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Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Predicting the polyproline type II (PPII) helix structure is crucial important in many research areas, such as the protein folding mechanisms, the drug targets, and the protein functions. However, many existing PPII helix prediction algorithms encode the protein sequence information in a single way, which causes the insufficient learning of protein sequence feature information. To improve the protein sequence encoding performance, this paper proposes a BERT-based PPII helix structure prediction algorithm (BERT-PPII), which learns the protein sequence information based on the BERT model. The BERT model's CLS vector can fairly fuse sample's each amino acid residue information. Thus, we utilize the CLS vector as the global feature to represent the sample's global contextual information. As the interactions among the protein chains' local amino acid residues have an important influence on the formation of PPII helix, we utilize the CNN to extract local amino acid residues' features which can further enhance the information expression of protein sequence samples. In this paper, we fuse the CLS vectors with CNN local features to improve the performance of predicting PPII structure. Compared to the state-of-the-art PPIIPRED method, the experimental results on the unbalanced dataset show that the proposed method improves the accuracy value by 1% on the strict dataset and 2% on the less strict dataset. Correspondingly, the results on the balanced dataset show that the AUCs of the proposed method are 0.826 on the strict dataset and 0.785 on less strict datasets, respectively. For the independent test set, the proposed method has the AUC value of 0.827 on the strict dataset and 0.783 on the less strict dataset. The above experimental results have proved that the proposed BERT-PPII method can achieve a superior performance of predicting the PPII helix. [ABSTRACT FROM AUTHOR]

As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) plays a crucial role in controlling gene replication, expression, cell cycle, DNA replication, and differentiation. The accurate identification of 4mC sites is necessary to understand biological functions. In the paper, we use ensemble learning to develop a model named i4mC-EL to identify 4mC sites in the mouse genome. Firstly, a multifeature encoding scheme consisting of Kmer and EIIP was adopted to describe the DNA sequences. Secondly, on the basis of the multifeature encoding scheme, we developed a stacked ensemble model, in which four machine learning algorithms, namely, BayesNet, NaiveBayes, LibSVM, and Voted Perceptron, were utilized to implement an ensemble of base classifiers that produce intermediate results as input of the metaclassifier, Logistic. The experimental results on the independent test dataset demonstrate that the overall rate of predictive accurate of i4mC-EL is 82.19%, which is better than the existing methods. The user-friendly website implementing i4mC-EL can be accessed freely at the following. [ABSTRACT FROM AUTHOR]

Malignant melanoma is one of the most threatening cancers to human health. Only 14% of patients with malignant melanoma have a remaining life span of 5 years. At present, there have been some studies looking for potential prognostic indicators of esophageal cancer from the level of genes and infiltrating immune cells, but there are still some problems that need to be resolved urgently. This paper proposes IGHG3 as the immune infiltration of malignant melanoma, which takes into account the computational mathematics. It aims to deduce the characteristics of immune cell infiltration in malignant melanoma and study the relationship between different immune cell infiltration characteristics and prognosis. The method in this article is to establish a computational mathematical model for the immunotherapy of melanoma, then study the method of identification of the affinity of the IGHG3 reagent, and finally obtain the gene expression of immune infiltration. The functions of these methods are, respectively, to predict the dynamic behavior of T cells with two different specificities through mathematical models and to test the matching degree of different concentrations of IGHG3 reagent with the human body. Then use the ssGSEA algorithm to obtain immune infiltration related data and calculate the difference between the weighted empirical cumulative distribution function of all genes in the effect of IGHG3 on melanoma that was carried out. The experimental results showed the computational mathematical method genome and all the remaining genes. In this study, a computational mathematical method to detect the IGHG3 gene expression had a significant inhibitory effect on A375 cells in the experimental group, and the knockdown efficiency reached 85.6%. [ABSTRACT FROM AUTHOR]

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods. [ABSTRACT FROM AUTHOR]

Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical staff and patients family members to make right medical decisions. As well known, cancer is a partly inherited disease with various important biological markers, especially the gene expression profile data and clinical data. Therefore, the accuracy of prediction model can be improved by integrating gene expression profile data and clinical data. In this paper, we proposed an end-to-end model, Attention-based Multi-NMF DNN (AMND), which combines clinical data and gene expression data extracted by Multiple Nonnegative Matrix Factorization algorithms (Multi-NMF) for the prognostic prediction of breast cancer. The innovation of this method is highlighted through using clinical data and combining multiple feature selection methods with the help of Attention mechanism. The results of comprehensive performance evaluation show that the proposed model reports better predictive performances than either models only using data of single modality, e.g., gene or clinical, or models based on any single NMF improved methods which only use one of the NMF algorithms to extract features. The performance of our model is competitive or even better than other previously reported models. Meanwhile, AMND can be extended to the survival prediction of other cancer diseases, providing a new strategy for breast cancer prognostic prediction. [ABSTRACT FROM AUTHOR]

Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine. [ABSTRACT FROM AUTHOR]

Gene expression network is also a type of complex network. It is challenging to analyze the gene expression network through relevant knowledge and algorithms of a complex network. In this paper, the existing characteristics of genes are analyzed from various indexes of the gene expression network to analyze key genes and TOP genes. Firstly, gene chip data are screened, gene data with obvious characteristics are selected, and relevant clustering characteristics are analyzed. Then, the complex gene network structure is established, and gene networks with different threshold shapes and different sizes are selected. Finally, the relevant indexes and PR values after the PageRank algorithm are analyzed for complex networks under different thresholds, thus establishing the TOP gene and PR sequence. [ABSTRACT FROM AUTHOR]

Background and Aim: Gastric cancer (GC) is the common leading cause of cancer-related death worldwide. Immune-related genes (IRGs) may potentially predict lymph node metastasis (LNM). We aimed to develop a preoperative model to predict LNM based on these IRGs. Methods: In this paper, we compared and evaluated three machine learning models to predict LNM based on publicly available gene expression data from TCGA-STAD. The Pearson correlation coefficient (PCC) method was utilized to feature selection according to its relationships with LN status. The performance of the model was assessed using the area under the curve (AUC) and F1 score. Results: The Naive Bayesian model showed better performance and was constructed based on 26 selected gene features, with AUCs of 0.741 in the training set and 0.688 in the test set. The F1 score in the training set and test set was 0.652 and 0.597, respectively. Furthermore, Naive Bayesian model based on 26 IRGs is the first diagnostic tool for the identification of LNM in advanced GC. Conclusion: These results indicate that our new methods have the value of auxiliary diagnosis with promising clinical potential. [ABSTRACT FROM AUTHOR]

