Predicting the clinical outcome of melanoma using an immune-related gene pairs signature.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33031392", "kind": "science-record", "revision": 7, "content_hash": "sha256:b51fc96fb0dc52c29fa79a876a713c82d9abb539ef2f19253ccdc0169add1b68", "created_at": "2026-11-01T15:41:12Z", "updated_at": "2026-11-01T21:23:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Predicting the clinical outcome of melanoma using an immune-related gene pairs signature.", "doi": "10.1371/journal.pone.0240331", "pmid": "33031392", "authors": ["Liangliang Meng", "Xiaoxi He", "Xiao Zhang", "Xiaobo Zhang", "Yingtian Wei", "Bin Wu", "Wei Li", "Jing Li", "Yueyong Xiao"], "journal": "PloS one", "year": 2020, "abstract": "OBJECTIVE: Melanoma is rare but dangerous skin cancer, and it can spread rather quickly in the advanced stages of the tumor. Abundant evidence suggests the relationship between tumor development and progression and the immune system. A robust gene risk model could provide an accurate prediction of clinical outcomes. The present study aimed to explore a robust signature of immune-related gene pairs (IRGPs) for estimating overall survival (OS) in malignant melanoma.\nMETHODS: Clinical and genetic data of skin cutaneous melanoma (SKCM) patients from The Cancer Genome Atlas (TCGA) was performed as a training dataset to identify candidate IRGPs for the prognosis of melanoma. Two independent datasets from the Gene Expression Omnibus (GEO) database (GSE65904) and TCGA dataset (TCGA-UVM) were selected for external validation. Univariate and multivariate Cox regression analyses were then performed to explore the prognostic power of the IRGPs signature and other clinical factors. CIBERSORTx was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues.\nRESULTS: A signature consisted of 33 IRGPs was established which was significantly associated with patients' survival in the TCGA-SKCM dataset (P = 2.0×10-16, Hazard Ratio (HR) = 4.220 (2.909 to 6.122)). We found the IRGPs signature exhibited an independent prognostic factor in all the three independent cohorts in both the univariate and multivariate Cox analysis (P<0.01). The prognostic efficacy of the signature remained unaffected regardless of whether BRAF or NRAS was mutated. As expected, the results were verified in the GSE65904 dataset and the TCGA-UVM dataset. We found an apparent shorter OS in patients of the high-risk group in the GSE65904 dataset (P = 2.1×10-3; HR = 1.988 (1.309 to 3.020)). The trend in the results of the survival analysis in TCGA-UVM was as we expected, but the result was not statistically significant (P = 0.117, HR = 4.263 (1.407 to 12.91)). CD8 T cells, activated dendritic cells (DCs), regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group in the TCGA-SKCM dataset(P <0.01).\nCONCLUSION: The results of the present study support the IRGPs signature as a promising marker for prognosis prediction in melanoma.", "claims": [{"id": "claim-data-access", "statement": "The HTSeq-FPKM RNA-seq expression data and clinical data of 471 skin cutaneous melanoma patients were retrieved from The TCGA-SKCM dataset (https://portal.gdc.cancer.gov).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33031392", "locator": "sections[3] \"Data sources of melanoma\" paragraph 2", "quote": "The HTSeq-FPKM RNA-seq expression data and clinical data of 471 skin cutaneous melanoma patients were retrieved from The TCGA-SKCM dataset (https://portal.gdc.cancer.gov)."}], "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:33031392"], "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:33031392", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0240331", "sha256": 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