis and constructed a risk model based on the three TLS-related genes."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}