One of the deadliest diseases which affects the large intestine is colon cancer. Older adults are typically affected by colon cancer though it can happen at any age. It generally starts as small benign growth of cells that forms on the inside of the colon, and later, it develops into cancer. Due to the propagation of somatic alterations that affects the gene expression, colon cancer is caused. A standardized format for assessing the expression levels of thousands of genes is provided by the DNA microarray technology. The tumors of various anatomical regions can be distinguished by the patterns of gene expression in microarray technology. As the microarray data is too huge to process due to the curse of dimensionality problem, an amalgamated approach of utilizing bilevel feature selection techniques is proposed in this paper. In the first level, the genes or the features are dimensionally reduced with the help of Multivariate Minimum Redundancy–Maximum Relevance (MRMR) technique. Then, in the second level, six optimization techniques are utilized in this work for selecting the best genes or features before proceeding to classification process. The optimization techniques considered in this work are Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), League Championship Optimization (LCO), Beetle Antennae Search Optimization (BASO), Crow Search Optimization (CSO), and Fruit Fly Optimization (FFO). Finally, it is classified with five suitable classifiers, and the best results show when IWO is utilized with MRMR, and then classified with Quadratic Discriminant Analysis (QDA), a classification accuracy of 99.16% is obtained. [ABSTRACT FROM AUTHOR]

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods. [ABSTRACT FROM AUTHOR]

Gene expression programming (GEP) uses simple linear coding to solve complex modeling problems. However, the performance is limited by the effectiveness of the selected method of evaluating population individuals, the breadth and depth of the search domain for the solution, and the ability of accuracy of correcting the solution based on historical data. Therefore, a new dual-mode GEP prediction algorithm based on irregularity and similar period is proposed. It takes measures to specialize origin data to reserve the elite individuals, reevaluate the target individuals, and process data and solutions via the similar period mode, which avoids the tendency to get stuck in local optimum and the complexity of the precisions of correcting complex modeling problems due to insufficiency scope of the search domain, and subsequently, better convergence results are obtained. If we take the leek price and the sunspot observation data as the sample to compare the new algorithm with the GEP simulation test, the results indicate that the new algorithm possesses more powerful exploration ability and higher precision. Under the same accuracy requirements, the new algorithm can find the individual faster. Additionally, the conclusion can be drawn that the performance of new algorithm is better on the condition that we take another set of sunspot observations as samples, combining the ARIMA algorithm and BP neural network prediction algorithm for simulation and comparison with the new algorithm. [ABSTRACT FROM AUTHOR]

The paper proposes an incremental Gene Expression Programming classifier. Its main features include using two-level ensemble consisting of base classifiers in form of genes and the upper-level classifier in the form of metagene. The approach enables us to deal with big datasets through controlling computation time using data reduction mechanisms. The user can control the number of attributes used to induce base classifiers as well as the number of base classifiers used to induce metagenes. To optimize the parameter setting phase, an approach based on the Orthogonal Experiment Design principles is proposed, allowing for statistical evaluation of the influence of different factors on the classifier performance. In addition, the algorithm is equipped with a simple mechanism for drift detection. A detailed description of the algorithm is followed by the extensive computational experiment. Its results validate the approach. Computational experiment results show that the proposed approach compares favourably with several state-of-the-art incremental classifiers. [ABSTRACT FROM AUTHOR]

Background. Non-small-cell lung cancer (NSCLC) is a major health problem that endangers human health. The prognosis of radiotherapy or chemotherapy is still unsatisfactory. This study is aimed at investigating the predictive value of glycolysis-related genes (GRGs) on the prognosis of NSCLC patients with radiotherapy or chemotherapy. Methods. Download the clinical information and RNA data of NSCLC patients receiving radiotherapy or chemotherapy from TCGA and geo databases and obtain GRGs from MsigDB. The two clusters were identified by consistent cluster analysis, the potential mechanism was explored by KEGG and GO enrichment analyses, and the immune status was evaluated by estimate, TIMER, and quanTIseq algorithms. Lasso algorithm is used to build the corresponding prognostic risk model. Results. Two clusters with different GRG expression were identified. The high-expression subgroup had poor overall survival. The results of KEGG and GO enrichment analyses suggest that the differential genes of the two clusters are mainly reflected in metabolic and immune-related pathways. The risk model constructed with GRGs can effectively predict the prognosis. The nomogram combined with the model and clinical characteristics has good clinical application potential. Conclusion. In this study, we found that GRGs are associated with tumor immune status and can assess the prognosis of NSCLC patients receiving radiotherapy or chemotherapy. [ABSTRACT FROM AUTHOR]

The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them. [ABSTRACT FROM AUTHOR]

As one of the most effective function mining algorithms, Gene Expression Programming (GEP) algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks. [ABSTRACT FROM AUTHOR]

Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms. [ABSTRACT FROM AUTHOR]

GINS2 is overexpressed in several cancers, but little is known about its role in osteosarcoma (OS). A series of in vivo and in vitro experiments were conducted to explore the role of GINS2 in OS. In this study, we demonstrated that GINS2 was found to be highly expressed in OS tissues and cell lines, which was associated with poor outcomes in OS patients. GINS2 knockdown hindered the growth and induced apoptosis in OS cell lines in vitro. Furthermore, GINS2 knockdown effectively inhibited the growth of a xenograft tumor in vivo. By using an Affymetrix gene chip and intelligent pathway analysis, it was demonstrated that the GINS2 knockdown could reduce the expression of several targeted genes and reduce the activity of the MYC signaling pathway. Mechanically, LC-MS, CoIP, and rescue experiments revealed that GINS2 promoted tumor progression through the STAT3/MYC axis in the OS. Moreover, GINS2 was associated with tumor immunity and may be a potential immunotherapeutic target for OS. [ABSTRACT FROM AUTHOR]

Background. Acute myeloid leukemia (LAML) is the most widely known acute leukemia in adults. Chemotherapy is the main treatment method, but eventually many individuals who have achieved remission relapse, the disease will ultimately transform into refractory leukemia. Therefore, for the improvement of the clinical outcome of patients, it is crucial to identify novel prognostic markers. Methods. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were utilized to retrieve RNA-Seq information and clinical follow-up details for patients with acute myeloid leukemia, respectively, whereas samples that received or did not receive ultrasound treatment were analyzed using differential expression analysis. For consistent clustering analysis, the ConsensusClusterPlus package was utilized, while by utilizing weighted correlation network analysis (WGCNA), important modules were found and the generation of the coexpression network of hub gene was generated using Cytoscape. CIBERSORT, ESTIMATE, and xCell algorithms of the "IOBR" R package were employed for the calculation of the relative quantity of immune infiltrating cells, whereas the mutation frequency of cells was estimated by means of the "maftools" R package. The pathway enrichment score was calculated using the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm of the "Gene Set Variation Analysis (GSVA)" R package. The IC50 value of the drug was predicted by utilizing the "pRRophetic." The indications linked with prognosis were selected by means of the least absolute shrinkage and selection operator (Lasso) Cox analysis. Results. Two categories of samples were created as follows: Cluster 1 and Cluster 2 depending on the differential gene consistent clustering of ultrasound treatment. The prognosis of patients in Cluster 2 was better than that in Cluster 1, and a considerable variation was observed in the immune microenvironment of Cluster 1 and Cluster 2. Lasso analysis finally obtained an 8-gene risk model (GASK1A, LPO, LTK, PRRT4, UGT3A2, BLOCK1S1, G6PD, and UNC93B1). The model acted as an independent risk factor for the patients' prognosis, and it showed good robustness in different datasets. Considerable variations were observed in the abundance of immune cell infiltration, genome mutation, pathway enrichment score, and chemotherapeutic drug resistance between the low and high-risk groups in accordance with the risk score (RS). Additionally, model-based RSs in the immunotherapy cohort were significantly different between complete remission (CR) and other response groups. Conclusion. The prognosis of people with LAML can be predicted using the 8-gene signature. [ABSTRACT FROM AUTHOR]

