Lim, Ee Chen, Lim, Shu Wen, Tan, Kenneth JunKai, Sathiya, Maran, Cheng, Wan Hee, Lai, Kok-Song, Loh, Jiun-Yan, and Yap, Wai-Sum (2022) In-Silico Analysis of Deleterious SNPs of FGF4 Gene and Their Impacts on Protein Structure, Function and Bladder Cancer Prognosis. Life, 12 (7). 1018.

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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.

Item ID:	83451
Item Type:	Article (Research - C1)
ISSN:	2075-1729
Copyright Information:	© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited:	19 Aug 2024 22:53
FoR Codes:	31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310204 Genomics and transcriptomics @ 100%
SEO Codes:	20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified @ 100%
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