Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach

Deciphering the molecular nexus between Omicron infection and acute kidney injury: a bioinformatics approach

Background

The ongoing global health crisis of COVID-19, and particularly the challenges posed by recurrent infections of the Omicron variant, have significantly strained healthcare systems worldwide. There is a growing body of evidence indicating an increased susceptibility to Omicron infection in patients suffering from Acute Kidney Injury (AKI). However, the intricate molecular interplay between AKI and Omicron variant of COVID-19 remains largely enigmatic.

Methods

This study employed a comprehensive analysis of human RNA sequencing (RNA-seq) and microarray datasets to identify differentially expressed genes (DEGs) associated with Omicron infection in the context of AKI. We engaged in functional enrichment assessments, an examination of Protein-Protein Interaction (PPI) networks, and advanced network analysis to elucidate the cellular signaling pathways involved, identify critical hub genes, and determine the relevant controlling transcription factors and microRNAs. Additionally, we explored protein-drug interactions to highlight potential pharmacological interventions.

Results

Our investigation revealed significant DEGs and cellular signaling pathways implicated in both Omicron infection and AKI. We identified pivotal hub genes, including EIF2AK2, PLSCR1, GBP1, TNFSF10, C1QB, and BST2, and their associated regulatory transcription factors and microRNAs. Notably, in the murine AKI model, there was a marked reduction in EIF2AK2 expression, in contrast to significant elevations in PLSCR1, C1QB, and BST2. EIF2AK2 exhibited an inverse relationship with the primary AKI mediator, Kim-1, whereas PLSCR1 and C1QB demonstrated strong positive correlations with it. Moreover, we identified potential therapeutic agents such as Suloctidil, Apocarotenal, 3'-Azido-3'-deoxythymidine, among others. Our findings also highlighted a correlation between the identified hub genes and diseases like myocardial ischemia, schizophrenia, and liver cirrhosis. To further validate the credibility of our data, we employed an independent validation dataset to verify the hub genes. Notably, the expression patterns of PLSCR1, GBP1, BST2, and C1QB were consistent with our research findings, reaffirming the reliability of our results.

Conclusion

Our bioinformatics analysis has provided initial insights into the shared genetic landscape between Omicron COVID-19 infections and AKI, identifying potential therapeutic targets and drugs. This preliminary investigation lays the foundation for further research, with the hope of contributing to the development of innovative treatment strategies for these complex medical conditions.

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

A bioinformatics approach to systematically analyze the molecular patterns of monkeypox virus-host cell interactions

Monkeypox has been spreading worldwide since May 2022, when the World Health Organization (WHO) declared the outbreak a "public health emergency of international concern." The spread of monkeypox has posed a serious threat to the health of people around the world, but few studies have been conducted, and the molecular mechanism of monkeypox after infection remains unclear. We therefore implemented a transcriptome analysis to identify signaling pathways and biomarkers in monkeypox-infected cells to help understand monkeypox-host cell interactions. In this study, datasets GSE36854 and GSE11234 were obtained from GEO. Of these, 84 significantly different genes were identified in the dataset GSE36854, followed by KEGG, GO analysis protein-protein interaction (PPI) construction, and Hub gene extraction. We also analyzed the expression regulation of hub genes and screened for drugs targeting hub genes. The results showed that monkeypox-infected cells significantly activated the cellular immune response. The top 10 hub genes are IER3, IFIT2, IL11, ZC3H12A, EREG, IER2, NFKBIE, FST, IFIT1 and AREG. AP-26113 and itraconazole can be used to counteract the inhibitory effect of monkeypox on IFIT1 and IFIT2 and serve as candidate drugs for the treatment of monkeypox virus infection. IRF1 may also be a transcription factor of IFIT. Our results provide a new entry point for understanding how monkeypox virus interacts with its host.

