Search Results (11)

Background: Single-nucleotide polymorphisms (SNPs) are associated with multiple disorders and various cancer types. In the context of cancer, alterations within non-coding regions, specifically 3′ untranslated regions (3′ UTR), have proven substantially important. Methods: In this study, we utilized various bioinformatics tools to examine the effect of SNPs in the 3′ UTR. We retrieved the 3′ UTR SNPs of the Signal Transducer and Activator of Transcription 1 (STAT1) gene from the National Centre for Biotechnology Information (NCBI) website. Next, we employed the Polymorphism in miRNAs and their corresponding target sites (PolymiRTS) database to predict the 3′ UTR SNPs that create new microRNA (miRNA) binding sites and their respective miRNAs. The effect of the 3′ UTR SNPs on the messenger RNA structure was studied using RNAfold server. We used Cscape tool to predict the oncogenic 3′ UTR SNPs. Then, we submitted the miRNAs to the miRNet database to visualize the miRNA-miRNAs’ target genes interaction, for which gene enrichment analysis was performed using ShinyGO. Protein–protein interactions were conducted using the STRING database. We conducted miRNA enrichment analysis utilizing miRPathDB, subsequently performing miRNA differential expression analysis through oncoMIR, and the StarBase database. The survival analysis of the upregulated miRNAs in cancer was investigated using the Kaplan–Meier Plotter. Result: Twelve SNPs were predicted to create new miRNA binding sites. Two of them, rs188557905 and rs190542524, were predicted to destabilize the mRNA structures. We predicted rs190542524, rs11305, rs186033487, and rs188557905 to be oncogenic 3′ UTR SNPs, with high-confidence predictions and scores > 0.5. Using miRNAs’ target genes enrichment analysis, this study indicated that the miRNA target genes were more likely to be involved in cancer-related pathways. Our comprehensive analysis of miRNAs, their functional enrichment, their expression in various types of cancer, and the correlation between miRNA expression and survival outcome yielded these results. Our research shows that the oncogenic 3′ UTR SNP rs190542524 creates a new binding site for the oncogenic miRNA hsa-miR-136-5p. This miRNA is significantly upregulated in BLCA, LUSC, and STAD and is linked to poor survival. Additionally, rs114360225 creates a new binding site for hsa-miR-362-3p, influencing LIHC. Conclusions: These analyses suggest that these 3′ UTR SNPs may have a functional impact on the STAT1 gene’s regulation through their predicted effect on miRNA binding sites. Future experimental validation could establish their potential role in the diagnosis and treatment of various diseases, including cancer. Full article

This study investigates the association between circulating microRNA (miRNA) expression and cardiovascular adverse events (CVAE) in multiple myeloma (MM) patients treated with a carfilzomib (CFZ)-based regimen. A cohort of 60 MM patients from the Prospective Observation of Cardiac Safety with Proteasome Inhibitor (PROTECT) study was analyzed. Among these, 31 patients (51.6%) developed CVAE post-CFZ treatment. The Taqman OpenArray Human microRNA panels were used for miRNA profiling. We identified 13 differentially expressed miRNAs at baseline, with higher expressions of miR-125a-5p, miR-15a-5p, miR-18a-3p, and miR-152-3p and lower expression of miR-140-3p in patients who later developed CVAE compared to those free of CVAE, adjusting for age, gender, race, and higher B-type natriuretic peptide levels. We also identified three miRNAs, including miR-150-5p, that were differentially expressed in patients with and without CVAE post-treatment. Additionally, five miRNAs responded differently to CFZ treatment in CVAE vs. non-CVAE patients, including significantly elevated post-treatment expression of miR-140-3p and lower expressions of miR-598, miR-152, miR-21, and miR-323a in CVAE patients. Pathway enrichment analysis highlighted the involvement of these miRNAs in cardiovascular diseases and vascular processes. These findings suggest that specific miRNAs could serve as predictive biomarkers for CVAE and provide insights into the underlying mechanisms of CFZ-CVAE. Further investigation is warranted before these findings can be applied in clinical settings. Full article

Atherosclerosis (AS) is a disease dangerous to human health and the main pathological cause of ischemic cardiovascular diseases. Although its pathogenesis is not fully understood, numerous basic and clinical studies have shown that AS is a chronic inflammatory disease existing in all stages of atherogenesis. It may be a common link or pathway in the pathogenesis of multiple atherogenic factors. Inflammation is associated with AS complications, such as plaque rupture and ischemic cerebral infarction. In addition to inflammation, apoptosis plays an important role in AS. Apoptosis is a type of programmed cell death, and different apoptotic cells have different or even opposite roles in the process of AS. Unlike linear RNA, circular RNA (circRNA) a covalently closed circular non-coding RNA, is stable and can sponge miRNA, which can affect the stages of AS by regulating downstream pathways. Ultimately, circRNAs play very important roles in AS by regulating inflammation, apoptosis, and some other mechanisms. The study of circular RNAs can provide new ideas for the prediction, prevention, and treatment of AS. Full article

