The use of a new bioinformatics pipeline allowed the identification of deregulated transcription factors (TFs) coexpressed in lung cancer that could become biomarkers of tumor establishment and progression. A gene regulatory network (GRN) of lung cancer was created with the normalized gene expression levels of differentially expressed genes (DEGs) from the microarray dataset GSE19804. Moreover, coregulatory and transcriptional regulatory network (TRN) analyses were performed for the main regulators identified in the GRN analysis. The gene targets and binding motifs of all potentially implicated regulators were identified in the TRN and with multiple alignments of the TFs’ target gene sequences. Six transcription factors (E2F3, FHL2, ETS1, KAT6B, TWIST1, and RUNX2) were identified in the GRN as essential regulators of gene expression in non-small-cell lung cancer (NSCLC) and related to the lung tumoral process. Our findings indicate that RUNX2 could be an important regulator of the lung cancer GRN through the formation of coregulatory complexes with other TFs related to the establishment and progression of lung cancer. Therefore, RUNX2 could become an essential biomarker for developing diagnostic tools and specific treatments against tumoral diseases in the lung after the experimental validation of its regulatory function.

In this paper, we compare semiparametric and parametric model adjustments for cancer mortality in breast and cervical cancer and prostate and lung cancer in men, according to age and period of death. Semiparametric models were adjusted for the number of deaths from the two localizations of greatest mortality by sex: breast and cervix in women; prostate and lungs in men. Adjustments in different semiparametric models were compared; which included making adjustments with different distributions and variable combinations in the parametric and non-parametric part, for localization as well as for scale. Finally, the semiparametric model with best adjustment was selected and compared to traditional model; that is, to the generalized lineal model with Poisson response and logarithmic link. Best results for the four kinds of cancer were obtained for the selected semiparametric model by comparing it to the traditional Poisson model based upon AIC, envelope correlation between estimated logarithm rate and real rate logarithm. In general, we observe that in estimation, rate increases with age; however, with respect to period, breast cancer and stomach cancer in men show a tendency to rise over time; on the other hand, for cervical cancer, it remains virtually constant, but for lung cancer in men, as of 2007, it tends to decrease.