Extracellular vesicles (EVs) are secreted from almost all human cells and mediate intercellular communication by transferring heterogeneous molecules (i.e., DNA, RNAs, proteins, and lipids). In this way, EVs participate in various biological processes, including immune responses. Viruses can hijack EV biogenesis systems for their dissemination, while EVs from infected cells can transfer viral proteins to uninfected cells and to immune cells in order to mask the infection or to trigger a response. Several studies have highlighted the role of native or engineered EVs in the induction of B cell and CD8(+) T cell reactions against viral proteins, strongly suggesting these antigen-presenting EVs as a novel strategy for vaccine design, including the emerging COVID-19. EV-based vaccines overcome some limitations of conventional vaccines and introduce novel unique characteristics useful in vaccine design, including higher bio-safety and efficiency as antigen-presenting systems and as adjuvants. Here, we review the state-of-the-art for antiviral EV-based vaccines, including the ongoing projects of some biotech companies in the development of EV-based vaccines for SARS-CoV-2. Finally, we discuss the limits for further development of this promising class of therapeutic agents.

The ACE2 receptor is, so far, the best-known host factor for SARS-CoV-2 entry, but another essential element, the TMPRSS2 protease, has recently been identified. Here, we have analysed TMPRSS2 expression data in the lung correlating them with age, sex, diabetes, smoking habits, exposure to pollutant and other stimuli, in order to highlight which factors might alter TMPRSS2 expression, and thus impact the susceptibility to infection and COVID-19 prognosis. Moreover, we reported TMPRSS2 polymorphisms affecting its expression and suggested the ethnic groups more prone to COVID-19. Finally, we also highlighted a gender-specific co-expression between TMPRSS2 and other genes related to SARS-CoV-2 entry, maybe explaining the higher observed susceptibility of infection in men. Our results could be useful in designing potential prevention and treatment strategies regarding the COVID-19.

CXCL12 is a chemokine that acts through CXCR4 and ACKR3 receptors and plays a physiological role in embryogenesis and haematopoiesis. It has an important role also in tumor development, since it is released by stromal cells of tumor microenvironment and alters the behavior of cancer cells. Many studies investigated the roles of CXCL12 in order to understand if it has an anti- or protumor role. In particular, it seems to promote tumor invasion, proliferation, angiogenesis, epithelial to mesenchymal transition (EMT), and metastasis in pancreatic cancer. Nevertheless, some evidence shows opposite functions; therefore research on CXCL12 is still ongoing. These discrepancies could be due to the presence of at least six CXCL12 splicing isoforms, each with different roles. Interestingly, three out of six variants have the highest levels of expression in the pancreas. Here, we report the current knowledge about the functions of this chemokine and then focus on pancreatic cancer. Moreover, we discuss the methods applied in recent studies in order to understand if they took into account the existence of the CXCL12 isoforms.

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

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