Dysregulation of fibroblast growth factors is linked to the pathogenesis of bladder cancer. The role of FGF1 and FGF3 is evident in bladder cancer; however, the role of FGF4 is vague. Despite being reported that FGF4 interacts with FGF1 and FGF3 in MAPK pathways, its pathogenesis and mechanism of action are yet to be elucidated. Therefore, this study aimed to elucidate pathogenic nsSNPs and their role in the prognosis of bladder cancer by employing in-silico analysis. The nsSNPs of FGF4 were retrieved from the NCBI database. Different in silico tools, PROVEAN, SIFT, PolyPhen-2, SNPs&GO, and PhD-SNP, were used for predicting the pathogenicity of the nsSNPs. Twenty-seven nsSNPs were identified as “damaging”, and further stability analysis using I-Mutant 2.0 and MUPro indicated 22 nsSNPs to cause decreased stability (DDG scores < −0.5). Conservation analysis predicted that Q97K, G106V, N164S, and N167S were highly conserved and exposed. Biophysical characterisation indicated these nsSNPs were not tolerated, and protein-protein interaction analysis showed their involvement in the GFR-MAPK signalling pathway. Furthermore, Kaplan Meier bioinformatics analyses indicated that the FGF4 gene deregulation affected the overall survival rate of patients with bladder cancer, leading to prognostic significance. Thus, based on these analyses, our study suggests that the reported nsSNPs of FGF4 may serve as potential targets for diagnoses and therapeutic interventions focusing on bladder cancer.

The Harvey rat sarcoma (HRAS) proto-oncogene belongs to the RAS family and is one of the pathogenic genes that cause cancer. Deleterious nsSNPs might have adverse consequences at the protein level. This study aimed to investigate deleterious nsSNPs in the HRAS gene in predicting structural alterations associated with mutants that disrupt normal protein–protein interactions. Functional and structural analysis was employed in analyzing the HRAS nsSNPs. Putative post-translational modification sites and the changes in protein–protein interactions, which included a variety of signal cascades, were also investigated. Five different bioinformatics tools predicted 33 nsSNPs as “pathogenic” or “harmful”. Stability analysis predicted rs1554885139, rs770492627, rs1589792804, rs730880460, rs104894227, rs104894227, and rs121917759 as unstable. Protein–protein interaction analysis revealed that HRAS has a hub connecting three clusters consisting of 11 proteins, and changes in HRAS might cause signal cascades to dissociate. Furthermore, Kaplan–Meier bioinformatics analyses indicated that the HRAS gene deregulation affected the overall survival rate of patients with breast cancer, leading to prognostic significance. Thus, based on these analyses, our study suggests that the reported nsSNPs of HRAS may serve as potential targets for different proteomic studies, diagnoses, and therapeutic interventions focusing on cancer.

The diagnosis of endometrial cancer involves sequential, invasive tests to assess the thickness of the endometrium by a transvaginal ultrasound scan. In 6–33% of cases, endometrial biopsy results in inadequate tissue for a conclusive pathological diagnosis and 6% of postmenopausal women with non-diagnostic specimens are later discovered to have severe endometrial lesions. Thus, identifying diagnostic biomarkers could offer a non-invasive diagnosis for community or home-based triage of symptomatic or asymptomatic women. Herein, this study identified high-risk pathogenic nsSNPs in the NRAS gene. The nsSNPs of NRAS were retrieved from the NCBI database. PROVEAN, SIFT, PolyPhen-2, SNPs&GO, PhD-SNP and PANTHER were used to predict the pathogenicity of the nsSNPs. Eleven nsSNPs were identified as “damaging”, and further stability analysis using I-Mutant 2.0 and MutPred 2 indicated eight nsSNPs to cause decreased stability (DDG scores < −0.5). Post-translational modification and protein–protein interactions (PPI) analysis showed putative phosphorylation sites. The PPI network indicated a GFR-MAPK signalling pathway with higher node degrees that were further evaluated for drug targets. The P34L, G12C and Y64D showed significantly lower binding affinity towards GTP than wild-type. Furthermore, the Kaplan–Meier bioinformatics analyses indicated that the NRAS gene deregulation affected the overall survival rate of patients with endometrial cancer, leading to prognostic significance. Findings from this could be considered novel diagnostic and therapeutic markers.

Harvey rat sarcoma (HRAS) protooncogene belongs to the RAS family and is one of the pathogenic genes that cause cancer. Deleterious nsSNPs might have adverse consequences at the protein level. This study aimed to investigate deleterious nsSNPs in the HRAS gene in predicting structural alterations associated with mutants that disrupt normal protein-protein interactions. Functional and structural analysis was em[loyed in analysing the HRAS nsSNPs. Putative post-translational modification sites and the changes in protein-protein interactions, which included a variety of signal cascades, were also investigated. Five different bioinformatics tools predicted 33 nsSNPs as “pathogenic” or “harmful”. Stability analysis predicted shortlisted rs1554885139, rs770492627, rs1589792804, rs730880460, rs104894227, rs104894227 and rs121917759 as unstable. Protein-protein interaction analysis revealed that HRAS as a hub connecting three clusters consisting of 11 proteins and changes in HRAS might cause signal cascades to dissociate. Furthermore, Kaplan Meier bioinformatics analyses indicated that the HRAS gene deregulation affected the overall survival rate of patients with breast cancer, leading to prognostic significance. Thus, based on these analyses, our study suggests that the reported nsSNPs of HRAS may serve as potential targets for different proteomic studies, diagnoses and therapeutic interventions focusing on cancer.