Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach.

MicroRNA (miRNA)-target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29.

Quantitative model for genome-wide cyclic AMP receptor protein binding site identification and characteristic analysis.

Cyclic AMP receptor proteins (CRPs) are important transcription regulators in many species. The prediction of CRP-binding sites was mainly based on position-weighted matrixes (PWMs). Traditional prediction methods only considered known binding motifs, and their ability to discover inflexible binding patterns was limited. Thus, a novel CRP-binding site prediction model called CRPBSFinder was developed in this research, which combined the hidden Markov model, knowledge-based PWMs and structure-based binding affinity matrixes. We trained this model using validated CRP-binding data from Escherichia coli and evaluated it with computational and experimental methods. The result shows that the model not only can provide higher prediction performance than a classic method but also quantitatively indicates the binding affinity of transcription factor binding sites by prediction scores. The prediction result included not only the most knowns regulated genes but also 1089 novel CRP-regulated genes. The major regulatory roles of CRPs were divided into four classes: carbohydrate metabolism, organic acid metabolism, nitrogen compound metabolism and cellular transport. Several novel functions were also discovered, including heterocycle metabolic and response to stimulus. Based on the functional similarity of homologous CRPs, we applied the model to 35 other species. The prediction tool and the prediction results are online and are available at: https://awi.cuhk.edu.cn/∼CRPBSFinder.

MED1 Regulates BMP/TGF-ß in Endothelium: Implication for Pulmonary Hypertension.

BACKGROUND: Dysregulated BMP (bone morphogenetic protein) or TGF-ß (transforming growth factor beta) signaling pathways are imperative in idiopathic and familial pulmonary arterial hypertension (PAH) as well as experimental pulmonary hypertension (PH) in rodent models. MED1 (mediator complex subunit 1) is a key transcriptional co-activator and KLF4 (Krüppel-like factor 4) is a master transcription factor in endothelium. However, MED1 and KLF4 epigenetic and transcriptional regulations of the BMP/TGF-ß axes in pulmonary endothelium and their dysregulations leading to PAH remain elusive. We investigate the MED1/KLF4 co-regulation of the BMP/TGF-ß axes in endothelium by studying the epigenetic regulation of BMPR2 (BMP receptor type II), ETS-related gene (ERG), and TGFBR2 (TGF-ß receptor 2) and their involvement in the PH. METHODS: High-throughput screening involving data from RNA-seq, MED1 ChIP-seq, H3K27ac ChIP-seq, ATAC-seq, and high-throughput chromosome conformation capture together with in silico computations were used to explore the epigenetic and transcriptional regulation of BMPR2, ERG, and TGFBR2 by MED1 and KLF4. In vitro experiments with cultured pulmonary arterial endothelial cells (ECs) and bulk assays were used to validate results from these in silico analyses. Lung tissue from patients with idiopathic PAH, animals with experimental PH, and mice with endothelial ablation of MED1 (EC-MED1-/-) were used to study the PH-protective effect of MED1. RESULTS: Levels of MED1 were decreased in lung tissue or pulmonary arterial endothelial cells from idiopathic PAH patients and rodent PH models. Mechanistically, MED1 acted synergistically with KLF4 to transactivate BMPR2, ERG, and TGFBR2 via chromatin remodeling and enhancer-promoter interactions. EC-MED1-/- mice showed PH susceptibility. In contrast, MED1 overexpression mitigated the PH phenotype in rodents. CONCLUSIONS: A homeostatic regulation of BMPR2, ERG, and TGFBR2 in ECs by MED1 synergistic with KLF4 is essential for the normal function of the pulmonary endothelium. Dysregulation of MED1 and the resulting impairment of the BMP/TGF-ß signaling is implicated in the disease progression of PAH in humans and PH in rodent models.

CircNet 2.0: an updated database for exploring circular RNA regulatory networks in cancers.

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA-target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA-miRNA-gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions.

MicroRNAs (miRNAs) are noncoding RNAs with 18-26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA-target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update's critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3' untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk.edu.cn/.

A Causal Regulation Modeling Algorithm for Temporal Events with Application to Escherichia coli's Aerobic to Anaerobic Transition.

