Effect of Sun exposure-induced ferroptosis mechanisms on pathology and potential biological processes of primary melanoma by microarray data analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36226170", "kind": "science-record", "revision": 5, "content_hash": "sha256:b2fd7e2df5101dde37aac82de07ad5aee52778431432cec4d6c22aca2017f1c2", "created_at": "2026-11-01T18:15:35Z", "updated_at": "2026-11-01T22:50:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Effect of Sun exposure-induced ferroptosis mechanisms on pathology and potential biological processes of primary melanoma by microarray data analysis.", "doi": "10.3389/fgene.2022.998792", "pmid": "36226170", "authors": ["Yakun Gao", "Qiang Hou", "Rong Guo", "Jianghui Ying", "Jiachao Xiong", "Hua Jiang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Objectives: Sunlight exposure is an important environmental factor in the pathogenesis of skin cutaneous melanoma (SKCM). Ultraviolet (UV) from sunlight can cause excessive intracellular production of reactive oxygen species (ROS), resulting in damage from oxidative stress to cells. As a major iron-rich and ROS-producing organelle, mitochondria are considered as an important place for cell ferroptosis. Thus, the pathology and potential biological process of UV exposure-induced ferroptosis in the development of SKCM has aroused our strong interest. Methods: Gene expression profile datasets of melanoma cell line datasets (GSE31909) and UV-irradiated mitochondria dataset (GSE3632) were downloaded from the Gene Expression Omnibus (GEO) database, and ferroptosis-related genes were obtained from the FerrDb v2 database. After identifying the common differentially expressed genes (DEGs), comprehensive analyzes were performed, including functional annotation, protein-protein interaction (PPI) network construction, hub gene identification, and gene and tissue protein expression levels, survival analysis, and immune cell infiltration analysis. Results: A total of 14 common DEGs was identified for subsequent analyses. Seven DEGs, including PSMB4, CRELD2, CDKN2A, TIMP1, NDRG1, ATF3 and JUND, have consistent performance in mRNA and protein expression in normal skin and SKCM tissues can be regarded as a good biomarker with SKCM diagnostic effectiveness. Functional enrichment analysis results indicate that HIF-1 signaling pathway and angiogenesis involved in the pathogenesis and development of SKCM. Induction of ferroptosis in tumor cells by enhancing the function of CD8+ T cells is expected to be an effective intervention to promote tumor therapy. Conclusion: Our study reveals the pathogenesis and potential biological processes of UV exposure-induced ferroptosis in the development of SKCM, which may provide potential immunotherapy targets for SKCM treatment via tumor cell ferroptosis mechanisms.", "claims": [{"id": "claim-method", "statement": "The gene expression profile data were read by GEOquery packages, and the differential expression fold change was calculated by Limma R packages.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36226170", "locator": "sections[4] \"Differential analysis of gene expression profiles\" paragraph 2", "quote": "The gene expression profile data were read by GEOquery packages, and the differential expression fold change was calculated by Limma R packages."}], "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:36226170", "locator": "sections[20] \"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": "A total of 14 common DEGs was identified for subsequent analyses.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36226170", "locator": "sections[12] \"Identification of differentially expressed genes\" paragraph 2", "quote": "After the Venn diagram intersection screening, 14 common DEGs were identified (Figure 1C) and visualized using a heat map (Figure 1D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 14 common DEGs was identified for subsequent analyses.", "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:36226170"], "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:GSE31909"], "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:GSE31909"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36226170", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.998792", "sha256": "sha256:2c4907acbb51901799797870d19cb3220cd00dfedc2836d49a40ccae8864b86a", "bytes": 45037, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.998792", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9548870", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31909", "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=GSE31909", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31909", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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