Identification of immune related gene signature for predicting prognosis of cholangiocarcinoma patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36817485", "kind": "science-record", "revision": 2, "content_hash": "sha256:c46785d17faab20272d4865270f62dec2de0937f727a99591ebcd90e6da0fbea", "created_at": "2026-11-01T19:01:24Z", "updated_at": "2026-11-01T21:30:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune related gene signature for predicting prognosis of cholangiocarcinoma patients.", "doi": "10.3389/fimmu.2023.1028404", "pmid": "36817485", "authors": ["Zi-Jian Zhang", "Yun-Peng Huang", "Zhong-Tao Liu", "Yong-Xiang Wang", "Hui Zhou", "Ke-Xiong Hou", "Ji-Wang Tang", "Li Xiong", "Yu Wen", "Sheng-Fu Huang"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Objective: To identify the gene subtypes related to immune cells of cholangiocarcinoma and construct an immune score model to predict the immunotherapy efficacy and prognosis for cholangiocarcinoma.\nMethods: Based on principal component analysis (PCA) algorithm, The Cancer Genome Atlas (TCGA)-cholangiocarcinoma, GSE107943 and E-MTAB-6389 datasets were combined as Joint data. Immune genes were downloaded from ImmPort. Univariate Cox survival analysis filtered prognostically associated immune genes, which would identify immune-related subtypes of cholangiocarcinoma. Least absolute shrinkage and selection operator (LASSO) further screened immune genes with prognosis values, and tumor immune score was calculated for patients with cholangiocarcinoma after the combination of the three datasets. Kaplan-Meier curve analysis determined the optimal cut-off value, which was applied for dividing cholangiocarcinoma patients into low and high immune score group. To explore the differences in tumor microenvironment and immunotherapy between immune cell-related subtypes and immune score groups of cholangiocarcinoma.\nResults: 34 prognostic immune genes and three immunocell-related subtypes with statistically significant prognosis (IC1, IC2 and IC3) were identified. Among them, IC1 and IC3 showed higher immune cell infiltration, and IC3 may be more suitable for immunotherapy and chemotherapy. 10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analysis showed that RiskScore had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CSRP1, LEP, MUC4 and SEMA4B) of 10 genes were abnormal expressions between cancer and paracancer tissue. Immune cells infiltration in high immune score group was generally increased, and it was more suitable for chemotherapy. In GSE112366-Crohn's disease dataset, 6 of 10 immune genes had expression differences between Crohn's disease and healthy control. The area under ROC obtained 0.671 based on 10-immune gene signature. Moreover, the model had a sound performance in Crohn's disease.\nConclusion: The prediction of tumor immune score model in predicting immune microenvironment, immunotherapy and chemotherapy in patients with cholangiocarcinoma has shown its potential for indicating the effect of immunotherapy on patients with cholangiocarcinoma.", "claims": [{"id": "claim-method", "statement": "Based on immune genes associated with prognosis, using the glmnet package, here LASSO regression was executed (15).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36817485", "locator": "sections[9] \"Developing a prognostic model for cholangiocarcinoma\" paragraph 2", "quote": "Based on immune genes associated with prognosis, using the glmnet package, here LASSO regression was executed (15)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36817485", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study screened 10 prognostic immune genes and constructed a tumor immune score model with prognostic prediction AUCs exceeding 0.64 across the analyzed cholangiocarcinoma datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36817485", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed."}, {"asset": "source:pmid:36817485", "locator": "sections[17] \"Validation of prognostic model\" paragraph 2", "quote": "From the current data, the model accuracy in prediction of 1‐, 3‐, and 5‐year survival rates in above datasets was higher, moreover, the area under the curve (AUC) exceeded 0.64."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study screened 10 prognostic immune genes and constructed a tumor immune score model with prognostic prediction AUCs exceeding 0.64 across the analyzed cholangiocarcinoma datasets.", "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:36817485"], "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:E-MTAB-6389"], "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:E-MTAB-6389"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36817485", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1028404", "sha256": "sha256:adf649720859f086f2514a82ec2dc8591a13287aa5d5de339f35fba8fc582eed", "bytes": 26900, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1028404", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9932535", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:E-MTAB-6389", "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=E-MTAB-6389", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "E-MTAB-6389", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE107943", "E-MTAB-6389", "GSE112366"], "resolved_accessions": ["GSE107943", "E-MTAB-6389", "GSE112366"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."], "basis": "The paper names GSE107943, E-MTAB-6389, GSE112366 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:36817485", "data:geo:E-MTAB-6389"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:E-MTAB-6389"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:28:14Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8182, "completion_tokens": 1640, "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:36817485", "type": "paper", "label": "pmid:36817485"}, {"id": "source:pmid:36817485", "type": "manuscript", "label": "PMID 36817485 full text"}, {"id": "data:geo:E-MTAB-6389", "type": "dataset", "label": "GEO E-MTAB-6389"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36817485", "type": "paper", "label": "pmid:36817485"}, {"id": "source:pmid:36817485", "type": "manuscript", "label": "PMID 36817485 full text"}, {"id": "data:geo:E-MTAB-6389", "type": "dataset", "label": "GEO E-MTAB-6389"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36817485", "type": "extraction", "used": ["source:pmid:36817485"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-E-MTAB-6389", "type": "accession_resolution", "used": ["data:geo:E-MTAB-6389"], "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:36817485", "parent_hash": "sha256:f931e199d36531ba39f183aa99f8eacb317ca264b3e70d8d2f1cd4f9263db82d", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:E-MTAB-6389 licence not read", "asset:data:geo:E-MTAB-6389 not downloaded", "asset:data:geo:GSE107943 licence not read", "asset:data:geo:GSE107943 not downloaded", "asset:data:geo:GSE112366 licence not read", "asset:data:geo:GSE112366 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": 0.93, "evidence": ["had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CS"], "order_basis": "paper_text_order:1733", "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": ["Based on immune genes associated with prognosis, using the glmnet package, here LASSO regression was executed (15)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}