Drugs targeting CMPK2 inhibit pyroptosis to alleviate severe pneumonia caused by multiple respiratory viruses

Severe pneumonia caused by respiratory viruses has become a major threat to humans, especially with the SARS-CoV-2 outbreak and epidemic. The aim of this study was to investigate the universal molecular mechanism of severe pneumonia induced by multiple respiratory viruses and to search for therapeutic strategies targeting this universal molecular mechanism. The common differential genes of four respiratory viruses, including respiratory syncytial virus (RSV), rhinovirus, influenza, and SARS-CoV-2, were screened by GEO database, and the hub gene was obtained by Sytohubba in Cytoscape. Then, the effect of hub genes on inflammasome and pyrodeath was investigated in the model of RSV infection in vitro and in vivo. Finally, through virtual screening, drugs targeting the hub gene were obtained, which could alleviate severe viral pneumonia in vitro and in vivo. The results showed that CMPK2 is one of the hub genes after infection by four respiratory viruses. CMPK2 activates the inflammasome by activating NLRP3, and promotes the releases of inflammatory factors interleukin (IL)-1β and IL-18 to induce severe viral pneumonia. Z25 and Z08 can reduce the expression level of CMPK2 mRNA and protein, thereby inhibiting NLRP3 and alleviating the development of severe viral pneumonia. In conclusion, the inflammatory response mediated by CMPK2 is the common molecular mechanism of severe pneumonia induced by viral infection, and Z25 and Z08 can effectively alleviate viral infection and severe pneumonia through this mechanism.

Bioinformatics and system biology approaches to determine the connection of SARS-CoV-2 infection and intrahepatic cholangiocarcinoma

INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide, which poses a severe threat to human health. COVID-19 is a systemic ailment affecting various tissues and organs, including the lungs and liver. Intrahepatic cholangiocarcinoma (ICC) is one of the most common liver cancer, and cancer patients are particularly at high risk of SARS-CoV-2 infection. Nonetheless, few studies have investigated the impact of COVID-19 on ICC patients.

METHODS

With the methods of systems biology and bioinformatics, this study explored the link between COVID-19 and ICC, and searched for potential therapeutic drugs.

RESULTS

This study identified a total of 70 common differentially expressed genes (DEGs) shared by both diseases, shedding light on their shared functionalities. Enrichment analysis pinpointed metabolism and immunity as the primary areas influenced by these common genes. Subsequently, through protein-protein interaction (PPI) network analysis, we identified SCD, ACSL5, ACAT2, HSD17B4, ALDOA, ACSS1, ACADSB, CYP51A1, PSAT1, and HKDC1 as hub genes. Additionally, 44 transcription factors (TFs) and 112 microRNAs (miRNAs) were forecasted to regulate the hub genes. Most importantly, several drug candidates (Periodate-oxidized adenosine, Desipramine, Quercetin, Perfluoroheptanoic acid, Tetrandrine, Pentadecafluorooctanoic acid, Benzo[a]pyrene, SARIN, Dorzolamide, 8-Bromo-cAMP) may prove effective in treating ICC and COVID-19.

CONCLUSION

This study is expected to provide valuable references and potential drugs for future research and treatment of COVID-19 and ICC.

New genetic biomarkers predicting 5-aminosalicylate-induced adverse events in patients with inflammatory bowel diseases

Background

Notably, 5-aminosalicylates (5-ASA) are vital in treating inflammatory bowel diseases (IBD). The adverse events of 5-ASA rarely occur but they could be fatal.

Objectives

We aimed to discover new genetic biomarkers predicting 5-ASA-induced adverse events in patients with IBD.

Design

This was a retrospective observational study.

Methods

We performed a genome-wide association study on patients with IBD in South Korea. We defined subset 1 as 39 all adverse events and 272 controls; subset 2 as 20 severe adverse events and 291 controls (mild adverse events and control); subset 3 as 20 severe adverse events and 272 controls; and subset 4 as 19 mild adverse events and 272 controls. Logistic regression analysis was performed and commonly found associated genes were determined as candidate single-nucleotide polymorphisms predicting 5-ASA adverse events.

