Genetic and functional interaction network analysis reveals global enrichment of regulatory T cell genes influencing basal cell carcinoma susceptibility

Abstract

Background Basal cell carcinoma (BCC) of the skin is the most common form of human cancer, with more than 90% of tumours presenting with clear genetic activation of the Hedgehog pathway. However, polygenic risk factors affecting mechanisms such as DNA repair and cell cycle checkpoints or which modulate the tumour microenvironment or host immune system play significant roles in determining whether genetic mutations culminate in BCC development. We set out to define background genetic factors that play a role in influencing BCC susceptibility via promoting or suppressing the effects of oncogenic drivers of BCC. Methods We performed genome-wide association studies (GWAS) on 17,416 cases and 375,455 controls. We subsequently performed statistical analysis by integrating data from population-based genetic studies of multi-omics data, including blood- and skin-specific expression quantitative trait loci and methylation quantitative trait loci, thereby defining a list of functionally relevant candidate BCC susceptibility genes from our GWAS loci. We also constructed a local GWAS functional interaction network (consisting of GWAS nearest genes) and another functional interaction network, consisting specifically of candidate BCC susceptibility genes. Results A total of 71 GWAS loci and 46 functional candidate BCC susceptibility genes were identified. Increased risk of BCC was associated with the decreased expression of 26 susceptibility genes and increased expression of 20 susceptibility genes. Pathway analysis of the functional candidate gene regulatory network revealed strong enrichment for cell cycle, cell death, and immune regulation processes, with a global enrichment of genes and proteins linked to TReg cell biology. Conclusions Our genome-wide association analyses and functional interaction network analysis reveal an enrichment of risk variants that function in an immunosuppressive regulatory network, likely hindering cancer immune surveillance and effective antitumour immunity.

A novel classification method for NSCLC based on the background interaction network and the edge-perturbation matrix

“…However, recent research on biological functional networks has discovered that heterogeneity can be avoided or minimized to a certain extent through the use of sequencing and bioinformatics analysis technology [ 10 – 14 ]. Thus, using the R language-based bioinformatics analysis technology, we designed the following research to re-evaluate the alterations in the NSCLC genome after excluding tumor heterogeneity: Based on edge perturbation [ 15 – 18 ], functional gene interaction networks were used to deduce the pathological environment of individual patients with NSCLC [ 19 – 22 ], and to identify cancer subtypes with the same or similar status, followed by a multi-dimensional and multi-omics comprehensive analysis for validation [ 23 – 26 ].…”

“…Based on edge perturbation [ 15 – 18 ], functional gene interaction networks were used to deduce the pathological environment of NSCLC patients at the individual level [ 19 – 22 ], and to identify cancer subtypes with the same or similar status, followed by a multi-dimensional and multi-omics comprehensive analysis for validation [ 23 – 26 ].…”

TGF-β/VEGF-A Genetic Variants Interplay in Genetic Susceptibility to Non-Melanocytic Skin Cancer

“…HLA class II , TP63, FOXP1 genes were associated with NMSC [ 68 , 69 ]. However, novel approaches that integrate skin expression-related single-nucleotide polymorphisms (eSNPs) and pathway analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), or BioCarta databases allowed identification of the role of genes of the TGF-β pathway interacting with genetic activation of the Hedgehog pathway [ 70 , 71 ]. The NO pathway in the BioCarta includes VEGFA SNPs and plays an important role in the regulation of vascular endothelial function facilitating, in particular, BCC development and an angiogenic response propagated by the growing skin cancer [ 72 ].…”

Integrative Transcriptome-Wide Analyses Uncover Novel Risk-Associated MicroRNAs in Hormone-Dependent Cancers

“…Deriving HEIDI test statistic is available in the Methods section of Zhu et al (2016). Recent SMR-HEIDI method-based studies have demonstrated that P HEIDI > 0.01 provides better predictions compared to the P HEIDI > 0.05 threshold defined in the original paper (Zhu et al, 2016;Wu Y. et al, 2018;Adolphe et al, 2021). Therefore, we used P HEIDI > 0.01 to exclude miRNA genes that belong to the linkage models.…”