Background: The Signal Transducer and Activator of Transcription 1 (STAT1) gene is an essential component of the JAK-STAT signaling pathway. This pathway plays a pivotal role in the regulation of different cellular processes, including immune responses, cell growth, and apoptosis. Mutations in the STAT1 gene contribute to a variety of immune system dysfunctions. Objectives: We aim to identify disease-susceptible single-nucleotide polymorphisms (SNPs) in STAT1 gene and predict structural changes associated with the mutations that disrupt normal protein–protein interactions using different computational algorithms. Methods: Several in silico tools, such as SIFT, Polyphen v2, PROVEAN, SNAP2, PhD-SNP, SNPs&GO, Pmut, and PANTHER, were used to determine the deleterious nsSNPs of the STAT1. Further, we evaluated the potentially deleterious SNPs for their effect on protein stability using I-Mutant, MUpro, and DDMUT. Additionally, we predicted the functional and structural effects of the nsSNPs using MutPred. We used Alpha-Missense to predict missense variant pathogenicity. Moreover, we predicted the 3D structure of STAT1 using an artificial intelligence system, alphafold, and the visualization of the 3D structures of the wild-type amino acids and the mutant residues was performed using ChimeraX 1.9 software. Furthermore, we analyzed the structural and conformational variations that have resulted from SNPs using Project Hope, while changes in the biological interactions between wild type, mutant amino acids, and neighborhood residues was studied using DDMUT. Conservational analysis and surface accessibility prediction of STAT1 was performed using ConSurf. We predicted the protein–protein interaction using STRING database. Results: In the current study, we identified six deleterious nsSNPs (R602W, I648T, V642D, L600P, I578N, and W504C) and their effect on protein structure, function, and stability. Conclusions: These findings highlight the potential of approaches to pinpoint pathogenic SNPs, providing a time- and cost-effective alternative to experimental approaches. To the best of our knowledge, this is the first comprehensive study in which we analyze STAT1 gene variants using both bioinformatics and artificial-intelligence-based model tools.

Background: Single-nucleotide polymorphisms (SNPs) are associated with multiple disorders and various cancer types. In the context of cancer, alterations within non-coding regions, specifically 3′ untranslated regions (3′ UTR), have proven substantially important. Methods: In this study, we utilized various bioinformatics tools to examine the effect of SNPs in the 3′ UTR. We retrieved the 3′ UTR SNPs of the Signal Transducer and Activator of Transcription 1 (STAT1) gene from the National Centre for Biotechnology Information (NCBI) website. Next, we employed the Polymorphism in miRNAs and their corresponding target sites (PolymiRTS) database to predict the 3′ UTR SNPs that create new microRNA (miRNA) binding sites and their respective miRNAs. The effect of the 3′ UTR SNPs on the messenger RNA structure was studied using RNAfold server. We used Cscape tool to predict the oncogenic 3′ UTR SNPs. Then, we submitted the miRNAs to the miRNet database to visualize the miRNA-miRNAs’ target genes interaction, for which gene enrichment analysis was performed using ShinyGO. Protein–protein interactions were conducted using the STRING database. We conducted miRNA enrichment analysis utilizing miRPathDB, subsequently performing miRNA differential expression analysis through oncoMIR, and the StarBase database. The survival analysis of the upregulated miRNAs in cancer was investigated using the Kaplan–Meier Plotter. Result: Twelve SNPs were predicted to create new miRNA binding sites. Two of them, rs188557905 and rs190542524, were predicted to destabilize the mRNA structures. We predicted rs190542524, rs11305, rs186033487, and rs188557905 to be oncogenic 3′ UTR SNPs, with high-confidence predictions and scores > 0.5. Using miRNAs’ target genes enrichment analysis, this study indicated that the miRNA target genes were more likely to be involved in cancer-related pathways. Our comprehensive analysis of miRNAs, their functional enrichment, their expression in various types of cancer, and the correlation between miRNA expression and survival outcome yielded these results. Our research shows that the oncogenic 3′ UTR SNP rs190542524 creates a new binding site for the oncogenic miRNA hsa-miR-136-5p. This miRNA is significantly upregulated in BLCA, LUSC, and STAD and is linked to poor survival. Additionally, rs114360225 creates a new binding site for hsa-miR-362-3p, influencing LIHC. Conclusions: These analyses suggest that these 3′ UTR SNPs may have a functional impact on the STAT1 gene’s regulation through their predicted effect on miRNA binding sites. Future experimental validation could establish their potential role in the diagnosis and treatment of various diseases, including cancer.

The Signal Transducer and Activator of Transcription 1 (STAT1) gene is an essential component of the JAK-STAT signalling pathway. This pathway has a pivotal role in regulating different cellular processes, including immune responses, cell growth, and apoptosis. Mutations in the STAT1 gene contribute to various body pathologies [OMIM #600555], including immune system dysfunction. In the current study, we used eighteen online computational approaches. Six pathogenic variants (R602W, I648T, V642D, L600P, I578N, and W504C) were predicted to significantly disrupt protein stability and function. These findings highlight the potential of approaches to pinpoint pathogenic single nucleotide polymorphisms, providing a time and cost effective alternative to experimental approaches. Up to the best of our knowledge, this is the original inclusive study, where we analyze STAT1 gene variants using both bioinformatics and artificial intelligence based model tools.

