An integrated single-cell transcriptomic dataset for non-small cell lung cancer.

As single-cell RNA sequencing (scRNA-seq) has emerged as a great tool for studying cellular heterogeneity within the past decade, the number of available scRNA-seq datasets also rapidly increased. However, reuse of such data is often problematic due to a small cohort size, limited cell types, and insufficient information on cell type classification. Here, we present a large integrated scRNA-seq dataset containing 224,611 cells from human primary non-small cell lung cancer (NSCLC) tumors. Using publicly available resources, we pre-processed and integrated seven independent scRNA-seq datasets using an anchor-based approach, with five datasets utilized as reference and the remaining two, as validation. We created two levels of annotation based on cell type-specific markers conserved across the datasets. To demonstrate usability of the integrated dataset, we created annotation predictions for the two validation datasets using our integrated reference. Additionally, we conducted a trajectory analysis on subsets of T cells and lung cancer cells. This integrated data may serve as a resource for studying NSCLC transcriptome at the single cell level.

Discovering biomarkers for hormone-dependent tumors: in silico study on signaling pathways implicated in cell cycle and cytoskeleton regulation.

Malignancies dependent on hormone homeostasis include breast, ovary, cervical, prostate, testis and uterine tumors. Hormones are involved in signal transduction which orchestrate processes, such as apoptosis, proliferation, cell cycle or cytoskeleton organization. Currently, there is a need for novel biomarkers which would help to diagnose cancers efficiently. In this study, the genes implicated in signaling that is important in hormone-sensitive carcinogenesis were investigated regarding their prognostic significance. Data of seven cancer cohorts were collected from FireBrowse. 54 gene sets implicated in specific pathways were browsed through MSig database. Profiling was assessed via Monocle3, while gene ontology through PANTHER. For confirmation, correlation analysis was performed using WGCNA. Protein-protein networks were visualized via Cytoscape and impact of genes on survival, as well as cell cycle or cytoskeleton-related prognostic signatures, was tested. Several differences in expression profile were identified, some of them allowed to distinguish histology. Functional annotation revealed that various regulation of cell cycle, adhesion, migration, apoptosis and angiogenesis underlie these differences. Clinical traits, such as histological type or cancer staging, were found during evaluation of module-trait relationships. Of modules, the TopHubs (COL6A3, TNR, GTF2A1, NKX3-1) interacted directly with, e.g., PDGFB, ITGA10, SP1 or AKT3. Among TopHubs and interacting proteins, many showed an impact on hazard ratio and affected the cell cycle or cytoskeleton-related prognostic signatures, e.g., COL1A1 or PDGFB. In conclusion, this study laid the foundation for further hormone-sensitive carcinogenesis research through identification of genes which prove that crosstalk between cell cycle and cytoskeleton exists, opening avenues for future therapeutic strategies.