Abstract
Single-cell sequencing technology provides insights into the pathology of complex diseases like cancer. Here, we proposed a novel computational framework to explore the molecular mechanisms of cancer called melanoma. We first constructed a disease-specific cell–cell interaction network after data preprocessing and dimensionality reduction. Second, the features of cells in the cell–cell interaction network were learned by node2vec which is a graph embedding technology proposed previously. Then, consensus clusters were identified by considering different clustering algorithms. Finally, cell markers and cancer-related genes were further analyzed by integrating gene regulation pairs. We exploited our model on two independent datasets, which showed interesting results that the differences between clusters obtained by consensus clustering based on network embedding (CCNE) were observed obviously through visualization. For the KEGG pathway analysis of clusters, we found that all clusters are extremely related to MicroRNAs in cancer and HTLV-I infection, and the hub genes in cluster specific regulatory networks, i.e., ETS1, TP53, E2F1, and GATA3 are highly associated with melanoma. Furthermore, our method can also be extended to other scRNA-seq data.
Highlights
Melanoma is a malignant tumor that develops from melanocytes and is a complex multifactorial disease caused by the interaction between genetic susceptibility factors and environmental exposure (Rastrelli et al, 2014; Situm et al, 2014)
We used existing methods to explore the molecular mechanisms of melanoma by this novel pipeline
We investigated the pathology of melanoma via scRNAseq, which revealed the significant impact of hub genes in the development of melanoma
Summary
Melanoma is a malignant tumor that develops from melanocytes and is a complex multifactorial disease caused by the interaction between genetic susceptibility factors and environmental exposure (Rastrelli et al, 2014; Situm et al, 2014). Many studies have been focused on the molecular mechanisms of melanoma, demonstrating that PI3K-Akt and MEK-ERK signaling pathways’ hyperactivation is highly correlated with the malignant transformation and progression of melanoma (Wei et al, 2019). There have been many significant studies based on bulk RNA-seq data of melanoma. Some limitations exist when only bulk RNA-seq data are available for the molecular expression level. As far as we know, few studies focusing on molecular mechanisms of melanoma at the single-cell level (Fattore et al, 2019; Durante et al, 2020)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
