Analysis of Functional Polymorphisms in Meningioma associated Genes

Abstract

Meningioma is the most common benign intracranial tumour that develops in the meningeal protective covering of the central nervous system (CNS). Globally, every nine individuals out of 100,000 are diagnosed with this cancer. Basic risk factors of meningioma comprise ionizing radiation, hormonal imbalance, and genetic aberrations. In this study, various bioinformatics tools, specialized for consensus-based identification, sequence-homology, and supervised learning, were employed to analyze and screen the deleterious mutational landscape of commonly associated genes of meningioma/genes commonly associated with meningioma. This study employed an in-silico approach aimed to utilize thirteen different tools to benchmark pathogenic single nucleotide polymorphisms (SNPs) in SMARCB1, AKT1, SMO, SUFU, NF2 and MTHFR genes related to meningioma. We identified six highly pathogenic SNPs related to meningioma: SMARCB1 (rs387906812, rs387906811, rs267607072), AKT1 (rs121434592), SMO (rs121918347), and SUFU (rs202247756). Additionally, several deleterious missense variants of NF2 and MTHFR genes were also identified. Hence, this study is a gateway for research on SNPs since they can be utilized to conduct a type-based diagnosis of meningioma for its early prognosis. They can also be utilized as genomic targets for a targeted therapy by developing inhibitors against mutated proteins. For this purpose, further wet-lab experiments and genome-wide association studies are required to genotype these SNPs in a large number of samples, collected from different populations belonging to various ethnicities, for the development of SNP(s) gene panels.

3D Bioprinting and Its Role in a Wound Healing Renaissance

“…[ 183 ] A streamlined bioprinting process in a clinical setting following this model might involve first scanning a patient's wound for its precise dimensions, with these measurements being subsequently used to construct a 3D CAD model for use with an in vitro bioprinter on‐site. [ 184 ] The resulting 3D bioprinted construct would then be a personalized wound treatment for the patient in question. [ 184 ] There would be some delay in treatment associated with this approach, however, associated with the time to scan the patients wound, create the 3D model, and subsequently bioprint the personalized construct.…”

“…[ 184 ] The resulting 3D bioprinted construct would then be a personalized wound treatment for the patient in question. [ 184 ] There would be some delay in treatment associated with this approach, however, associated with the time to scan the patients wound, create the 3D model, and subsequently bioprint the personalized construct. [ 183 ]…”

Detection of C677T, rs75582903 and SNV64878785 in Methylenetetra hydrofolate Reductase Gene (MTHFR) in Meningioma among Sudanese patients

“…[71] CODEN (USA): JDDTAO grade I meningioma also missense mutations in SMARCB1, SUFU, MTHFR, MTRR, BRIP1, SRSF2, and UNC13C genes are associated with meningioma 4 .…”

“…The etiologies of meningiomas are not yet known; it may be related to radiation exposure, genetic disorders (Neurofibromatosis type2), injuries in the skull and surrounding membranes, hormonal imbalance (progesterone) and single nucleotide polymorphisms (SNPs) are important molecular markers that occur in may site through whole genome; they are sufficient for disrupting a protein's structural and functional viability 4 .…”

“…The World Health Organization (WHO) defines 15 subtypes of meningiomas and divides them into 3 grades of malignancy based on histopathological characteristics, the most common type of meningioma accounts for grade 1 (80%), atypical grade 2 (18%), and anaplastic grade 3 (2% Grade II meningiomas includes: Atypical meningioma, Clear-cell meningioma and Chordoid meningioma. Grade III meningiomas include the following: anaplastic (malignant) meningioma, rhabdoid meningioma and papillary meningioma 4,6 .…”