Increased expression of ORMDL3 in allergic asthma: a case control and in vitro study.

BACKGROUND: Asthma is the most frequent chronic disease in children. One of the most replicated genetic findings in childhood asthma is the ORMDL3 gene confirmed in several GWA studies in several pediatric populations. OBJECTIVES: The purpose of this study was to analyze ORMDL3 variants and expression in childhood asthma in the Polish population. METHODS: In the study we included 416 subject, 223 asthmatic children and 193 healthy control subjects. The analysis of two SNPs (rs3744246 and rs8076131) was performed using genotyping with TaqMan probes. The methylation of the ORMDL3 promoter was examined with Methylation Sensitive HRM (MS-HRM), covering 9 CpG sites. The expression of ORMDL3 was analyzed in PBMCs from pediatric patients diagnosed with allergic asthma and primary human bronchial epithelial cells derived from healthy subjects treated with IL-13, IL-4, or co-treatment with both cytokines to model allergic airway inflammation. RESULTS: We found that ORMDL3 expression was increased in allergic asthma both in PBMCs from asthmatic patients as well as in human bronchial epithelial cells stimulated with the current cytokines. We did not observe significant differences between cases and controls either in the genotype distribution of analyzed SNPs (rs3744246 and rs8076131) nor in the level of promoter methylation. CONCLUSIONS: Increased ORMDL3 expression is associated with pediatric allergic asthma and upregulated in the airways upon Th2-cytokines stimulation, but further functional studies are required to fully understand its role in this disease.

The Landscape of Transmembrane Protein Family Members in Head and Neck Cancers: Their Biological Role and Diagnostic Utility.

Background: Transmembrane proteins (TMEM) constitute a large family of proteins spanning the entirety of the lipid bilayer. However, there is still a lack of knowledge about their function or mechanism of action. In this study, we analyzed the expression of selected TMEM genes in patients with head and neck squamous cell carcinoma (HNSCC) to learn their role in tumor formation and metastasis. Materials and Methods: Using TCGA data, we analyzed the expression levels of different TMEMs in both normal and tumor samples and compared those two groups depending on clinical-pathological parameters. We selected four TMEMs whose expression was highly correlated with patient survival status and subjected them to further analysis. The pathway analysis using REACTOME and the gene set enrichment analysis (GSEA) were performed to evaluate the association of those TMEMs with genes involved in hallmarks of cancer as well as in oncogenic and immune-related pathways. In addition, the fractions of different immune cell subpopulations depending on TMEM expression were estimated in analyzed patients. The results for selected TMEMs were validated using GEO data. All analyses were performed using the R package, Statistica, and Graphpad Prism. Results: We demonstrated that 73% of the analyzed TMEMs were dysregulated in HNSCC and depended on tumor localization, smoking, alcohol consumption, or HPV infection. The expression levels of ANO1, TMEM156, TMEM173, and TMEM213 correlated with patient survival. The four TMEMs were also upregulated in HPV-positive patients. The elevated expression of those TMEMs correlated with the enrichment of genes involved in cancer-related processes, including immune response. Specifically, overexpression of TMEM156 and TMEM173 was associated with immune cell mobilization and better survival rates, while the elevated ANO1 expression was linked with metastasis formation and worse survival. Conclusions: In this work, we performed a panel of in silico analyses to discover the role of TMEMs in head and neck squamous cell carcinoma. We found that ANO1, TMEM156, TMEM173, and TMEM213 correlated with clinical status and immune responses in HNSCC patients, pointing them as biomarkers for a better prognosis and treatment. This is the first study describing such the role of TMEMs in HNSCC. Future clinical trials should confirm the potential of those genes as targets for personalized therapy of HNSCC.

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine.

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.