Comprehensive exploration of tumor immune microenvironment feature and therapeutic response in colorectal cancer based on a novel immune-related long non-coding RNA prognostic signature.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36092924", "kind": "science-record", "revision": 4, "content_hash": "sha256:fd3b5a25500db53cbf88e78a43a898fad695e052a4481846a482820e3ef2e739", "created_at": "2026-11-01T18:15:20Z", "updated_at": "2026-11-01T22:07:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive exploration of tumor immune microenvironment feature and therapeutic response in colorectal cancer based on a novel immune-related long non-coding RNA prognostic signature.", "doi": "10.3389/fgene.2022.962575", "pmid": "36092924", "authors": ["Xueliang Zhou", "Batuer Aikemu", "Shuchun Li", "Yanfei Shao", "Hongtao Jia", "Ling Huang", "Hiju Hong", "Sen Zhang", "Qiushi Tang", "Ruijun Pan", "Jing Sun", "Minhua Zheng"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Colorectal cancer (CRC) is one of the most common malignant tumors with a high incidence rate and mortality. LncRNA is an important regulator of the immune system. It is of great significance to study immune-related lncRNAs (IR-lncRNAs) for CRC. In this study, we screened IR-lncRNAs differentially expressed in normal and CRC tissues, and Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator were applied to construct IR-lncRNA prognostic signature in TCGA training dataset, and its predictive capability for the prognosis of CRC patients was verified in GSE39582 validation dataset. The novel signature was identified as an independent predictor of prognosis in CRC patients. In addition, the signature could accurately predict the feature of the immune microenvironment and therapeutic response in CRC patients. The CMap database was adopted to screen for small molecule candidate drugs that can reverse and treat high-risk CRC patients. Finally, the expression of six IR-lncRNAs were verified by qRT-PCR in clinical specimens from our patient cohort. In conclusion, we construct an IR-lncRNA prognostic signature, which is a powerful biomarker of CRC and can accurately predict the prognosis, immune microenvironment feature, and therapeutic response of CRC patients.", "claims": [{"id": "claim-method", "statement": "The optimal lambda value was obtained by LASSO Cox regression and cross-validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36092924", "locator": "sections[17] \"Construction of IR-lncRNA prognostic signature\" paragraph 2", "quote": "The optimal lambda value was obtained by LASSO Cox regression and cross-validation."}], "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:36092924", "locator": "sections[27] \"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": "In conclusion, we construct an IR-lncRNA prognostic signature, which is a powerful biomarker of CRC and can accurately predict the prognosis, immune microenvironment feature, and therapeutic response of CRC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36092924", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In conclusion, we construct an IR-lncRNA prognostic signature, which is a powerful biomarker of CRC and can accurately predict the prognosis, immune microenvironment feature, and therapeutic response of CRC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In conclusion, we construct an IR-lncRNA prognostic signature, which is a powerful biomarker of CRC and can accurately predict the prognosis, immune microenvironment feature, and therapeutic response of CRC patients.", "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:36092924"], "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. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:36092924", "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": "2026-11-01T18:05:07Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14824, "completion_tokens": 2909, "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:36092924", "type": "paper", "label": "pmid:36092924"}, {"id": "source:pmid:36092924", "type": "manuscript", "label": "PMID 36092924 full text"}, {"id": "data:geo:GSE39582", "type": "dataset", "label": "GEO GSE39582"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36092924", "type": "paper", "label": "pmid:36092924"}, {"id": "source:pmid:36092924", "type": "manuscript", "label": "PMID 36092924 full text"}, {"id": "data:geo:GSE39582", "type": "dataset", "label": "GEO GSE39582"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36092924", "type": "extraction", "used": ["source:pmid:36092924"], "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:36092924", "parent_hash": "sha256:934c879c64153589f024f707e2ff8ea2e1860e7ed09c544ac5fbc36c3e7a0502", "fork_of": null, "generation": 3}, "gaps": ["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": "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": ["The optimal lambda value was obtained by LASSO Cox regression and cross-validation."], "order_basis": "analysis_only", "id": "assay:1"}]}