Time-series experiments are crucial for understanding the transient and dynamic nature of biological phenomena. These experiments, leveraging advanced classification and clustering algorithms, allow for a deep dive into the cellular processes. However, while these approaches effectively identify patterns and trends within data, they often need to improve in elucidating the causal mechanisms behind these changes. Building on this foundation, our study introduces a novel algorithm for temporal causal signaling modeling, integrating established knowledge networks with sequential gene expression data to elucidate signal transduction pathways over time. Focusing on Escherichia coli's (E. coli) aerobic to anaerobic transition (AAT), this research marks a significant leap in understanding the organism's metabolic shifts. By applying our algorithm to a comprehensive E. coli regulatory network and a time-series microarray dataset, we constructed the cross-time point core signaling and regulatory processes of E. coli's AAT. Through gene expression analysis, we validated the primary regulatory interactions governing this process. We identified a novel regulatory scheme wherein environmentally responsive genes, soxR and oxyR, activate fur, modulating the nitrogen metabolism regulators fnr and nac. This regulatory cascade controls the stress regulators ompR and lrhA, ultimately affecting the cell motility gene flhD, unveiling a novel regulatory axis that elucidates the complex regulatory dynamics during the AAT process. Our approach, merging empirical data with prior knowledge, represents a significant advance in modeling cellular signaling processes, offering a deeper understanding of microbial physiology and its applications in biotechnology.

MethHC 2.0: information repository of DNA methylation and gene expression in human cancer.

DNA methylation is an important epigenetic regulator in gene expression and has several roles in cancer and disease progression. MethHC version 2.0 (MethHC 2.0) is an integrated and web-based resource focusing on the aberrant methylomes of human diseases, specifically cancer. This paper presents an updated implementation of MethHC 2.0 by incorporating additional DNA methylomes and transcriptomes from several public repositories, including 33 human cancers, over 50 118 microarray and RNA sequencing data from TCGA and GEO, and accumulating up to 3586 manually curated data from >7000 collected published literature with experimental evidence. MethHC 2.0 has also been equipped with enhanced data annotation functionality and a user-friendly web interface for data presentation, search, and visualization. Provided features include clinical-pathological data, mutation and copy number variation, multiplicity of information (gene regions, enhancer regions, and CGI regions), and circulating tumor DNA methylation profiles, available for research such as biomarker panel design, cancer comparison, diagnosis, prognosis, therapy study and identifying potential epigenetic biomarkers. MethHC 2.0 is now available at http://awi.cuhk.edu.cn/∼MethHC.

A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.

Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug-target interactions. We compared the performance of CapBM-DTI with state-of-the-art methods using four experimentally validated DTI datasets of different sizes, including human (Homo sapiens) and worm (Caenorhabditis elegans) species datasets, as well as three subsets (new compounds, new proteins, and new pairs). Our results demonstrate that the proposed model achieved robust performance and powerful generalization ability in all experiments. The case study on treating COVID-19 demonstrates the applicability of the model in virtual screening.

Effects of Natural Products on Enzymes Involved in Ferroptosis: Regulation and Implications.

Ferroptosis is a form of regulated cell death that is characterized by the accumulation of iron-dependent lipid peroxides. The regulation of ferroptosis involves both non-enzymatic reactions and enzymatic mechanisms. Natural products have demonstrated potential effects on various enzymes, including GPX4, HO-1, NQO1, NOX4, GCLC, and GCLM, which are mainly involved in glutathione metabolic pathway or oxidative stress regulation, and ACSL3 and ACSL4, which mainly participate in lipid metabolism, thereby influencing the regulation of ferroptosis. In this review, we have provided a comprehensive overview of the existing literature pertaining to the effects of natural products on enzymes involved in ferroptosis and discussed their potential implications for the prevention and treatment of ferroptosis-related diseases. We also highlight the potential challenge that the majority of research has concentrated on investigating the impact of natural products on the expression of enzymes involving ferroptosis while limited attention is given to the regulation of enzyme activity. This observation underscores the considerable potential and scope for exploring the influence of natural products on enzyme activity.

miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database.

