ArticleScientific reports2019
Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 78 citations in OpenAlex.
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- Establishing and validating a new metabolic marker-driven prognosis signature for cutaneous melanoma.Scientific reports · 2025Article
- CDKN2A, a key gene in copper-induced cell death model, influencing melanoma invasion and apoptosis.Discover oncology · 2025Article
- regionalpcs improve discovery of DNA methylation associations with complex traits.Nature communications · 2025Article
- Identification of multiple prognostic biomarker sets for risk stratification in SKCM.Frontiers in bioinformatics · 2025Article
- What is the real value of omics data? Enhancing research outcomes and securing long-term data excellence.Nucleic acids research · 2024Article
- Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer.Scientific reports · 2024Article
- Review
- GLI Transcriptional Targets S100A7 and KRT16 Show Upregulated Expression Patterns in Epidermis Overlying the Tumor Mass in Melanoma Samples.International journal of molecular sciences · 2024Article
- Molecular Markers in Melanoma Progression: A Study on the Expression of miRNA Gene Subtypes in Tumoral vs. Benign Nevi.Current oncology (Toronto, Ont.) · 2024Observational
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- Advances in cancer mechanobiology: Metastasis, mechanics, and materials.APL bioengineering · 2024Review
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression Using Transfer Learning.Genes · 2022Article
- Article
- Bioinformatic analysis identifies epidermal development genes that contribute to melanoma progression.Medical oncology (Northwood, London, England) · 2022Article
- Assessment of the Predictive Value of Spectrophotometric Skin Color Parameters and Environmental and Behavioral Factors in Estimating the Risk of Skin Cancer: A Case-Control Study.Journal of clinical medicine · 2022Article
- Long non-coding RNA (FALEC) promotes malignant behaviors of gastric cancer cells by regulating miR-203b/PIM3 axis.Annals of translational medicine · 2022Article
- A Pancancer Analysis of the Oncogenic Role of S100 Calcium Binding Protein A7 (S100A7) in Human Tumors.Biology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The metastatic Skin Cutaneous Melanoma (SKCM) has been associated with diminished survival rates and high mortality rates worldwide. Thus, segregating metastatic melanoma from the primary tumors is crucial to employ an optimal therapeutic strategy for the prolonged survival of patients. The SKCM mRNA, miRNA and methylation data of TCGA is comprehensively analysed to recognize key genomic features that can segregate metastatic and primary tumors. Further, machine learning models have been developed using selected features to distinguish the same. The Support Vector Classification with Weight (SVC-W) model developed using the expression of 17 mRNAs achieved Area under the Receiver Operating Characteristic (AUROC) curve of 0.95 and an accuracy of 89.47% on an independent validation dataset. This study reveals the genes C7, MMP3, KRT14, LOC642587, CASP7, S100A7 and miRNAs hsa-mir-205 and hsa-mir-203b as the key genomic features that may substantially contribute to the oncogenesis of melanoma. Our study also proposes genes ESM1, NFATC3, C7orf4, CDK14, ZNF827, and ZSWIM7 as novel putative markers for cutaneous melanoma metastasis. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP ( http://webs.iiitd.edu.in/raghava/cancerspp/ ).
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