AUTHOR=Wang Fengdi , Cheng Fanjun , Zheng Fang 

TITLE=Bioinformatic-based genetic characterizations of neural regulation in skin cutaneous melanoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1166373

DOI=10.3389/fonc.2023.1166373

ISSN=2234-943X

ABSTRACT=Background: Recent discoveries uncovered the complex interactions among cancer-nerve in several cancer types including skin cutaneous melanoma (SKCM). However, the genetic characterization of neural regulation in SKCM is unclear.
Methods: Transcriptomic expression data were collected from TCGA and GTEx portal and the differences in cancer-nerve crosstalk associated genes expressions between normal skin and SKCM tissues were analyzed. cBioPortal dataset was utilized to implement the gene mutation analysis. PPI analysis was performed by STRING database. Functional enrichment analysis was analyzed by R package clusterProfiler. K-M plotter, univariate, multivariate and LASSO regression were used for prognostic analysis and verification. GEPIA dataset was performed to analyze the association of gene expression with SKCM clinical stage. ssGSEA and GSCA dataset were used for immune cell infiltration analysis. GSEA was used to elucidate the significant function and pathway differences. 
Results: 66 cancer-nerve crosstalk associated genes were identified, 60 of which were up or down regulated in SKCM and KEGG analysis suggested that they mainly enriched in calcium signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway and so on. A gene prognostic model including eight genes (GRIN3A, CCR2, CHRNA4, CSF1, NTN1, ADRB1, CHRNB4, CHRNG). was built and verified by independent cohort GSE59455 and GSE19234. A nomogram was constructed containing clinical characteristics and eight genes above, and the AUCs of the 1-, 3-, and 5-year ROC was 0.850, 0.811, and 0.792 respectively. Expression of CCR2, GRIN3A and CSF1 were associated with SKCM clinical stages. There existed broad and strong correlations of the prognostic gene set with immune infiltration and immune checkpoint genes. CHRNA4 and CHRNG were independent poor prognostic genes, and multiple metabolic pathways were enriched in high CHRNA4 expression cells.
Conclusion: Comprehensive bioinformatics analysis of cancer-nerve crosstalk associated genes in SKCM were performed, and an effective prognostic model was constructed based on clinical characteristics and eight genes (GRIN3A, CCR2, CHRNA4, CSF1, NTN1, ADRB1, CHRNB4, CHRNG)., which were widely related with the clinical stages and immunological features. Our work may be helpful for further investigation in the molecular mechanisms correlated with neural regulation in SKCM, and in searching new therapeutic targets.