ArticleScientific reports2017
Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 40 citations in OpenAlex.
- MOFR: Multi-omics Feature Reconstruction for Cancer Classification and Metastasis Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Distinct Transcriptomic and Tumor Microenvironment Profiles in Sinonasal Mucosal Melanoma and Aggressive Cutaneous Melanomas.Cancers · 2024Article
- Genetic effects on the skin methylome in healthy older twins.American journal of human genetics · 2024Article
- 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
- Mutation p.Arg127Pro in the 1A Domain of KRT16 Causes Pachyonychia Congenita in Chinese Patient: A Case Report of PC Associated with Acral Melanoma.Clinical, cosmetic and investigational dermatology · 2024Article
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- The transmembrane proteins (TMEM) and their role in cell proliferation, migration, invasion, and epithelial-mesenchymal transition in cancer.Frontiers in oncology · 2023Review
- RNA-seq and ChIP-seq Identification of Unique and Overlapping Targets of GLI Transcription Factors in Melanoma Cell Lines.Cancers · 2022Article
- Bioinformatic analysis identifies epidermal development genes that contribute to melanoma progression.Medical oncology (Northwood, London, England) · 2022Article
- Analysis of Melanoma Gene Expression Signatures at the Single-Cell Level Uncovers 45-Gene Signature Related to Prognosis.Biomedicines · 2022Article
- Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma.JAMA network open · 2022Article
- A new blood-based RNA signature (RiScience · 2022Article
- Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View.International journal of molecular sciences · 2022Review
- Incidence Trends and Survival Prediction of Malignant Skin Cancer: A SEER-Based Study.International journal of general medicine · 2022Article
- PathExt: a general framework for path-based mining of omics-integrated biological networks.Bioinformatics (Oxford, England) · 2021Article
- VBEBioMedicine · 2021Article
- Transcriptional signatures underlying dynamic phenotypic switching and novel disease biomarkers in a linear cellular model of melanoma progression.Neoplasia (New York, N.Y.) · 2021Article
- Transcript levels of keratin 1/5/6/14/15/16/17 as potential prognostic indicators in melanoma patients.Scientific reports · 2021Article
- Machine learning and deep learning methods that use omics data for metastasis prediction.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.