Results

Patients with Crohn's disease (CD) were significantly negatively associated with the development of adverse events compared to patients with ulcerative colitis (UC) (5.3% versus 22.9%). However, sex and age at diagnosis were unassociated with the adverse events of 5-ASA. rs13898676 [odds ratio (OR), 20.33; 95% confidence interval (CI), 5.69-72.67; p = 3.57 × e-6], rs12681590 (OR, 7.35; 95% CI, 2.85-19.00; p = 3.78 × e-5), rs10967320 (OR, 4.51; 95% CI, 2.18-9.31; p = 4.72 × e-5), and rs78726924 (OR, 3.54; 95% CI, 1.69-7.40; p = 7.96 × e-5) were genetic biomarkers predicting 5-ASA-induced severe adverse events in patients with IBD.

Conclusion

The adverse events of 5-ASA were more common in patients with UC than those with CD in our study. We found that novel rs13898676 nearby WSB2 was the most significant genetic locus contributing to 5-ASA's adverse event risk.

Discovering common pathogenetic processes between COVID-19 and tuberculosis by bioinformatics and system biology approach

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, stemming from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has persistently threatened the global health system. Meanwhile, tuberculosis (TB) caused by Mycobacterium tuberculosis (M. tuberculosis) still continues to be endemic in various regions of the world. There is a certain degree of similarity between the clinical features of COVID-19 and TB, but the underlying common pathogenetic processes between COVID-19 and TB are not well understood.

Methods

To elucidate the common pathogenetic processes between COVID-19 and TB, we implemented bioinformatics and systematic research to obtain shared pathways and molecular biomarkers. Here, the RNA-seq datasets (GSE196822 and GSE126614) are used to extract shared differentially expressed genes (DEGs) of COVID-19 and TB. The common DEGs were used to identify common pathways, hub genes, transcriptional regulatory networks, and potential drugs.

Results

A total of 96 common DEGs were selected for subsequent analyses. Functional enrichment analyses showed that viral genome replication and immune-related pathways collectively contributed to the development and progression of TB and COVID-19. Based on the protein-protein interaction (PPI) network analysis, we identified 10 hub genes, including IFI44L, ISG15, MX1, IFI44, OASL, RSAD2, GBP1, OAS1, IFI6, and HERC5. Subsequently, the transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory network identified 61 TFs and 29 miRNAs. Notably, we identified 10 potential drugs to treat TB and COVID-19, namely suloctidil, prenylamine, acetohexamide, terfenadine, prochlorperazine, 3'-azido-3'-deoxythymidine, chlorophyllin, etoposide, clioquinol, and propofol.

Conclusion

This research provides novel strategies and valuable references for the treatment of tuberculosis and COVID-19.

Identification of genes critical for inducing ulcerative colitis and exploring their tumorigenic potential in human colorectal carcinoma

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease leading to continuous mucosal inflammation in the rectum extending proximally towards the colon. Chronic and/or recurrent UC is one of the critical predisposing mediators of the oncogenesis of human colorectal carcinoma (CRC). Perturbations of the differential expression of the UC-critical genes exert an intense impact on the neoplastic transformation of the affected tissue(s). Herein, a comprehensive exploration of the UC-critical genes from the transcriptomic profiles of UC patients was conducted to study the differential expression, functional enrichment, genomic alterations, signal transduction pathways, and immune infiltration level encountered by these genes concerning the oncogenesis of CRC. The study reveals that WFDC2, TTLL12, THRA, and EPHB3 play crucial roles as UC-CRC critical genes and are positively correlated with the molecular transformation of UC to CRC. Taken together, these genes can be used as potential biomarkers and therapeutic targets for combating UC-induced human CRC.