MicroRNAs (miRNAs) are small non-coding RNAs (typically consisting of 18-25 nucleotides) that negatively control expression of target genes at the post-transcriptional level. Owing to the biological significance of miRNAs, miRTarBase was developed to provide comprehensive information on experimentally validated miRNA-target interactions (MTIs). To date, the database has accumulated >13,404 validated MTIs from 11,021 articles from manual curations. In this update, a text-mining system was incorporated to enhance the recognition of MTI-related articles by adopting a scoring system. In addition, a variety of biological databases were integrated to provide information on the regulatory network of miRNAs and its expression in blood. Not only targets of miRNAs but also regulators of miRNAs are provided to users for investigating the up- and downstream regulations of miRNAs. Moreover, the number of MTIs with high-throughput experimental evidence increased remarkably (validated by CLIP-seq technology). In conclusion, these improvements promote the miRTarBase as one of the most comprehensively annotated and experimentally validated miRNA-target interaction databases. The updated version of miRTarBase is now available at http://miRTarBase.cuhk.edu.cn/.

Enzyme Activity of Natural Products on Cytochrome P450.

Drug-metabolizing enzymes, particularly the cytochrome P450 (CYP450) monooxygenases, play a pivotal role in pharmacokinetics. CYP450 enzymes can be affected by various xenobiotic substrates, which will eventually be responsible for most metabolism-based herb-herb or herb-drug interactions, usually involving competition with another drug for the same enzyme binding site. Compounds from herbal or natural products are involved in many scenarios in the context of such interactions. These interactions are decisive both in drug discovery regarding the synergistic effects, and drug application regarding unwanted side effects. Herein, this review was conducted as a comprehensive compilation of the effects of herbal ingredients on CYP450 enzymes. Nearly 500 publications reporting botanicals' effects on CYP450s were collected and analyzed. The countries focusing on this topic were summarized, the identified herbal ingredients affecting enzyme activity of CYP450s, as well as methods identifying the inhibitory/inducing effects were reviewed. Inhibitory effects of botanicals on CYP450 enzymes may contribute to synergistic effects, such as herbal formulae/prescriptions, or lead to therapeutic failure, or even increase concentrations of conventional medicines causing serious adverse events. Conducting this review may help in metabolism-based drug combination discovery, and in the evaluation of the safety profile of natural products used therapeutically.

Multi-omics profiling reveals microRNA-mediated insulin signaling networks.

BACKGROUND: MicroRNAs (miRNAs) play a key role in mediating the action of insulin on cell growth and the development of diabetes. However, few studies have been conducted to provide a comprehensive overview of the miRNA-mediated signaling network in response to glucose in pancreatic beta cells. In our study, we established a computational framework integrating multi-omics profiles analyses, including RNA sequencing (RNA-seq) and small RNA sequencing (sRNA-seq) data analysis, inverse expression pattern analysis, public data integration, and miRNA targets prediction to illustrate the miRNA-mediated regulatory network at different glucose concentrations in INS-1 pancreatic beta cells (INS-1), which display important characteristics of the pancreatic beta cells. RESULTS: We applied our computational framework to the expression profiles of miRNA/mRNA of INS-1, at different glucose concentrations. A total of 1437 differentially expressed genes (DEGs) and 153 differentially expressed miRNAs (DEmiRs) were identified from multi-omics profiles. In particular, 121 DEmiRs putatively regulated a total of 237 DEGs involved in glucose metabolism, fatty acid oxidation, ion channels, exocytosis, homeostasis, and insulin gene regulation. Moreover, Argonaute 2 immunoprecipitation sequencing, qRT-PCR, and luciferase assay identified Crem, Fn1, and Stc1 are direct targets of miR-146b and elucidated that miR-146b acted as a potential regulator and promising target to understand the insulin signaling network. CONCLUSIONS: In this study, the integration of experimentally verified data with system biology framework extracts the miRNA network for exploring potential insulin-associated miRNA and their target genes. The findings offer a potentially significant effect on the understanding of miRNA-mediated insulin signaling network in the development and progression of pancreatic diabetes.

Atheroprotective Flow Upregulates ITPR3 (Inositol 1,4,5-Trisphosphate Receptor 3) in Vascular Endothelium via KLF4 (Krüppel-Like Factor 4)-Mediated Histone Modifications.

