Delineating the mechanistic relevance of the TP53 gene and its mutational impact on gene expression and patients' survival in bladder cancer.

Bladder carcinoma (BLCA) is a widespread urological malignancy causing significant global mortality, often hindered by delayed diagnosis and limited treatments. BLCA frequently exhibits TP53 mutations, playing a pivotal role in its pathogenesis and underscoring the potential of targeting TP53 as a therapeutic approach for this prevalent urological malignancy. Tumor tissues from 50 bladder cancer patients were used for mutational analysis in TP53's mutation-rich exons (5, 7, & 8). The gene expression of the TP53 gene, along with a TP53-target gene B-cell translocation gene 2 (BTG2) was also assessed in the cDNA samples from the same BLCA tissues and 15 urine controls of healthy people. The analysis revealed 22 % of patients with somatic hotspot mutations, 18 % with pathogenic missense mutations, and 12 % with intronic variants. Patients with somatic mutations exhibited the worst prognosis, supported by survival analysis from The Cancer Genome Atlas (TCGA) BLCA data. Interestingly, H296Y missense mutation correlated with higher TP53 expression and improved survival, while intronic SNPs were linked to worse outcomes. Additionally, upregulated BTG2 expression in mutated patients was observed which was correlated with poor prognosis, emphasizing the role of TP53 mutations in bladder cancer progression. The multivariate analysis highlighted the predictive power of TP53 mutations, with a high frequency of high-grade tumors (78.57 %) in mutated patients, underscoring their role in cancer progression. In conclusion, this study emphasizes the crucial role of TP53 mutations in bladder cancer patients from Bangladesh.

Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation.

The urea transporter UT-B1, encoded by the SLC14A1 gene, has been hypothesized to be a significant protein whose deficiency and dysfunction contribute to the pathogenesis of bladder cancer and many other diseases. Several studies reported the association of genetic alterations in the SLC14A1 (UT-B1) gene with bladder carcinogenesis, suggesting a need for thorough characterization of the UT-B1 protein's coding and non-coding variants. This study used various computational techniques to investigate the commonly occurring germ-line missense and non-coding SNPs (ncSNPs) of the SLC14A1 gene (UT-B1) for their structural, functional, and molecular implications for disease susceptibility and dysfunctionality. SLC14A1 missense variants, primarily identified from the ENSEMBL genome browser, were screened through twelve functionality prediction tools leading to two variants D280Y (predicted detrimental by maximum tools) and D280N (high global MAF) for rs1058396. Subsequently, the ConSurf and NetSurf tools revealed the D280 residue to be in a variable site and exposed on the protein surface. According to I-Mutant2.0 and MUpro, both variants are predicted to cause a significant effect on protein stability. Analysis of molecular docking anticipated these two variants to decrease the binding affinity of UT-B1 protein for the examined ligands to a significant extent. Molecular dynamics also disclosed the possible destabilization of the UT-B1 protein due to single nucleotide polymorphism compared to wild-type protein which may result in impaired protein function. Furthermore, several non-coding SNPs were estimated to affect transcription factor binding and regulation of SLC14A1 gene expression. Additionally, two ncSNPs were found to affect miRNA-based post-transcriptional regulation by creating new seed regions for miRNA binding. This comprehensive in-silico study of SLC14A1 gene variants may serve as a springboard for future large-scale investigations examining SLC14A1 polymorphisms.

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis.

Being a frequent malignant tumor of the genitourinary system, Bladder Urothelial Carcinoma (BLCA) has a poor prognosis. This study focused on identifying and validating prognostic biomarkers utilizing methylation, transcriptomics, and clinical data from The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA BLCA) cohort. The impact of altered differentially methylated hallmark pathway genes was subjected to clustering analysis to observe changes in the transcriptional landscape on BLCA patients and identify two subtypes of patients from the TCGA BLCA population where Subtype 2 was associated with the worst prognosis with a p-value of 0.00032. Differential expression and enrichment analysis showed that subtype 2 was enriched in immune-responsive and cancer-progressive pathways, whereas subtype 1 was enriched in biosynthetic pathways. Following, regression and network analyses revealed Epidermal Growth Factor Receptor (EGFR), Fos-related antigen 1 (FOSL1), Nuclear Factor Erythroid 2 (NFE2), ADP-ribosylation factor-like protein 4D (ARL4D), SH3 domain containing ring finger 2 (SH3RF2), and Cadherin 3 (CDH3) genes to be the most significant prognostic gene markers. These genes were used to construct a risk model that separated the BLCA patients into high and low-risk groups. The risk model was also validated in an external dataset by performing survival analysis between high and low-risk groups with a p-value < 0.001 and the result showed the high group was significantly associated with poor prognosis compared to the low group. Single-cell analyses revealed the elevated level of these genes in the tumor microenvironment and associated with immune response. High-grade patients also tend to have a high expression of these genes compared to low-grade patients. In conclusion, this research developed a six-gene signature that is pertinent to the prediction of overall survival (OS) and might contribute to the advancement of precision medicine in the management of bladder cancer.

