Detection of novel paraja ring finger 2-fer tyrosine kinase mRNA chimeras is associated with poor postoperative prognosis in non-small cell lung cancer

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.

Identification of hub genes in bladder cancer based on weighted gene co-expression network analysis from TCGA database

Background

Muscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment.

Aim

We aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer.

Methods and results

Gene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up‐regulated genes and 2961 down‐regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up‐regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co‐expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan–Meier survival analysis to identify further hub genes, the screening criteria at p ≤ .05, we found CNKSR1, HIP1R, CFL2, TPM1, CSRP1, SYNM, POPDC2, PJA2, and RBBP8NL genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that CNKSR1 plays a vital role in the tumorigenic context of MIBC.

Conclusion

The research suggests that CNKSR1, POPDC2, and PJA2 may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate CNKSR1 as a therapeutic target for MIBC which may help to improve the prognosis for MIBC.

m6A demethylase FTO suppresses pancreatic cancer tumorigenesis by demethylating PJA2 and inhibiting Wnt signaling

Pancreatic cancer is the deadliest malignancy of the digestive system and is the seventh most common cause of cancer-related deaths worldwide. The incidence and mortality of pancreatic cancer continue to increase, and its 5-year survival rate remains the lowest among all cancers. N6-methyladenine (m6A) is the most abundant reversible RNA modification in various eukaryotic messenger and long noncoding RNAs and plays crucial roles in the occurrence and development of cancers. However, the role of m6A in pancreatic cancer remains unclear. The present study aimed to explore the role of m6A and its regulators in pancreatic cancer and assess its underlying molecular mechanism associated with pancreatic cancer cell proliferation, invasion, and metastasis. Reduced expression of the m6A demethylase, fat mass and obesity-associated protein (FTO), was responsible for the high levels of m6A RNA modification in pancreatic cancer. Moreover, FTO demethylated the m6A modification of praja ring finger ubiquitin ligase 2 (PJA2), thereby reducing its mRNA decay, suppressing Wnt signaling, and ultimately restraining the proliferation, invasion, and metastasis of pancreatic cancer cells. Altogether, this study describes new, potential molecular therapeutic targets for pancreatic cancer that could pave the way to improve patient outcome.

Regulatory function of praja ring finger ubiquitin ligase 2 mediated by the P2rx3/P2rx7 axis in mouse hippocampal neuronal cells

Praja2 (Pja2), a member of the growing family of mammalian RING E3 ubiquitin ligases, is reportedly involved in not only several types of cancer but also neurological diseases and disorders, but the genetic mechanism underlying the regulation of Pja2 in the nervous system remains unclear. To study the cellular and molecular functions of Pja2 in mouse hippocampal neuronal cells (MHNCs), we used gain- and loss-of-function manipulations of Pja2 in HT-22 cells and tested their regulatory effects on three Alzheimer's disease (AD) genes and cell proliferation. The results revealed that the expression of AD markers, including amyloid beta precursor protein (App), microtubule-associated protein tau (Mapt), and gamma-secretase activating protein (Gsap), could be inhibited by Pja2 overexpression and activated by Pja2 knockdown. In addition, HT-22 cell proliferation was enhanced by Pja2 upregulation and suppressed by its downregulation. We also evaluated and quantified the targets that responded to the enforced expression of Pja2 by RNA-Seq, and the results showed that purinergic receptor P2X, ligand-gated ion channel 3 and 7 (P2rx3 and P2rx7), which show different expression patterns in the critical calcium signaling pathway, mediated the regulatory effect of Pja2 in HT-22 cells. Functional studies indicated that Pja2 regulated HT-22 cells development and AD marker genes by inhibiting P2rx3 but promoting P2rx7, a gene downstream of P2rx3. In conclusion, our results provide new insights into the regulatory function of the Pja2 gene in MHNCs and thus underscore the potential relevance of this molecule to the pathophysiology of AD.

YY1 inhibits the migration and invasion of pancreatic ductal adenocarcinoma by downregulating the FER/STAT3/MMP2 signaling pathway

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis and a high mortality rate. The transcription factor YY1 acts as an inhibitor of many types of tumors. We found that YY1 knockdown promoted the invasion and migration of PANC-1 and BxPC-3 cells; FER knockdown partially restored the promotion of pancreatic cancer caused by YY1 knockdown. In vivo experiments yielded the same results. According to luciferase reporter gene, electrophoretic mobility shift (EMSA) and chromatin immunoprecipitation (ChIP) assays, YY1 directly binds to the FER promoter region. Moreover, higher level FER expression results in a worse TNM stage and prognosis for patients with PDAC. Furthermore, by downregulating FER, YY1 inhibits the formation of the STAT3-MMP2 complex, thereby suppressing expression of MMP2 and ultimately inhibiting the migration and invasion of pancreatic cancer. Our study demonstrates that the YY1/FER/STAT3/MMP2 axis is associated with the progression of pancreatic cancer and may provide a new therapeutic target for the treatment of pancreatic cancer.

