Background: Although immunoglobulin A nephropathy (IgAN) is one of the foremost primary glomerular disease, treatment of IgAN is still in infancy. Non-invasive biomarkers are urgently needed for IgAN diagnosis. We investigate the difference in expression profiles of exosomal long non-coding-RNAs (lncRNAs) in plasma from IgAN patients compared with their healthy first-degree relatives, which may reveal novel non-invasive IgAN biomarkers. Methods: We isolated exosomes from the plasma of both IgAN patients and their healthy first-degree relatives. High-throughput RNA sequencing and real-time quantitative polymerase chain reaction (qRT-PCR) was used to validate lncRNA expression profiles. Pathway enrichment analysis was used to predict their nearest protein-coding genes. Results: lncRNA-G21551 was significantly down-regulated in IgAN patients. Interestingly, the nearest protein-coding gene of lncRNA-G21551 was found to be encoding the low affinity receptor of the Fc segment of immunoglobulin G (FCGR3B). Conclusions: Exosomal lncRNA-G21551, with FCGR3B as the nearest protein-coding gene, was down-regulated in IgAN patients, indicating its potential to serve as a non-invasive biomarker for IgAN.

The accumulating evidence demonstrates that the apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC), DNA-editing protein plays an important role in the molecular pathogenesis of cancer. In particular, the APOBEC3 family was shown to induce tumor mutations by an aberrant DNA editing mechanism. However, knowledge regarding the reconstitution of the APOBEC family genes across cancer types is still lacking. Here, we systematically analyzed the molecular alterations, immuno-oncological features, and clinical relevance of the APOBEC family in pan-cancer. We found that APOBEC genes were widely and significantly differentially expressed between normal and cancer samples in 16 cancer types, and that their expression levels are significantly correlated with the prognostic value in 17 cancer types. Moreover, two patterns of APOBEC-mediated stratification with distinct immune characteristics were identified in different cancer types, respectively. In ACC, for example, the first pattern of APOBEC-mediated stratification was closely correlated with the phenotype of immune activation, which was characterized by a high immune score, increased infiltration of CD8 T cells, and higher survival. The other pattern of APOBEC-mediated stratification was closely correlated with the low-infiltration immune phenotype, which was characterized by a low immune score, lack of effective immune infiltration, and poorer survival. Further, we found the APOBEC-mediated pattern with low-infiltration immune was also highly associated with the advanced tumor subtype and the CIMP-high tumor subtype (CpG island hypermethylation). Patients with the APOBEC-mediated pattern with immune activation were more likely to have therapeutic advantages in ICB (immunological checkpoint blockade) treatment. Overall, our results provide a valuable resource that will be useful in guiding oncologic and therapeutic analyses of the role of APOBEC family in cancer.

As one of the hallmarks of cancer, gene fusions play an important role in tumorigenesis, and have been established as biomarkers and therapeutic targets. Although recent years have witnessed the development of gene fusion databases, a tool with interactive analytic functions remains lacking. Here, we introduce fusion profiling interactive analysis (FPIA), a web server to perform interactive and customizable analysis on fusion genes. With this platform, researchers can easily explore fusion‐associated biological and molecular differences including gene expression, tumor purity and ploidy, mutation, copy number variations, protein expression, immune cell infiltration, stemness, telomere length, microsatellite instability, survival and novel peptides based on 33 cancer types from The Cancer Genome Atlas (TCGA) data. Currently, it contains 31 633 fusion events from 6910 patients. FPIA complements the existing gene fusion annotation databases with its multiomics analytic capacity, integrated analysis features, customized analysis selection and user‐friendly design. The comprehensive data analyses by FPIA will greatly facilitate data mining, hypothesis generation and therapeutic target discovery. FPIA is available at http://bioinfo-sysu.com/fpia.

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Diabetic cutaneous ulcers (DCU) are a complication of diabetes patient, which mostly occurring in foot and causing non-healing diabetic foot ulcer. Mesenchymal stem cells (MSCs)-based therapy is currently being investigated...

