Identification of prognostic gene biomarkers for metastatic skin cancer using data mining

Screening out molecular pathways and prognostic biomarkers of ultraviolet-mediated melanoma through computational techniques

PURPOSE

Ultraviolet radiation causes skin cancer, but the exact mechanism by which it occurs and the most effective methods of intervention to prevent it are yet unknown. For this purpose, our study will use bioinformatics and systems biology approaches to discover potential biomarkers of skin cancer for early diagnosis and prevention of disease with applicable clinical treatments.

METHODS

This study compared gene expression and protein levels in ultraviolet-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database. Then, pathway analysis was employed with a selection of hub genes from the protein-protein interaction (PPI) network and the survival and expression profiles. Finally, potential clinical biomarkers were validated by receiver operating characteristic (ROC) curve analysis.

RESULTS

We identified 32 shared differentially expressed genes (DEGs) by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to the control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation, and activation of the NIMA kinase pathways. The cytoHubba plugin in Cytoscape identified 12 hub genes from PPI; among these 3 DEGs, namely, AURKA, CDK4, and PLK1 were significantly associated with survival (P < 0.05) and highly expressed in skin cancer tissues. For validation purposes, ROC curve analysis indicated two biomarkers: AURKA (area under the curve (AUC) value = 0.8) and PLK1 (AUC value = 0.7), which were in an acceptable range.

CONCLUSIONS

Further translational research, including clinical experiments, teratogenicity tests, and in-vitro or in-vivo studies, will be performed to evaluate the expression of these identified biomarkers regarding the prognosis of skin cancer patients.

Integrative bioinformatics approaches to map key biological markers and therapeutic drugs in Extramammary Paget's disease of the scrotum

Extramammary Paget’s disease (EMPD) is an intra-epidermal adenocarcinoma. Till now, the mechanisms underlying the pathogenesis of scrotal EMPD is poorly known. This present study aims to explore the knowledge of molecular mechanism of scrotal EMPD by identifying the hub genes and candidate drugs using integrated bioinformatics approaches. Firstly, the microarray datasets (GSE117285) were downloaded from the GEO database and then analyzed using GEO2R in order to obtain differentially expressed genes (DEGs). Moreover, hub genes were identified on the basis of their degree of connectivity using Cytohubba plugin of cytoscape tool. Finally, GEPIA and DGIdb were used for the survival analysis and selection of therapeutic candidates, respectively. A total of 786 DEGs were identified, of which 10 genes were considered as hub genes on the basis of the highest degree of connectivity. After the survival analysis of ten hub genes, a total of 5 genes were found to be altered in EMPD patients. Furthermore, 14 drugs of CHEK1, CCNA2, and CDK1 were found to have therapeutic potential against EMPD. This study updates the information and yields a new perspective in the context of understanding the pathogenesis of EMPD. In future, hub genes and candidate drugs might be capable of improving the personalized detection and therapies for EMPD.

Identification of Signatures of Prognosis Prediction for Melanoma Using a Hypoxia Score

Melanoma is one of the most aggressive cancers. Hypoxic microenvironment affects multiple cellular pathways and contributes to tumor progression. The purpose of the research was to investigate the association between hypoxia and melanoma, and identify the prognostic value of hypoxia-related genes. Based on the GSVA algorithm, gene expression profile collected from The Cancer Genome Atlas (TCGA) was used for calculating the hypoxia score. The Kaplan–Meier plot suggested that a high hypoxia score was correlated with the inferior survival of melanoma patients. Using differential gene expression analysis and WGCNA, a total of 337 overlapping genes associated with hypoxia were determined. Protein-protein interaction network and functional enrichment analysis were conducted, and Lasso Cox regression was performed to establish the prognostic gene signature. Lasso regression showed that seven genes displayed the best features. A novel seven-gene signature (including ABCA12, PTK6, FERMT1, GSDMC, KRT2, CSTA, and SPRR2F) was constructed for prognosis prediction. The ROC curve inferred good performance in both the TCGA cohort and validation cohorts. Therefore, our study determined the prognostic implication of the hypoxia score in melanoma and showed a novel seven-gene signature to predict prognosis, which may provide insights into the prognosis evaluation and clinical decision making.