Objective- The topographical distribution of atherosclerosis in vasculature underscores the importance of shear stress in regulating endothelium. With a systems approach integrating sequencing data, the current study aims to explore the link between shear stress-regulated master transcription factor and its regulation of endothelial cell (EC) function via epigenetic modifications. Approach and Results- H3K27ac (acetylation of histone 3 lysine 27)-ChIP-seq (chromatin immunoprecipitation followed by high throughput sequencing), ATAC-seq (an assay for transposase-accessible chromatin-sequencing), and RNA-seq (RNA-sequencing) were performed to investigate the genome-wide epigenetic regulations in ECs in response to atheroprotective pulsatile shear stress (PS). In silico prediction revealed that KLF4 binding motifs were enriched in the PS-enhanced H3K27ac regions. By integrating PS- and KLF4-modulated H3K27ac, we identified 18 novel PS-upregulated genes. The promoter regions of these genes showed an overlap between the KLF4-enhanced assay for transposase-accessible chromatin signals and the PS-induced H3K27ac peaks. Experiments using ECs isolated from mouse aorta, lung ECs from EC-KLF4-TG versus EC-KLF4-KO mice, and atorvastatin-treated ECs showed that ITPR3 (inositol 1,4,5-trisphosphate receptor 3) was robustly activated by KLF4 and statins. KLF4 ATAC-qPCR (quantitative polymerase chain reaction) and ChIP-qPCR further demonstrated that a specific locus in the promoter region of the ITPR3 gene was essential for KLF4 binding, H3K27ac enrichment, chromatin accessibility, RNA polymerase II recruitment, and ITPR3 transcriptional activation. Deletion of this KLF4 binding locus in ECs by using CRISPR-Cas9 resulted in blunted calcium influx, reduced expression of endothelial nitric oxide synthase, and diminished nitric oxide bioavailability. Conclusions- These results from a novel multiomics study suggest that KLF4 is crucial for PS-modulated H3K27ac that allow the transcriptional activation of ITPR3. This novel mechanism contributes to the Ca2+-dependent eNOS (endothelial nitric oxide synthase) activation and EC homeostasis.

AMP-activated Protein Kinase Phosphorylation of Angiotensin-Converting Enzyme 2 in Endothelium Mitigates Pulmonary Hypertension.

RATIONALE: Endothelial dysfunction plays an integral role in pulmonary hypertension (PH). AMPK (AMP-activated protein kinase) and ACE2 (angiotensin-converting enzyme 2) are crucial in endothelial homeostasis. The mechanism by which AMPK regulates ACE2 in the pulmonary endothelium and its protective role in PH remain elusive. OBJECTIVES: We investigated the role of AMPK phosphorylation of ACE2 Ser680 in ACE2 stability and deciphered the functional consequences of this post-translational modification of ACE2 in endothelial homeostasis and PH. METHODS: Bioinformatics prediction, kinase assay, and antibody against phospho-ACE2 Ser680 (p-ACE2 S680) were used to investigate AMPK phosphorylation of ACE2 Ser680 in endothelial cells. Using CRISPR-Cas9 genomic editing, we created gain-of-function ACE2 S680D knock-in and loss-of-function ACE2 knockout (ACE2-/-) mouse lines to address the involvement of p-ACE2 S680 and ACE2 in PH. The AMPK-p-ACE2 S680 axis was also validated in lung tissue from humans with idiopathic pulmonary arterial hypertension. MEASUREMENTS AND MAIN RESULTS: Phosphorylation of ACE2 by AMPK enhanced the stability of ACE2, which increased Ang (angiotensin) 1-7 and endothelial nitric oxide synthase-derived NO bioavailability. ACE2 S680D knock-in mice were resistant to PH as compared with wild-type littermates. In contrast, ACE2-knockout mice exacerbated PH, a similar phenotype found in mice with endothelial cell-specific deletion of AMPKα2. Consistently, the concentrations of phosphorylated AMPK, p-ACE2 S680, and ACE2 were decreased in human lungs with idiopathic pulmonary arterial hypertension. CONCLUSIONS: Impaired phosphorylation of ACE2 Ser680 by AMPK in pulmonary endothelium leads to a labile ACE2 and hence is associated with the pathogenesis of PH. Thus, AMPK regulation of the vasoprotective ACE2 is a potential target for PH treatment.

