Characterization of Aging-Related Genes to Predict Prognosis and Evaluate the Tumor Immune Microenvironment in Malignant Melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35368886", "kind": "science-record", "revision": 4, "content_hash": "sha256:f56c99fad31ac311e0199f0e2f6fe39d280fa97626e4838f9eeab05ec0625b3c", "created_at": "2026-11-01T18:14:11Z", "updated_at": "2026-11-01T22:06:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of Aging-Related Genes to Predict Prognosis and Evaluate the Tumor Immune Microenvironment in Malignant Melanoma.", "doi": "10.1155/2022/1271378", "pmid": "35368886", "authors": ["Ni Zeng", "Chenrui Guo", "Yajun Wang", "Lin Li", "Xi Chen", "Shaoying Gao", "Feng Jiang", "Bilan Cao"], "journal": "Journal of oncology", "year": 2022, "abstract": "Objective: Malignant melanoma (MM) is one of the most malignant types of skin cancer and its incidence and mortality rates are increasing worldwide. Aging is well recognized as a significant risk factor for cancer. However, few studies have analyzed in depth the association between aging-related genes (AGs) and malignant melanoma prognosis with tumor immune microenvironment.\nMethods: Here, we downloaded 471 MM patients from The Cancer Genome Atlas (TCGA) with RNA sequence and clinicopathological data. 58 AGs from the TCGA dataset were examined using Cox regression and the LASSO assay. As a result, a gene signature for aging-related genes was created. The time-dependent ROC curve and Kaplan-Meier analysis were calculated to determine its predictive capability. Moreover, we created a nomogram for the clinicopathologic variables and the AGs gene signature to determine overall survival (OS). We also explored the association between three immune checkpoints, immune cell infiltration, and the aging-related gene signature.\nResults: We established an aging risk model to identify and predict the immune microenvironment in malignant melanoma. Then we developed and validated a prognosis risk model using three AGs (CSNK1E, C1QA, and SOD-2) in the GSE65904 dataset. The aging signature was positively associated with clinical and molecular characteristics and can be used as a prognostic factor for malignant melanoma. The low aging risk score was associated with a poor prognosis and indicated an immunosuppressive microenvironment.\nConclusions: To summarize, we established and validated a model of aging risk based on three aging-related genes that acted as an independent prognostic predictor of overall survival. Besides, it also characterized the immune response in the malignant melanoma microenvironment and could provide a potential indicator of individualized immunotherapy in malignant melanoma.", "claims": [{"id": "claim-method", "statement": "58 AGs from the TCGA dataset were examined using Cox regression and the LASSO assay.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35368886", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "58 AGs from the TCGA dataset were examined using Cox regression and the LASSO assay."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are obtained from publicly accessible datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35368886", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are obtained from publicly accessible datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established and validated a prognostic aging-risk model based on three aging-related genes, C1QA, CSNK1E, and SOD-2, that characterized the melanoma immune microenvironment and independently predicted overall survival.", "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:35368886"], "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:GSE65904"], "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:GSE65904"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35368886", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/1271378", "sha256": 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