Snail is a prominent epithelial-mesenchymal transition (EMT) transcription factor and promotes metastasis. However, Snail protein is unstable and is quickly degraded through ubiquitination-mediated proteasome pathway. Deubiquitinases prevent Snail degradation by regulating the ubiquitination-mediated hydrolysis process. Our studies demonstrate that a deubiquitinating enzyme (DUB) family member, USP37, can deubiquitinate Snail and prevent degradation of Snail. USP37 is co-localized with Snail in the nucleus. Biologically, upregulated expression of USP37 promotes lung cancer cell migration, while depletion of Snail abolishes the effect of USP37. These data demonstrate that USP37 is a Snail-specific deubiquitinase and also indicate a potential therapeutic target for metastasis.

After liver transplantation, the liver function of a patient is gradually restored over a period of time that can be divided into a convalescence period (CP) and a stabilizing period (SP). The plasma concentration of tacrolimus, an immunosuppressant commonly used to prevent organ rejection, varies as a result of variations in its metabolism. The effects of genetic and clinical factors on the plasma concentration of tacrolimus appear to differ in the CP and SP. To establish a model explaining the variation in tacrolimus trough concentration between individuals in the CP and SP, we conducted a retrospective, single-center, discovery study of 115 pairs of patients (115 donors and 115 matched recipients) who had undergone liver transplantation. Donors and recipients were genotyped by a genome-wide association study (GWAS) using an exome chip. Novel exons were identified that influenced tacrolimus trough concentrations and were verified with bootstrap analysis. In donors, two single-nucleotide polymorphisms showed an effect on the CP (rs1927321, rs1057192) and four showed an effect on the SP (rs776746, rs2667662, rs7980521, rs4903096); in recipients, two single-nucleotide polymorphisms showed an effect in the SP (rs7828796, rs776746). Genetic factors played a crucial role in tacrolimus metabolism, accounting for 44.8% in the SP, which was higher than previously reported. In addition, we found that CYP3A5, which is known to affect the metabolism of tacrolimus, only influenced tacrolimus pharmacokinetics in the SP.

Background: Breast cancer (BRCA) is the most frequent malignancy. Identification of potential biomarkers could help to better understand and combat the disease at early stages. Methods: We selected the overlapping genes of differential expressed genes and genes in BRCA-highly correlated modules by Weighted Gene Co-Expression Network Analysis (WGCNA) in TCGA and GEO data and performed KEGG and GO enrichment. PPARG was achieved from Protein-Protein Interaction (PPI) network analysis and prognostic analysis. TIMER, UALCAN, GEO, TCGA, and western blot analysis were used to validate the expression of PPARG in BRCA. PPARG was further analyzed by DNA methylation, immune parameters, and tumor mutation burden. Results: Among 381 overlapping genes, the lipid metabolic process was identified as highly enriched pathways in BRCA by TCGA and GEO data. When the prognostic analysis of 10 core genes by PPI network was performed, results revealed that high expression of PPARG was significantly correlated to a better prognosis. PPARG was lesser expression in BRCA according to TIMER, UALCAN, GEO, TCGA, and western blot in both mRNA level and protein level. PPARG had several high DNA methylation level sites and the methylation level is negatively correlated to expression. PPARG is also correlated to TNM stages, tumor microenvironment, and tumor burden. Conclusions: Findings of our study identified the PPARG as a potential biomarker by confirming its low expression in BRCA and its correlation to prognosis. Moreover, its correlation to DNA methylation and tumor microenvironment may guide new therapeutic strategies for BRCA patients.

Introduction: Abdominal aortic aneurysms (AAA) are among the most lethal non-cancerous diseases. A comprehensive analysis of the AAA-related disease model has yet to be conducted. Methods: Weighted correlation network analysis (WGCNA) was performed for the AAA-related genes. Machine learning random forest and LASSO regression analysis were performed to develop the AAA-related score. Immune characteristics and epigenetic characteristics of the AAA-related score were explored. Results: Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation. Discussion: The pathogenic roles of four model genes, UBE2K, TMEM230, VAMP7, and PUM2, in AAA, need further validation by in vitro and in vivo experiments.