Sarcoma, the second common type of solid tumor in children and adolescents, has a wide variety of subtypes that are often not properly diagnosed at an early stage, leading to late metastases and causing serious loss of life and property to patients and families. It exhibits a high degree of heterogeneity at the cellular, molecular, and epigenetic levels, where DNA methylation has been proposed to play a role in the diagnosis of sarcoma subtypes. Thus, this study is aimed at finding potential biomarkers at the DNA methylation level to distinguish different sarcoma subtypes. A machine learning process was designed to analyse sarcoma samples, each of which was represented by lots of methylation sites. Irrelevant sites were removed using the Boruta method, and remaining sites related to the target variables were kept for further analyses. Afterward, three feature ranking methods (LASSO, LightGBM, and MCFS) were adopted to rank these features, and six classification models were constructed by combining incremental feature selection and two classification algorithms (decision tree and random forest). Among these models, the performance of RF model was higher than that of DT model under all three ranking conditions. The specific expression of genes obtained from the annotation of highly correlated methylation site features, such as PRKAR1B, INPP5A, and GLI3, was proven to be associated with sarcoma by publications. Moreover, the quantitative rules obtained by decision tree algorithm helped us to understand the essential differences between various sarcoma types and classify sarcoma subtypes, providing a new means of clinical identification and determining new therapeutic targets. [ABSTRACT FROM AUTHOR]

Background. Ferroptosis, a form of cell death driven by iron-dependent lipid peroxidation, may be a potential treatment for many cancers, including cervical cancer (CC). However, the regulation of long noncoding RNAs (lncRNAs) in the process of ferroptosis and whether ferroptosis inducers could increase the cytotoxicity of conventional chemotherapy drugs remain to be further elucidated. Methods. We analyzed the variation of 55 differentially ferroptosis-related genes (FRGs) and the influence of mutations in CC patients. The patients with CC were classified into two ferroptosis clusters by the non-negative matrix factorization (NMF) algorithm. The principal components analysis (PCA) was used to measure the ferroptosis score (FerroScore) in patients with CC. Besides, FerroScore was used to predict the sensitivity of chemotherapy and responses to immunotherapy in patients with CC. Finally, experiments were performed to verify the regulatory effect of AC026790.1 on erastin-induced ferroptosis, as well as the effect of erastin in combination with cisplatin on the toxicity of CC cells (SiHa, HeLa). Result. There were significant differences in the overall survival and immune cell infiltration between the two ferroptosis clusters. Patients with low FerroScore were more sensitive to chemotherapy drugs such as cisplatin and docetaxel. The low-FerroScore group had higher CD8+ T cell infiltration and immune checkpoint expression, demonstrating that patients with lower FerroScores were more sensitive to immunotherapy, which was consistent with the result of the submap method. In vitro, overexpression of AC026790.1 could promote erastin-induced ferroptosis, and the combination of erastin and cisplatin could increase the toxicity of CC cells. Conclusion. FerroScore has a potential prognostic value for CC that may provide a reference for chemotherapy and immunotherapy. LncRNA AC026790.1 can influence ferroptosis, and the combination of ferroptosis inducers and chemotherapy drugs can provide a new perspective on cancer treatment. [ABSTRACT FROM AUTHOR]

Background. Rheumatoid arthritis (RA) is recognized as a chronic inflammatory disease featured by pathological synovial inflammation. Currently, the underlying pathophysiological mechanisms of RA remain unclear. In the study, we attempted to explore the underlying mechanisms of RA and provide potential targets for the therapy of RA via bioinformatics analysis. Methods. We downloaded four microarray datasets (GSE77298, GSE55235, GSE12021, and GSE55457) from the GEO database. Firstly, GSE77298 and GSE55457 were identified DEGs by the "limma" and "sva" packages of R software. Then, we performed GO, KEGG, and GSEA enrichment analyses to further analyze the function of DEGs. Hub genes were screened using LASSO analysis and SVM-RFE analysis. To further explore the differences of the expression of hub genes in healthy control and RA patient synovial tissues, we calculated the ROC curves and AUC. The expression levels of hub genes were verified in synovial tissues of normal and RA rats by qRT-PCR and western blot. Furthermore, the CIBERSORTx was implemented to assess the differences of infiltration in 22 immune cells between normal and RA synovial tissues. We explored the association between hub genes and infiltrating immune cells. Results. CRTAM, CXCL13, and LRRC15 were identified as RA's potential hub genes by machine learning and LASSO algorithms. In addition, we verified the expression levels of three hub genes in the synovial tissue of normal and RA rats by PCR and western blot. Moreover, immune cell infiltration analysis showed that plasma cells, T follicular helper cells, M0 macrophages, M1 macrophages, and gamma delta T cells may be engaged in the development and progression of RA. Conclusions. In brief, our study identified and validated that three hub genes CRTAM, CXCL13, and LRRC15 might involve in the pathological development of RA, which could provide novel perspectives for the diagnosis and treatment with RA. [ABSTRACT FROM AUTHOR]

