Bioinformatics Research Research Articles

ITGB2 related to immune cell infiltration as a potential therapeutic target of inflammatory bowel disease using bioinformatics and functional research.

Inflammatory bowel disease (IBD) is a chronic systemic inflammatory condition regarded as a major risk factor for colitis-associated cancer. However, the underlying mechanisms of IBD remain unclear. First, five GSE data sets available in GEO were used to perform 'batch correction' and Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs). Candidate molecules were identified using CytoHubba, and their diagnostic effectiveness was predicted. The CIBERSORT algorithm evaluated the immune cell infiltration in the intestinal epithelial tissues of patients with IBD and controls. Immune cell infiltration in the IBD and control groups was determined using the least absolute shrinkage selection operator algorithm and Cox regression analysis. Finally, a total of 51 DEGs were screened, and nine hub genes were identified using CytoHubba and Cytoscape. GSE87466 and GSE193677 were used as extra data set to validate the expression of the nine hub genes. CD4-naïve T cells, gamma-delta T cells, M1 macrophages and resting dendritic cells (DCs) are the main immune cell infiltrates in patients with IBD. Signal transducer and activator of transcription 1, CCR5 and integrin subunit beta 2 (ITGB2) were significantly upregulated in the IBD mouse model, and suppression of ITGB2 expression alleviated IBD inflammation in mice. Additionally, the expression of ITGB2 was upregulated in IBD-associated colorectal cancer (CRC). The silence of ITGB2 suppressed cell proliferation and tumour growth invitro and invivo. ITGB2 resting DCs may provide a therapeutic strategy for IBD, and ITGB2 may be a potential diagnostic marker for IBD-associated CRC.

Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression

Leveraging deep learning (DL) approaches in genomics data has led to significant advances in cancer prediction. The continuous availability of gene expression datasets over the preceding years has made them one of the most accessible sources of genome-wide data, advancing cancer bioinformatics research and advanced prediction of cancer genomic data. To contribute to this topic, the proposed work is based on DL prediction in both convolutional neural network (CNN) and recurrent neural network (RNN) for five classes in brain cancer using gene expression data obtained from Curated Microarray Database (CuMiDa). This database is used for cancer classification and is publicly accessible on the official CuMiDa website. This paper implemented DL approaches using a One Dimensional-Convolutional Neural Network (1D-CNN) followed by an RNN classifier with and without Bayesian hyperparameter optimization (BO). The accuracy of this hybrid model combination of (BO + 1D-CNN + RNN) produced the highest classification accuracy of 100% instead of the 95% for the ML model in prior work and 90% for the (1D-CNN + RNN) algorithm considered in the paper. Therefore, the classification of brain cancer gene expression according to the hybrid model (BO + 1D-CNN + RNN) provides more accurate and useful assessments for patients with different types of brain cancers. Thus, gene expression data are used to create a DL classification-based- hybrid model that will hold senior promise in the treatment of brain cancer.

Characteristics of Rapeseed (Brassica rapa L.) genome DREB family demonstrate their roles in stress

Dehydration-responsive element-binding (DREB) proteins are crucial transcription factors in plant regulatory mechanisms, playing a key role in responding to environmental stresses. Dehydration-responsive element-binding (DREB) proteins are crucial transcription factors in plant regulatory mechanisms, playing a key role in responding to environmental stresses. This study employs bioinformatics research methods to identify and characterize the DREB gene family in rapeseed (Brassica rapa L.) under abiotic stress conditions. The approach facilitates a detailed exploration of the genomic architecture and potential regulatory mechanisms that these genes may exhibit in response to environmental stresses. A whole-genome survey identified 75 DREB genes in rapeseed, which were classified into six subgroups (Ⅰ-Ⅵ) based on phylogenetic comparison with Arabidopsis homologs. The amino acid lengths of these genes ranged from 144 to 530, with predicted molecular weights between 15.63 kDa and 60.36 kDa, and isoelectric points from 4.48 to 9.32. Chromosomal localization revealed that these genes are distributed across 10 chromosomes, predominantly near telomeric regions, with more than 10 DREB genes located on Chr3 and Chr9 respectively. Structural domain analysis indicated that 57 DREB genes possess a single exon. Promoter cis-element analyses suggested that BrDREB genes could respond to diverse stressors, including drought, heat, hormones, and light. Transcriptomic analysis under cold stress revealed significant upregulation of BrDREB17, 24, 49, 50, and 51 in response to low temperature, while five BrDREB genes were not expressed. Specifically, BrDREB17, 24, 49, 50, and 51 showed high expression levels at 3 h of cold stress, whereas BrDREB30, 42, and 67 were highly expressed at 24 h of cold stress. Gene Ontology (GO) analysis demonstrated that the majority of biological processes mediated by BrDREB are associated with plant stress resistance. Protein interaction network analysis revealed that BrDREB9 and BrDREB26 interact with 15 proteins each, underscoring their significant biological roles within the DREB family in rapeseed. These findings provide a theoretical framework for further examination of the physiological and biochemical properties of the DREB gene family components in rapeseed. Our results may help to screen candidate genes for cold resistance in rapeseed and select rapeseed varieties better suited to the Tibetan locality to improve quality and yield in agricultural production.