Krüppel-Like Factor 4 Regulation of Cholesterol-25-Hydroxylase and Liver X Receptor Mitigates Atherosclerosis Susceptibility.

BACKGROUND: Atherosclerosis is a multifaceted inflammatory disease involving cells in the vascular wall (eg, endothelial cells [ECs]), as well as circulating and resident immunogenic cells (eg, monocytes/macrophages). Acting as a ligand for liver X receptor (LXR), but an inhibitor of SREBP2 (sterol regulatory element-binding protein 2), 25-hydroxycholesterol, and its catalyzing enzyme cholesterol-25-hydroxylase (Ch25h) are important in regulating cellular inflammatory status and cholesterol biosynthesis in both ECs and monocytes/macrophages. METHODS: Bioinformatic analyses were used to investigate RNA-sequencing data to identify cholesterol oxidation and efflux genes regulated by Krüppel-like factor 4 (KLF4). In vitro experiments involving cultured ECs and macrophages and in vivo methods involving mice with Ch25h ablation were then used to explore the atheroprotective role of KLF4-Ch25h/LXR. RESULTS: Vasoprotective stimuli increased the expression of Ch25h and LXR via KLF4. The KLF4-Ch25h/LXR homeostatic axis functions through suppressing inflammation, evidenced by the reduction of inflammasome activity in ECs and the promotion of M1 to M2 phenotypic transition in macrophages. The increased atherosclerosis in apolipoprotein E-/-/Ch25h-/- mice further demonstrates the beneficial role of the KLF4-Ch25h/LXR axis in vascular function and disease. CONCLUSIONS: KLF4 transactivates Ch25h and LXR, thereby promoting the synergistic effects between ECs and macrophages to protect against atherosclerosis susceptibility.

The aquatic animals' transcriptome resource for comparative functional analysis.

BACKGROUND: Aquatic animals have great economic and ecological importance. Among them, non-model organisms have been studied regarding eco-toxicity, stress biology, and environmental adaptation. Due to recent advances in next-generation sequencing techniques, large amounts of RNA-seq data for aquatic animals are publicly available. However, currently there is no comprehensive resource exist for the analysis, unification, and integration of these datasets. This study utilizes computational approaches to build a new resource of transcriptomic maps for aquatic animals. This aquatic animal transcriptome map database dbATM provides de novo assembly of transcriptome, gene annotation and comparative analysis of more than twenty aquatic organisms without draft genome. RESULTS: To improve the assembly quality, three computational tools (Trinity, Oases and SOAPdenovo-Trans) were employed to enhance individual transcriptome assembly, and CAP3 and CD-HIT-EST software were then used to merge these three assembled transcriptomes. In addition, functional annotation analysis provides valuable clues to gene characteristics, including full-length transcript coding regions, conserved domains, gene ontology and KEGG pathways. Furthermore, all aquatic animal genes are essential for comparative genomics tasks such as constructing homologous gene groups and blast databases and phylogenetic analysis. CONCLUSION: In conclusion, we establish a resource for non model organism aquatic animals, which is great economic and ecological importance and provide transcriptomic information including functional annotation and comparative transcriptome analysis. The database is now publically accessible through the URL http://dbATM.mbc.nctu.edu.tw/ .

Whole exome sequencing in Dandy-Walker variant with intellectual disability reveals an activating CIP2A mutation as novel genetic cause.

