Ferroptosis patterns and tumor microenvironment infiltration characterization in esophageal squamous cell cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36568375", "kind": "science-record", "revision": 3, "content_hash": "sha256:20535db293ce47aae24a3c69c36e78a9dc6940cb61108dcf0e859ab2557fff76", "created_at": "2026-11-01T19:01:07Z", "updated_at": "2026-11-01T22:08:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Ferroptosis patterns and tumor microenvironment infiltration characterization in esophageal squamous cell cancer.", "doi": "10.3389/fgene.2022.1047382", "pmid": "36568375", "authors": ["Lu-Lu Zhang", "Wei-Jie Zhu", "Xin-Xin Zhang", "Da Feng", "Xi-Cheng Wang", "Ying Ding", "Dong-Xia Wang", "Yi-Yang Li"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Esophageal Squamous Cell Cancer (ESCC) is an aggressive disease associated with a poor prognosis. As a newly defined form of regulated cell death, ferroptosis plays a crucial role in cancer development and treatment and might be a promising therapeutic target. However, the expression patterns of ferroptosis-related genes (FRGs) in ESCC remain to be systematically analyzed. Methods: First, we retrieved the transcriptional profile of ESCC from TCGA and GEO datasets (GSE47404, GSE23400, and GSE53625) and performed unsupervised clustering to identify different ferroptosis patterns. Then, we used the ssGSEA algorithm to estimate the immune cell infiltration of these patterns and explored the differences in immune cell abundance. Common genes among patterns were finally identified as signature genes of ferroptosis patterns. Results: Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C. Cluster C was abundant in CD8+ T cells and other immune cell infiltration, while cluster A was immune-barren. By comparing the differently expressed genes between clusters of diverse datasets, we defined a gene signature for each cluster and successfully validated it in the TCGA-ESCC dataset. Conclusion: We provided a comprehensive insight into the expression pattern of ferroptosis genes and their interaction with immune cell infiltration. Additionally, we established a gene signature to define the ferroptosis patterns, which might be used to predict the response to immunotherapy.", "claims": [{"id": "claim-method", "statement": "Next, we performed unsupervised clustering based on the expression of ferroptosis genes to identify distinct ferroptosis patterns and classify patients for further analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36568375", "locator": "sections[5] \"Unsupervised clustering for ferroptosis genes\" paragraph 2", "quote": "Next, we performed unsupervised clustering based on the expression of ferroptosis genes to identify distinct ferroptosis patterns and classify patients for further analysis."}], "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:36568375", "locator": "sections[17] \"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": "Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36568375", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C.", "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:36568375"], "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:GSE23400"], "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:group_comparison_wilcoxon", "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:GSE23400"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36568375", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.1047382", "sha256": "sha256:9d3c60c71cbf12d409298cc2503012fcdc4e73cb8b3837c56ea6c2dacc920ec8", "bytes": 41463, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.1047382", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9780266", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE23400", "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=GSE23400", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE23400", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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