HapKled: a haplotype-aware structural variant calling approach for Oxford nanopore sequencing data.

Introduction: Structural Variants (SVs) are a type of variation that can significantly influence phenotypes and cause diseases. Thus, the accurate detection of SVs is a vital part of modern genetic analysis. The advent of long-read sequencing technology ushers in a new era of more accurate and comprehensive SV calling, and many tools have been developed to call SVs using long-read data. Haplotype-tagging is a procedure that can tag haplotype information on reads and can thus potentially improve the SV detection; nevertheless, few methods make use of this information. In this article, we introduce HapKled, a new SV detection tool that can accurately detect SVs from Oxford Nanopore Technologies (ONT) long-read alignment data. Methods: HapKled utilizes haplotype information underlying alignment data by conducting haplotype-tagging using Whatshap on the reads to improve the detection performance, with three unique calling mechanics including altering clustering conditions according to haplotype information of signatures, determination of similar SVs based on haplotype information, and slack filtering conditions based on haplotype quality. Results: In our evaluations, HapKled outperformed state-of-the-art tools and can deliver better SV detection results on both simulated and real sequencing data. The code and experiments of HapKled can be obtained from https://github.com/CoREse/HapKled. Discussion: With the superb SV detection performance that HapKled can deliver, HapKled could be useful in bioinformatics research, clinical diagnosis, and medical research and development.

Integrating metabolomics and bioinformatics to reveal the mechanism of Epimedium-induced liver injury.

Epimedium is a traditional Chinese medicine with a wide range of clinical applications; however, there have been numerous reports of adverse reactions in recent years. The most common side effect of Epimedium is liver injury. In this study, the liquid chromatography-mass spectrometry (LC-MS) method has been established to study the components of Epimedium and to identify the components absorbed into the blood of rats. Bioinformatics was used to screen out potential toxic components, and the integrating metabolomics method was used to explore the molecular mechanism of Epimedium-induced liver injury. The chemical constituents of Epimedium were identified by LC-MS, and 62 compounds were obtained, including 57 flavonoids, four organic acids and one alkaloid. The toxicity network of "Epimedium-component-target-liver injury" was constructed using bioinformatics research methods, and then the key hepatotoxic component icaritin was identified. Integrating metabolomics was used to investigate the changes in the metabolic profile of L-02 cells with different durations of icaritin administration compared with the control group, and 106 different metabolites were obtained. A total of 14 potential biomarkers significantly associated with cell survival were screened by Pearson correlation analysis combined with the L-02 cell survival rate. Our study preliminarily revealed the mechanism of hepatotoxicity induced by Epimedium.

