An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38406803", "kind": "science-record", "revision": 4, "content_hash": "sha256:47928d7850970459c18246e3d185ef621094baf33ce1cc0cb5b1eaaa81ab8ac9", "created_at": "2026-11-01T19:20:21Z", "updated_at": "2026-11-01T22:53:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.", "doi": "10.3389/fonc.2024.1294440", "pmid": "38406803", "authors": ["Pei Shu", "Ning Liu", "Xu Luo", "Yuanling Tang", "Zhebin Chen", "Dandan Li", "Dong Miao", "Jiayu Duan", "Ouying Yan", "Leiming Sheng", "Ganlu Ouyang", "Sen Wang", "Dan Jiang", "Xiangbing Deng", "Ziqiang Wang", "Qingyun Li", "Xin Wang"], "journal": "Frontiers in oncology", "year": 2024, "abstract": "Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes.\nMethods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted.\nResults: In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group.\nConclusions: The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.", "claims": [{"id": "claim-method", "statement": "To obtain the optimal panel, elastic net logistic regression was carried out on the candidate genes. We screened a 20-gene signature with the best concordance index ( Figure 2A ). Then, we constructed the optimized prognostic model by using the random forest method, which assigned each sample a prognostic risk score based on the identified coefficient of each gene ( Figure 2B ; details in Supplementary Table S4 ).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38406803", "locator": "sections[11] \"Prognostic risk model construction\" paragraph 2", "quote": "To obtain the optimal panel, elastic net logistic regression was carried out on the candidate genes. We screened a 20-gene signature with the best concordance index ( Figure 2A ). Then, we constructed the optimized prognostic model by using the random forest method, which assigned each sample a prognostic risk score based on the identified coefficient of each gene ( Figure 2B ; details in Supplementary Table S4 )."}], "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/s.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38406803", "locator": "sections[18] \"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/s."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The Kaplan−Meier curves showed that the low-risk group was associated with a significantly longer median disease-free survival (mDFS) than the high-risk group (unreached vs. 20 months; p< 0.0001; hazard ratio (HR), 0.05; 95% confidence interval (95% CI), 0.03–0.11; Figure 2C ).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The Kaplan−Meier curves showed that the low-risk group was associated with a significantly longer median disease-free survival (mDFS) than the high-risk group (unreached vs. 20 months; p< 0.0001; hazard ratio (HR), 0.05; 95% confidence interval (95% CI), 0.03–0.11; Figure 2C )."}, "evidence": [{"asset": "source:pmid:38406803", "locator": "sections[11] \"Prognostic risk model construction\" paragraph 2", "quote": "The Kaplan−Meier curves showed that the low-risk group was associated with a significantly longer median disease-free survival (mDFS) than the high-risk group (unreached vs. 20 months; p< 0.0001; hazard ratio (HR), 0.05; 95% confidence interval (95% CI), 0.03–0.11; Figure 2C )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The Kaplan−Meier curves showed that the low-risk group was associated with a significantly longer median disease-free survival (mDFS) than the high-risk group (unreached vs. 20 months; p< 0.0001; hazard ratio (HR), 0.05; 95% confidence interval (95% CI), 0.03–0.11; Figure 2C ).", "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:38406803"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest", "step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38406803", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2024.1294440", "sha256": "sha256:aa51439a7e18583795f6e8b92540a19eaf821fbc6e827b7da5c326e27563594d", "bytes": 46064, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2024.1294440", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10889124", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "To obtain the optimal panel, elastic net logistic regression was carried out on the candidate genes. We screened a 20-gene signature with the best concordance index ( Figure 2A ). Then, we constructed the optimized prognostic model by using the random forest method, which assigned each sample a prognostic risk score based on the identified coefficient of each gene ( Figure 2B ; details in Supplementary Table S4 )."}}, {"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": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.8, "band": "high", "quote": "logistic regression"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "glm_logit_fit", "expected_value": 95.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}, {"id": "target-published-estimate-p-value", "claim_id": "claim-target-result", "operation": "glm_logit_fit", "expected_value": 0.0001, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold."}], "gaps": ["method.reader_differs_from_extractor", "method.machine_learning_random_forest"]}, "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "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": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:38406803"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:10:12Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13300, "completion_tokens": 2059, "cached_tokens": 256}}, "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:38406803", "type": "paper", "label": "pmid:38406803"}, {"id": "source:pmid:38406803", "type": "manuscript", "label": "PMID 38406803 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38406803", "type": "paper", "label": "pmid:38406803"}, {"id": "source:pmid:38406803", "type": "manuscript", "label": "PMID 38406803 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38406803", "type": "extraction", "used": ["source:pmid:38406803"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "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:38406803", "parent_hash": "sha256:66e3fa8f91146bea8f73c9ce63ed1337a843b9da42dace4f526b66710a4ddd16", "fork_of": null, "generation": 3}, "gaps": ["execution.not_run", "method.machine_learning_random_forest", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["To obtain the optimal panel, elastic net logistic regression was carried out on the candidate genes. We screened a 20-gene signature with the best concordance index ( Figure 2A ). Then, we constructed the optimized prognostic model by using the random forest method, which assigned each sample a prognostic risk score based on the identified coefficient of each gene ( Figure 2B ; details in Supplementary Table S4 )."], "order_basis": "analysis_only", "id": "assay:1"}]}