A scoping review on deep learning for next-generation RNA-Seq. data analysis

HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data

Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.

Investigating the impact of regulatory B cells and regulatory B cell-related genes on bladder cancer progression and immunotherapeutic sensitivity

BACKGROUND

Regulatory B cells (Bregs), a specialized subset of B cells that modulate immune responses and maintain immune tolerance in malignant tumors, have not been extensively investigated in the context of bladder cancer (BLCA). This study aims to elucidate the roles of Bregs and Breg-related genes in BLCA.

METHODS

We assessed Breg infiltration levels in 34 pairs of BLCA and corresponding paracancerous tissues using immunohistochemical staining. We conducted transwell and wound healing assays to evaluate the impact of Bregs on the malignant phenotype of SW780 and T24 cells. Breg-related genes were identified through gene sets and transcriptional analysis. The TCGA-BLCA cohort served as the training set, while the IMvigor210 and 5 GEO cohorts were used as external validation sets. We employed LASSO regression and random forest for feature selection and developed a risk signature using Cox regression. Primary validation of the risk signature was performed through immunohistochemical staining and RT-qPCR experiments using the 34 local BLCA samples. Additionally, we employed transfection assays and flow cytometry to investigate Breg expansion ability and immunosuppressive functions.

RESULTS

Breg levels in BLCA tissues were significantly elevated compared to paracancerous tissues (P < 0.05) and positively correlated with tumor malignancy (P < 0.05). Co-incubation of SW780 and T24 cells with Bregs resulted in enhanced invasion and migration abilities (all P < 0.05). We identified 27 Breg-related genes, including CD96, OAS1, and CSH1, which were integrated into the risk signature. This signature demonstrated robust prognostic classification across the 6 cohorts (pooled HR = 2.25, 95% CI = 1.52-3.33). Moreover, the signature exhibited positive associations with advanced tumor stage (P < 0.001) and Breg infiltration ratios (P < 0.05) in the local samples. Furthermore, the signature successfully predicted immunotherapeutic sensitivity in three cohorts (all P < 0.05). Knockdown of CSH1 in B cells increased Breg phenotype and enhanced suppressive ability against CD8 + T cells (all P < 0.05).

CONCLUSIONS

Bregs play a pro-tumor role in the development of BLCA. The Breg-related gene signature established in this study holds great potential as a valuable tool for evaluating prognosis and predicting immunotherapeutic response in BLCA patients.

O. Improved meta-analysis pipeline ameliorates distinctive gene regulators of diabetic vasculopathy in human endothelial cell (hECs) RNA-Seq data

Enormous gene expression data generated through next-generation sequencing (NGS) technologies are accessible to the scientific community via public repositories. The data harboured in these repositories are foundational for data integrative studies enabling large-scale data analysis whose potential is yet to be fully realized. Prudent integration of individual gene expression data i.e. RNA-Seq datasets is remarkably challenging as it encompasses an assortment and series of data analysis steps that requires to be accomplished before arriving at meaningful insights on biological interrogations. These insights are at all times latent within the data and are not usually revealed from the modest individual data analysis owing to the limited number of biological samples in individual studies. Nevertheless, a sensibly designed meta-analysis of select individual studies would not only maximize the sample size of the analysis but also significantly improves the statistical power of analysis thereby revealing the latent insights. In the present study, a custom-built meta-analysis pipeline is presented for the integration of multiple datasets from different origins. As a case study, we have tested with the integration of two relevant datasets pertaining to diabetic vasculopathy retrieved from the open source domain. We report the meta-analysis ameliorated distinctive and latent gene regulators of diabetic vasculopathy and uncovered a total of 975 i.e. 930 up-regulated and 45 down-regulated gene signatures. Further investigation revealed a subset of 14 DEGs including CTLA4, CALR, G0S2, CALCR, OMA1, and DNAJC3 as latent i.e. novel as these signatures have not been reported earlier. Moreover, downstream investigations including enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs revealed durable disease association signifying their potential as novel transcriptomic biomarkers of diabetic vasculopathy. While the meta-analysis of individual whole transcriptomic datasets for diabetic vasculopathy is exclusive to our comprehension, however, the novel meta-analysis pipeline could very well be extended to study the mechanistic links of DEGs in other disease conditions.