Drought and high salinity are key limiting factors for cotton production. Therefore, research is increasingly focused on the underlying stress response mechanisms of cotton. We first identified and cloned a novel gene encoding the 525 amino acids in cotton, namely GhWRKY6. qRT-PCR analysis indicated that GhWRKY6 was induced by NaCl, PEG 6000 and ABA. Analyses of germination rate and root length indicated that overexpression of GhWRKY6 in Arabidopsis resulted in hypersensitivity to ABA, NaCl, and PEG 6000. In contrast, the loss-of-function mutant wrky6 was insensitive and had slightly longer roots than the wild-type did under these treatment conditions. Furthermore, GhWRKY6 overexpression in Arabidopsis modulated salt- and drought-sensitive phenotypes and stomatal aperture by regulating ABA signaling pathways, and reduced plant tolerance to abiotic stress through reactive oxygen species (ROS) enrichment, reduced proline content, and increased electrolytes and malondialdehyde (MDA). The expression levels of a series of ABA-, salt- and drought-related marker genes were altered in overexpression seedlings. Virus-induced gene silencing (VIGS) technology revealed that down-regulation of GhWRKY6 increased salt tolerance in cotton. These results demonstrate that GhWRKY6 is a negative regulator of plant responses to abiotic stress via the ABA signaling pathway.

Mutually exclusive splicing is an important mechanism for expanding protein diversity. An extreme example is the Down syndrome cell adhesion molecular (Dscam1) gene of insects, containing four clusters of variable exons (exons 4, 6, 9, and 17), which potentially generates tens of thousands of protein isoforms through mutually exclusive splicing, of which regulatory mechanisms are still elusive. Here, we systematically analyzed the variable exon 4, 6, and 9 clusters of Dscam1 in Coleoptera species. Through comparative genomics and RNA secondary structure prediction, we found apparent evidence that the evolutionarily conserved RNA base pairing mediates mutually exclusive splicing in the Dscam1 exon 4 cluster. In contrast to the fly exon 6, most exon 6 selector sequences in Coleoptera species are partially located in the variable exon region. Besides, bidirectional RNA-RNA interactions are predicted to regulate the mutually exclusive splicing of variable exon 9 of Dscam1. Although the docking sites in exon 4 and 9 clusters are clade specific, the docking sites-selector base pairing is conserved in secondary structure level. In short, our result provided a mechanistic framework for the application of long-range RNA base pairings in regulating the mutually exclusive splicing of Coleoptera Dscam1.

Alzheimer's disease (AD) is a degenerative disease of central nervous system with unclear pathogenesis, accounting for 60%-70% of dementia cases. Long noncoding RNAs (LncRNAs) play an important function in the development of AD. This study aims to explore the role of differentially expressed lncRNAs in AD patients' serum in the pathogenesis of AD. Microarray analysis was performed in the serum of AD patients and healthy controls to establish lncRNAs and mRNAs expression profiles. GO analysis and KEGG pathway analysis revealed that G1/S transition of mitotic cell cycle might be involved in the development of AD. The result showed that RP11-59J16.2 was up-regulated and MCM2 was down-regulated in serum of AD patients. SH-SY5Y cells were treated with Aβ 1-42 to establish AD cell model. Dual luciferase reporter gene analysis verified that RP11-59J16.2 could directly interact with 3'UTR of MCM2 and further regulate the expression of MCM2. Inhibition of RP11-59J16.2 or overexpression of MCM2, CCK-8 assay and Annexin V FITC/PI apoptosis assay kit results showed that RP11-59J16.2 could reduce cell viability, aggravate apoptosis and increase Tau phosphorylation in AD cell model by inhibiting MCM2. In short, our study revealed a novel lncRNA RP11-59J16.2 that could promote neuronal apoptosis and increase Tau phosphorylation by regulating MCM2 in AD model, and indicated that lncRNA RP11-59J16.2 might be a potential target molecule for AD development.