Purpose. Hepatocellular carcinoma (HCC) has poor prognosis and high mortality among gastrointestinal tumors because of its insidious onset and strong invasiveness. However, there was little understanding of their pathogenesis. The purpose of this study was to use bioinformatics analysis to identify genes associated with the immune microenvironment in HBV-related HCC and to develop new therapeutic targets to prevent and treat cancer. Methods. RNA-seq data of HBV-related HCC cases were downloaded from TCGA-LIHC database. ESTIMATE and Deseq2 algorithms were used to screen out differentially expressed genes (DEGs). WGCNA was used to construct gene coexpression networks. In key modules, functional enrichment analysis was performed. Protein-protein interaction (PPI) was used to screen hub genes, and survival analysis was conducted to assess their prognostic significance. Following, we search for key genes differentially expressed between cancerous and paracancerous tissues in GSE136247 and GSE121248 datasets. Reveal the potential links between key genes in immune infiltration by using TIMER. Finally, in TCGA-LIHC database, integration of key genes with clinical data were used to further validate their correlation with prognosis. Results. In the cohort of HBV-related HCC patients, immune/stromal/ESTIMATE scores were not significantly associated with patient prognosis. After bioinformatics analysis, screening out five key genes was significantly related to the prognosis of HBV-related HCC. Downregulation of SLAMF1 and TRAF3IP3 suggested poor prognosis and was related to a variety of immune cell infiltration. Furthermore, compared with adjacent nontumor tissues, TRAF3IP3 and SLAMF1 were highly expressed in tumor tissues and were linked to tumor recurrences. Conclusion. In conclusion, SLAMF1 and TRAF3IP3 were identified with higher expression in tumor tissues and associated with tumor recurrence. It will be a new research direction of tumor progress and treatment. [ABSTRACT FROM AUTHOR]

The effect of pyroptosis-related genes (PRGs) on the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unclear. Thus, this study is aimed at evaluating the prognostic value of PRGs in patients with LUAD and to elucidate their role in the TME and their effect on immunotherapy. Transcriptomic and clinical data were obtained from the Cancer Genome Atlas and the Gene Expression Omnibus databases (GSE3141, GSE31210). Patients with LUAD were classified using consistent clustering, and the differences in the TME for each type were determined using the ESTIMATE and CIBERSORT algorithms. PRGs were screened using univariate regression analysis, and a prognostic risk model was constructed using LASSO regression analysis. The tumor mutational burden and the tumor immune dysfunction and exclusion algorithms were used to predict therapeutic sensitivity in LUAD patients. Then, we evaluated the potential therapeutic interventions using the GDSC database. LUAD patients in cluster 2 had significantly shorter overall survival and progression-free survival rates, lower immune scores, and higher infiltration of T follicular helper cells than those in cluster 1. We used five PRGs to classify patients with LUAD into different risks groups and found that the high-risk group is sensitive to immunotherapy; however, its immune-related pathways were inhibited, which may be related to tumor metabolic reprogramming. Lastly, we identified several potential therapeutic drugs for application in low-risk patients who were less sensitive to immunotherapy. Overall, our findings showed that PRGs can be used to predict prognosis and may aid in the development of personalized therapeutic strategies in LUAD patients. [ABSTRACT FROM AUTHOR]

The purpose of this study was to identify the potential diagnostic biomarkers in hepatocellular carcinoma (HCC) by machine learning (ML) and to explore the significance of immune cell infiltration in HCC. From GEO datasets, the microarray datasets of HCC patients were obtained and downloaded. Differentially expressed genes (DEGs) were screened from five datasets of GSE57957, GSE84402, GSE112790, GSE113996, and GSE121248, totalling 125 normal liver tissues and 326 HCC tissues. In order to find the diagnostic indicators of HCC, the LASSO regression and the SVM-RFE algorithms were utilized. The prognostic value of VIPR1 was analyzed. Finally, the difference of immune cell infiltration between HCC tissues and normal liver tissues was evaluated by CIBERSORT algorithm. In this study, a total of 232 DEGs were identified in 125 normal liver tissues and 326 HCC tissues. 11 diagnostic markers were identified by LASSO regression and SVM-RFE algorithms. FCN2, ECM1, VIRP1, IGFALS, and ASPG genes with AUC>0.85 were regarded as candidate biomarkers with high diagnostic value, and the above results were verified in GSE36376. Survival analyses showed that VIPR1 and IGFALS were significantly correlated with the OS, while ASPG, ECM1, and FCN2 had no statistical significance with the OS. Multivariate assays indicated that VIPR1 gene could be used as an independent prognostic factor for HCC, while FCN2, ECM1, IGFALS, and ASPG could not be used as independent prognostic factors for HCC. Immune cell infiltration analyses showed that the expression of VIPR1 in HCC was positively correlated with the levels of several immune cells. Overall, VIPR1 gene can be used as a diagnostic feature marker of HCC and may be a potential target for the diagnosis and treatment of liver cancer in the future. [ABSTRACT FROM AUTHOR]

Subcellular localization attempts to assign proteins to one of the cell compartments that performs specific biological functions. Finding the link between proteins, biological functions, and subcellular localization is an effective way to investigate the general organization of living cells in a systematic manner. However, determining the subcellular localization of proteins by traditional experimental approaches is difficult. Here, protein–protein interaction networks, functional enrichment on gene ontology and pathway, and a set of proteins having confirmed subcellular localization were applied to build prediction models for human protein subcellular localizations. To build an effective predictive model, we employed a variety of robust machine learning algorithms, including Boruta feature selection, minimum redundancy maximum relevance, Monte Carlo feature selection, and LightGBM. Then, the incremental feature selection method with random forest and support vector machine was used to discover the essential features. Furthermore, 38 key features were determined by integrating results of different feature selection methods, which may provide critical insights into the subcellular location of proteins. Their biological functions of subcellular localizations were discussed according to recent publications. In summary, our computational framework can help advance the understanding of subcellular localization prediction techniques and provide a new perspective to investigate the patterns of protein subcellular localization and their biological importance. [ABSTRACT FROM AUTHOR]

Based on TCGA, GTEx, and TIMER databases and various bioinformatics analysis methods, the potential biological roles of cuprotosis-related genes in pancreatic cancer were deeply explored, and a predictive model for pancreatic cancer patients was constructed. We downloaded the RNA-Seq data and clinicopathological and predictive data of 179 pancreatic cancer tissues and 332 adjacent normal tissues from TCGA and GTEx databases. The differential expression of cuprotosis-related genes in pancreatic cancer tissue and adjacent normal tissue was analyzed, and the LASSO regression algorithm was used to construct a prediction model and verify the validity of the model prediction. Based on the LASSO regression algorithm, a predictive model composed of three genes LIPT1, LIAS, and DLAT was screened. The corresponding survival curves showed that the constructed prediction model could significantly distinguish the prognosis of pancreatic cancer patients, and the prognosis of patients in the high-risk group was worse (P = 0.00557). The ROC curve showed that the area under the curve of the predictive model for predicting the 4-, 5-, and 6-year survival rates in pancreatic cancer was 0.816, 0.836, and 0.956, respectively. The AUC value of this risk model was significantly higher than 0.7, which could more accurately predict the prognosis of pancreatic cancer patients. This study determined a risk-scoring model of cuprotosis-related genes, which can provide an essential basis for judging the prognosis of pancreatic cancer patients. [ABSTRACT FROM AUTHOR]

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. [ABSTRACT FROM AUTHOR]

