Current Genomics - Volume 24, Issue 5, 2023

Spinal muscular atrophy (SMA) is one of the most common genetic disorders worldwide, and genetic testing plays a key role in its diagnosis and prevention. The last decade has seen a continuous flow of new methods for SMA genetic testing that, along with traditional approaches, have affected clinical practice patterns to some degree. Targeting different application scenarios and selecting the appropriate technique for genetic testing have become priorities for optimizing the clinical pathway for SMA. In this review, we summarize the latest technological innovations in genetic testing for SMA, including MassArray^®, digital PCR (dPCR), next-generation sequencing (NGS), and third-generation sequencing (TGS). Implementation recommendations for rationally choosing different technical strategies in the tertiary prevention of SMA are also explored.

Background: Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people.Objectives: In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making.Methods: An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape- cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots.Results: Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM.Conclusion: A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Introduction: Aminoacyl tRNA synthetase complex interacting with multifunctional protein 2 (AIMP2) is a significant regulator of cell proliferation and apoptosis. Despite its abnormal expression in various tumor types, the specific functions and effects of AIMP2 on tumor immune cell infiltration, proliferation, and migration remain unclear.Materials and Methods: To assess AIMP2's role in tumor immunity, we conducted a pan-cancer multi-database analysis using the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Lines Encyclopedia (CCLE) datasets, examining expression levels, prognosis, tumor progression, and immune microenvironment. Additionally, we investigated AIMP2's impact on breast cancer (BRCA) proliferation and migration using cell counting kit 8 (CCK-8) assay, transwell assays, and western blot analysis.Results: Our findings revealed that AIMP2 was overexpressed in 24 tumor tissue types compared to normal tissue and was associated with four tumor stages. Survival analysis indicated that AIMP2 expression was strongly correlated with overall survival (OS) in certain cancer patients, with high AIMP2 expression linked to poorer prognosis in five cancer types.Conclusion: Finally, siRNA-mediated AIMP2 knockdown inhibited BRCA cell proliferation and migration in vitro. In conclusion, our pan-cancer analysis suggests that AIMP2 may play a crucial role in tumor immunity and could serve as a potential prognostic marker, particularly in BRCA.

Background: Dysferlinopathies represent a group of limb girdle or distal muscular dystrophies with an autosomal-recessive inheritance pattern resulting from the presence of pathogenic variants in the dysferlin gene (DYSF).Objective: In this work, we describe a population from a small city in Brazil carrying the c.5979dupA pathogenic variant of DYSF responsible for limb girdle muscular dystrophy type 2R and distal muscular dystrophy.Methods: Genotyping analyses were performed by qPCR using customized probe complementary to the region with the duplication under analysis in the DYSF.Results: A total of 104 individuals were examined. c.5979dupA was identified in 48 (46.15%) individuals. Twenty-three (22%) were homozygotes, among whom 13 (56.5%) were female. A total of 91.3% (21) of homozygous individuals had a positive family history, and seven (30.4%) reported consanguineous marriages. Twenty-five (24%) individuals were heterozygous (25.8±16 years) for the same variant, among whom 15 (60%) were female. The mean CK level was 697 IU for homozygotes, 140.5 IU for heterozygotes and 176 IU for wild-type homo-zygotes. The weakness distribution pattern showed 17.3% of individuals with a proximal pattern, 13% with a distal pattern and 69.6% with a mixed pattern. Fatigue was present in 15 homozygotes and one heterozygote.Conclusion: The high prevalence of this variant in individuals from this small community can be explained by a possible founder effect associated with historical, geographical and cultural aspects.