Trans-spliced long non-coding RNA: an emerging regulator of pluripotency

With dual capacities for unlimited self-renewal and pluripotent differentiation, pluripotent stem cells (PSCs) give rise to many cell types in our body and PSC culture systems provide an unparalleled opportunity to study early human development and disease. Accumulating evidence indicates that the molecular mechanisms underlying pluripotency maintenance in PSCs involve many factors. Among these regulators, recent studies have shown that long non-coding RNAs (lncRNAs) can affect the pluripotency circuitry by cooperating with master pluripotency-associated factors. Additionally, trans-spliced RNAs, which are generated by combining two or more pre-mRNA transcripts to produce a chimeric RNA, have been identified as regulators of various biological processes, including human pluripotency. In this review, we summarize and discuss current knowledge about the roles of lncRNAs, including trans-spliced lncRNAs, in controlling pluripotency.

Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA

BACKGROUND

It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation.

RESULTS

The proposed software pipeline and package, SNV Discriminant Analysis (SNV-DA), was applied on two RNA-Seq datasets with varying sample sizes sequenced at different depths: a dataset containing primary tumors from twenty patients with different disease outcomes in lung adenocarcinoma and a larger dataset of primary tumors representing two major breast cancer subtypes, estrogen receptor positive and triple negative. Predictive models were generated using the machine learning algorithm, sparse projections to latent structures discriminant analysis. Training sets composed of RNA-Seq SNV features limited to genomic regions of origin (e.g. exonic or intronic) and/or RNA-editing sites were shown to produce models with accurate predictive performances, were discriminant towards true label groupings, and were able to produce SNV rankings significantly different from than univariate tests. Furthermore, the utility of the proposed methodology is supported by its comparable performance to traditional models as well as the enrichment of selected SNVs located in genes previously associated with cancer and genes showing allele-specific expression. As proof of concept, we highlight the discovery of a previously unannotated intergenic locus that is associated with epigenetic regulatory marks in cancer and whose significant allele-specific expression is correlated with ER+ status; hereafter named ER+ associated hotspot (ERPAHS).

CONCLUSION

The use of models from RNA-Seq SNVs to identify and prioritize candidate molecular targets for biomarker discovery is supported by the ability of the proposed method to produce significantly accurate predictive models that are discriminant towards true label groupings. Importantly, the proposed methodology allows investigation of mutations outside of exonic regions and identification of interesting expressed loci not included in traditional gene annotations. An implementation of the proposed methodology is provided that allows the user to specify SNV filtering criteria and cross-validation design during model creation and evaluation.

Evolutionary Insights into RNA trans-Splicing in Vertebrates

Pre-RNA splicing is an essential step in generating mature mRNA. RNA trans-splicing combines two separate pre-mRNA molecules to form a chimeric non-co-linear RNA, which may exert a function distinct from its original molecules. Trans-spliced RNAs may encode novel proteins or serve as noncoding or regulatory RNAs. These novel RNAs not only increase the complexity of the proteome but also provide new regulatory mechanisms for gene expression. An increasing amount of evidence indicates that trans-splicing occurs frequently in both physiological and pathological processes. In addition, mRNA reprogramming based on trans-splicing has been successfully applied in RNA-based therapies for human genetic diseases. Nevertheless, clarifying the extent and evolution of trans-splicing in vertebrates and developing detection methods for trans-splicing remain challenging. In this review, we summarize previous research, highlight recent advances in trans-splicing, and discuss possible splicing mechanisms and functions from an evolutionary viewpoint.

The chimeric transcript RUNX1-GLRX5: a biomarker for good postoperative prognosis in Stage IA non-small-cell lung cancer

Stage IA non-small-cell lung cancer cases have been recognized as having a low risk of relapse; however, occasionally, relapse may occur. To predict clinical outcome in Stage IA non-small-cell lung cancer patients, we searched for chimeric transcripts that can be used as biomarkers and identified a novel chimeric transcript, RUNX1–GLRX5, comprising RUNX1, a transcription factor, and GLRX5. This chimera was detected in approximately half of the investigated Stage IA non-small-cell lung cancer patients (44/104 cases, 42.3%). Although there was no significant difference in the overall survival rate between RUNX1–GLRX5-positive and -negative cases (P = 0.088), a significantly lower relapse rate was observed in the RUNX1–GLRX5-positive cases (P = 0.039), indicating that this chimera can be used as a biomarker for good prognosis in Stage IA patients. Detection of the RUNX1–GLRX5 chimeric transcript may therefore be useful for the determination of a postoperative treatment plan for Stage IA non-small-cell lung cancer patients.