Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUV_max, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75–10.05, p = 5.41x10^-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

In recent years, the importance of the gut microbiome in human health and disease has increased. Growing evidence suggests that gut dysbiosis might be a crucial risk factor for coronary artery disease (CAD). Therefore, we conducted a systematic review and meta-analysis to determine whether or not CAD is associated with specific changes in the gut microbiome. The V3–V4 regions of the 16S rDNA from fecal samples were analyzed to compare the gut microbiome composition between CAD patients and controls. Our search yielded 1181 articles, of which 21 met inclusion criteria for systematic review and 7 for meta-analysis. The alpha-diversity, including observed OTUs, Shannon and Simpson indices, was significantly decreased in CAD, indicating the reduced richness of the gut microbiome. The most consistent results in a systematic review and meta-analysis pointed out the reduced abundance of Bacteroidetes and Lachnospiraceae in CAD patients. Moreover, Enterobacteriaceae, Lactobacillus, and Streptococcus taxa demonstrated an increased trend in CAD patients. The alterations in the gut microbiota composition are associated with qualitative and quantitative changes in bacterial metabolites, many of which have pro-atherogenic effects on endothelial cells, increasing the risk of developing and progressing CAD.

Despite knowledge of classical coronary artery disease (CAD) risk factors, the morbidity and mortality associated with this disease remain high. Therefore, new factors that may affect the development of CAD, such as the gut microbiome, are extensively investigated. This study aimed to evaluate gut microbiome composition in CAD patients in relation to the control group. We examined 169 CAD patients and 166 people in the control group, without CAD, matched in terms of age and sex to the study group. Both populations underwent a detailed health assessment. The microbiome analysis was based on the V3–V4 region of the 16S rRNA gene (NGS method). Among 4074 identified taxonomic units in the whole population, 1070 differed between study groups. The most common bacterial types were Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Furthermore, a higher Firmicutes/Bacteroidetes ratio in the CAD group compared with the control was demonstrated. Firmicutes/Bacteroidetes ratio, independent of age, sex, CAD status, LDL cholesterol concentration, and statins treatment, was related to altered phosphatidylcholine concentrations obtained in targeted metabolomics. Altered alpha-biodiversity (Kruskal–Wallis test, p = 0.001) and beta-biodiversity (Bray–Curtis metric, p < 0.001) in the CAD group were observed. Moreover, a predicted functional analysis revealed some taxonomic units, metabolic pathways, and proteins that might be characteristic of the CAD patients’ microbiome, such as increased expressions of 6-phospho-β-glucosidase and protein-N(pi)-phosphohistidine-sugar phosphotransferase and decreased expressions of DNA topoisomerase, oxaloacetate decarboxylase, and 6-beta-glucosidase. In summary, CAD is associated with altered gut microbiome composition and function.

Due to a global increase in the prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need for early identification of prediabetes, as these people have the highest risk of developing diabetes. Circulating miRNAs have shown potential as progression biomarkers in other diseases. This study aimed to conduct a baseline comparison of serum-circulating miRNAs in prediabetic individuals, with the distinction between those who later progressed to T2DM and those who did not. The expression levels of 798 miRNAs using NanoString technology were examined. Spearman correlation, receiver operating characteristic (ROC) curve analysis, and logistic regression modeling were performed. Gene ontology (GO) and canonical pathway analysis were used to explore the biological functions of the miRNA target genes. The study revealed that three miRNAs were upregulated in the serum samples of patients who later progressed to T2DM. Pathway analysis showed that the miRNA target genes were mainly significantly enriched in neuronal NO synthase (nNOS) signaling in neurons, amyloid processing, and hepatic cholestasis. ROC analysis demonstrated that miR-491-5p, miR-1307-3p, and miR-298 can be introduced as a diagnostic tool for the prediction of T2DM (area under the curve (AUC) = 94.0%, 88.0%, and 84.0%, respectively). Validation by real-time quantitative polymerase chain reaction (qRT-PCR) confirmed our findings. The results suggest that circulating miRNAs can potentially be used as predictive biomarkers of T2DM in prediabetic patients.