Current Bioinformatics - Volume 16, Issue 7, 2021

Objective: Benign Prostatic Hyperplasia (BPH) is a common disease prevalent in elderly men, but the genetic determinants of BPH still remain unclear. Since metabolic syndrome, especially diabetes, may influence the progression of benign prostatic hyperplasia, we investigated whether susceptibility loci for diabetes would increase the risk of BPH development and progression in elderly Chinese men. Material and Methods: Fifteen SNPs associated with the diabetes risk in a Chinese population were genotyped in 377 BPH cases (152 aggressive and 225 non-aggressive BPH cases) and 1,008 controls. The association between the SNPs and the risk of BPH development was evaluated through logistic regression. Additionally, the effects of the 15 SNPs on BPH related clinical parameters, including Body Mass Index (BMI) International Prostate Symptom Score (IPSS), Quality of Life (QoL) and Prostate Volume (PV) were also evaluated. Results: SNP rs9864104 in IGF2BP2 at 3q27 (OR=1.24, P =0.0148) was significantly associated with BPH development. In addition, SNP rs9863780, rs9864104, rs10229583 and rs17727841 were significantly associated with baseline clinical parameters in BPH patients. Moreover, the risk allele of rs6763887 (C) and rs17727841 (C) was significantly associated with the change of storage score and voiding score after treatment. No SNPs were associated with the risk of BPH progression. Conclusion: This is a systematic investigation of the contributions of diabetes susceptibility loci to the risk of BPH development and progression. Our findings advance the understanding of the genetic basis of BPH and provide new insights into the genetic determinants shared between BPH and metabolic syndrome.

Background: Liver cancer is one of the most deadly malignancies worldwide. Tumor metastasis is the main cause of liver cancer-related death. So far, the mechanism of liver cancer metastasis is far away from fully elucidated. In this study, we aimed to discover key regulators involved in liver cancer metastasis by data mining. Methods: Two different types of data, including mRNA microarray (GSE6222 and GSE6764) and miRNA microarray (GSE67138), were analyzed. A total of 83 intersectant differently expressed genes (DEGs) with the same expression pattern in GSE6222 and GSE6764 were identified. One hundred and thirty-one differently expressed miRNAs (DEMs) were identified in GSE 67138. Furthermore, a total of 26 pairs of miRNA-target, including 18 DEMs and 13 DEGs were identified as critical miRNA-target axis via miRNA-target gene interaction analysis. Results and Conclusion: Among the 18 DEMs and 13 DEGs, 10 miRNAs and 10 target genes are significantly correlated with patients’ survival (p < 0.05). Our results and methods might be interesting for data mining and helpful for further experimental functional validation.

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endanger human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis, or treatment measures. Methods: Firstly, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation- based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by a Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold crossvalidation test and the independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein-protein interaction (PPI) network. Conclusion: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

Background: Recent studies revealed that the hypoglycemic hormone, glucagon-like peptide-1 (GLP-1), acted as an important modulator in osteogenesis of bone marrow derived mesenchymal stem cells (BMSCs). Objectives: The aim of this study was to identify the specific microRNA (miRNA) using bioinformatics analysis and validate the presence of differentially expressed microRNAs with their target genes after GLP-1 receptor agonist (GLP-1RA) administration involved in osteogenesis of BMSCs. Methods: MiRNAs were extracted from BMSCs after 5 days’ treatment and sent for highthroughput sequencing for differentially expressed (DE) miRNAs analyses. Then, the expression of the DE miRNAs was verified by the real-time RT-PCR analyses. Target genes were predicted, and highly enriched GOs and KEGG pathway analysis were conducted using bioinformatics analysis. For the functional study, two of the target genes, SRY (sex determining region Y)-box 5 (SOX5) and G protein-coupled receptor 84 (GPR84), were identified. Results: A total of 5 miRNAs (miRNA-509-5p, miRNA-547-3p, miRNA-201-3p, miRNA-201-5p, and miRNA-novel-272-mature) were identified and differentially expressed among groups. The expression of miRNA-novel-272-mature was decreased during the osteogenic differentiation of BMSCs, and GLP-1RA further decreased its expression. MiRNA-novel-272-mature might interact with its target mRNAs to enhance osteogenesis. The lower expression of miRNA-novel-272- mature led to an increase in SOX5 and a decrease in GPR84 mRNA expression, respectively. Conclusion: Taken together, these results provide further insights to the pharmacological properties of GLP-1RA and expand our knowledge on the role of miRNAs-mRNAs regulation network in BMSCs’ differentiation.

