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.

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Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered proteinprotein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment. Welding processes join rigid material pieces (usually metal) at their contact interface by using high temperatures to cause fusion. This process can be hazardous because it exposes the operator to extremely toxic fumes and to radiant energy 1. The International Agency for Research on Cancer (IARC) has recognized WFs and UV radiation from welding as Group 1 carcinogens 2. WFs are mainly composed of metallic oxides, silicates and fluorides, including those of magnesium, manganese, zinc, aluminum, beryllium, copper, chromium, cadmium, lead, iron, nickel and vanadium 3 .

Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high‐calorie diet and high‐fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein–protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors’ approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP.

Developing type 2 diabetes (T2D) can increase patient risk of developing other common diseases and exacerbate their severity, including diseases that affect bone and joints. Such comorbidity interactions are hard to study in detail by traditional endocrinological methods. Thus, we developed tissue transcript analytical approaches to identify common pathways through which these diseases can interact. We examined RNAseq and microarray transcript datasets from studies of T2D and chronic bone and joint diseases, namely rheumatoid arthritis (RA), osteoarthritis (OA), juvenile idiopathic arthritis (JIA) and low peak bone density, a key osteoporosis (OP) determinant. These datasets contained data from affected individuals and matched controls. Differentially expressed genes (DEGs) for each condition were compared with T2D DEG. Overlapping DEGs (i.e., those common to T2D and a bone or joint condition) were subjected to gene enrichment by pathway analyses and by gene ontology methods, and the results were evaluated by using SNP-disease linkage (dbGaP) and gene-disease association (OMIM) databases that indicate gene involvement in pathologies. By examining gene targets of transcription factors (TFs) and microRNA (miRNAs), we also constructed DEG-TF and DEG-miRNA interactions networks for analysis. We identified strong candidate genes in common pathways, notably including SYK, UCP3, ROR1, PPARG, BUB1, AKT2, ADCY2 and CCR5. The DEG-TF network and DEG-miRNA interactions network analyses revealed a number of TFs (GATA2, FOXC1, USF2, YY1, E2F1, JUN, RELA, CREB1, TFAP2A, NFB1) and miRNAs (mir-335-5p, mir-16-5p, mir-26b-5p, mir-124-3p, mir-218-5p, mir-98-5p, mir-29b-3p, mir-3135b, mir-29c-3p, mir-1-1) that can regulate the identified DEGs at the transcriptional and post-transcriptional levels. Thus this data-driven approach has enabled identification and validation of regulatory factors and cell pathways by which T2D may influence bone and joint conditions, which may suggest new ways to interfere with the pathogenic processes involved.INDEX TERMS T2 diabetes, network-based approach, joint diseases, bone diseases.