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancer

Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Comprehensive analysis of pan-cancer reveals the potential of SLC16A1 as a prognostic and immunological biomarker

SLC16A1 plays an important role in the development of multiple cancer types. Pan-cancer analysis may have significant impacts on the exploration of the relationship between SLC16A1 gene expression, prognosis and the molecular mechanisms of tumorigenesis. In this study, through the analysis of TCGA and GEO datasets, we explored the expression level and survival prognosis of SLC16A1 in pan-cancer, and further explored the differences in SLC16A1 gene mutation, methylation, and phosphorylation between tumor and normal tissues. In addition, we focused on the biological function of this gene and the relationship between the prognosis and immune infiltration by immune infiltration analysis and enrichment analysis, in order to evaluate the diagnostic and prognostic significance of SLC16A1 in carcinomas. The study found that SLC16A1 was highly expressed in 14 kinds of tumors, and there were statistically significant differences in the prognosis of 9 tumors. The phosphorylation level of S467 increased in OV, RCC, and UCEC. There was a statistically negative correlation between the CD8+ T-cell infiltration level and the SLC16A1 expression in HNSC, LUSC, SARC, TGCT, and KIRC. The cancer-related fibroblasts were positively correlated with SLC16A1 expression in BLCA, BRCA, KIRC, KIRP, PAAD, PCPG, and THCA. The enrichment analysis indicated that the tumorigenesis mechanism of this gene was mainly related to "glycolysis and glucose metabolism synthesis." SLC16A1 was a promising prognostic and immunological biomarker in pan-cancer.

Highlighting the Undetectable - Fluorescence Molecular Imaging in Gastrointestinal Endoscopy

Flexible high-definition white-light endoscopy is the current gold standard in screening for cancer and its precursor lesions in the gastrointestinal tract. However, miss rates are high, especially in populations at high risk for developing gastrointestinal cancer (e.g., inflammatory bowel disease, Lynch syndrome, or Barrett's esophagus) where lesions tend to be flat and subtle. Fluorescence molecular endoscopy (FME) enables intraluminal visualization of (pre)malignant lesions based on specific biomolecular features rather than morphology by using fluorescently labeled molecular probes that bind to specific molecular targets. This strategy has the potential to serve as a valuable tool for the clinician to improve endoscopic lesion detection and real-time clinical decision-making. This narrative review presents an overview of recent advances in FME, focusing on probe development, techniques, and clinical evidence. Future perspectives will also be addressed, such as the use of FME in patient stratification for targeted therapies and potential alliances with artificial intelligence. KEY MESSAGES: • Fluorescence molecular endoscopy is a relatively new technology that enables safe and real-time endoscopic lesion visualization based on specific molecular features rather than on morphology, thereby adding a layer of information to endoscopy, like in PET-CT imaging. • Recently the transition from preclinical to clinical studies has been made, with promising results regarding enhancing detection of flat and subtle lesions in the colon and esophagus. However, clinical evidence needs to be strengthened by larger patient studies with stratified study designs. • In the future fluorescence molecular endoscopy could serve as a valuable tool in clinical workflows to improve detection in high-risk populations like patients with Barrett's esophagus, Lynch syndrome, and inflammatory bowel syndrome, where flat and subtle lesions tend to be malignant up to five times more often. • Fluorescence molecular endoscopy has the potential to assess therapy responsiveness in vivo for targeted therapies, thereby playing a role in personalizing medicine. • To further reduce high miss rates due to human and technical factors, joint application of artificial intelligence and fluorescence molecular endoscopy are likely to generate added value.

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.

Identifying the Effect of COVID-19 Infection in Multiple Myeloma and Diffuse Large B-Cell Lymphoma Patients Using Bioinformatics and System Biology