Dandy-Walker malformation (DWM) has been reported to have heterogeneous causes, including mutations in genes of fibroblast growth factors and in genes in the sonic hedgehog (Shh) signaling pathway. Here, we identified an activating cancerous inhibitor of protein phosphatase 2A (CIP2A) p.D269V mutation, located at the predicted protein-protein interaction groove, as a novel genetic cause of Dandy-Walker variant (DWV). CIP2A has been reported as an oncoprotein promoting tumor survival via inhibition of protein phosphatase 2A (PP2A). However, the impact of human germline CIP2A mutation is unknown. We report a novel heterozygous CIP2A p.D269V mutation via whole exome sequencing in two siblings with DWV and severe intellectual disability who were born to non-consanguineous parents. Only the older brother developed a slow-growing sacral leiomyoma in his teens. The CIP2A p.D269V mutation is associated with increased PP2A, mTOR, and c-Myc protein levels in peripheral blood mononuclear cells (PBMCs). The PP2A phosphatase activity, however, was not suppressed. Deep sequencing revealed that the father carries 16% of somatic CIP2A p.D269V mutation, suggesting potential inheritance from the mosaic sperm populations. Our study is the first to describe a pathogenic CIP2A mutation in humans, which might disrupt neuronal development via enhancing mTOR and c-Myc protein expressions, shedding light in mechanisms of DWV pathogenesis.

G6PD as a predictive marker for glioma risk, prognosis and chemosensitivity.

PURPOSE: Glucose-6-phosphate dehydrogenase (G6PD) is a key enzyme preventing cells from oxidative damage and has been reported to have tumor-promoting roles. This study aims to comprehensively evaluate the predictive values of G6PD on brain tumor risk, prognosis and chemo-resistance. METHODS: A retrospective 13-year cohort study analyzing cancer risk using the Taiwan National Health Insurance Research Database (4066 G6PD deficiency patients and 16,264 controls) was conducted. Furthermore, RNAseq and clinical data of grade II-III glioma (LGG, n = 515) and glioblastoma (GBM, n = 155) were downloaded from The Cancer Genome Atlas (TCGA) and analyzed. Bioinformatics methods were applied to build a glioma prognostication model and to predict response to chemotherapy based on tumor G6PD-related gene expressions. The predicted results were validated in another glioma cohort GSE 16011 and in KALS1 cell line. RESULTS: G6PD-dificient patients were found to have an increased risk for cancers, especially for brain tumor (adjusted hazard ratio (HR) 10.5, 95% CI 1.03-7.60). Furthermore, higher tumor G6PD expression was associated with poor patient survival in LGG, but not in GBM. A prognostication model using expression levels of G6PD and 9 related genes (PSMA2, PSMB8, SHFM1, GSS, GSTK1, MGST2, POLD3, MSH2, MSH6) could independently predict LGG patient survival. Boosted decision tree analysis on 213 cancer cell line database revealed predictive values of G6PD expression on response to gemcitabine and bortezomib. Knockdown of G6PD in KALS1 cell line enhanced its sensitivity to both chemotherapeutic agents. CONCLUSIONS: Our study suggests that G6PD could be a marker predicting glioma risk, prognosis and chemo-sensitivity.

Prognostic Value of RNASEH2A-, CDK1-, and CD151-Related Pathway Gene Profiling for Kidney Cancers.

The nucleotide degrading enzyme gene RNASEH2A (ribonuclease H2 subunit A) has been found to be overexpressed in cancers. Our aim was to understand the role of RNASEH2A in cancer prognostication and to establish a scoring system based on the expressions of genes interacting with RNASEH2A. We screened the nucleotide degrading enzyme gene expression in RNAseq data of 14 cancer types derived from The Cancer Genome Atlas (TCGA) and found that RNASEH2A overexpression was associated with poor patient survival only in renal cell carcinomas (RCCs). Further cluster analyses of samples with poor outcomes revealed that cluster of differentiation 151 (CD151) upregulation correlated with low cyclin dependent kinase 1 (CDK1) and high RNASEH2A expression. The combination of low CD151 expression and high RNASEH2A expression resulted in impaired proliferation in four kidney cancer cell lines, suggesting potential synthetic dosage lethality (SDL) interactions between the two genes. A prognostication scoring system was established based on the expression levels of RNASEH2A-, CDK1-, and CD151-related genes, which could effectively predict the overall survival in a TCGA clear cell RCC cohort (n = 533, 995.3 versus 2242.2 days, p < 0.0001), in another clear cell renal cell carcinoma (ccRCC) cohort E-GEOD-22541 (n = 44, 390.0 versus 1889.2 days, p = 0.0007), and in a TCGA papillary RCC (pRCC) cohort (n = 287, 741.6 versus 1623.7 days, p < 0.0001). Our results provide a clinically applicable prognostication scoring system for renal cancers.