The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Background: Many microRNAs have been identified as critical mediators in the progression of melanoma through its regulation of genes involved in different cellular processes such as melanogenesis, cell cycle control, and senescence. However, microRNAs’ concurrent participation in syngeneic mouse B16F1 melanoma cells simultaneously induced decreased proliferation and differential pigmentation by exposure to 5-Brd-2′-dU (5’Bromo-2-deoxyuridine) and L-Tyr (L-Tyrosine) respectively, is poorly understood. Aim: To evaluate changes in the expression of microRNAs and identify which miRNAs in-network may contribute to the functional bases of phenotypes of differential pigmentation and reduction of proliferation in B16F1 melanoma cells exposed to 5-Brd-2′-dU and L-Tyr. Methods: Small RNAseq evaluation of the expression profiles of miRNAs in B16F1 melanoma cells exposed to 5-Brd-2′-dU (2.5 μg/mL) and L-Tyr (5 mM), as well as the expression by qRT-PCR of some molecular targets related to melanogenesis, cell cycle, and senescence. By bioinformatic analysis, we constructed network models of regulation and co-expression of microRNAs. Results: We confirmed that stimulation or repression of melanogenesis with L-Tyr or 5-Brd-2′-dU, respectively, generated changes in melanin concentration, reduction in proliferation, and changes in expression of microRNAs 470-3p, 470-5p, 30d-5p, 129-5p, 148b-3p, 27b-3p, and 211-5p, which presented patterns of coordinated and reciprocal co-expression, related to changes in melanogenesis through their putative targets Mitf, Tyr and Tyrp1, and control of cell cycle and senescence: Cyclin D1, Cdk2, Cdk4, p21, and p27. Conclusions: These findings provide insights into the molecular biology of melanoma of the way miRNAs are coordinated and reciprocal expression that may operate in a network as molecular bases for understanding changes in pigmentation and decreased proliferation induced in B16F1 melanoma cells exposed to L-Tyr and 5-Brd-2′-dU.