Circular RNA Expression Profile in Cervical Cancer and Construction of the Circular RNA‑MicroRNA‑Messenger RNA Regulatory Network

Introduction. Cervical cancer (CC) remains the most common cancer in women worldwide. However, effective and specific biomarkers for the diagnosis and prognosis of cervical cancer are yet to be found. In recent years, the potential of circular RNAs (circRNAs) as new diagnostic, prognostic and therapeutic tools has received much attention. The current study involved an in-depth bioinformatics research to explore the circRNA-microRNA (miRNA)-messenger RNA (mRNA) regulatory network in order to identify important molecular processes and biological pathways supposedly associated with CC. Materials and methods. The study collected data on the expression of circRNA (GSE102686), miRNA (GSE30656) and mRNA of target genes (GSE9750), based on the Gene Expression Omnibus (GEO) database, in squamous cell carcinoma of the cervix samples and normal squamous epithelium of the cervix, dividing them into study and control groups. Protein-protein interaction (PPI), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to further understand the function of circRNAs for their target genes. Results. A total of 105 differentially expressed circular RNAs (DECs), 144 differentially expressed microRNAs (DEMs), and 539 differentially expressed target genes (DEGs) were identified for cervical cancer. Concurrently, functional enrichment analysis of GO and KEGG pathways was performed for DEGs. Subsequently, searching databases for circRNA, miRNA and mRNA target genes, as well as PPI network analysis and functional enrichment revealed 3 DECs with significantly high expression levels (hsa_circ_0000745, hsa_circ_0084927 and hsa_circ_0002762), 6 DEMs with reduced expression levels (hsa -miR-145, hsa-miR-876-3p, hsa-miR-1229, hsa-miR-182, hsa-miR-520h and hsa-miR-1252) and 9 key genes such as ANGPT2, COL11A1, MEST, KIF20A, CLN6, FNDC3B, USP18, DLGAP5 and CXCL9, suggesting a potentially significant role in cervical cancer. Conclusion.Understanding the circRNA-miRNA-mRNA regulatory network is of great importance for evaluating the oncogenesis of CC, as well as discoverying new circRNAs as the main regulatory molecules in this network. This is considered to be a new direction in the diagnosis and targeted therapy of cervical cancer.

Expression profiles of serum transfer RNA-derived fragments and their potential roles in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is one of the malignant tumors with the highest morbidity and mortality in the world. Early diagnosis can significantly improve the prognosis of patients. Transfer RNA (tRNA)-derived fragments (tRFs) have been found to have a crucial function in the pathophysiology of cancers. However, the role of tRFs/tRNA halves (tiRNAs) in NSCLC is yet unknown. The present study aimed to investigate unique expression profiles of tRFs/tiRNAs in NSCLC and search novel biomarkers for the diagnosis. RNA-sequencing was utilized for determining differently expressed tRFs/tiRNAs in serum in NSCLC and healthy controls. Stem-loop quantitative polymerase chain reaction (PCR) was used to confirm the selected tRFs/tiRNAs expressions. Their possible roles in NSCLC were predicted using bioinformatic research. Eleven up-regulated tRFs/tiRNAs and 18 down-regulated tRFs/tiRNAs were determined. Levels of tRF-31-87R8WP9N1EWJ0 and tRF-31-79MP9P9NH57SD were significantly higher in NSCLC serum samples than those of healthy controls; the receiver operating characteristic (ROC) curve suggested that they could serve as new diagnostic biomarkers. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis hinted that tRF-31-87R8WP9N1EWJ0 and tRF-31-79MP9P9NH57SD might influence the development and manifestation of NSCLC. In NSCLC patients' serum, the tRFs/tiRNAs were abnormally regulated and that tRF-31-87R8WP9N1EWJ0 and tRF-31-79MP9P9NH57SD might be the potential biological markers for NSCLC.

Identification of pancreatic adenocarcinoma immune subtype associated with tumor neoantigen from aberrant alternative splicing.