Identification of rhythmic gene expression from metabolic cycles to circadian rhythms is crucial for understanding the gene regulatory networks and functions of these biological processes. Recently, two algorithms, JTK_CYCLE and ARSER, have been developed to estimate periodicity of rhythmic gene expression. JTK_CYCLE performs well for long or less noisy time series, while ARSER performs well for detecting a single rhythmic category. However, observing gene expression at high temporal resolution is not always feasible, and many scientists are interested in exploring both ultradian and circadian rhythmic categories simultaneously. In this paper, a new algorithm, named autoregressive Bayesian spectral regression (ABSR), is proposed. It estimates the period of time-course experimental data and classifies gene expression profiles into multiple rhythmic categories simultaneously. Through the simulation studies, it is shown that ABSR substantially improves the accuracy of periodicity estimation and clustering of rhythmic categories as compared to JTK_CYCLE and ARSER for the data with low temporal resolution. Moreover, ABSR is insensitive to rhythmic patterns. This new scheme is applied to existing time-course mouse liver data to estimate period of rhythms and classify the genes into ultradian, circadian, and arrhythmic categories. It is observed that 49.2% of the circadian profiles detected by JTK_CYCLE with 1-hour resolution are also detected by ABSR with only 4-hour resolution. [ABSTRACT FROM AUTHOR]

Aims. The clinical diagnosis of Kawasaki disease (KD) is not easy because of many atypical manifestations. This study is aimed at finding potential diagnostic markers and therapeutic targets for KD and analysing their correlation with immune cell infiltrations. Methods. First, we downloaded the KD dataset from the Gene Expression Omnibus (GEO) database and used R software to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then, CIBERSORT algorithm was used to evaluate immune cell infiltrations in samples. Coexpression analysis between DEGs and infiltrating immune cells was performed to screen the main infiltrating immune cells. Subsequently, the least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to screen the core genes related to KD. Finally, correlation analysis between the core genes and the main infiltrating immune cells was performed. Results. 327 DEGs were screened out in this study. Among them, 72 shared genes were the category of genes most likely to be disease-causing for they did not change before and after treatment. After analysis, it was found that expression level of IL2RB in KD tissues was significantly upregulated, the number of resting CD4+ memory T cells was decreased, and the decrease was significantly negatively correlated with the upregulated expression of IL2RB. Therefore, it was speculated that the upregulated expression of IL2RB disrupted Th1/Th2 cell differentiation balance, which led to a decrease of resting CD4+ memory T cells and finally caused disorder of immune microenvironment in patients with KD. Conclusions. Upregulated expression of IL2RB leads to disorder of immune microenvironment in patients with KD and eventually causes the occurrence and development of KD. Therefore, IL2RB may serve as a diagnostic marker and potential therapeutic target for KD. [ABSTRACT FROM AUTHOR]

Background. Immunotherapy has been considered as a promising cancer treatment for hepatocellular carcinoma (HCC). However, due to the particular immune environment of the liver, identifying patients who could benefit from immunotherapy is critical in clinical practice. Methods. The pyroptosis gene expression database of 54 candidates from The Cancer Genome Atlas (TCGA) were collected to discover the critical prognostic-related pyroptosis genes. A novel pyroptosis gene model was established to calculate the risk score. Kaplan–Meier analysis and receiver operating characteristic curve (ROC) were used to verify its predictive ability. The International Cancer Genome Consortium (ICGC) data was collected as external validation data to verify the model's accuracy. We employed multiple bioinformatics tools and algorithms to evaluate the tumor immune microenvironment (TIME) and the response to immunotherapy. Results. Our study found that most pyroptosis genes were expressed differently in normal and tumor tissues and that their expression was associated with the prognosis. Then, a precise four-pyroptosis gene model was generated. The one-year area under the curves (AUCs) among the training, internal, and external validation patients were 0.901, 0.727, and 0.671, respectively. An analysis of survival data revealed that individuals had a worse prognosis than patients with low risk. The analysis of TIME revealed that the low-risk group had more antitumor cells, fewer immunosuppressive cells, stronger immune function, less immune checkpoint gene expression, and better immunotherapy response than the high-risk group. Immunophenoscore (IPS) analysis also demonstrated that the low-risk score was related to superior immune checkpoint inhibitors therapy. Conclusion. A nomogram based on the four-pyroptosis gene signature was a novel tool to predict the effectiveness of immunotherapy for HCC. Therefore, individualized treatment targeting the pyroptosis genes may influence TIME and play an essential role in improving the prognosis in HCC patients. [ABSTRACT FROM AUTHOR]

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a progressively and fatally neurodegenerative disorder and leads to irreversibly cognitive and memorial damage in different brain regions. The identification and analysis of the dysregulated pathways and subnetworks among affected brain regions will provide deep insights for the pathogenetic mechanism of AD. In this paper, commonly and specifically significant subnetworks were identified from six AD brain regions. Protein-protein interaction (PPI) data were integrated to add molecular biological information to construct the functional modules of six AD brain regions by Heinz algorithm. Then, the simulated annealing algorithm based on edge weight is applied to predicting and optimizing the maximal scoring networks for common and specific genes, respectively, which can remove the weak interactions and add the prediction of strong interactions to increase the accuracy of the networks. The identified common subnetworks showed that inflammation of the brain nerves is one of the critical factors of AD and calcium imbalance may be a link among several causative factors in AD pathogenesis. In addition, the extracted specific subnetworks for each brain region revealed many biologically functional mechanisms to understand AD pathogenesis. [ABSTRACT FROM AUTHOR]

Diagnostic genes are usually used to distinguish different disease phenotypes. Most existing methods for diagnostic genes finding are based on either the individual or combinatorial discriminative power of gene(s). However, they both ignore the common expression trends among genes. In this paper, we devise a novel sequence rule, namely, top-k irreducible covering contrast sequence rules (TopkIRs for short), which helps to build a sample classifier of high accuracy. Furthermore, we propose an algorithm called MineTopkIRs to efficiently discover TopkIRs. Extensive experiments conducted on synthetic and real datasets show that MineTopkIRs is significantly faster than the previous methods and is of a higher classification accuracy. Additionally, many diagnostic genes discovered provide a new insight into disease diagnosis. [ABSTRACT FROM AUTHOR]

