Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34422810", "kind": "science-record", "revision": 7, "content_hash": "sha256:0bbf663cdff4a211caea0dba8175b575d5d798512e56e33944fe6d39e1336bcf", "created_at": "2026-11-01T15:41:48Z", "updated_at": "2026-11-01T22:04:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach.", "doi": "10.3389/fcell.2021.687245", "pmid": "34422810", "authors": ["Simin Li", "Zhaoyi Mai", "Wenli Gu", "Anthony Chukwunonso Ogbuehi", "Aneesha Acharya", "George Pelekos", "Wanchen Ning", "Xiangqiong Liu", "Yupei Deng", "Hanluo Li", "Bernd Lethaus", "Vuk Savkovic", "Rüdiger Zimmerer", "Dirk Ziebolz", "Gerhard Schmalz", "Hao Wang", "Hui Xiao", "Jianjiang Zhao"], "journal": "Frontiers in cell and developmental biology", "year": 2021, "abstract": "Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCC-related genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumor-infiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area.", "claims": [{"id": "claim-method", "statement": "The R package survival (Lin and Zelterman, 2002) was employed to mine ISGs that were significant prognostic indicators using Cox proportional hazards model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34422810", "locator": "sections[13] \"The Survival Analysis of ISGs\" paragraph 2", "quote": "The R package survival (Lin and Zelterman, 2002) was employed to mine ISGs that were significant prognostic indicators using Cox proportional hazards model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: GSE41613 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613), GSE42743 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42743), and GSE75538 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75538).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34422810", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: GSE41613 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613), GSE42743 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42743), and GSE75538 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75538)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:34422810"], "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:GSE41613"], "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:GSE41613"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34422810", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2021.687245", "sha256": "sha256:0d1b4ff5769d93698bbb561e200434e6b996ed68abca00134350635a9bdbb03a", "bytes": 92936, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcell.2021.687245", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8375681", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE41613", "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=GSE41613", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE41613", "version": null, "note": "Publicly available datasets were analyzed in this study. 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6}, "table": {"label": "TABLE 1", "caption": "The detailed data information (i.e., genes number, sample size in the expression profile, sample size with the clinical information, and events of dead and alive, respectively) of the inferring (TCGA dataset) and cohort datasets (i.e., GSE41613, GSE42743, GSE75538, and ICGC dataset) analyzed in the current research.", "rows": [["Dataset", "Genes number", "Samples from the expression profile", "Samples with clinical information", "Events (alive)", "Events (dead)"], ["TCGA", "19754", "335", "317", "203", "114"], ["GSE41613", "23516", "97", "97", "46", "51"], ["GSE42743", "23516", "103", "71", "31", "40"], ["GSE75538", "20818", "28", "14", "11", "3"], ["ICGC", "24003", "40", "40", "32", "8"]], "row_count": 6, "column_count": 6, "numeric_cells": 28, "header_rows": 1, "locator": "section[3]/table-wrap[1]#T1"}}], "gaps": ["asset:data:geo:GSE41613 not downloaded", "asset:data:geo:GSE41613 licence not read", "asset:data:geo:GSE42743 not 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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": ["GSE41613", "GSE42743", "GSE75538"], "resolved_accessions": ["GSE41613", "GSE42743", "GSE75538"], "statements": ["Publicly available datasets were analyzed in this study. This data can be found here: GSE41613 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613), GSE42743 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42743), and GSE75538 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75538)."], "basis": "The paper names GSE41613, GSE42743, GSE75538 in NCBI 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:34422810", "data:geo:GSE41613", "asset:table:pmid:34422810:1"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE41613"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:34:06Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 70217, "completion_tokens": 2571, "cached_tokens": 776}}, "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:34422810", "type": "paper", "label": "pmid:34422810"}, {"id": "source:pmid:34422810", "type": "manuscript", "label": "PMID 34422810 full text"}, {"id": "data:geo:GSE41613", "type": "dataset", "label": "GEO GSE41613"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34422810", "type": "paper", "label": "pmid:34422810"}, {"id": "source:pmid:34422810", "type": "manuscript", "label": "PMID 34422810 full text"}, {"id": "data:geo:GSE41613", "type": "dataset", "label": "GEO GSE41613"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34422810", "type": "extraction", "used": ["source:pmid:34422810"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE41613", "type": "accession_resolution", "used": ["data:geo:GSE41613"], "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:34422810", "parent_hash": "sha256:6ab9b7e7e572a0d1f7d6f64c908f0078799544cc36d19fb1704470e099585d7b", "fork_of": null, "generation": 6}, "gaps": ["asset:data:geo:GSE41613 licence not read", "asset:data:geo:GSE41613 not downloaded", "asset:data:geo:GSE42743 licence not read", "asset:data:geo:GSE42743 not downloaded", "asset:data:geo:GSE75538 licence not read", "asset:data:geo:GSE75538 not downloaded", "execution.not_run", "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 R package survival (Lin and Zelterman, 2002) was employed to mine ISGs that were significant prognostic indicators using Cox proportional hazards model."], "order_basis": "analysis_only", "id": "assay:1"}]}