Definition of immune molecular subtypes with distinct immune microenvironment, recurrence, and PANoptosis features to aid clinical therapeutic decision-making.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36313466", "kind": "science-record", "revision": 4, "content_hash": "sha256:761fa3a6dba606f7c004b41ee78fbf63533a6489bf2d1695482fb01019c77a2f", "created_at": "2026-11-01T18:15:44Z", "updated_at": "2026-11-01T22:07:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Definition of immune molecular subtypes with distinct immune microenvironment, recurrence, and PANoptosis features to aid clinical therapeutic decision-making.", "doi": "10.3389/fgene.2022.1007108", "pmid": "36313466", "authors": ["Sufeng Qiang", "Fei Fu", "Jianjun Wang", "Chunyan Dong"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Objective: Cervical cancer poses a remarkable health burden to females globally. Despite major advances in early detection and treatment modalities, some patients still relapse. The present study proposed a novel immune molecular classification that reflected distinct recurrent risk and therapeutic responses in cervical cancer. Methods: We retrospectively collected two cervical cancer cohorts: TCGA and GSE44001. Consensus clustering approach was conducted based on expression profiling of recurrence- and immune-related genes. The abundance of immune cells was inferred via five algorithms. Immune functions and signatures were quantified through ssGSEA. Genetic mutations were analyzed by maftools package. Immunotherapeutic response was inferred via tumor mutation burden (TMB), Tumor Immune Dysfunction and Exclusion (TIDE), and Submap methods. Finally, we developed a LASSO model for recurrence prediction. Results: Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2). IC2 exhibited better disease free survival (DFS), increased immune cell infiltration within the immune microenvironment, higher expression of immune checkpoints, higher activity of immune-relevant pathways (APC co-inhibition and co-stimulation, inflammation-promoting, MHC class I, IFN response, leukocyte and stromal fractions, macrophage regulation, and TCR Shannon), and higher frequencies of genetic mutations. This molecular classification exhibited a remarkable difference with existing immune subtypes, with diverse PANoptosis (pyroptosis, apoptosis and necroptosis) features. Patients in IC2 were more likely to respond to immunotherapy and targeted, and chemotherapeutic agents. The immune subtype-relevant signature was quantified to predict patients' recurrence risk. Conclusion: Altogether, we developed an immune molecular classification, which can be utilized in clinical practice to aid decision-making on recurrence management.", "claims": [{"id": "claim-method", "statement": "Univariate cox regression analysis of IRGs was implemented based on recurrence time and status both in the TCGA and GSE44001 datasets. In accordance with p < 0.05, recurrence-related IRGs were obtained, with hazard ratio (HR)>1 as a risk factor and HR < 1 as a protective factor. Then, protective and risk factors of the two datasets were separately intersected for subsequent consensus clustering analysis. Unsupervised consensus clustering was implemented for constructing an immune molecular classification using ConsensusClusterPlus package (version 1.52.0) based on recurrence-related IRGs (Wilkerson and Hayes, 2010).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36313466", "locator": "sections[4] \"Unsupervised consensus clustering analysis\" paragraph 2", "quote": "Univariate cox regression analysis of IRGs was implemented based on recurrence time and status both in the TCGA and GSE44001 datasets. In accordance with p < 0.05, recurrence-related IRGs were obtained, with hazard ratio (HR)>1 as a risk factor and HR < 1 as a protective factor. Then, protective and risk factors of the two datasets were separately intersected for subsequent consensus clustering analysis. Unsupervised consensus clustering was implemented for constructing an immune molecular classification using ConsensusClusterPlus package (version 1.52.0) based on recurrence-related IRGs (Wilkerson and Hayes, 2010)."}], "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:36313466", "locator": "sections[28] \"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": "Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36313466", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2).", "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:36313466"], "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:GSE44001"], "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:GSE44001"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36313466", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.1007108", "sha256": "sha256:eedbac81e16e350ef8d1222be1c6251e67e4634d81842d59f4fa2d10f4613cc4", "bytes": 39177, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.1007108", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9606342", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE44001", "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=GSE44001", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE44001", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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Patients in IC2 w", "E S3The gene sets of PANoptosis (pyroptosis, apoptosis and necroptosis). SUPPLEMENTARY TABLE S4The"], "order_basis": "paper_text_order:1668", "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": ["Univariate cox regression analysis of IRGs was implemented based on recurrence time and status both in the TCGA and GSE44001 datasets. In accordance with p < 0.05, recurrence-related IRGs were obtained, with hazard ratio (HR)>1 as a risk factor and HR < 1 as a protective factor. Then, protective and risk factors of the two datasets were separately intersected for subsequent consensus clustering analysis. 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