Background. Chronic sinusitis (CRS) was a chronic inflammation that originated in the nasal mucosa and affected the health of most people around the world. Chronic rhinosinusitis with nasal polyps (CRSwNP) was one kind of chronic sinusitis. Emerging research had suggested that long noncoding RNAs (lncRNAs) played vital parts in inflammatories and inflammation development. Methods. We acquired GEO data to analyze the differential expression between the miRNA, immune genes, TF, and lncRNA data in CRSWNP and the corresponding control tissues. Bioinformatic analysis by coexpression of endogenous RNA network and competitive way enrichment, analysis, and forecasting functions of these noncoding RNA. The different pathway expressions in CRSwNP patients were confirmed using GSVA to analyze the differentially expressed immune genes and TF data sets in CRSwNP patients. The differential immune gene and transcription factor data set in CRSwNP perform functional notes and protein-protein interaction (PPI) network structure. We predicted the potential genes and RNAs related to CRSWNP by constructing a ceRNA network. In addition, we also used 19 hub immune genes to predict the potential drugs of CRSWNP. lncRNA biomarkers in CRSwNP were identified by lncRNAs LASSO regression. The CIBERSORT algorithm was used to contrast the divergence in immune infiltrations between CRSwNP and usual inferior turbinate organizations in 22 immunocyte subgroups. Results. We identified a total of 48 miRNAs, 304 lncRNAs, 92 TFs, and 525 immune genes as CRSwNP-specific RNAs. GO and KEGG pathways both analyzed differentially expressed immune genes and transcription factor data sets. We predicted the potential genes GNG7, TUSC8, LINC01198, and has-miR-6776-5p by constructing ceRNA and PPI networks. At the same time, we found that the above genes were involved in two important pathways: chemokine signal path and PI3K/AKT signal path. In addition, we predicted 5 small molecule drugs to treat CRSwNP by analyzing 19 central immune genes, namely, danazol, ikarugamycin, semustine, cefamandole, and molindone. Finally, we identified 5 biomarkers in CRSwNP, namely, LINC01198, LINC01094, LINC01798, LINC01829, and LINC01320. Conclusions. We had identified CRSwNP-related miRNAs, lncRNAs, TFs, and immune genes, which may be making use of latent therapeutic target for CRSwNP. At the same time, we identified 5 lncRNA biomarkers in CRSwNP. The results of this study showed that LINC01198 promoted the progression of CRSwNPs through spongy miR-6776-5p. Our studies provide a new way for further analyses of the pathogenesis of CRSwNP. [ABSTRACT FROM AUTHOR]

COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4+ cell, CD8+ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMR, and the resulting ranked feature lists were then fed into the incremental feature selection method to determine the optimal number of features with decision tree and random forest algorithms. Meanwhile, we extracted the classification rules in each cell type from the optimal decision tree classifiers. The best gene sets discovered in this study were analyzed by GO and KEGG pathway enrichment, and some important biomarkers like TLR2, ITK, CX3CR1, IL1B, and PRDM1 were validated by recent literature. The findings reveal that the optimal gene sets for each cell type can accurately classify COVID-19 disease severity and provide insight into the molecular mechanisms involved in disease progression. [ABSTRACT FROM AUTHOR]

Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, patient prognosis is identified based on individual biocell appearance. Classification of advanced artificial neural network subtypes attains improved performance compared to previous enhanced artificial neural network (EANN) biocell subtype investigation. In this research, the proposed features are selected based on improved gene expression programming (IGEP) with modified brute force algorithm. Then, the maximum and minimum term survivals are classified by using PCA with enhanced artificial neural network (EANN). In this, the improved gene expression programming (IGEP) effectual features are selected by using remainder performance to improve the prognosis efficiency. This system is estimated by using the Cancer Genome Atlas (CGA) dataset. Simulation outputs present improved gene expression programming (IGEP) with modified brute force algorithm which achieves accurate efficiency of 96.37%, specificity of 96.37%, sensitivity of 98.37%, precision of 78.78%, F -measure of 80.22%, and recall of 64.29% when compared to generalized regression neural network (GRNN), improved extreme learning machine (IELM) with minimum redundancy maximum relevance (MRMR) method, and support vector machine (SVM). [ABSTRACT FROM AUTHOR]

Background. Myocardial ischemia-reperfusion injury (MIRI) has become a thorny and unsolved clinical problem. The pathological mechanisms of MIRI are intricate and unclear, so it is of great significance to explore potential hub genes and search for some natural products that exhibit potential therapeutic efficacy on MIRI via targeting the hub genes. Methods. First, the differential expression genes (DEGs) from GSE58486, GSE108940, and GSE115568 were screened and integrated via a robust rank aggregation algorithm. Then, the hub genes were identified and verified by the functional experiment of the MIRI mice. Finally, natural products with protective effects against MIRI were retrieved, and molecular docking simulations between hub genes and natural products were performed. Results. 230 integrated DEGs and 9 hub genes were identified. After verification, Emr1, Tyrobp, Itgb2, Fcgr2b, Cybb, and Fcer1g might be the most significant genes during MIRI. A total of 75 natural products were discovered. Most of them (especially araloside C, glycyrrhizic acid, ophiopogonin D, polyphyllin I, and punicalagin) showed good ability to bind the hub genes. Conclusions. Emr1, Tyrobp, Itgb2, Fcgr2b, Cybb, and Fcer1g might be critical in the pathological process of MIRI, and the natural products (araloside C, glycyrrhizic acid, ophiopogonin D, polyphyllin I, and punicalagin) targeting these hub genes exhibited potential therapeutic efficacy on MIRI. Our findings provided new insights to explore the mechanism and treatments for MIRI and revealed new therapeutic targets for natural products with protective properties against MIRI. [ABSTRACT FROM AUTHOR]

Background. Coronary heart disease (CHD) is the most prevalent disease with an unelucidated pathogenetic mechanism and is mediated by complex molecular interactions of exosomes. Here, we aimed to identify differentially expressed exosome genes for the disease development and prognosis of CHD. Method. Six CHD samples and 32 normal samples were downloaded from the exoRbase database to identify the candidate genes in the CHD. The differentially expressed genes (DEGs) were identified. And then, weighted gene correlation network analysis (WGCNA) was used to investigate the modules in coexpressed genes between CHD samples and normal samples. DEGs and the module of the WGCNA were intersected to obtain the most relevant exosome genes. After that, the function enrichment analyses and protein-protein interaction network (PPI) were performed for the particular module using STRING and Cytoscape software. Finally, the CIBERSORT algorithm was used to analyze the immune infiltration of exosome genes between CHD samples and normal samples. Result. We obtain a total of 715 overlapping exosome genes located at the intersection of the DEGs and key modules. The Gene Ontology enrichment of DEGs in the blue module included inflammatory response, neutrophil degranulation, and activation of CHD. In addition, protein-protein networks were constructed, and hub genes were identified, such as LYZ, CAMP, HP, ORM1, and LTF. The immune infiltration profiles varied significantly between normal controls and CHD. Finally, we found that mast cells activated and eosinophils had a positive correlation. B cell memory had a significant negative correlation with B cell naive. Besides, neutrophils and mast cells were significantly increased in CHD patients. Conclusion. The underlying mechanism may be related to neutrophil degranulation and the immune response. The hub genes and the difference in immune infiltration identified in the present study may provide new insights into the diagnostic and provide candidate targets for CHD. [ABSTRACT FROM AUTHOR]