Lung cancer is the tumor with the highest morbidity and mortality, and has become a global public health problem. The incidence of lung cancer in men has declined in some countries and regions, while the incidence of lung cancer in women has been slowly increasing. Therefore, the aim is to explore whether estrogen-related genes are associated with the incidence and prognosis of lung cancer.

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA-disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA-disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA-disease associations.

Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer-related death in young women. Several prognostic and predictive transcription factor (TF) markers have been reported for BC; however, they are inconsistent due to small datasets, the heterogeneity of BC, and variation in data pre-processing approaches. This study aimed to identify an effective predictive TF signature for the prognosis of patients with BC. We analyzed the TF data of 868 patients with BC in The Cancer Genome Atlas (TCGA) database to investigate TF biomarkers relevant to recurrence-free survival (RFS). These patients were separated into training and internal validation datasets, with GSE2034 and GSE42568 used as external validation sets. A nine-TF signature was identified as crucially related to the RFS of patients with BC by univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox proportional hazard analysis in the training dataset. Kaplan-Meier analysis revealed that the nine-TF signature could significantly distinguish high- and low-risk patients in both the internal validation dataset and the two external validation sets. Receiver operating characteristic (ROC) analysis further verified that the nine-TF signature showed a good performance for predicting the RFS of patients with BC. In addition, we developed a nomogram based on risk score and lymph node status, with C-index, ROC, and calibration plot analysis, suggesting that it displays good performance and clinical value. In summary, we used integrated bioinformatics approaches to identify an effective predictive nine-TF signature which may be a potential biomarker for BC prognosis.

An increasing number of experiments had verified that miRNA expression is related to human diseases. The miRNA expression profile may be an indicator of clinical diagnosis and provides a new direction for the prevention and treatment of complex diseases. In this work, we present a weighted voting-based model for predicting miRNA-disease association (WVMDA). To reasonably build a network of similarity, we established credibility similarity based on the reliability of known associations and used it to improve the original incomplete similarity. To eliminate noise interference as much as possible while maintaining more reliable similarity information, we developed a filter. More importantly, to ensure the fairness and efficiency of weighted voting, we focus on the design of weighting. Finally, cross-validation experiments and case studies are undertaken to verify the efficacy of the proposed model. The results showed that WVMDA could efficiently identify miRNAs associated with the disease.

Objective: The efficacy of immunotherapy for cholangiocarcinoma (CCA) is blocked by a high degree of tumor heterogeneity. Cell communication contributes to heterogeneity in the tumor microenvironment. This study aimed to explore critical cell signaling and biomarkers induced via cell communication during immune exhaustion in CCA. Methods: We constructed empirical Bayes and Markov random field models eLBP to determine transcription factors, interacting genes, and associated signaling pathways involved in cell-cell communication using single-cell RNAseq data. We then analyzed the mechanism of immune exhaustion during CCA progression. Results: We found that VEGFA-positive macrophages with high levels of LGALS9 could interact with HAVCR2 to promote the exhaustion of CD8+ T cells in CCA. Transcription factors SPI1 and IRF1 can upregulate the expression of LGALS9 in VEGFA-positive macrophages. Subsequently, we obtained a panel containing 54 genes through the model, which identified subtype S2 with high expression of immune checkpoint genes that are suitable for immunotherapy. Moreover, we found that patients with subtype S2 with a higher mutation ratio of MUC16 had immune-exhausted genes, such as HAVCR2 and TIGIT. Finally, we constructed a nine-gene eLBP-LASSO-COX risk model, which was designated the tumor microenvironment risk score (TMRS). Conclusion: Cell communication-related genes can be used as important markers for predicting patient prognosis and immunotherapy responses. The TMRS panel is a reliable tool for prognostic prediction and chemotherapeutic decision-making in CCA.