Pancreatic adenocarcinoma (PAAD) is referred to as an immunologically "cold" tumor that responds poorly to immunotherapy. A fundamental theory that explains the low immunogenicity of PAAD is the dramatically low tumor mutation burden (TMB) of PAAD tumors, which fails to induce sufficient immune response. Alternative splicing of pre-mRNA, which could alter the proteomic diversity of many cancers, has been reported to be involved in neoantigen production. Therefore, we aim to identify novel PAAD antigens and immune subtypes through systematic bioinformatics research. Data for splicing analysis were downloaded from The Cancer Genome Atlas (TCGA) SpliceSeq database. Among the available algorithms, we chose CIBERSORT to evaluate the immune cell distribution among PAADs. The TCGA-PAAD expression matrix was used to construct a co-expression network. Single-cell analysis was performed based on the Seurat workflow. Integrated analysis of aberrantly upregulated genes, alternatively spliced genes, genes associated with nonsense-mediated RNA decay (NMD) factors, antigen presentation and overall survival (OS) in TCGA-PAAD revealed that PLEC is a promising neoantigen for PAAD-targeted therapy. We identified a C2 TCGA-PAAD subtype that had better prognosis and more CD8+ T-cell infiltration. We propose a novel immune subtyping system for PAAD to indicate patient prognosis and opportunities for immunotherapy, such as immune checkpoint (ICP) inhibitors. In conclusion, the present study used a transcriptome-guided approach to screen neoantigen candidates based on alternative splicing, NMD factors, and antigen-presenting signatures for PAAD. A prognosis model with guidance of immunotherapy will aid in patient selection for appropriate treatment.

Implementasi Algoritma Needleman-Wunsch dalam Pengujian Tingkat Kemiripan DNA Babi Sulawesi Utara

Data is a collection generated from various media used by humans. DNA data is a form of data, which is data in the form of genetic code text to store biological identities and characteristics of living things. This study used DNA to test the similarity level of North Sulawesi pigs. The method used is the Pairwise Sequence Alignment method. With sequences of different DNA lengths, the researcher implemented the Needleman-Wunsch algorithm for alignment. Matching is used using the BioPython module, which is in pairwise2. And this study produces a model and proportion of the similarity level of North Sulawesi Pigs that can be used as future references for biological research on molecular biology or for the field of informatics in implementing Needleman-Wunsch algorithm in Python, or references and additional knowledge for research in Bioinformatics.

SymNOM-GED: Symmetric neighbor outlier mining in gene expression datasets

The accurate detection of outliers in gene expression datasets plays a crucial role in the unraveling of intricate biological processes. This research introduces "SymNOM-GED," an innovative algorithm for outlier mining in gene expression datasets, with a focus on Esophageal Squamous Cell Carcinoma (ESCC). SymNOM-GED leverages symmetric neighbor to effectively identify outliers by considering local and global gene expression patterns. Extensive experiments demonstrate that SymNOM-GED outperforms existing algorithms in terms of accuracy, robustness, and scalability. The algorithm's performance is validated using clustering coefficient, graph density, and modularity, confirming its superiority. SymNOM-GED's precise and reliable outlier detection capabilities contribute significantly to bioinformatics research, offering insights into gene expression patterns in diverse biological contexts.

Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential

Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to detect complex patterns inherent in the data. The integration of CNN and transformers' attention mechanisms allows for the extraction of relevant information, leading to accurate predictions of hemolytic potential. The proposed method was trained on three distinct data sets of peptide sequences known as recurrent neural network-hemolytic (RNN-Hem), Hlppredfuse, and Combined. Our computational results demonstrated the superior efficacy of our models compared to existing methods. The proposed approach demonstrated impressive Matthews correlation coefficients of 0.5962, 0.9111, and 0.7788 respectively, indicating its effectiveness in predicting hemolytic activity. With its potential to guide experimental efforts in peptide design and drug development, this method holds great promise for practical applications. Integrating CNNs and transformers proves to be a powerful tool in the fields of bioinformatics and therapeutic research, highlighting their potential to drive advancement in this area.

Molecular epidemiology and genetic evolution of avian influenza H5N1 subtype in Nigeria, 2006 to 2021.