Astragalus membranaceus has complex components as a natural drug and has multilevel, multitarget, and multichannel effects on dilated cardiomyopathy (DCM). However, the immune mechanism, gene module, and molecular subtype of astragalus membranaceus in the treatment of DCM are still not revealed. Microarray information of GSE84796 was downloaded from the GEO database, including RNA sequencing data of seven normal cardiac tissues and ten DCM cardiac tissues. A total of 4029 DCM differentially expressed genes were obtained, including 1855 upregulated genes and 2174 downregulated genes. GO/KEGG/GSEA analysis suggested that the activation of T cells and B cells was the primary cause of DCM. WGCNA was used to obtain blue module genes. The blue module genes are primarily ADCY7, BANK1, CD1E, CD19, CD38, CD300LF, CLEC4E, FLT3, GPR18, HCAR3, IRF4, LAMP3, MRC1, SYK, and TLR8, which successfully divided DCM into three molecular subtypes. Based on the CIBERSORT algorithm, the immune infiltration profile of DCM was analyzed. Many immune cell subtypes, including the abovementioned immune cells, showed different levels of increased infiltration in the myocardial tissue of DCM. However, this infiltration pattern was not obviously correlated with clinical characteristics, such as age, EF, and sex. Based on network pharmacology and ClueGO, 20 active components of Astragalus membranaceus and 40 components of DMCTGS were obtained from TCMSP. Through analysis of the immune regulatory network, we found that Astragalus membranaceus effectively regulates the activation of immune cells, such as B cells and T cells, cytokine secretion, and other processes and can intervene in DCM at multiple components, targets, and levels. The above mechanisms were verified by molecular docking results, which confirmed that AKT1, VEGFA, MMP9, and RELA are promising potential targets of DCM. [ABSTRACT FROM AUTHOR]

Choroidal neovascularization (CNV) is a type of eye disease that can cause vision loss. In recent years, many studies have attempted to investigate the major pathological processes and molecular pathogenic mechanisms of CNV. Because many diseases are related to genes, the genes associated with CNV need to be identified. In this study, we proposed a network-based approach for identifying novel CNV-associated genes. To execute such method, we first employed a protein-protein interaction network reported in STRING. Then, we applied a network diffusion algorithm, Laplacian heat diffusion, on this network by selecting validated CNV-related genes as the seed nodes. As a result, some novel genes that had unknown but strong relationships with validated genes were identified. Furthermore, we used a screening procedure to extract the most essential genes. Eleven latent CNV-related genes were finally obtained. Extensive analyses were performed to confirm that these genes are novel CNV-related genes. [ABSTRACT FROM AUTHOR]

With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method—threshold gradient descent regularization (TGDR)—stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. We proposed a new novel feature selection algorithm for longitudinal outcomes—GEE-TGDR. In the GEE-TGDR method, the corresponding quasilikelihood function of a GEE model is the objective function to be optimized, and the optimization and feature selection are accomplished by the TGDR method. Long noncoding RNAs (lncRNAs) are posttranscriptional and epigenetic regulators and have lower expression levels and are more tissue-specific compared with protein-coding genes. So far, the implication of lncRNAs in psoriasis remains largely unexplored and poorly understood even though some evidence in the literature supports that lncRNAs and psoriasis are highly associated. In this study, we applied the GEE-TGDR method to a lncRNA expression dataset that examined the response of psoriasis patients to immune treatments. As a result, a list including 10 relevant lncRNAs was identified with a predictive accuracy of 70% that is superior to the accuracies achieved by two competitive methods and meaningful biological interpretation. A widespread application of the GEE-TGDR method in omics longitudinal data analysis is anticipated. [ABSTRACT FROM AUTHOR]

Objective. To investigate the genetic crosstalk mechanisms that link periodontitis and Alzheimer's disease (AD). Background. Periodontitis, a common oral infectious disease, is associated with Alzheimer's disease (AD) and considered a putative contributory factor to its progression. However, a comprehensive investigation of potential shared genetic mechanisms between these diseases has not yet been reported. Methods. Gene expression datasets related to periodontitis were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis was performed to identify differentially expressed genes (DEGs). Genes associated with AD were downloaded from the DisGeNET database. Overlapping genes among the DEGs in periodontitis and the AD-related genes were defined as crosstalk genes between periodontitis and AD. The Boruta algorithm was applied to perform feature selection from these crosstalk genes, and representative crosstalk genes were thus obtained. In addition, a support vector machine (SVM) model was constructed by using the scikit-learn algorithm in Python. Next, the crosstalk gene-TF network and crosstalk gene-DEP (differentially expressed pathway) network were each constructed. As a final step, shared genes among the crosstalk genes and periodontitis-related genes in DisGeNET were identified and denoted as the core crosstalk genes. Results. Four datasets (GSE23586, GSE16134, GSE10334, and GSE79705) pertaining to periodontitis were included in the analysis. A total of 48 representative crosstalk genes were identified by using the Boruta algorithm. Three TFs (FOS, MEF2C, and USF2) and several pathways (i.e., JAK-STAT, MAPK, NF-kappa B, and natural killer cell-mediated cytotoxicity) were identified as regulators of these crosstalk genes. Among these 48 crosstalk genes and the chronic periodontitis-related genes in DisGeNET, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 were shared and identified as the most pivotal candidate links between periodontitis and AD. Conclusions. Exploration of available transcriptomic datasets revealed C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 as the top candidate molecular linkage genes between periodontitis and AD. [ABSTRACT FROM AUTHOR]

One of the problems of optimization of concrete is to formulate a mathematical equation that shows the relationship between the various constituents of concrete and its properties. In this work, modelling of the compressive strength of concrete admixed with metakaolin was carried out using the Gene Expression Programming (GEP) algorithm. The dataset from laboratory experimentation was used for the analysis. The mixture proportions were made of three different water/binder ratios (0.4, 0.5, and 0.6), and the grades of concrete produced were grade M15 and M20. The compressive strength of the concrete was determined after 28 days of curing. The parameters used in the GEP algorithm are the input variables which include cement content, water, metakaolin content, and fine and coarse aggregate, while the response was designated as the compressive strength. The model was trained and tested using the parameters. The R-square value from the GEP algorithm was compared with the use of conventional stepwise regression analysis. With a coefficient of determination (R-square value) of 0.95, the GEP algorithm has shown to be a good alternative for modelling concrete compressive strength. [ABSTRACT FROM AUTHOR]

