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.

Non-coding RNAs (ncRNAs) have been the focus of many studies over the last few decades, and their fundamental roles in human diseases have been well established. Transfer RNAs (tRNAs) are housekeeping ncRNAs that deliver amino acids to ribosomes during protein biosynthesis. tRNA fragments (tRFs) are a novel class of small ncRNAs produced through enzymatic cleavage of tRNAs and have been shown to play key regulatory roles similar to microRNAs. Development and application of high-throughput sequencing technologies has provided accumulating evidence of dysregulated tRFs in cancer. Aberrant expression of tRFs has been found to participate in cell proliferation, invasive metastasis, and progression in several human malignancies. These newly identified functional tRFs also have great potential as new biomarkers and therapeutic targets for cancer treatment. In this review, we focus on the major biological functions of tRFs including RNA silencing, translation regulation, and epigenetic regulation; summarize recent research on the roles of tRFs in different types of cancer; and discuss the potential of using tRFs as clinical biomarkers for cancer diagnosis and prognosis and as therapeutic targets for cancer treatment.

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.

Cervical cancer screening through detection and treatment of high-grade cervical intraepithelial neoplasia (CIN) is most successful in cancer prevention. However, the accuracy of the current cervical cancer screening tests is still low. The aim of this study was to develop a more accurate method based on circulating exosomal miRNAs. The miRNA sequencing was performed to identify candidate exosomal miRNAs as diagnostic biomarkers in 121 plasma samples from healthy volunteers, cervical carcinoma patients, and CIN patients. A panel with eight differentially expressed exosomal miRNAs was identified to distinguish patients in the CIN II+ group (including advanced CIN II patients) from those in the CIN I− group (including CIN I patients and healthy volunteers). Let-7d-3p and miR-30d-5p showed significant difference between cervical tumors and adjacent normal tissues (P < 0.005), exhibited a consistent trend in plasma samples, and were further validated in 203 independent plasma samples. Integrating these two miRNAs yielded an AUC value of 0.828 to distinguish patients in CIN II+ group from those in CIN I− group. Further integrating them into a cytological test-based model resulted in a higher AUC of 0.887, while the AUC value based on the cytological test alone was 0.766. In summary, plasma exosomal miR-30d-5p and let-7d-3p are valuable diagnostic biomarkers for non-invasive screening of cervical cancer and its precursors. Further validation using large sample sizes is required for clinical diagnosis.