All physiological events in living organisms originated as specific chemical/biochemical signals on the cell surface and transmitted into the cytoplasm. This signal is translated within milliseconds–hours to a specific and unique order required to maintain optimum performance and homeostasis of living organisms. Examples of daily biological functions include neuronal communication and neurotransmission in the process of learning and memory, secretion (hormones, sweat, and saliva), muscle contraction, cellular growth, differentiation and migration during wound healing, and immunity to fight infections. Among the different transducers for such life-dependent signals is the large family of G protein-coupled receptors (GPCRs). GPCRs constitute roughly 800 genes, corresponding to 2% of the human genome. While GPCRs control a plethora of pathophysiological disorders, only approximately one-third of GPCR families have been deorphanized and characterized. Recent drug data show that around 40% of the recommended drugs available in the market target mainly GPCRs. In this review, we presented how such system signals, either through G protein or via other players, independent of G protein, function within the biological system. We also discussed drugs in the market or clinical trials targeting mainly GPCRs in various diseases, including cancer.

Breast, colon, and lung carcinomas are classified as aggressive tumors with poor relapse-free survival (RFS), progression-free survival (PF), and poor hazard ratios (HRs) despite extensive therapy. Therefore, it is essential to identify a gene expression signature that correlates with RFS/PF and HR status in order to predict treatment efficiency. RNA-binding proteins (RBPs) play critical roles in RNA metabolism, including RNA transcription, maturation, and post-translational regulation. However, their involvement in cancer is not yet fully understood. In this study, we used computational bioinformatics to classify the functions and correlations of RBPs in solid cancers. We aimed to identify molecular biomarkers that could help predict disease prognosis and improve the therapeutic efficiency in treated patients. Intersection analysis summarized more than 1659 RBPs across three recently updated RNA databases. Bioinformatics analysis showed that 58 RBPs were common in breast, colon, and lung cancers, with HR values < 1 and >1 and a significant Q-value < 0.0001. RBP gene clusters were identified based on RFS/PF, HR, p-value, and fold induction. To define union RBPs, common genes were subjected to hierarchical clustering and were classified into two groups. Poor survival was associated with high genes expression, including CDKN2A, MEX3A, RPL39L, VARS, GSPT1, SNRPE, SSR1, and TIA1 in breast and colon cancer but not with lung cancer; and poor survival was associated with low genes expression, including PPARGC1B, EIF4E3, and SMAD9 in breast, colon, and lung cancer. This study highlights the significant contribution of PPARGC1B, EIF4E3, and SMAD9 out of 11 RBP genes as prognostic predictors in patients with breast, colon, and lung cancers and their potential application in personalized therapy.

Breast, colon and lung carcinomas are classified as aggressive tumors that have poor relapse-free survival (RFS) or progression-free survival (PF) and poor hazard ratios (HRs) despite of extensive therapy. Therefore, it is essential to identify a gene expression signature correlating with RFS/PF and HR status to predict the efficiency of treatment. RNA Binding Proteins (RBPs) play a critical role in RNA metabolic activities including RNA transcription, maturation and posttranslational regulation. However, their particular involvement in cancers is not yet understood. In this study, we used computational bioinformatics to classify the function and the correlation of RBPs among solid cancers. We aimed to identify the molecular biomarker that would help in disease prognosis prediction or improve therapeutic efficiency in treated patients. The intersection analysis summarized more than 1659 RBPs across three recently updated RNA databases. The bioinformatics analysis showed that 58 RBPs were common in breast, colon and lung cancers with HR values < 1 and > 1 and a significant Q-value < 0.0001. RBP gene clusters were identified based on RFS/PF, HR, P-value and fold of induction. In order to define union RBPs, the common genes were subjected to hierarchical clustering and classified into two groups. Poor survival with high-risk HR genes included CDKN2A, MEX3A, RPL39L and VARS (valine cytoplasmic-localized aminoacyl-tRNA synthetase) and poor survival with low-risk HR genes included GSPT1, SNRPE, SSR1 and TIA1, PPARGC1B, EIF4E3 and SMAD9. This study may highlight the significant contribution of the 11 RBP genes as prognostic predictors in breast, colon and lung cancer patient and their potential application in personalized therapy.