Background. Bladder cancer (BCa) is a common urothelial malignancy. The Cancer Genome Atlas (TCGA) database allows for an opportunity to analyze the relationship between gene expression and clinical outcomes in bladder cancer patients. This study is aimed at identifying prognosis-related genes in the bladder cancer microenvironment. Methods. Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We divided bladder cancer patients into high and low groups based on their immune/stromal scores. Then, differentially expressed genes (DEGs) were identified in bladder cancer patients based on the TCGA database. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. Finally, we validated identified genes associated with bladder cancer prognosis through a cohort study in the Gene Expression Omnibus (GEO) database. Results. A higher stromal score was associated with female (vs. male p = 0.037), age > 65 (vs. age ≤ 65 p = 0.015), T3/4 (vs. T1/2, p < 0.001), N status p = 0.016 , and pathological high grade (vs. low grade P < 0.001). By analyzing DEGs, there were 1125 genes commonly upregulated, and 209 genes were commonly downregulated. Protein-protein interaction networks further showed the important protein that may be involved in the biological behavior and prognosis of BCa, such as FN1, CXCL12, CD3E, LCK, and ZAP70. A total of 14 DEGs were found to be associated with overall survival of bladder cancer. After validation by a cohort of 165 BCa cases with detailed follow-up information from GSE13507, 10 immune-associated DEGs were demonstrated to be predictive of prognosis in BCa. Among them, 5 genes have not been reported previously associated with the prognosis of BCa, including BTBD16, OLFML2B, PRRX1, SPINK4, and SPON2. Conclusions. Our study elucidated tight associations between stromal score and clinical characteristics as well as prognosis in BCa. Moreover, we obtained a group of genes closely related to the prognosis of BCa in the tumor microenvironment. [ABSTRACT FROM AUTHOR]

Background. The incidence of lung cancer is the highest of all cancers, and it has the highest death rate. Lung adenocarcinoma (LUAD) is a major type of lung cancer. This study is aimed at identifying the prognostic value of immune-related long noncoding RNAs (lncRNAs) in LUAD. Materials and Methods. Gene expression profiles and the corresponding clinicopathological features of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was performed on the prognostic immune-related lncRNAs to calculate the risk scores, and a risk signature was constructed. Survival analysis was performed to assess the prognostic value of the risk signature. A nomogram was also constructed based on the clinicopathological features and risk signature. Results. A total of 437 LUAD patients with gene expression data and clinicopathological features were obtained in this study, which was considered the combination set. They were randomly and equally divided into a training set and a validation set. Seven immune-related lncRNAs (AC092794.1, AL034397.3, AC069023.1, AP000695.1, AC091057.1, HLA-DQB1-AS1, and HSPC324) were identified and used to construct a risk signature. The patients were divided into the low- and high-risk groups based on the median risk score of -0.04074. Survival analysis suggested that patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (p = 1.478 e − 02). A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients (C-index of the nomogram was 0.755, and the AUCs for the 1-, 3-, and 5-year survivals were 0.826, 0.719, and 0.724, respectively). The validation and combination sets confirmed these results. Conclusion. Our study identified seven novel immune-related lncRNAs and generated a risk signature, as well as a nomogram, that could predict the prognosis of LUAD patients. [ABSTRACT FROM AUTHOR]

ATPase family AAA domain-containing protein 2 (ATAD2), a chromatin regulator and an oncogenic transcription cofactor, is frequently overexpressed in many cancers, particularly in hepatocellular carcinoma (HCC). By integrating open-access online mRNA datasets and our institutional tissue data on HCC, the clinical role and functions of ATAD2 were analyzed by bioinformatic algorithms. We systematically examined ATAD2 expression in HCC based on a large sample population, integrating data from our institution and the GEO, Oncomine, and TCGA datasets. Aberrant ATAD2 expression related to pathways was identified by bioinformatic algorithms. The effects of ATAD2 downregulation on the cycle cell were also determined. A pooled analysis from 28 datasets indicated that ATAD2 overexpression was found in HCC (SMD = 8.88 , 95% CI: 5.96–11.81, P < 0.001) and was correlated with poor survival. Subgroup analysis of Asian patients with a serum alpha-fetoprotein (AFP) concentration < 200 ng/ml in stage I + II showed that the ATAD2-high group had a more unfavorable overall survival (OS) rate than the ATAD2-low group. The receiver operating characteristic curve indicated that the efficiency of ATAD2 for HCC diagnosis was considerable (area under the curve = 0.89 , 95% CI: 0.86–0.91). Functional analysis based on bioinformatic algorithms demonstrated that ATAD2 participates in cell division, mitotic nuclear division, DNA replication, repair, and cell cycle processes. ATAD2 knockout in HCC cells downregulated cyclin C and cyclin D1 protein levels and resulted in G1/S phase arrest in vitro. The kinesin family member C1 (KIFC1), shugoshin 1 (SGO1), GINS complex subunit 1 (GINS1), and TPX2 microtubule nucleation factor (TPX2) genes were closely related to ATAD2 upregulation. ATAD2 may interact with TTK protein kinase (TTK) to accelerate HCC carcinogenesis. ATAD2 plays a vital role in HCC carcinogenesis by disturbing the interaction between chromatin proteins and DNA. Targeting ATAD2 represents a promising method for the development of therapeutic treatments for cancer. [ABSTRACT FROM AUTHOR]

Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD. [ABSTRACT FROM AUTHOR]

Background. To elucidate the correlations between tumor microenvironment and clinical characteristics as well as prognosis in clear cell renal cell cancer (ccRCC) and investigate the immune-associated genes by a comprehensive analysis of 1e Cancer Genome Atlas (TCGA) database. Methods. We collected mRNA expression profiles of 537 ccRCC samples from the TCGA database. Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. 1e differentially expressed genes (DEGs) were screened between high immune/stromal score and low immune/stromal score groups by the cutoff of |log (fold change)| > 1, P value <0.05 by using package "limma" in R. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction network of intersected DEGs between stromal score and immune score groups was conducted using the STRING database.1eKaplan-Meier method was used to explore DEGs with predictive values in overall survival, and the prognostic DEGs were further validated in a Gene Expression Omnibus (GEO) dataset GSE29609. Results. A higher immune score was associated with T3/4 (vs. T1/2, P < 0.001), N1 (vs. N0, P = 0.05), M1 (vs. M0, P = 0.004), G3/4 (vs. G1/2, P < 0.001), advanced AJCC stage (P < 0.001), and shorter overall survival (P = 0.04). Intersected DEGs between immune and stromal score groups were 48 upregulated and 47 downregulated genes, with 43 DEGs associated with overall survival in ccRCC. After validation by a cohort of 39 ccRCC cases with detailed follow-up information from GSE29609, six immune-associated DEGs including CASP5, HSD11B1, VSIG4, HMGCS2, HSD11B2, and OGDHL were demonstrated to be predictive of prognosis in ccRCC. Conclusions. Our study elucidated tight associations between immune score and clinical characteristics as well as prognosis in ccRCC. Moreover, six DEGs were explored and validated to exert predictive values in overall survival of ccRCC. [ABSTRACT FROM AUTHOR]

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable. [ABSTRACT FROM AUTHOR]