Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0278883 (metastatic melanoma)
 6,224 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Genes in the S100 family are abnormally expressed in a variety of tumor cells and are associated with clinical pathology, but their prognostic value in melanoma patients has not yet been fully elucidated. In this study, we extracted and profiled S100 family mRNA expression data and corresponding clinical data from the Gene Expression Omnibus database to analyze how expression of these genes correlates with clinical pathology. Compared with normal skin, S100A1, S100A13, and S100B were expressed at significantly higher levels in melanoma samples. S100A2, S100A7, S100A8, S100A9, S100A10, S100A11, and S100P were all highly expressed in primary melanoma samples but were expressed at low levels in metastatic melanoma, and all of these genes were strongly correlated with each other (P<0.001). We found the expression of these S100 family genes to be significantly correlated with both lymphatic and distant melanoma metastasis, as well as with American Joint Committee on Cancer grade but not with Clark's grade, age, or sex. This suggests that expression of these genes may be related to the degree of tumor invasion. Although further validation through basic and clinical trials is needed, our results suggest that the S100 family genes have the potential to play an important role in the diagnosis of melanoma. S100 expression may be related to tumor invasion and may facilitate the early diagnosis of melanoma, allowing for a more accurate prognosis. Targeted S100 therapies are also potentially viable strategies in the context of melanoma.
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PMID:Expression and clinical significance of S100 family genes in patients with melanoma. 3021

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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PMID:Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients. 3167 75