Comprehensive characterization of coding and non-coding single nucleotide polymorphisms of the Myoneurin (MYNN) gene using molecular dynamics simulation and docking approaches.

Genome-wide association studies (GWAS) identified a coding single nucleotide polymorphism, MYNN rs10936599, at chromosome 3q. MYNN gene encodes myoneurin protein, which has been associated with several cancer pathogenesis and disease development processes. However, there needed to be a more detailed characterization of this polymorphism's (and other coding and non-coding polymorphisms) structural, functional, and molecular impact. The current study addressed this gap and analyzed different properties of rs10936599 and non-coding SNPs of MYNN via a thorough computational method. The variant, rs10936599, was predicted functionally deleterious by nine functionality prediction approaches, like SIFT, PolyPhen-2, and REVEL, etc. Following that, structural modifications were estimated through the HOPE server and Mutation3D. Moreover, the mutation was found in a conserved and active residue, according to ConSurf and CPORT. Further, the secondary structures were predicted, followed by tertiary structures, and there was a significant deviation between the native and variant models. Similarly, molecular simulation also showed considerable differences in the dynamic pattern of the wildtype and mutant structures. Molecular docking revealed that the variant binds with better docking scores with ligand NOTCH2. In addition to that, non-coding SNPs located at the MYNN locus were retrieved from the ENSEMBL database. These were found to disrupt the transcription factor binding regulatory regions; nonetheless, only two affect miRNA target sites. Again, eight non-coding variants were detected in the testes with normalized expression, whereas HaploReg v4.1 unveiled annotations for non-coding variants. In summary, in silico comprehensive characterization of coding and non-coding single nucleotide polymorphisms of MYNN gene will assist researchers to work on MYNN gene and establish their association with certain types of cancers.

A multi-epitope based vaccine against the surface proteins expressed in cyst and trophozoite stages of parasite Entamoeba histolytica.

Entamoeba histolytica, an anaerobic parasite, infects humans and other primates and causes fatal diseases, such as amebiasis, amebic liver abscesses, and many others. Thousands of people are infected and dying due to the need for a proper protective cure, especially in poor sanitizing regions, such as Latin America, Asia, and Africa. Around 10% of the world population is infected by E. histolytica every year. Consequently, novel preventive approaches are required to eliminate the threats of the parasite. A designed vaccine targeting the exposed proteins that are common between cyst and trophozoite stages of the parasite's life cycle would be an effective way to repress the impact of the parasite. Therefore, an in silico bioinformatics approach was performed to design an effective vaccine targeting surface proteins common between both stages of the parasite's life cycle using B-cell and T-cell epitopes. The epitopes derived from the conserved portions of the proteins and their corresponding isomers specific to the parasite suggested that the vaccine could benefit cross-protection. Furthermore, the three-dimensional structure of the designed vaccine was modelled, refined, and validated using multiple bioinformatics tools. The physiological properties and solubility were also predicted using different algorithmic tools and found to be highly soluble in nature. The vaccine was found interactcted with TLR immune receptors, and the stability was observed via dynamics simulation. Codon optimization and cloning were performed for expression analysis. Immune simulation prediction anticipated significant immune responses with a high IgG and IgM antibodies expression, Th and Tc cells population, B-cell population, memory cells, INF-γ, and IL-2 cytokines. Therefore, the constructed multi-epitope putative vaccine can effectively neutralize the parasite's harmful effects.

Transcriptomic analysis of esophageal cancer reveals hub genes and networks involved in cancer progression.

Esophageal carcinoma (ESCA) has a 5-year survival rate of fewer than 20%. The study aimed to identify new predictive biomarkers for ESCA through transcriptomics meta-analysis to address the problems of ineffective cancer therapy, lack of efficient diagnostic tools, and costly screening and contribute to developing more efficient cancer screening and treatments by identifying new marker genes. Nine GEO datasets of three kinds of esophageal carcinoma were analyzed, and 20 differentially expressed genes were detected in carcinogenic pathways. Network analysis revealed four hub genes, namely RAR Related Orphan Receptor A (RORA), lysine acetyltransferase 2B (KAT2B), Cell Division Cycle 25B (CDC25B), and Epithelial Cell Transforming 2 (ECT2). Overexpression of RORA, KAT2B, and ECT2 was identified with a bad prognosis. These hub genes modulate immune cell infiltration. These hub genes modulate immune cell infiltration. Although this research needs lab confirmation, we found interesting biomarkers in ESCA that may aid in diagnosis and treatment.