Stomach adenocarcinoma is a major contributor to worldwide mortality and poses a substantial challenge to improving life expectancy. The main objective of the current work was to identify diagnostic and prognostic biomarkers in stomach adenocarcinoma in order to advance translational medicine and patient outcomes. By accomplishing this objective, the research seeks to provide significant insights into the field of translational medicine. Seven novel unfavourable prognosis-associated genes (NALCN, CALCR, CPT1C, ELAVL3, FLJ16779, MYOZ3, and TPST1) were first identified. Additionally, 41 potential miRNAs were predicted. ELAVL3-hsa-mir-29a-3p and CALCR-hsa-mir-29a-3p axes were identified as two critical pathways in the carcinogenesis of stomach adenocarcinoma via a bioinformatics analysis. Following that, lncRNAs binding to hsa-mir-29a-3p were predicted via starBase and miRNet databases for predicting lncRNA binding sites. After conducting both expression and survival analyses for these predicted lncRNAs, we found that only one lncRNA (KCNQ1OT1) was markedly overexpressed in stomach adenocarcinoma, and its elevated expression was associated with an unfavourable prognosis. Next, we established a comprehensive mRNA-miRNA-lncRNA triple ceRNA network linked to the prognosis of patients with stomach adenocarcinoma. In summary, the current study provides an extensive ceRNA network that highlights novel diagnostic and prognostic biomarkers for stomach adenocarcinoma.

Background: The Myelin Basic Protein (MBP) gene is essential for myelin sheath formation in the central nervous system. Coding and noncoding single-nucleotide polymorphisms (SNPs) can impair the protein structure and function, contributing to demyelinating diseases exemplified by multiple sclerosis. This study aimed to assess the impact of SNPs in the MBP gene on protein structure and function. Methods: We employed a comprehensive approach to investigate the impact of both noncoding and coding SNPs of the MBP gene. Initially, we utilized RegulomeDB to assess the regulatory roles of SNPs located in the 3′ untranslated regions (3′ UTRs). Subsequently, we examined the influence of the 3’ UTR SNPs on microRNA (miRNA) binding sites using PolymiRTS. Furthermore, we analyzed the functional 3′ UTR SNPs using RNAfold to evaluate their impact on RNA structure. To predict deleterious nonsynonymous SNPs (nsSNPs), various bioinformatics tools, including SIFT, PolyPhen-2, PROVEAN, META-SNP, ESNPs&GO, PANTHER, and AlphaMissense, were employed. Protein stability was assessed using I-Mutant2.0, MUpro, and DDMut. Structural modeling was performed with AlphaFold, and both wild-type and mutant proteins were visualized in UCSF ChimeraX. Conservation analysis was conducted using the ConSurf tool, and protein interaction networks were explored using the STRING database. Results: Eight noncoding SNPs were identified as potential regulatory SNPs, affecting the miRNA binding sites. Moreover, three nsSNPs, rs1971676214 (D173E), rs1242552448 (D173H), and rs772570115 (G176W), were consistently predicted to be pathogenic and to destabilize the protein structure. These variants were located in highly conserved sites and disrupted hydrogen bonds. STRING analysis revealed interactions between MBP and other myelin-related, immune, and signaling proteins, linking it to CNS and autoimmune pathways. Conclusions: This study identified eight noncoding 3′ UTR SNPs and three potentially pathogenic nsSNPs that may compromise gene expression and protein structure and function, respectively, offering insight into genetic mechanisms of demyelination.

Background: RNF114 gene encodes an E3 ubiquitin ligase involved in immune signaling and regulation of inflammation. Genetic variants, particularly nonsynonymous single-nucleotide polymorphisms (nsSNPs), may interfere with protein function and cause immune diseases such as psoriasis. Although significant, the structural and functional impact of RNF114 nsSNPs is not well understood. Methods: We used comprehensive bioinformatics analyses to predict the functional impact of RNF114 nsSNPs. Deleterious variants were predicted by SIFT, PolyPhen-2, PROVEAN, META-SNP, ESNP&GO, PANTHER, and Alpha-Missense. Protein stability was examined by I-Mutant2.0, and MUpro further contextualized variant effects. Structural modeling was performed by AlphaFold and visualized using UCSF ChimeraX 1.10.1. Additionally, we studied the Conservation using ConSurf and protein-protein interaction by STRING tools. Results: Among 252 available nsSNPs, three mutations—C49R (rs1600868749), R68C (rs745318334), and R68H (rs758000156)—were predicted to have a deleterious and destabilizing effects on the protein structure by all the tools. All three variants were located in extremely conserved residues and were predicted to significantly destabilize the protein structure. Structural modeling demonstrated disruptions in the RNF114 domain structure. STRING analysis revealed interactions of RNF114 with key immune regulators, and pathway enrichment pointed to roles in NF-κB signaling, ubiquitin-mediated proteolysis, and autoimmune disease pathways. Conclusions: In the current study, we predicted three novel, potentially pathogenic RNF114 variants with protein-destabilizing effect that could lead to immune dysregulation.