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also referred to as COVID-19, has spread to several countries and caused a serious threat to human health worldwide. Patients with confirmed COVID-19 infection spread the disease rapidly throughout the region. Multiple myeloma (MM) and diffuse large B-cell lymphoma (DLBCL) are risk factors for COVID-19, although the molecular mechanisms underlying the relationship among MM, DLBCL, and COVID-19 have not been elucidated so far. In this context, transcriptome analysis was performed in the present study to identify the shared pathways and molecular indicators of MM, DLBCL, and COVID-19, which benefited the overall understanding of the effect of COVID-19 in patients with MM and DLBCL. Three datasets (GSE16558, GSE56315, and GSE152418) were downloaded from the Gene Expression Omnibus (GEO) and searched for the shared differentially expressed genes (DEGs) in patients with MM and DLBCL who were infected with SARS-CoV-2. The objective was to detect similar pathways and prospective medicines. A total of 29 DEGs that were common across these three datasets were selected. A protein-protein interaction (PPI) network was constructed using data from the STRING database followed by the identification of hub genes. In addition, the association of MM and DLBCL with COVID-19 infection was analyzed through functional analysis using ontologies terms and pathway analysis. Three relationships were observed in the evaluated datasets: transcription factor-gene interactions, protein-drug interactions, and an integrated regulatory network of DEGs and miRNAs with mutual DEGs. The findings of the present study revealed potential pharmaceuticals that could be beneficial in the treatment of COVID-19.

Bioinformatics and systems biology approaches to identify molecular targeting mechanism influenced by COVID-19 on heart failure

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a contemporary hazard to people. It has been known that COVID-19 can both induce heart failure (HF) and raise the risk of patient mortality. However, the mechanism underlying the association between COVID-19 and HF remains unclear. The common molecular pathways between COVID-19 and HF were identified using bioinformatic and systems biology techniques. Transcriptome analysis was performed to identify differentially expressed genes (DEGs). To identify gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, common DEGs were used for enrichment analysis. The results showed that COVID-19 and HF have several common immune mechanisms, including differentiation of T helper (Th) 1, Th 2, Th 17 cells; activation of lymphocytes; and binding of major histocompatibility complex class I and II protein complexes. Furthermore, a protein-protein interaction network was constructed to identify hub genes, and immune cell infiltration analysis was performed. Six hub genes (FCGR3A, CD69, IFNG, CCR7, CCL5, and CCL4) were closely associated with COVID-19 and HF. These targets were associated with immune cells (central memory CD8 T cells, T follicular helper cells, regulatory T cells, myeloid-derived suppressor cells, plasmacytoid dendritic cells, macrophages, eosinophils, and neutrophils). Additionally, transcription factors, microRNAs, drugs, and chemicals that are closely associated with COVID-19 and HF were identified through the interaction network.

A Bioinformatic Approach Based on Systems Biology to Determine the Effects of SARS-CoV-2 Infection in Patients with Hypertrophic Cardiomyopathy

Recently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide. While COVID-19 generally affects the lungs, it also damages other organs, including those of the cardiovascular system. Hypertrophic cardiomyopathy (HCM) is a common genetic cardiovascular disorder. Studies have shown that HCM patients with COVID-19 have a higher mortality rate; however, the reason for this phenomenon is not yet elucidated. Herein, we conducted transcriptomic analyses to identify shared biomarkers between HCM and COVID-19 to bridge this knowledge gap. Differentially expressed genes (DEGs) were obtained using the Gene Expression Omnibus ribonucleic acid (RNA) sequencing datasets, GSE147507 and GSE89714, to identify shared pathways and potential drug candidates. We discovered 30 DEGs that were common between these two datasets. Using a combination of statistical and biological tools, protein-protein interactions were constructed in response to these findings to support hub genes and modules. We discovered that HCM is linked to COVID-19 progression based on a functional analysis under ontology terms. Based on the DEGs identified from the datasets, a coregulatory network of transcription factors, genes, proteins, and microRNAs was also discovered. Lastly, our research suggests that the potential drugs we identified might be helpful for COVID-19 therapy.

Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma

Background

The coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma.

Methods

Two sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury.

Results

We discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes.

Conclusion

We identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.