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system has been widely used for genome engineering and transcriptional regulation in many different organisms. Current CRISPR-activation (CRISPRa) platforms often require multiple components because of inefficient transcriptional activation. Here, we fused different phase-separation proteins to dCas9-VPR (dCas9-VP64-P65-RTA) and observed robust increases in transcriptional activation efficiency. Notably, human NUP98 (nucleoporin 98) and FUS (fused in sarcoma) IDR domains were best at enhancing dCas9-VPR activity, with dCas9-VPR-FUS IDR (VPRF) outperforming the other CRISPRa systems tested in this study in both activation efficiency and system simplicity. dCas9-VPRF overcomes the target strand bias and widens gRNA designing windows without affecting the off-target effect of dCas9-VPR. These findings demonstrate the feasibility of using phase-separation proteins to assist in the regulation of gene expression and support the broad appeal of the dCas9-VPRF system in basic and clinical applications.

Various abnormalities of transcriptional regulation revealed by RNA sequencing (RNA-seq) have been reported in cancers. However, strategies to integrate multi-modal information from RNA-seq, which would help uncover more disease mechanisms, are still limited. Here, we present PipeOne, a cross-platform one-stop analysis workflow for large-scale transcriptome data. It was developed based on Nextflow, a reproducible workflow management system. PipeOne is composed of three modules, data processing and feature matrices construction, disease feature prioritization, and disease subtyping. It first integrates eight different tools to extract different information from RNA-seq data, and then used random forest algorithm to study and stratify patients according to evidences from multiple-modal information. Its application in five cancers (colon, liver, kidney, stomach, or thyroid; total samples n = 2024) identified various dysregulated key features (such as PVT1 expression and ABI3BP alternative splicing) and pathways (especially liver and kidney dysfunction) shared by multiple cancers. Furthermore, we demonstrated clinically-relevant patient subtypes in four of five cancers, with most subtypes characterized by distinct driver somatic mutations, such as TP53, TTN, BRAF, HRAS, MET, KMT2D, and KMT2C mutations. Importantly, these subtyping results were frequently contributed by dysregulated biological processes, such as ribosome biogenesis, RNA binding, and mitochondria functions. PipeOne is efficient and accurate in studying different cancer types to reveal the specificity and cross-cancer contributing factors of each cancer.It could be easily applied to other diseases and is available at GitHub.

The importance of the network defined by phosphatidylinositol-3-kinase (PI3K), AKT and mammalian target of rapamycin (mTOR) downstream of Receptor Tyrosine Kinase (RTK) has been recognized for many years. However, the central role of RICTOR (rapamycin-insensitive companion of mTOR) in this pathway has only recently come to light. The function of RICTOR in pan-cancer still needs to be systematically elucidated. In this study, we examined RICTOR’s molecular characteristics and clinical prognostic value by pan-cancer analysis. Our findings indicate that RICTOR was overexpressed in twelve cancer types, and a high RICTOR expression was linked to poor overall survival. Moreover, the CRISPR Achilles’ knockout analysis revealed that RICTOR was a critical gene for the survival of many tumor cells. Function analysis revealed that RICTOR-related genes were mainly involved in TOR signaling and cell growth. We further demonstrated that the RICTOR expression was significantly influenced by genetic alteration and DNA-methylation in multiple cancer types. Additionally, we found a positive relationship between RICTOR expression and the immune infiltration of macrophages and cancer-associated fibroblasts in Colon adenocarcinoma and Head and Neck squamous cell carcinoma. Finally, we validated the ability of RICTOR in sustaining tumor growth and invasion in the Hela cell line using cell-cycle analysis, the cell proliferation assay, and wound-healing assay. Our pan-cancer analysis highlights the critical role of RICTOR in tumor progression and its potential as a prognostic marker for various cancer types.