The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Background: Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and play fundamental roles in various types of cancer. Current developments in transcriptome analyses unveiled the existence of lncRNAs; however, their functional characterization remains a challenge. Methods: A bioinformatics screen was performed by integration of multiple omics data in hepatocellular carcinoma (HCC) prioritizing a novel oncogenic lncRNA, LINC01132. Expression of LINC01132 in HCC and control tissues was validated by qRT-PCR. Cell viability and migration activity was examined by MTT and transwell assays. Finally, our results were confirmed in vivo mouse model and ex vivo patient derived tumor xenograft experiments to determine the mechanism of action and explore LINC01132-targeted immunotherapy. Results: Systematic investigation of lncRNAs genome-wide expression patterns revealed LINC01132 as an oncogene in HCC. LINC01132 is significantly overexpressed in tumor and associated with poor overall survival of HCC patients, which is mainly driven by copy number amplification. Functionally, LINC01132 overexpression promoted cell growth, proliferation, invasion and metastasis in vitro and in vivo. Mechanistically, LINC01132 acts as an oncogenic driver by physically interacting with NRF and enhancing the expression of DPP4. Notably, LINC01132 silencing triggers CD8+ T cells infiltration, and LINC01132 knockdown combined with anti-PDL1 treatment improves antitumor immunity, which may prove a new combination therapy in HCC. Conclusions: LINC01132 functions as an oncogenic driver that induces HCC development via the NRF1/DPP4 axis. Silencing LINC01132 may enhance the efficacy of anti-PDL1 immunotherapy in HCC patients.

Background: The tripartite motif (TRIM) proteins function as important regulators in innate immunity, tumorigenesis, cell differentiation and ontogenetic development. However, we still lack knowledge about the genetic and transcriptome alterations landscape of TRIM proteins across cancer types. Methods: We comprehensively reviewed and characterized the perturbations of TRIM genes across > 10,000 samples across 33 cancer types. Genetic mutations and transcriptome of TRIM genes were analyzed by diverse computational methods. A TRIMs score index was calculated based on the expression of TRIM genes. The correlation between TRIMs scores and clinical associations, immune cell infiltrations and immunotherapy response were analyzed by correlation coefficients and gene set enrichment analysis. Results: Alterations in TRIM genes and protein levels frequently emerge in a wide range of tumors and affect expression of TRIM genes. In particular, mutations located in domains are likely to be deleterious mutations. Perturbations of TRIM genes are correlated with expressions of immune checkpoints and immune cell infiltrations, which further regulated the cancer- and immune-related pathways. Moreover, we proposed a TRIMs score index, which can accurately predict the clinical outcome of cancer patients. TRIMs scores of patients are correlated with clinical survival and immune therapy response across cancer types. Identifying the TRIM genes with genetic and transcriptome alterations will directly contribute to cancer therapy in the context of predictive, preventive, and personalized medicine. Conclusions: Our study provided a comprehensive analysis and resource for guiding both mechanistic and therapeutic analyses of the roles of TRIM genes in cancer.