Network-Based Data Analysis Reveals Ion Channel-Related Gene Features in COVID-19: A Bioinformatic Approach

Coronavirus disease 2019 (COVID-19) seriously threatens human health and has been disseminated worldwide. Although there are several treatments for COVID-19, its control is currently suboptimal. Therefore, the development of novel strategies to treat COVID-19 is necessary. Ion channels are located on the membranes of all excitable cells and many intracellular organelles and are key components involved in various biological processes. They are a target of interest when searching for drug targets. This study aimed to reveal the relevant molecular features of ion channel genes in COVID-19 based on bioinformatic analyses. The RNA-sequencing data of patients with COVID-19 and healthy subjects (GSE152418 and GSE171110 datasets) were obtained from the Gene Expression Omnibus (GEO) database. Ion channel genes were selected from the Hugo Gene Nomenclature Committee (HGNC) database. The RStudio software was used to process the data based on the corresponding R language package to identify ion channel-associated differentially expressed genes (DEGs). Based on the DEGs, Gene Ontology (GO) functional and pathway enrichment analyses were performed using the Enrichr web tool. The STRING database was used to generate a protein-protein interaction (PPI) network, and the Cytoscape software was used to screen for hub genes in the PPI network based on the cytoHubba plug-in. Transcription factors (TF)-DEG, DEG-microRNA (miRNA) and DEG-disease association networks were constructed using the NetworkAnalyst web tool. Finally, the screened hub genes as drug targets were subjected to enrichment analysis based on the DSigDB using the Enrichr web tool to identify potential therapeutic agents for COVID-19. A total of 29 ion channel-associated DEGs were identified. GO functional analysis showed that the DEGs were integral components of the plasma membrane and were mainly involved in inorganic cation transmembrane transport and ion channel activity functions. Pathway analysis showed that the DEGs were mainly involved in nicotine addiction, calcium regulation in the cardiac cell and neuronal system pathways. The top 10 hub genes screened based on the PPI network included KCNA2, KCNJ4, CACNA1A, CACNA1E, NALCN, KCNA5, CACNA2D1, TRPC1, TRPM3 and KCNN3. The TF-DEG and DEG-miRNA networks revealed significant TFs (FOXC1, GATA2, HINFP, USF2, JUN and NFKB1) and miRNAs (hsa-mir-146a-5p, hsa-mir-27a-3p, hsa-mir-335-5p, hsa-let-7b-5p and hsa-mir-129-2-3p). Gene-disease association network analysis revealed that the DEGs were closely associated with intellectual disability and cerebellar ataxia. Drug-target enrichment analysis showed that the relevant drugs targeting the hub genes CACNA2D1, CACNA1A, CACNA1E, KCNA2 and KCNA5 were gabapentin, gabapentin enacarbil, pregabalin, guanidine hydrochloride and 4-aminopyridine. The results of this study provide a valuable basis for exploring the mechanisms of ion channel genes in COVID-19 and clues for developing therapeutic strategies for COVID-19.

Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.

Inflammatory bowel disease: an overview of Chinese herbal medicine formula-based treatment

Inflammatory bowel disease (IBD) is a chronic recurrent inflammatory disease of the intestine, including Crohn’s disease (CD) and ulcerative colitis (UC), whose etiology and pathogenesis have not been fully understood. Due to its prolonged course and chronic recurrence, IBD imposes a heavy economic burden and psychological stress on patients. Traditional Chinese Herbal Medicine has unique advantages in IBD treatment because of its symptomatic treatment. However, the advantages of the Chinese Herbal Medicine Formula (CHMF) have rarely been discussed. In recent years, many scholars have conducted fundamental studies on CHMF to delay IBD from different perspectives and found that CHMF may help maintain intestinal integrity, reduce inflammation, and decrease oxidative stress, thus playing a positive role in the treatment of IBD. Therefore, this review focuses on the mechanisms associated with CHMF in IBD treatment. CHMF has apparent advantages. In addition to the exact composition and controlled quality of modern drugs, it also has multi-component and multi-target synergistic effects. CHMF has good prospects in the treatment of IBD, but its multi-agent composition and wide range of targets exacerbate the difficulty of studying its treatment of IBD. Future research on CHMF-related mechanisms is needed to achieve better efficacy.