Nigeria recorded one of the earliest outbreaks of the Highly Pathogenic Avian Influenza (HPAI) virus H5N1 in 2006, which spread to other African countries. In 2023, 18 countries reported outbreaks of H5N1 in poultry, with human cases documented in Egypt, Nigeria, and Djibouti. There is limited information on the molecular epidemiology of HPAI H5N1 in Nigeria. We determined the molecular epidemiology and genetic evolution of the virus from 2006 to 2021. We investigated the trend and geographical distribution across Nigeria. The evolutionary history of 61 full-length genomes was performed from 13 countries worldwide, and compared with sequences obtained from the early outbreaks in Nigeria up to 2021. MEGA 11 was used to determine the phylogenetic relationships of H5N1 strains, which revealed close ancestry between sequences in Nigeria and those from other African countries. Clade classification was performed using the subspecies classification tool for Bacterial and Viral Bioinformatics Research Center (BV-BRC) version 3.35.5. H5N1 Clade 2.2 was observed in 2006, with 2.3.2, 2.3.2.1f clades observed afterwards and 2.3.4.4b in 2021. Our findings underscore the need for genomics surveillance to track antigenic variation and clades switching to monitor the epidemiological of the virus and safeguard human and animal health.Impacts Specific variations in the hemagglutinin (HA) and neuraminidase (NA) genes of Avian influenza virus are consistent in different geographical regions. H5N1 Clade 2.2 was reported in 2006, with 2.3.2, 2.3.2.1f afterwards and 2.3.4.4b in 2021. Nigeria is an epicentre for avian influenza with three major migratory routes for wild birds transversing the country. It is plausible that the Avian influenza in Northern Nigeria may be linked to wild bird sanctuaries in the region.

Pregnant Women with Multiple Sclerosis: An Overview of Gene Expression and Molecular Interaction Using Bioinformatics Analysis.

Multiple sclerosis (MS) is a common disease in young women of reproductive age, characterized by demyelination of the central nervous system (CNS). Understanding how genes related to MS are expressed during pregnancy can provide insights into the potential mechanisms by which pregnancy affects the course of this disease. This review article presents evidence-based studies on these patients' gene expression patterns. In addition, it constructs interaction networks using bioinformatics tools, such as STRING and KEGG pathways, to understand the molecular role of each of these genes. Bioinformatics research identified 25 genes and 21 signaling pathways, which allows us to understand pregnancy patients' genetic and biological phenomena and formulate new questions about MS during pregnancy.

Identification and bioinformatics analysis of genes associated with pyroptosis in spinal cord injury of rat and mouse

The mechanism of spinal cord injury (SCI) is highly complex, and an increasing number of studies have indicated the involvement of pyroptosis in the physiological and pathological processes of secondary SCI. However, there is limited bioinformatics research on pyroptosis-related genes (PRGs) in SCI. This study aims to identify and validate differentially expressed PRGs in the GEO database, perform bioinformatics analysis, and construct regulatory networks to explore potential regulatory mechanisms and therapeutic targets for SCI. We obtained high-throughput sequencing datasets of SCI in rats and mice from the GEO database. Differential analysis was conducted using the “limma” package in R to identify differentially expressed genes (DEGs). These genes were then intersected with previously reported PRGs, resulting in a set of PRGs in SCI. GO and KEGG enrichment analyses, as well as correlation analysis, were performed on the PRGs in both rat and mouse models of SCI. Additionally, a protein–protein interaction (PPI) network was constructed using the STRING website to examine the relationships between proteins. Hub genes were identified using Cytoscape software, and the intersection of the top 5 hub genes in rats and mice were selected for subsequent experimentally validated. Furthermore, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory mechanisms. The gene expression profiles of GSE93249, GSE133093, GSE138637, GSE174549, GSE45376, GSE171441_3d and GSE171441_35d were selected in this study. We identified 10 and 12 PRGs in rats and mice datasets respectively. Six common DEGs were identified in the intersection of rats and mice PRGs. Enrichment analysis of these DEGs indicated that GO analysis was mainly focused on inflammation-related factors, while KEGG analysis showed that the most genes were enriched on the NOD-like receptor signaling pathway. We constructed a ceRNA regulatory network that consisted of five important PRGs, as well as 24 miRNAs and 34 lncRNAs. This network revealed potential regulatory mechanisms. Additionally, the three hub genes obtained from the intersection were validated in the rat model, showing high expression of PRGs in SCI. Pyroptosis is involved in secondary SCI and may play a significant role in its pathogenesis. The regulatory mechanisms associated with pyroptosis deserve further in-depth research.

