Circular RNAs (circRNAs), a new class of regulatory noncoding RNAs, play important roles in human diseases. While a growing number of circRNAs have been characterized with biological functions, it is necessary to integrate all the information to facilitate studies on circRNA functions and regulatory networks in human diseases. Circ2Disease database contains 273 manually curated associations between 237 circRNAs and 54 human diseases with strong experimental evidence from 120 studies. Each association includes circRNA name, disease name, expression pattern, experimental method, a brief functional description of the circRNA-disease relationship, and other detailed information. The experimentally validated miRNAs that may be ‘sponged up’ by these circRNAs and their validated targets were also integrated to form a comprehensive regulatory network. Circ2Disease provides a user-friendly interface to browse, search, analyze regulatory network and download data. With the rapidly increasing interest in circRNAs, Circ2Disease will significantly improve our understanding of circRNA deregulation in diseases and is a useful resource for studying posttranscriptional regulatory roles of circRNAs in human diseases.

Endometrial cancer (EC) is a major cause of death among gynecologic malignancies. To improve early detection of EC in patients, we carried out a large plasma-derived exosomal microRNA (miRNA) studies for diagnostic biomarker discovery in EC. Small RNA sequencing was performed to identify candidate exosomal miRNAs as diagnostic biomarkers in 56 plasma samples from healthy subjects and EC patients. These miRNA candidates were further validated in 202 independent plasma samples by droplet digital PCR (ddPCR), 32 pairs of endometrial tumors and adjacent normal tissues by quantitative real-time PCR (qRT-PCR), and matched plasma samples of 12 patients before and after surgery by ddPCR. miR-15a-5p, miR-106b-5p, and miR107 were significantly upregulated in exomes isolated from plasma samples of EC patients compared with healthy subjects. Particularly, miR-15a-5p alone yielded an AUC value of 0.813 to distinguish EC patients with stage I from healthy subjects. The integration of miR-15a-5p and serum tumor markers (CEA and CA125) achieved a higher AUC value of 0.899. There was also a close connection between miR-15a-5p and clinical manifestations in EC patients. Its exosomal expression was not only associated with the depth of muscular infiltration and aggressiveness of EC, but also correlated with levels of reproductive hormones such as TTE and DHEAS. Collectively, plasma-derived exosomal miR-15a-5p is a promising and effective diagnostic biomarker for the early detection of endometrial cancer.

Occurring in over 60% of human genes, alternative polyadenylation (APA) results in numerous transcripts with differing 3’ends, thus greatly expanding the diversity of mRNAs and of proteins derived from a single gene. As a key molecular mechanism, APA is involved in various gene regulation steps including mRNA maturation, mRNA stability, cellular RNA decay, and protein diversification. APA is frequently dysregulated in cancers leading to changes in oncogenes and tumor suppressor gene expressions. Recent studies have revealed various APA regulatory mechanisms that promote the development and progression of a number of human diseases, including cancer. Here, we provide an overview of four types of APA and their impacts on gene regulation. We focus particularly on the interaction of APA with microRNAs, RNA binding proteins and other related factors, the core pre-mRNA 3’end processing complex, and 3’UTR length change. We also describe next-generation sequencing methods and computational tools for use in poly(A) signal detection and APA repositories and databases. Finally, we summarize the current understanding of APA in cancer and provide our vision for future APA related research.

Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predicted from these computational tools lack consistency and are prone to false positives. Here, we developed an approach (DriverML) integrating Rao’s score test and supervised machine learning to identify cancer driver genes. The weight parameters in the score statistics quantified the functional impacts of mutations on the protein. To obtain optimized weight parameters, the score statistics of prior driver genes were maximized on pan-cancer training data. We conducted rigorous and unbiased benchmark analysis and comparisons of DriverML with 20 other existing tools in 31 independent datasets from The Cancer Genome Atlas (TCGA). Our comprehensive evaluations demonstrated that DriverML was robust and powerful among various datasets and outperformed the other tools with a better balance of precision and sensitivity. In vitro cell-based assays further proved the validity of the DriverML prediction of novel driver genes. In summary, DriverML uses an innovative, machine learning-based approach to prioritize cancer driver genes and provides dramatic improvements over currently existing methods. Its source code is available at https://github.com/HelloYiHan/DriverML.

Integration of human papillomavirus (HPV) DNA into the human genome is considered as a key event in cervical carcinogenesis. Here, we perform comprehensive characterization of large-range virus-human integration events in 16 HPV16-positive cervical tumors using the Nanopore long-read sequencing technology. Four distinct integration types characterized by the integrated HPV DNA segments are identified with Type B being particularly notable as lacking E6/E7 genes. We further demonstrate that multiple clonal integration events are involved in the use of shared breakpoints, the induction of inter-chromosomal translocations and the formation of extrachromosomal circular virus-human hybrid structures. Combined with the corresponding RNA-seq data, we highlight LINC00290, LINC02500 and LENG9 as potential driver genes in cervical cancer. Finally, we reveal the spatial relationship of HPV integration and its various structural variations as well as their functional consequences in cervical cancer. These findings provide insight into HPV integration and its oncogenic progression in cervical cancer.

Background: Long non-coding RNAs (lncRNAs) are important epigenetic regulators, which play critical roles in diverse physiological and pathological processes. However, the regulatory mechanism of lncRNAs in lung carcinogenesis remains elusive. Here, we characterized a novel oncogenic lncRNA, designated as Lung Cancer Associated Transcript 3 (LCAT3). Methods: We predicted and validated LCAT3 by analyzing RNA-sequencing (RNA-seq) data of lung cancer tissues from TCGA. Methylated RNA immunoprecipitation was performed to assess m6A modification on LCAT3. The LCAT3-FUBP1-MYC axis was assessed by dual-luciferase reporter, RNA immunoprecipitation and Chromatin immunoprecipitation assays. Signaling pathways altered by LCAT3 knockdown were identified using RNA-seq. Furthermore, the mechanism of LCAT3 was investigated using loss-of-function and gain-of-function assays in vivo and in vitro. Results: LCAT3 was found to be up-regulated in lung adenocarcinomas (LUAD), and its over-expression was associated with the poor prognosis of LUAD patients. LCAT3 upregulation is attributable to N6-methyladenosine (m6A) modification mediated by methyltransferase like 3 (METTL3), leading to LCAT3 stabilization. Biologically, loss-of-function assays revealed that LCAT3 knockdown significantly suppressed lung cancer cell proliferation, migration and invasion in vitro, and inhibited tumor growth and metastasis in vivo. LCAT3 knockdown induced cell cycle arrest at the G1 phase. Mechanistically, LCAT3 recruited Far Upstream Element Binding Protein 1 (FUBP1) to the MYC far-upstream element (FUSE) sequence, thereby activating MYC transcription to promote proliferation, survival, invasion and metastasis of lung cancer cells. Conclusions: Taken together, we identified and characterized LCAT3 as a novel oncogenic lncRNA in the lung, and validated the LCAT3-FUBP1-MYC axis as a potential therapeutic target for LUAD.