Background: An improved comprehension of the oral microbiota function in the pathogenesis of disease will contribute to the diagnosis and treatment of both hypertension and periodontal disease. In our study, a comparison of the salivary microbiome between hypertension and Non-hypertension cohorts was designed to reveal microbial signatures. Methods: Patients were divided into four sub-groups: Gingivitis, and Periodontitis (stage 2, 3, and 4). Then, hypertension and non-hypertension cohorts were split into periodontal health and periodontitis subgroups. The salivary samples were processed for DNA extraction (n=246). The V3-V4 hypervariable regions of microbiome 16S rRNA genes were amplified. Finally, sequencing libraries were constructed and subjected to bioinformatics and statistical analyses. Results: The oral microbial diversity decreased in both hypertension and periodontal disease groups compared to the healthy ones. At the genus level, the diversity showed 100 different operational taxonomic units (OTUs) for differential abundance testing. The first trend showed OTUs decreased in relative abundance with increasing periodontal disease, as well as hypertension and nonhypertensive groups. For this trend, OTUs comprise of a mix of primarily anaerobic commensals and potential acute diarrhea pathogens. The second trend was that the diversity of genera was decreased in the hypertension group relative to the non-hypertension group, including other anaerobic bacteria related to periodontal disease. Conclusion: Microbiota diversity decreased in the hypertension group and different stages of periodontal disease groups. However, Neisseria and Solobacterium genera increased in the coexisting hypertension and periodontal disease group. Certainly, these findings indicate that the abundance of genera continues to change due to additional stresses caused by co-existing conditions.

Deep Learning (DL) is a novel type of Machine Learning (ML) model. It is showing an increasing promise in medicine, study and treatment of diseases and injuries, to assist in data classification, novel disease symptoms and complicated decision making. Deep learning is one of form of machine learning typically implemented via multi-level neural networks. This work discusses the pros and cons of using DL in clinical cardiology that is also applied in medicine in general while proposing certain directions as more viable for clinical use. DL models called Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have been applied to arrhythmias, electrocardiogram, ultrasonic analysis, genomes and endomyocardial biopsy. Convincingly, the results of the trained model are satisfactory, demonstrating the power of more expressive deep learning algorithms for clinical predictive modeling. In the future, more novel deep learning methods are expected to make a difference in the field of clinical medicines.

Objective: The aim of this study was to screen for compounds with relatively high inhibitory activity on acetylcholinesterase. Methods: Classification models for acetylcholinesterase inhibitors based on KNN (1-nearest neighbors), and a quantitative prediction model based on support vector machine regression were used. The interaction of the compounds and receptors was analyzed using the molecular simulation method. Results: The radial basis kernel function was selected as the kernel function for support vector machine regression, and a total of 19 descriptors were selected to construct the quantitative prediction model.

Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and an understanding of its application prospect in animal diseases.

