Comparative study of bioinformatic tools for the identification of chimeric RNAs from RNA Sequencing

Oncogenic SLC2A11-MIF fusion protein interacts with polypyrimidine tract binding protein 1 to facilitate bladder cancer proliferation and metastasis by regulating mRNA stability

Chimeric RNAs, distinct from DNA gene fusions, have emerged as promising therapeutic targets with diverse functions in cancer treatment. However, the functional significance and therapeutic potential of most chimeric RNAs remain unclear. Here we identify a novel fusion transcript of solute carrier family 2-member 11 (SLC2A11) and macrophage migration inhibitory factor (MIF). In this study, we investigated the upregulation of SLC2A11-MIF in The Cancer Genome Atlas cohort and a cohort of patients from Sun Yat-Sen Memorial Hospital. Subsequently, functional investigations demonstrated that SLC2A11-MIF enhanced the proliferation, antiapoptotic effects, and metastasis of bladder cancer cells in vitro and in vivo. Mechanistically, the fusion protein encoded by SLC2A11-MIF interacted with polypyrimidine tract binding protein 1 (PTBP1) and regulated the mRNA half-lives of Polo Like Kinase 1, Roundabout guidance receptor 1, and phosphoinositide-3-kinase regulatory subunit 3 in BCa cells. Moreover, PTBP1 knockdown abolished the enhanced impact of SLC2A11-MIF on biological function and mRNA stability. Furthermore, the expression of SLC2A11-MIF mRNA is regulated by CCCTC-binding factor and stabilized through RNA N4-acetylcytidine modification facilitated by N-acetyltransferase 10. Overall, our findings revealed a significant fusion protein orchestrated by the SLC2A11-MIF-PTBP1 axis that governs mRNA stability during the multistep progression of bladder cancer.

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data

Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.

Chimeric RNAs and their implication in prostate cancer

•Chimeric ribonucleic acids (RNAs) form through gene fusion, trans-splicing, or cis-splicing between adjacent genes (cis-SAGe) mechanisms•Chimeric RNAs influence prostate cancer (PCa) progression by acting as long noncoding RNAs (lncRNAs) or circular RNAs (circRNAs), coding fusion proteins, and misregulating parental genes•Misregulated chimeric RNAs represent a novel repertoire for biomarkers and targets for PCa therapy.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

RTCpredictor: Identification of Read-Through Chimeric RNAs from RNA Sequencing Data

Read-through chimeric RNAs are gaining attention in cancer and other research fields, yet current tools often fail in predicting them. We have thus developed the first read-through chimeric RNA specific prediction method, RTCpredictor, utilizing a fast ripgrep algorithm to search for all possible exon-exon combinations of parental gene pairs. Compared with other ten popular tools, RTCpredictor achieved top performance on both simulated and real datasets. We randomly selected up to 30 candidate read-through chimeras predicted from each software method and experimentally validated a total of 109 read-throughs and on this set, RTCpredictor outperformed all the other methods. In addition, RTCpredictor ( https://github.com/sandybioteck/RTCpredictor ) has less memory requirements and faster execution time.

Recent advances in cancer fusion transcript detection

Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts.

Chimeric RNAs Discovered by RNA Sequencing and Their Roles in Cancer and Rare Genetic Diseases

Chimeric RNAs are transcripts that are generated by gene fusion and intergenic splicing events, thus comprising nucleotide sequences from different parental genes. In the past, Northern blot analysis and RT-PCR were used to detect chimeric RNAs. However, they are low-throughput and can be time-consuming, labor-intensive, and cost-prohibitive. With the development of RNA-seq and transcriptome analyses over the past decade, the number of chimeric RNAs in cancer as well as in rare inherited diseases has dramatically increased. Chimeric RNAs may be potential diagnostic biomarkers when they are specifically expressed in cancerous cells and/or tissues. Some chimeric RNAs can also play a role in cell proliferation and cancer development, acting as tools for cancer prognosis, and revealing new insights into the cell origin of tumors. Due to their abilities to characterize a whole transcriptome with a high sequencing depth and intergenically identify spliced chimeric RNAs produced with the absence of chromosomal rearrangement, RNA sequencing has not only enhanced our ability to diagnose genetic diseases, but also provided us with a deeper understanding of these diseases. Here, we reviewed the mechanisms of chimeric RNA formation and the utility of RNA sequencing for discovering chimeric RNAs in several types of cancer and rare inherited diseases. We also discussed the diagnostic, prognostic, and therapeutic values of chimeric RNAs.