Alterations in proprotein convertase genes’ expression during malignization of human lung cells have a limited set of scenarios

Aim. The current study aims to investigate changes in the expression of proprotein convertases (PCs) genes during the development of lung tumors. PCs is a family of highly specific serine endopeptidases of mammals that process precursors of various proteins and peptides. Nine genes encoding PCs have been identified in the human genome that are essential for normal body functioning. Additionally, PCs can activate proteins involved in carcinogenesis, as well as the expression levels of their genes were shown to correlate with tumor aggressiveness and patient survival. We previously evaluated the expression of all nine PC genes using quantitative PCR on paired samples of lung tumor and adjacent normal tissue. For the first time, we identified four distinct patterns of PCs gene expression change in tumor tissue, which we have called «scenarios». For three of them, covering more than two thirds of the samples, a dominant change in the expression of one PC gene in the tumor tissue was shown. These results may indicate the existence of a limited number of possible options for changes in PCs gene expression during the malignant transformation of lung cells. However, these results need to be confirmed using expanded cohort of tumor samples. Material and Methods. To confirm our previous findings, we analyzed the expression of PCs genes using modern methods of mathematical statistics and data from three previously published studies, which evaluated gene expression through high-throughput RNA sequencing in paired tumor and normal tissue samples from 194 patients with non-small cell lung cancer. Results. Our meta-analysis confirmed that the changes in PCs gene expression in lung tumor tissue compared to surrounding normal tissue follow a limited set of possible scenarios, each having a unique profile of PCs gene expression. Conclusion. The reasons for implementing each scenario may be linked to the origin of tumors, their mutation status, characteristics of the tumor microenvironment, and other factors. Correspondingly, these scenarios may correlate, for example, with tumor aggressiveness and resistance to therapy, and therefore may potentially be used to choose the treatment approach and/or to predict the course of the disease. However, this issue requires further research.