Background: Heart failure (HF) is a leading cause of mortality rate worldwide, but studied less for its underlying biomolecular mechanisms. With the advances in gene sequence analysis, many non-coding RNAs, especially from long non-coding RNA (lncRNA) genre are found to be involved in regulating HF conditions. Recent studies are based on competing endogenous RNA (ceRNA) theory in which lncRNA-miRNA-mRNA compounds control many disease conditions. Method: In this paper, we present a topic model based network cluster analysis to identify the role of lncRNAs in HF. The network is constructed based on the differentially expressed long noncoding RNAs (lncRNAs), micro RNAs (miRNAs), and messenger RNAs (mRNAs) of heart failure patients and control samples from the gene expression omnibus (GEO) database. Further, we extend the primary ceRNA network by adding pathways as additional nodes. Deep belief network based feature learning is used to extract the features from the network automatically. Results: We obtained two clusters where each cluster was a mixture of lncRNAs, mRNAs, miRNAs and pathways. The analysis included the identification of key lncRNAs, enriched pathways, and gene ontology terms from each cluster. Based on the shreds of evidence from literature, one of the clusters obtained was identified to be an essential functional module in HF mechanism. The potential of lncRNA as a diagnostic biomarker in HF was also analysed. Conclusion: The proposed work gives more insight on involvement of lncRNAs in the HF mechanism. The cluster analysis also brought some significant lncRNA-pathway associations into notice, which are supported by recent literature. The identification of lncRNA biomarkers can help in the early detection of HF and discover the missing links in disease pathology.

Background: Metagenomics is the study of genomic content in mass from an environment of interest such as the human gut or soil. Taxonomy is one of the most important fields of metagenomics, which is the science of defining and naming groups of microbial organisms that share the same characteristics. The problem of taxonomy classification is the identification and quantification of microbial species or higher-level taxa sampled by high throughput sequencing. Objective: Although many methods exist to deal with the taxonomic classification problem, assignment to low taxonomic ranks remains an important challenge for binning methods as is scalability to Gbsized datasets generated with deep sequencing techniques. Methods: In this paper, we introduce NLP-MeTaxa, a novel composition-based method for taxonomic binning, which relies on the use of words embeddings and deep learning architecture. The new proposed approach is word-based, where the metagenomic DNA fragments are processed as a set of overlapping words by using the word2vec model to vectorize them in order to feed the deep learning model. NLP-MeTaxa output is visualized as NCBI taxonomy tree, this representation helps to show the connection between the predicted taxonomic identifiers. NLP-MeTaxa was trained on large-scale data from the NCBI RefSeq, more than 14,000 complete microbial genomes. The NLP-MeTaxa code is available at the website: https://github.com/padriba/NLP_MeTaxa/. Results: We evaluated NLP-MeTaxa with a real and simulated metagenomic dataset and compared our results to other tools' results. The experimental results have shown that our method outperforms the other methods especially for the classification of low-ranking taxonomic class such as species and genus. Conclusion: In summary, our new method might provide novel insight for understanding the microbial community through the identification of the organisms it might contain.

Background: Hepatocellular carcinoma (HCC) is a serious disease and is the third main cause of death in the world. Hepatitis B virus infection can lead to HCC. The virus introduces genetic material into the host, damages DNA, and interferes with the activity of the apoptotic and tumor suppressors to trigger the formation of an oncogene. However, most of these cases are discovered after cancer enters stage three or four. Objective: Early detection of HCC through machine learning algorithm approach using data set: DNA sequence of HBx HepB virus. Methods: The research method used is the development of a Support Vector Machine classifier algorithm for carcinoma detection. The large data volume and unbalance data distribution in class can decrease the accuracy rate and sensitivity. Therefore, this paper proposed a hybrid of Hierarchical k-Means clustering and SVM algorithms to detect HCC disease using HBx DNA sequences. In this method, the SVM algorithm was applied in each cluster using the Hierarchical k- Means method. Results: The experimental result showed an accuracy rate of 97.18%, a sensitivity of 98.9%, and AUC of 0.918. This means the performance was increased to 9.52%, 95.3%, and 0.4 above the conventional SVM method. Conclusion: Detection of HCC can be applied using the SVM algorithm based on clustering. The proposed method, by hybrid hierarchical k-Means and SVM, increased the performance of classification results for the detection.