Gene selection based on recursive spider wasp optimizer guided by marine predators algorithm

Detecting tumors using gene analysis in microarray data is a critical area of research in artificial intelligence and bioinformatics. However, due to the large number of genes compared to observations, feature selection is a central process in microarray analysis. While various gene selection methods have been developed to select the most relevant genes, these methods’ efficiency and reliability can be improved. This paper proposes a new two-phase gene selection method that combines the ReliefF filter method with a novel version of the spider wasp optimizer (SWO) called RSWO-MPA. In the first phase, the ReliefF filter method is utilized to reduce the number of genes to a reasonable number. In the second phase, RSWO-MPA applies a recursive spider wasp optimizer guided by the marine predators algorithm (MPA) to select the most informative genes from the previously selected ones. The MPA is used in the initialization step of recursive SWO to narrow down the search space to the most relevant and accurate genes. The proposed RSWO-MPA has been implemented and validated through extensive experimentation using eight microarray gene expression datasets. The enhanced RSWO-MPA is compared with seven widely used and recently developed meta-heuristic algorithms, including Kepler optimization algorithm (KOA), marine predators algorithm (MPA), social ski-driver optimization (SSD), whale optimization algorithm (WOA), Harris hawks optimization (HHO), artificial bee colony (ABC) algorithm, and original SWO. The experimental results demonstrate that the developed method yields the highest accuracy, selects fewer features, and exhibits more stability than other compared algorithms and cutting-edge methods for all the datasets used. Specifically, it achieved an accuracy of 100.00%, 94.51%, 98.13%, 95.63%, 100.00%, 100.00%, 92.97%, and 100.00% for Yeoh, West, Chiaretti, Burcyznski, leukemia, ovarian cancer, central nervous system, and SRBCT datasets, respectively.

Understanding the bacteria in Mycobacterium avium complex (MAC) from a bioinformatic perspective – a review

Mycobacterium avium complex (MAC) houses a group of non-tuberculous mycobacteria causing pulmonary and disseminated infections. They are accountable for nodular bronchiectatic and fibrocavitary lung diseases in humans, Johne’s disease in ruminants, and respiratory diseases in birds. MAC infections pose challenges, owing to antibiotic resistance, prolonged therapy with antibiotic combinations, side effects, and risk of reinfections. Our objective was to summarize the outcome of computational research on the bacteria in MAC. This aimed to advance our understanding of characteristics, pathogenicity, and transmission dynamics to control infections. We incorporated information from the research on genomes, microbiomes, phylogeny, transcriptomes, proteomes, antibiotic resistance, and vaccine/drug target development to enhance our knowledge. It illuminated the significance of computational studies in distinguishing MAC species/subspecies and recognizing: virulence factors, lineage-specific markers, and transmission clusters. Moreover, it assisted in understanding: genomic diversity, resistance patterns, impact of polymorphisms in disease susceptibility, and taxa-induced dysbiosis in microbiomes. Additionally, this work highlighted the outcome of bioinformatic studies in predicting suitable vaccine epitopes, and novel drug targets to combat MAC infections. Bioinformatic research on bacteria within MAC has contributed to a deeper insight into the pathogens. These would facilitate better diagnosis, improved: therapeutic strategies, patient-specific surveillance, and community-level awareness.

MicroRNAs and Their Targets Could Have a Crucial Role in Breast Cancer Drug Resistance: A Bioinformatics Research

Background MicroRNAs(miRNAs) have been demonstrated to contribute to cancer development by playing essential roles in processes including proliferation, migration, invasion, and metastasis. One of the most serious issues in breast cancer (BRCA) is drug resistance. Recent research suggests that miRNAs may possibly play a role in drug resistance. Using diverse datasets and in silico approaches, we focused on the BRCA/drug resistance/miRNA/mRNA link in our study. Methods GSE73736 and GSE71142 geo datasets (for miRNAs) and GSE162187 geodataset (for genes) were obtained from the GEO database to detect differently expressed miRNAs and genes using the R software “LIMMA” package. Potential target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted using miRMap, miRTarbase, and miRNet tools. Differently expressed genes (DE-genes) were filtered and common DE-genes were identified via TCGA data and miRNet. Afterward, Enrichr, and Funrich tools were used to perform GO annotation and KEGG pathway enrichment analysis. KMplot and GEPIA2 web tools were utilized to investigate further hub miRNAs and genes' expression and prognostic effects. Results 3 miRNAs that were considerably downregulated and had prognostic significance in BRCA were identified using the criteria defined in the investigated geo datasets. MiR-586, which is expected to be more closely linked to BRCA, has been found to have the ability to target 5 genes involved in BRCA resistance to therapy. GO, KEGG, and survival analysis showed that the probable target genes of miR-586 could be closely connected to BRCA. Conclusion In this study, a comprehensive BRCA-drug resistance-miRNA-gene network was established and new targets for the treatment and prognosis of BRCA were revealed using bioinformatics data.

