SRY-Related Transcription Factors in Head and Neck Squamous Cell Carcinomas: In Silico Based Analysis.

Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer and the fifth cause of cancer-related deaths worldwide with a poor 5-year survival. SOX family genes play a role in the processes involved in cancer development such as epithelial-mesenchymal transition (EMT), the maintenance of cancer stem cells (CSCs) and the regulation of drug resistance. We analyzed the expression of SOX2-OT, SOX6, SOX8, SOX21, SOX30 and SRY genes in HNSCC patients using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, to assess their biological role and their potential utility as biomarkers. We demonstrated statistically significant differences in expression between normal and primary tumor tissues for SOX6, SOX8, SOX21 and SOX30 genes and pointed to SOX6 as the one that met the independent diagnostic markers criteria. SOX21 or SRY alone, or the panel of six SRY-related genes, could be used to estimate patient survival. SRY-related genes are positively correlated with immunological processes, as well as with keratinization and formation of the cornified envelope, and negatively correlated with DNA repair and response to stress. Moreover, except SRY, all analyzed genes were associated with a different tumor composition and immunological profiles. Based on validation results, the expression of SOX30 is higher in HPV(+) patients and is associated with patients' survival. SRY-related transcription factors have vast importance in HNSCC biology. SOX30 seems to be a potential biomarker of HPV infection and could be used as a prognostic marker, but further research is required to fully understand the role of SOX family genes in 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.