Comprehensive analysis and identification of key genes and signaling pathways in the occurrence and metastasis of cutaneous melanoma

Bioinformatic analysis identifies epidermal development genes that contribute to melanoma progression

Several diagnostic and prognostic markers for melanoma have been identified in last few years. However, their actual contribution to melanoma progression have not been investigated in detail. This study was aimed to identify genes, biological processes, and signaling pathways implicated in melanoma progression by applying bioinformatics analysis. We identified nine differentially expressed genes (DEGs) (IL36RN, KRT6A, KRT6B, KRT16, S100A7, SPRR1A, SPRR1B, SPRR2B, and KLK7) that were upregulated in primary melanoma compared with metastatic melanoma in all five datasets analyzed. All these genes except IL36RN, both form a protein-protein interaction network and have cellular functions associated with constitutive processes of keratinocytes. Thus, they were generically termed Epidermal Development and Cornification (EDC) genes. The differential expression of these genes in primary and metastatic melanoma was confirmed in the TCGA-SKCM cohort. High expression of the EDC genes correlated with reduced tumor thickness in primary melanoma and shorter survival in metastatic melanoma. Analysis of DEGs from primary melanoma patients displaying high or low expression of all eight EDC revealed that the upregulated genes are enriched in biological process related to cell migration, extracellular matrix organization, invasion, and Epithelial-Mesenchymal Transition. Further analysis of enriched curated oncogenic genesets together with RPPA data of phosphorylated proteins revealed the activation of MEK, ATF2, and EGFR pathways in tumors displaying high expression of EDC genes. Thus, EDC genes may contribute to melanoma progression by promoting the activation of MEK, ATF2, and EGFR pathways together with biological processes associated with tumor aggressiveness.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

BACKGROUND

Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.

METHODS

In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.

RESULTS

A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.

CONCLUSIONS

The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.