Hepatocellular carcinoma (HCC) is a common type of liver cancer and one of the highly lethal diseases worldwide. Hypoxia plays an important role in the development and prognosis of HCC. This study aimed to construct a new hypoxia-related prognosis signature and investigate its potential ceRNA axes in HCC. RNA profiles and hypoxia genes were downloaded, respectively, from the Cancer Genome Atlas hepatocellular carcinoma database and Gene Set Enrichment Analysis website. Cox regression analyses were performed to select the prognostic genes and construct the risk model. The ENCORI database was applied to build the lncRNA-miRNA–mRNA prognosis-related network. The TIMER and CellMiner databases were employed to analyze the association of gene expression in ceRNA with immune infiltration and drug sensitivity, respectively. Finally, the co-expression analysis was carried out to construct the potential lncRNA/miRNA/mRNA regulatory axes. We obtained a prognostic signature including eight hypoxia genes (ENO2, KDELR3, PFKP, SLC2A1, PGF, PPFIA4, SAP30, and TKTL1) and further established a hypoxia-related prognostic ceRNA network including 17 lncRNAs, six miRNAs, and seven mRNAs for hepatocellular carcinoma. Then, the analysis of immune infiltration and drug sensitivity showed that gene expression in the ceRNA network was significantly correlated with the infiltration abundance of multiple immune cells, the expression level of immune checkpoints, and drug sensitivity. Finally, we identified three ceRNA regulatory axes (SNHG1/miR-101-3p/PPFIA4, SNHG1/miR-101-3p/SAP30, and SNHG1/miR-101-3p/TKTL1) associated with the progression of HCC under hypoxia. Here, we constructed a prognosis gene signature and a ceRNA network related to hypoxia for hepatocellular carcinoma. Among the ceRNA network, six highly expressed lncRNAs (AC005540.1, AC012146.1, AC073529.1, AC090772.3, AC138150.2, AL390728.6) and one highly expressed mRNA (PPFIA4) were the potential biomarkers of hepatocellular carcinoma which we firstly reported. The three predicted hypoxia-related regulatory axes may play a vital role in the progression of hepatocellular carcinoma. Full article

MicroRNAs (miRNAs) are small, non-coding RNAs that post-transcriptionally regulate gene expression by binding specific cell mRNA targets, preventing their translation. miRNAs are implicated in the regulation of important physiological and pathological pathways. Liver disease, including injury, fibrosis, metabolism dysregulation, and tumor development disrupts liver-associated miRNAs. In addition to their effect in the originating tissue, miRNAs can also circulate in body fluids. miRNA release is an important form of intercellular communication that plays a role in the physiological and pathological processes underlying multiple diseases. Circulating plasma levels of miRNAs have been identified as potential disease biomarkers. One of the main challenges clinics face is the lack of available noninvasive biomarkers for diagnosing and predicting the different stages of liver disease (e.g., nonalcoholic fatty liver disease and nonalcoholic steatohepatitis), particularly among individuals infected with human immunodeficiency virus type 1 (HIV-1). Liver disease is a leading cause of death unrelated to acquired immunodeficiency syndrome (AIDS) among people living with HIV-1 (PLWH). Here, we review and discuss the utility of circulating miRNAs as biomarkers for early diagnosis, prognosis, and assessment of liver disease in PLWH. Remarkably, the identification of dysregulated miRNA expression may also identify targets for new therapeutics. Full article

Even though it is only the 6th most common malignancy at the modal level, head and neck cancers are distinguished by a considerable treatment failure rate, especially by locoregional recurrences, the intrinsic tumor radioresistance being one of the causes of this phenomenon. The efforts of radiobiological research of these cancers are oriented towards the identification of biomarkers associated with radioresistance and radiosensitivity in order to modulate the treatment so that the therapeutic benefit is maximum. Micro-RNAs (miRNAs, miRs), small single-stranded non-coding RNA molecules are currently being extensively evaluated as potential biomarkers in numerous diseases, including cancer. The evaluation of the potential of miRNAs to modulate or predict radiosensitivity or radioresistance, to anticipate the risk of recurrence and metastasis, and to differentiate different tumor subtypes is based on multiple mechanisms by which mRNAs control proliferation and apoptosis and interact with cell cycle phases or act as oncogenes with the potential to influence invasion promotion or tumor suppression. A refinement of radiosensitivity based on miRNAs with clinical and radiobiological application in head and neck cancers can lead to a personalization of radiotherapy. Thus, a miRNA signature can anticipate the risk of toxicity associated with chemoradiation, the possibility of obtaining locoregional control after treatment, and the recurrence and distant metastasis risk. The potential of miRNAs as an intrinsic predictor of sensitivity to chemotherapy may also guide the therapeutic decision toward choosing an escalation or de-escalation of concurrent or sequential systemic treatment. The choice of the irradiated dose, the fractional dose, the fractionation scheme, and the refining of the dose-volume constraints depending on the radiosensitivity of each tissue type estimated on a case-by-case basis by miRNAs profile are possible concepts for the future radiotherapy and radiobiology of head and neck cancers. Full article

