Cell type identification from single-cell transcriptomes in melanoma

Background Single-cell sequencing approaches allow gene expression to be measured at the single-cell level, providing opportunities and challenges to study the aetiology of complex diseases, including cancer. Methods Based on single-cell gene and lncRNA expression levels, we proposed a computational framework for cell type identification that fully considers cell dropout characteristics. First, we defined the dropout features of the cells and identified the dropout clusters. Second, we constructed a differential co-expression network and identified differential modules. Finally, we identified cell types based on the differential modules. Results The method was applied to single-cell melanoma data, and eight cell types were identified. Enrichment analysis of the candidate cell marker genes for the two key cell types showed that both key cell types were closely related to the physiological activities of the major histocompatibility complex (MHC); one key cell type was associated with mitosis-related activities, and the other with pathways related to ten diseases. Conclusions Through identification and analysis of key melanoma-related cell types, we explored the molecular mechanism of melanoma, providing insight into melanoma research. Moreover, the candidate cell markers for the two key cell types are potential therapeutic targets for melanoma.

A non-commutative differential module approach to Alexander modules

The theory of R. Crowell on derived modules is approached within the theory of non-commutative differential modules. We also seek analogies to the theory of cotangent complex from differentials in the commutative ring setting. Finally, we give examples motivated from the theory of Galois coverings of curves.

Identifying Breast Cancer-induced gene perturbations and its application in guiding drug repurposing

Background: Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored. Objective: We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures. Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks was constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed. Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin. Conclusion: As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication

Sevoflurane-medicated the pathway of chemokine receptors bind chemokines in patients undergoing CABG

BackgroundWe aim to identify sevoflurane-induced modules and pathways in patients following coronary artery bypass graft (CABG) surgery, and to further elucidate the molecular mechanisms of the cardioprotective effects of sevoflurane.MethodsDifferential co-expression network (DCN) was constructed. Candidate modules were identified via three steps: selection of seed genes, search of modules using snowball sampling, and refinement of modules. Afterwards, the significance of the candidate modules was assessed. Ultimately, pathway analyses for genes in differential modules were implemented to illuminate the biological processes.ResultsOverall, 122 genes were identified to serve as seed genes. From every seed gene, we extracted 122 modules and the mean node size in a module was 3. By setting the classification accuracy cutoff at 0.9 and the number of nodes in a module at 5, 7 candidate modules were identified, including module 80, 82, 82, 84, 85, 86 and 89. Based on the random permutation test, we found that these 7 candidate modules were all differential ones. Moreover, pathway analysis showed that genes in the differential modules 80, 82, and 85 were all enriched in the pathway of chemokine receptors bind chemokines.ConclusionSevoflurane might exert cardioprotective functions in patients following CABG, partially through regulating the pathway of chemokine receptors bind chemokines.