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Molecular-based studies have revealed heterogeneity in Breast cancer BC while also improving classification and treatment. However, efforts are underway to distinguish between distinct subtypes of breast cancer. In this study, the results of several microarray studies were combined to identify genes and pathways specific to each BC subtype.Meta-analysis of multiple gene expression profile datasets was screened to find differentially expressed genes (DEGs) across subtypes of BC and normal breast tissue samples. Protein-protein interaction network and gene set enrichment analysis were used to identify critical genes and pathways associated with BC subtypes. The differentially expressed genes from meta-analysis was validated using an independent comprehensive breast cancer RNA-sequencing dataset obtained from the Cancer Genome Atlas (TCGA).We identified 110 DEGs (13 DEGs in all and 97 DEGs in each subtype) across subtypes of BC. All subtypes had a small set of shared DEGs enriched in the Chemokine receptor bind chemokine pathway. Luminal A specific were enriched in the translational elongation process in mitochondria, and the enhanced process in luminal B subtypes was interferon-alpha/beta signaling. Cell cycle and mitotic DEGs were enriched in the basal-like group. All subtype-specific DEG genes (100%) were successfully validated for Luminal A, Luminal B, ERBB2, and Normal-like. However, the validation percentage for Basal-like group was 77.8%.Integrating researches such as a meta-analysis of gene expression might be more effective in uncovering subtype-specific DEGs and pathways than a single-study analysis. It would be more beneficial to increase the number of studies that use matched BC subtypes along with GEO profiling approaches to reach a better result regarding DEGs and reduce probable biases. However, achieving 77.8% overlap in basal-specific genes and complete concordance in specific genes related to other subtypes can implicate the strength of our analysis for discovering the subtype-specific genes.

This population-based longitudinal study is the first investigation that assesses the association of common MC4R SNPs with the obesity-related parameters over time and determines the effect of risk alleles during the three adulthood life periods (early, middle, and late) in a large Iranian cohort, a population with a unique genetic make-up that has been understudied and relatively unexplored. We obtained the genotype of 5370 unrelated adults who participated in the ongoing Tehran Cardiometabolic Genetic Study (TCGS) cohort project for the common MC4R SNPs. Linear regression and linear mixed model analyses were performed to examine the effect of MC4R polymorphisms on maximum BMI and other obesity-related factors over time. We recognized that several SNPs associated with the maximum BMI and the increased BMI, waist circumference, and waist-hip ratio across Iranian adults over a lifetime. Interestingly, we found that rs9954571-A has a yet unreported protective role against obesity-related factors, including BMI, waist circumference, waist-hip ratio, and triglyceride level. Additionally, a survey of the impact of the MC4R risk score throughout the adulthood life periods indicated that the MC4R risk score is influenced both the elevated BMI and waist circumference only during the early adulthood period. Our findings can expand our knowledge about the MC4R genetic variant's contributions to adulthood obesity and highlight the importance of evaluating the genetic components affecting obesity over a lifetime, which could be considered for obesity clinical screening and treatment.

Recent genome-wide association studies (GWAS) highlighted the importance of genetic variations on SLC22A3 and MIA3 genes in developing coronary heart disease (CHD) among different ethnicities. However, the influence of these variations is not recognized within the Iranian population. Hence, in the present study, we aim to investigate two key single nucleotide polymorphisms (SNPs) on CHD incidence in this population. For this purpose, from Tehran Cardiometabolic Genetic Study (TCGS), 453 individuals with CHD were selected as a case and 453 individuals as a control that matched their age and gender. After quality control of two selected SNPs, rs2048327 (SLC22A3) and rs17465637 (MIA3), we used genotyps resulted from chip-typing technology and conducted the logistic regression analysis adjusted for non-genetic risk factors to detect the possible association of these SNPs with the CHD development. Our findings demonstrated the rs2048327-G and rs17465637-C can significantly increase the risk of CHD development about two times in only males and females, respectively. Interestingly, in the male carriers of the risk allele (G) of rs2048327, the low high-density lipoprotein (HDL) level can significantly predispose them to develop coronary heart disease in the future. According to our results, paying more attention to gender and genetic markers can help more efficient coronary heart disease screening and diagnosis.