Network models of primary melanoma microenvironments identify key melanoma regulators underlying prognosis

Abstract

Melanoma is the most lethal skin malignancy, driven by genetic and epigenetic alterations in the complex tumour microenvironment. While large-scale molecular profiling of melanoma has identified molecular signatures associated with melanoma progression, comprehensive systems-level modeling remains elusive. This study builds up predictive gene network models of molecular alterations in primary melanoma by integrating large-scale bulk-based multi-omic and single-cell transcriptomic data. Incorporating clinical, epigenetic, and proteomic data into these networks reveals key subnetworks, cell types, and regulators underlying melanoma progression. Tumors with high immune infiltrates are found to be associated with good prognosis, presumably due to induced CD8+ T-cell cytotoxicity, via MYO1F-mediated M1-polarization of macrophages. Seventeen key drivers of the gene subnetworks associated with poor prognosis, including the transcription factor ZNF180, are tested for their pro-tumorigenic effects in vitro. The anti-tumor effect of silencing ZNF180 is further validated using in vivo xenografts. Experimentally validated targets of ZNF180 are enriched in the ZNF180 centered network and the known pathways such as melanoma cell maintenance and immune cell infiltration. The transcriptional networks and their critical regulators provide insights into the molecular mechanisms of melanomagenesis and pave the way for developing therapeutic strategies for melanoma.

KIF2C Is a Novel Prognostic Biomarker and Correlated with Immune Infiltration in Endometrial Cancer

“…A prior study reported that liver metastasis induced tumor-specific CD8 + T cell loss in preclinical models, which mirror the systemic CD8 + T cell loss and reduced immunotherapy efficacy observed in patients with liver metastasis [ 24 ]. High CD8 + T cells immune infiltration is found to be correlated with a good prognosis in melanoma [ 25 ]. The high density of direct CD8 + T/B cell interactions also can predict patients with an excellent prognosis, who would receive less invasive treatment in oropharyngeal squamous cell carcinoma [ 26 ].…”

The landscape of human tissue and cell type specific expression and co-regulation of senescence genes

“…We constructed gene co-expression networks from normalized and covariate-adjusted gene expression data. As age may influence senescence geneexpression, we constructed two sets of gene co-expression networks through the established multiscale gene co-expression network analysis (MEGENA) [ 27 – 30 ], one based on the data adjusted for age and the other from the data without age adjustment. Briefly, Pearson correlation coefficients (PCCs) were computed for all gene pairs.…”

“…The in vivo expression patterns of SnGs across multiple human tissues remain unknown, and the cell-type specific molecular signatures of cellular senescence have not been revealed. In this study, we applied our previously established system biology approach [ 27 – 30 ] to identify co-expressed networks of SnGs in 50 non-diseased human tissues. We identified co-expression structures of SnGs and constructed a consensus senescence network conserved across multiple healthy tissues.…”

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

“…In addition, single-cell techniques have been applied in identifying biomarkers to predict prognosis of cancer patients. For instance, by integrating large-scale bulk multi-omics and single-cell transcriptomic data of primary melanoma, a predictive model was constructed and 17 genes associated with the poor prognosis of patients were identified (Song et al, 2021). Finally, single-cell sequencing has been used to reveal the prognostic roles of stromal cell heterogeneity in multiple Cancers (Savas et al, 2018;Dominguez et al, 2020;Gong et al, 2021).…”