SAA1 gene polymorphisms in osteoporosis patients

A Novel Five-Gene Signature Related to Clinical Outcome and Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most frequent cancer in women and the main cause of cancer-related deaths in the globe, according to the World Health Organization. The need for biomarkers that can help predict survival or guide treatment decisions in BC patients is critical in order to provide each patient with an individualized treatment plan due to the wide range of prognoses and therapeutic responses. A reliable prognostic model is essential for determining the best course of treatment for patients. Patients’ clinical and pathological data, as well as their mRNA expression levels at level 3, were gleaned from the TCGA databases. Differentially expressed genes (DEGs) between BC and non-tumor specimens were identified. Tumor immunity analyses have been utilized in order to decipher molecular pathways and their relationship to the immune system. The expressions of KIF4A in BC cells were determined by RT-PCR. To evaluate the involvement of KIF4A in BC cell proliferation, CCK-8 tests were used. In this study, utilizing FC > 4 and p < 0.05, we identified 140 upregulated genes and 513 down-regulated genes. A five-gene signature comprising SFRP1, SAA1, RBP4, KIF4A and COL11A1 was developed for the prediction of overall survivals of BC. Overall survival was distinctly worse for patients in the high-risk group than those in the low-risk group. Cancerous and aggressiveness-related pathways and decreased B cell, T cell CD4⁺, T cell CD8⁺, Neutrophil and Myeloid dendritic cells levels were seen in the high-risk group. In addition, we found that KIF4A was highly expressed in BC and its silence resulted in the suppression of the proliferation of BC cells. Taken together, as a possible prognostic factor for BC, the five-gene profile created and verified in this investigation could guide the immunotherapy selection.

Association of the Sp1 binding site and -1997 promoter variations in COL1A1 with osteoporosis risk: The application of meta-analysis and bioinformatics approaches offers a new perspective for future research

As a complex disease, osteoporosis is influenced by several genetic markers. Many studies have examined the link between the Sp1 binding site +1245 G > T (rs1800012) and -1997 G > T (rs1107946) variations in the COL1A1 gene with osteoporosis risk. However, the findings of these studies have been contradictory; therefore, we performed a meta-analysis to aggregate additional information and obtain increased statistical power to more efficiently estimate this correlation. A meta-analysis was conducted with studies published between 1991-2020 that were identified by a systematic electronic search of the Scopus and Clarivate Analytics databases. Studies with bone mineral density (BMD) data and complete genotypes of the single-nucleotide variations (SNVs) for the overall and postmenopausal female population were included in this meta-analysis and analyzed using the R metaphor package. A relationship between rs1800012 and significantly decreased BMD values at the lumbar spine and femoral neck was found in individuals carrying the "ss" versus the "SS" genotype in the overall population according to a random effects model (p < 0.0001). Similar results were also found in the postmenopausal female population (p = 0.003 and 0.0002, respectively). Such findings might be an indication of increased osteoporosis risk in both studied groups in individuals with the "ss" genotype. Although no association was identified between the -1997 G > T and low BMD in the overall population, those individuals with the "GT" genotype showed a higher level of BMD than those with "GG" in the subgroup analysis (p = 0.007). To determine which transcription factor (TF) might bind to the -1997 G > T in COL1A1, 45 TFs were identified based on bioinformatics predictions. According to the GSE35958 microarray dataset, 16 of 45 TFs showed differential expression profiles in osteoporotic human mesenchymal stem cells relative to normal samples from elderly donors. By identifying candidate TFs for the -1997 G > T site, our study offers a new perspective for future research.

Genetic variation in NOD1/CARD4 and NOD2/CARD15 immune sensors and risk of osteoporosis

The present study was aimed to investigate the relationship between NOD1/CARD4 and NOD2/CARD15 gene polymorphisms and osteoporosis in the Turkish population. The first time we thought that the functional polymorphisms in NOD1/CARD4 and NOD2/CARD15 genes might have triggered the development of osteoporosis. The objective of our study was to determine the relationship between NOD1/CARD4 and NOD2/CARD15 SNPs and osteoporosis. The NOD1/CARD4 (rs5743336) and NOD2/CARD15 (rs2066847) SNPs were analyzed by PCR restriction fragment length polymorphism (PCR-RFLP) in 94 healthy controls and 164 subjects with osteoporosis. PCR products were digested with restriction enzymes AvaI for NOD1/CARD4 and ApaI for NOD2/CARD15. We found that NOD1/CARD4 genotype distribution of AA, GA and GG were 15, 44 and 41% for patients and 17, 46 and 37% for controls, respectively. NOD2/CARD15 mutation was found only in three patients (1.8%) as heterozygote. The results did not show any statistical difference between NOD1/CARD4 and NOD2/CARD15 genotype distribution of patients and healthy groups (χ² = 1.740, P=0.187; χ² = 1.311, P=0.519). However, the most frequent AG genotype (46%) of NOD1/CARD4 was observed in healthy controls, GG genotype (44%) of NOD1/CARD4 was observed as the most frequent in osteoporotic patients. NOD2/CARD15 WT/WT genotype, the most frequent genotype, was observed in both groups. Statistical analysis revealed that NOD1/CARD4 and NOD2/CARD15 polymorphisms are not associated with osteoporosis. However, a definite judgement is difficult to be made due to restricted number of patients and small size of control group. Further research is sorely warranted in this direction.

Characterization of differentially expressed plasma proteins in patients with acute myocardial infarction

Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide. Novel biomarkers are needed to identify NSTEMI in AMI patients. The study objective was to use proteomics to identify novel plasma biomarkers for STEMI and NSTEMI patients. iTRAQ analysis was performed on pooled samples from 8 healthy controls and 12 STEMI and 12 NSTEMI patients. Bioinformatics analysis identified 95 differentially expressed proteins that were differentially expressed in the plasma of AMI patients and healthy controls; 28 of these proteins were found in STEMI/Con (22 upregulated and 6 downregulated), 48 in NSTEMI/Con (12 upregulated and 36 downregulated), and 44 in NSTEMI/STEMI (11 upregulated and 33 downregulated). Protein network analysis was then performed using STRING software. Functional analysis revealed that the identified plasma proteins were mainly involved with carbon metabolism, toll-like receptor signaling pathway, and hypertrophic cardiomyopathy. Nine of the proteins (SSA1, MDH1, FCN2, GPI, S100A8, LBP, vinculin, VDBP, and RBP4) that changed levels during AMI progression were further validated by ELISA. The constructed plasma proteome could reflect the AMI pathogenesis molecular mechanisms and provide a method for the early identification of NSTEMI in AMI patients. SIGNIFICANCE: The aim of this study was to use proteomics to identify novel predictive plasma biomarkers for patients with acute myocardial infarction (AMI), which would allow for either identification of individuals at risk of an infarction, and early identification of NSTEMI in patients with AMI. Using an approach that combined iTRAQ with LC-MS/MS, we found 95 proteins that showed significant differences in expression levels among the AMI patients and healthy controls. The proteins SSA1, MDH1, FCN2, GPI, S100A8, LBP, vinculin, VDBP, and RBP4 were found to play crucial roles in the pathogenesis of AMI. Using bioinformatics analysis, we found that dysregulation of carbon metabolism, toll-like receptor signaling pathway, and hypertrophic cardiomyopathy may be the major driving forces for cardiac damage during myocardial infarction. However, further investigations are needed to verify the mechanisms involved in the development of AMI especially NSTEMI. Taken together, our findings lay the foundation for understanding the molecular mechanisms underlying the pathogenic processes of AMI, and suggest potential applications for specific biomarkers in early diagnosis and determination of prognosis.