Yazar "Afzal, Muhammad" seçeneğine göre listele

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    Non-negative matrix factorization and differential expression analyses identify hub genes linked to progression and prognosis of glioblastoma multiforme
    (Elsevier, 2022) Akçay, Sevinç; Güven, Emine; Afzal, Muhammad; Kazmi, Imran
    One of the most prevailing primary brain tumors in adult human male is glioblastoma multiforme (GBM), which is categorized by rapid cellular growth. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate, on average, is 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and defiance against chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE124145 was retrieved; the non-negative matrix factorization (NMF) method was applied on GBM dataset, and differentially expressed genes analysis (DEGs) was performed. After which, overlapping genes between metagenes and DEGs were detected to examine the Gene Ontology (GO) categories in the biological process (BP) in the stemness of GBM. The common hub genes were used to construct protein-protein interaction (PPI) network and further GO, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was utilized to pinpoint the real hub genes. The analysis of hub genes particular for the same GO categories demonstrated that specific hub genes triggered distinct features of the same biological processes. After utilizing GSE124145 and The Cancer Genome Atlas (TCGA) dataset for survival analysis, we screened five real hub genes: GUCA1A, RFC2, GNG11, MMP19, and NRG1, which are strongly associated with the progression and prognosis of GBM. The DEGs analysis revealed that all real hub genes were overexpressed in GBM and TCGA datasets, which further validates our results. The constructed study of PPI, GO, and KEGG pathway on common hub genes was performed. Finally, the KEGG pathways performed on the top 15 candidate hub genes (including six real hub genes) of the PPI network in the GBM gene expression dataset study found mitogen-activated protein kinase (Mapk) signaling pathway to be the most significant pathway. The rest of the hub genes reviewed throughout the analysis might be favorable targets for diagnosing and treating GBM and lower-grade gliomas.
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    Polycystic Ovarian Syndrome: A Complex Disease with a Genetics Approach
    (Mdpi, 2022) Nautiyal, Himani; Imam, Syed Sarim; Alshehri, Sultan; Ghoneim, Mohammed M.; Afzal, Muhammad; Alzarea, Sami I.; Güven, Emine
    Polycystic ovarian syndrome (PCOS) is a complex endocrine disorder affecting females in their reproductive age. The early diagnosis of PCOS is complicated and complex due to overlapping symptoms of this disease. The most accepted diagnostic approach today is the Rotterdam Consensus (2003), which supports the positive diagnosis of PCOS when patients present two out of the following three symptoms: biochemical and clinical signs of hyperandrogenism, oligo, and anovulation, also polycystic ovarian morphology on sonography. Genetic variance, epigenetic changes, and disturbed lifestyle lead to the development of pathophysiological disturbances, which include hyperandrogenism, insulin resistance, and chronic inflammation in PCOS females. At the molecular level, different proteins and molecular and signaling pathways are involved in disease progression, which leads to the failure of a single genetic diagnostic approach. The genetic approach to elucidate the mechanism of pathogenesis of PCOS was recently developed, whereby four phenotypic variances of PCOS categorize PCOS patients into classic, ovulatory, and non-hyperandrogenic types. Genetic studies help to identify the root cause for the development of this PCOS. PCOS genetic inheritance is autosomal dominant but the latest investigations revealed it as a multigene origin disease. Different genetic loci and specific genes have been identified so far as being associated with this disease. Genome-wide association studies (GWAS) and related genetic studies have changed the scenario for the diagnosis and treatment of this reproductive and metabolic condition known as PCOS. This review article briefly discusses different genes associated directly or indirectly with disease development and progression.
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    Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer's disease
    (Elsevier Ltd, 2023) Afzal, Muhammad; Alharbi, K.S.; Alzarea, S.I.; Alyamani, N.M.; Kazmi, I.; Güven, E.
    Background: A factor leading to Alzheimer's Disease (AD), portrayed by peripheral insulin resistance, is Type 2 diabetes mellitus (T2D). The likelihood of T2D cases would be at boosted danger in alternating AD cases has severe social consequences. Several genes have been detected via gene expression profiling or different techniques; despite the consideration of the utility of numerous of these genes stays insufficient. Methods: This project is designed to uncover the mutual genomics motifs between AD and T2D via non-negative matrix factorization (NMF) of differentially expressed genes (DEGs) of T2D Mellitus of human cortical neurons of the neurovascular unit gene expression data. A rank factorization value is calculated by employing the combination of the NMF model with the unit invariant knee (UIK) point method. The metagenes are further determined by remarking the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment tools. In this study, the most highly expressed genes of metagenes are subjected to protein-protein interaction (PPI) network study to discover the most significant biomarkers of T2D Mellitus in the ageing brain. Results: We screened the most important shared genes (CDKN1A, COL22A1, EIF4A, GFAP, SLC1A1, and VIM) and essential human molecular pathways that motivate these diseases. The study aimed to validate the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D. Conclusions: Using in silico tools, the computational pipeline has broadly examined transformed pathways and discovered promising biomarkers and drug targets. We validated the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D. These consequences on brain cells hypothetically reserve to diabetic Alzheimer's so-called type 3 diabetes (T3D) and may offer promising methodologies for curative intrusion. © 2022 The Author(s)
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    Screening the Significant Hub Genes by Comparing Tumor Cells, Normoxic and Hypoxic Glioblastoma Stem-like Cell Lines Using Co-Expression Analysis in Glioblastoma
    (Mdpi, 2022) Güven, Emine; Afzal, Muhammad; Kazmi, Imran
    Glioblastoma multiforme (GBM) is categorized by rapid malignant cellular growth in the central nervous system (CNS) tumors. It is one of the most prevailing primary brain tumors, particularly in human male adults. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate is on average 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and counteracting chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE45117 was retrieved and differentially expressed genes (DEGs) were spotted. The co-expression network analysis was employed on DEGs to find the significant modules. The most significant module resulting from co-expression analysis was the turquoise module. The turquoise module related to the tumor cells, hypoxia, normoxic treatments of glioblastoma tumor (GBT), and GSCs were screened. Sixty-one common genes in the turquoise module were selected generated through the co-expression analysis and protein-protein interaction (PPI) network. Moreover, the GO and KEGG pathway enrichment results were studied. Twenty common hub genes were screened by the NetworkAnalyst web instrument constructed on the PPI network through the STRING database. After survival analysis via the Kaplan-Meier (KM) plotter from The Cancer Genome Atlas (TCGA) database, we identified the five most significant hub genes strongly related to the progression of GBM. We further observed these five most significant hub genes also up-regulated in another GBM gene expression dataset. The protein-protein interaction (PPI) network of the turquoise module genes was constructed and a KEGG pathway enrichments study of the turquoise module genes was performed. The VEGF signaling pathway was emphasized because of the strong link with GBM. A gene-disease association network was further constructed to demonstrate the information of the progression of GBM and other related brain neoplasms. All hub genes assessed through this study would be potential markers for the prognosis and diagnosis of GBM.