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In-Silico Analysis of Deleterious SNPs of FGF4 Gene and Their Impacts on Protein Structure, Function and Bladder Cancer Prognosis

ABSTRACT: 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.

SUBMITTER: Lim E

PROVIDER: S-EPMC9316931 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHepatocellular carcinoma (HCC) is a life-threatening and refractory malignancy with poor outcome. Genetic mutations are the hallmark of cancer. Thus far, there is no comprehensive prognostic model constructed by mutation-gene transcriptome in HCC. The prognostic value of mutation-gene signature in HCC remains elusive.MethodsRNA expression profiles and the corresponding clinical information were recruited from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to establish gene signature. Kaplan-Meier curve and time-dependent receiver operating characteristic curve were implemented to evaluate the prognostic value. The Wilcoxon test was performed to analyze the expression of immune checkpoint genes, cell cycle genes, and tumor drug resistance genes in different risk groups. Finally, quantitative real-time PCR (qRT-RCR) and immunohistochemistry (IHC) were performed to validate the mRNA and protein expression between HCC and adjacent nontumorous tissues in an independent cohort.ResultsA prognostic model consisting of five mutated genes was established by LASSO Cox regression analysis. The prognostic model classified patients into high- and low-risk groups. Compared with the low-risk group, patients in the high-risk group had significantly worse survival results. The prognostic model can accurately predict the overall survival of HCC patients and predict overall survival more accurately when combined with stage. Furthermore, the immune checkpoint genes, cell cycle genes, and tumor drug resistance genes were higher expressed in the high-risk group compared in the low-risk group. In addition, the expression level of prognostic signature genes was validated in an independent sample cohort, which was consistent with RNA sequencing expression in the TCGA database.ConclusionThe prediction model of HCC constructed using mutation-related genes is of great significance for clinical decision making and the personalized treatment of patients with HCC.

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In-Silico Analysis of Deleterious SNPs of FGF4 Gene and Their Impacts on Protein Structure, Function and Bladder Cancer Prognosis

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