MicroRNAs (miRNAs) are a class of small non-coding RNA that can down-regulate their targets by selectively binding to the 3′ untranslated region (3′UTR) of most messenger RNAs (mRNAs) in the human genome. Single nucleotide variants (SNVs) located in miRNA target sites (MTS) can disrupt the binding of targeting miRNAs. Anti-correlated miRNA–mRNA pairs between normal and tumor tissues obtained from The Cancer Genome Atlas (TCGA) can reveal important information behind these SNVs on MTS and their associated oncogenesis. In this study, using previously identified anti-correlated miRNA–mRNA pairs in 15 TCGA cancer types and publicly available variant annotation databases, namely dbNSFP (database for nonsynonymous SNPs’ functional predictions) and dbMTS (database of miRNA target site SNVs), we identified multiple functional variants and their gene products that could be associated with various types of cancers. We found two genes from dbMTS and 33 from dbNSFP that passed our stringent filtering criteria (e.g., pathogenicity). Specifically, from dbMTS, we identified 23 candidate genes, two of which (BMPR1A and XIAP) were associated with diseases that increased the risk of cancer in patients. From dbNSFP, we identified 65 variants located in 33 genes that were likely pathogenic and had a potential causative relationship with cancer. This study provides a novel way of utilizing TCGA data and integrating multiple publicly available databases to explore cancer genomics. Full article

Porcine circovirus type 2 (PCV2) can cause severe disease in infected pigs, resulting in massive economic loss for the swine industry. Transcriptomic and proteomic approaches have been widely employed to identify the underlying molecular mechanisms of the PCV2 infection. Numerous differentially expressed mRNAs, miRNAs, and proteins, together with their associated signaling pathways, have been identified during PCV2 infection, paving the way for analysis of their biological functions. Long noncoding RNAs (lncRNAs) are important regulators of multiple biological processes. However, little is known regarding their role in the PCV2 infection. Hence, in our study, RNA-seq was performed by infecting PK-15 cells with PCV2. Analysis of the differentially expressed genes (DEGs) suggested that the cytoskeleton, apoptosis, cell division, and protein phosphorylation were significantly disturbed. Then, using stringent parameters, six lncRNAs were identified. Additionally, potential targets of the lncRNAs were predicted using both cis- and trans-prediction methods. Interestingly, we found that the HOXB (Homeobox B) gene cluster was probably the target of the lncRNA LOC106505099. Enrichment analysis of the target genes showed that numerous developmental processes were altered during PCV2 infection. Therefore, our study revealed that lncRNAs might affect porcine embryonic development through the regulation of the HOXB genes. Full article

Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA–disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases, without considering the topological structure information among these nodes, which is important for predicting lncRNA–disease associations. We propose a method based on information flow propagation and convolutional neural networks, called LDAPred, to predict disease-related lncRNAs. LDAPred not only integrates the similarities, associations, and interactions among lncRNAs, diseases, and miRNAs, but also exploits the topological structures formed by them. In this study, we construct a dual convolutional neural network-based framework that comprises the left and right sides. The embedding layer on the left side is established by utilizing lncRNA, miRNA, and disease-related biological premises. On the right side of the frame, multiple types of similarity, association, and interaction relationships among lncRNAs, diseases, and miRNAs are calculated based on information flow propagation on the bi-layer networks, such as the lncRNA–disease network. They contain the network topological structure and they are learned by the right side of the framework. The experimental results based on five-fold cross-validation indicate that LDAPred performs better than several state-of-the-art methods. Case studies on breast cancer, colon cancer, and osteosarcoma further demonstrate LDAPred’s ability to discover potential lncRNA–disease associations. Full article

It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA–disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs. Full article

Increasing evidence suggests that dysregulation of microRNAs (miRNAs) may lead to a variety of diseases. Therefore, identifying disease-related miRNAs is a crucial problem. Currently, many computational approaches have been proposed to predict binary miRNA-disease associations. In this study, in order to predict underlying miRNA-disease association types, a semi-supervised model called the network-based label propagation algorithm is proposed to infer multiple types of miRNA-disease associations (NLPMMDA) by mutual information derived from the heterogeneous network. The NLPMMDA method integrates disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity information of miRNAs and diseases to construct a heterogeneous network. NLPMMDA is a semi-supervised model which does not require verified negative samples. Leave-one-out cross validation (LOOCV) was implemented for four known types of miRNA-disease associations and demonstrated the reliable performance of our method. Moreover, case studies of lung cancer and breast cancer confirmed effective performance of NLPMMDA to predict novel miRNA-disease associations and their association types. Full article