Building a Computer Cluster for Bioinformatics and Biomedical Research from Scratch to Production

Building a Computer Cluster for Bioinformatics and Biomedical Research from Scratch to Production

Metformin is a potential therapeutic for COVID-19/LUAD by regulating glucose metabolism

Lung adenocarcinoma (LUAD) is the most common and aggressive subtype of lung cancer, and coronavirus disease 2019 (COVID-19) has become a serious public health threat worldwide. Patients with LUAD and COVID-19 have a poor prognosis. Therefore, finding medications that can be used to treat COVID-19/LUAD patients is essential. Bioinformatics analysis was used to identify 20 possible metformin target genes for the treatment of COVID-19/LUAD. PTEN and mTOR may serve as hub target genes of metformin. Metformin may be able to cure COVID-19/LUAD comorbidity through energy metabolism, oxidoreductase NADH activity, FoxO signalling pathway, AMPK signalling system, and mTOR signalling pathway, among other pathways, according to the results of bioinformatic research. Metformin has ability to inhibit the proliferation of A549 cells, according to the results of colony formation and proliferation assays. In A549 cells, metformin increased glucose uptake and lactate generation, while decreasing ATP synthesis and the NAD+/NADH ratio. In summary, PTEN and mTOR may be potential targets of metformin for the treatment of COVID-19/LUAD. The mechanism by which metformin inhibits lung adenocarcinoma cell proliferation may be related to glucose metabolism regulated by PI3K/AKT signalling and mTOR signalling pathways. Our study provides a new theoretical basis for the treatment of COVID-19/LUAD.

Beneficial effects of oxymatrine from Sophora flavescens on alleviating Ulcerative colitis by improving inflammation and ferroptosis

Ethnopharmacological relevanceSophora flavescens is often used in traditional Chinese medicine for skin issues, diarrhea, and vaginal itching (Plant names have been checked with http://www.the/plant/list.org on Feb 22nd, 2024). Oxymatrine (OY), a major bioactive compound from Sophora flavescens, is commonly used in China to treat ulcerative colitis, but its mechanisms are still unclear. Aim of the studyRecent studies have found that the crosstalk between ferroptosis and inflammation is an important mechanism in the pathogenesis of UC. The aim of this study was to investigate the potential underlying mechanisms of OY treatment on DSS-induced ulcerative colitis, specifically focusing on the processes of ferroptosis and inflammation. Materials and methodsBioinformatics methods were used to identify key targets of OY for ferroptosis and inflammation in ulcerative colitis, based on GEO data and FerrDb database. Then, 4% DSS solution was used to induce UC model. OY’s impact on morphological changes was assessed using colon views, Hematoxylin and eosin (HE) staining, and transmission electron microscopy (TEM). Ferroptosis phenotype index and inflammations factors were detected by ELISA or chem-bio detection kits.The screen out hub related genes about ferroptosis and inflammation were verified by RT-PCR, immunohistochemistry (IHC), and western blotting (WB) respectively. ResultsBioinformatics results show that there are 16 key target genes involved in ferroptosis and inflammation interaction of OY treatment for UC, such as IL6, NOS2, IDO1, SOCS1, and DUOX. The results of animal experiments show that OY could depress inflammatory factors (IL-1β, IL-6, TNF-α, HMGB1, and NLRP3) and reduce iron deposition (Fe2+, GSH). Additionally, OY suppressed the hub genes or proteins expression involved in ferroptosis and inflammation, including IL-1β, IL-6, NOS2, HIF1A, IDO1, TIMP1, and DUOX2. ConclusionThis present study combines bioinformatics, molecular biology, and animal experimental research evidently demonstrated that OY attenuates UC by improving ferroptosis and inflammation, mainly target to the expression of IL-1β, IL-6, NOS2, HIF1A, IDO1, TIMP1, and DUOX2.