Identification of the shared gene signatures and pathways between sarcopenia and type 2 diabetes mellitus

Background

Sarcopenia is characterized by the age-associated loss of skeletal muscle mass and strength that develops progressively and plays an important role in the disability of the elderly. It has received growing attention over the last decade and has been implicated as both a cause and consequence of type 2 diabetes mellitus (T2DM). The existence of T2DM could increase the risk of developing sarcopenia through multiple mechanisms including advanced glycation end-product accumulation. Meanwhile, sarcopenia would alter glucose disposal and may contribute to the development and progression of T2DM due to reduced muscle mass.

Methods

We implemented transcriptomic analysis of skeletal muscle biopsy specimens in sarcopenia patients and proliferating myoblasts or differentiated myotubes from individuals with T2DM. Related microarray data were selected from Gene Expression Omnibus (GEO) to screen the genes, which were differentially expressed for sarcopenia and T2DM. Multiple combinatorial statistical methods and bioinformatics tools were used to analyze the common DEGs. Meanwhile, functional enrichment analysis was also carried out. Furthermore, we constructed the protein-protein interaction (PPI), as well as transcription factor (TF)-gene interactions network and TF-miRNA coregulatory network. Finally, based on the common DEGs, drug compounds were speculated using the Drug Signatures database (DSigDB).

Results

A total of 1765 and 2155 DEGs of sarcopenia and T2DM were screened, respectively. 15 common genes (LXN, CIB2, PEA15, KANK2, FGD1, NMRK1, PLCB1, SEMA4G, ADARB1, UPF3A, CSTB, COL3A1, CD99, ETV3, FJX1) correlated with sarcopenia and T2DM simultaneously were then identified, and 3 genes (UPF3A, CSTB and PEA15) of them were regarded as hub genes. Functional enrichment analysis revealed several shared pathways between two diseases. In addition, according to the TF-gene interactions network and TF-miRNA coregulatory network, part of TF and miRNA may be identified as key regulator in sarcopenia and T2DM at the same time (e.g., CREM and miR-155). Notably, drug compounds for T2DM and sarcopenia were also suggested, such as coenzyme Q10.

Conclusion

This study revealed that sarcopenia and T2DM may share similar pathogenesis and provided new biological targets and ideas for early diagnosis and effective treatment of sarcopenia and T2DM.

Immune-related biomarkers shared by inflammatory bowel disease and liver cancer

It has been indicated that there is an association between inflammatory bowel disease (IBD) and hepatocellular carcinoma (HCC). However, the molecular mechanism underlying the risk of developing HCC among patients with IBD is not well understood. The current study aimed to identify shared genes and potential pathways and regulators between IBD and HCC using a system biology approach. By performing the different gene expression analyses, we identified 871 common differentially expressed genes (DEGs) between IBD and HCC. Of these, 112 genes overlapped with immune genes were subjected to subsequent bioinformatics analyses. The results revealed four hub genes (CXCL2, MMP9, SPP1 and SRC) and several other key regulators including six transcription factors (FOXC1, FOXL1, GATA2, YY1, ZNF354C and TP53) and five microRNAs (miR-124-3p, miR-34a-5p, miR-1-3p, miR-7-5p and miR-99b-5p) for these disease networks. Protein-drug interaction analysis discovered the interaction of the hub genes with 46 SRC-related and 11 MMP9- related drugs that may have a therapeutic effect on IBD and HCC. In conclusion, this study sheds light on the potential connecting mechanisms of HCC and IBD.

High expression of RIPK2 is associated with Taxol resistance in serous ovarian cancer

Background

Taxol resistance in serous ovarian cancer is responsible for its poor prognosis, yet the underlying mechanism is still poorly understood. Thus, we probed the mechanism of Taxol resistance in serous ovarian cancer with multiple bioinformatic methods to provide novel insights into potential therapies.

Methods

The differentially expressed genes (DEGs) in Taxol-sensitive and Taxol-resistant cell lines and their relationship with the overall survival (OS) and progression-free interval (PFI) of ovarian cancer patients were analyzed using gene expression datasets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The role of receptor interacting serine/threonine kinase 2 (RIPK2) was validated via identification of its coexpressed genes, functional analysis and generation of a protein-protein interaction (PPI) network. The single sample gene set enrichment analysis (ssGSEA) was used to explore immune infiltration, and genomic alterations of RIPK2 were also analyzed via cBio Cancer Genomics Portal (cBioProtal).

Results

RIPK2 was highly expressed in Taxol resistant ovarian cancer cell lines, and its high expression was also linked with shorter OS and PFI in serous ovarian cancer patients. The PPI network analysis and pathway analysis demonstrated that RIPK2 might participate in the positive regulation of NF-κB transcription factor activity. RIPK2 expression was related to tumor microenvironment alterations, which might participate in the formation of Taxol resistance.

Conclusions

Our studies suggested that high expression of RIPK2 is related to Taxol resistance in serous ovarian cancer, and that RIPK2 induces Taxol resistance through NOD1/RIPK2/NF-κB inflammatory pathway activation and tumor microenvironment changes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00986-2.

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.

MicroRNA expression in inflammatory bowel disease-associated colorectal cancer

MicroRNAs (miRNAs) are non-coding RNA molecules composed of 19–25 nucleotides that regulate gene expression and play a central role in the regulation of several immune-mediated disorders, including inflammatory bowel diseases (IBD). IBD, represented by ulcerative colitis and Crohn’s disease, is characterized by chronic intestinal inflammation associated with an increased risk of colorectal cancer (CRC). CRC is one of the most prevalent tumors in the world, and its main risk factors are obesity, physical inactivity, smoking, alcoholism, advanced age, and some eating habits, in addition to chronic intestinal inflammatory processes and the use of immunosuppressants administered to IBD patients. Recent studies have identified miRNAs associated with an increased risk of developing CRC in this population. The identification of miRNAs involved in this tumorigenic process could be useful to stratify cancer risk development for patients with IBD and to monitor and assess prognosis. Thus, the present review aimed to summarize the role of miRNAs as biomarkers for the diagnosis and prognosis of IBD-associated CRC. In the future, therapies based on miRNA modulation could be used both in clinical practice to achieve remission of the disease and restore the quality of life for patients with IBD, and to identify the patients with IBD at high risk for tumor development.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein–protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors–genes interaction, protein–drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.

Molecular Characterization of the Oncogene BTF3 and Its Targets in Colorectal Cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed and leading causes of cancer mortality worldwide, and the prognosis of patients with CRC remains unsatisfactory. Basic transcription factor 3 (BTF3) is an oncogene and hazardous prognosticator in CRC. Although two distinct functional mechanisms of BTF3 in different cancer types have been reported, its role in CRC is still unclear. In this study, we aimed to molecularly characterize the oncogene BTF3 and its targets in CRC. Here, we first identified the transcriptional targets of BTF3 by applying combined RNA-Seq and ChIP-Seq analysis, identifying CHD1L as a transcriptional target of BTF3. Thereafter, we conducted immunoprecipitation (IP)-MS and E3 ubiquitin ligase analysis to identify potential interacting targets of BTF3 as a subunit of the nascent-polypeptide-associated complex (NAC). The analysis revealed that BTF3 might also inhibit E3 ubiquitin ligase HERC2-mediated p53 degradation. Finally, miRNAs targeting BTF3 were predicted and validated. Decreased miR-497-5p expression is responsible for higher levels of BTF3 post-transcriptionally. Collectively, we concluded that BTF3 is an oncogene, and there may exist a transcription factor and NAC-related proteolysis mechanism in CRC. This study provides a comprehensive basis for understanding the oncogenic mechanisms of BTF3 in CRC.