Browsing by Author "Alotaibi, Naif"

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    An Integrated Framework to Identify Prognostic Biomarkers and Novel Therapeutic Targets in Hepatocellular Carcinoma-Based Disabilities
    (2024-11-24) Rahman, Md Okibur; Das, Asim; Naeem, Nazratun; Tahnim, Jabeen-E-; Hossain, Md Ali; Alam, Md Nur; Azad, Akm; Alyami, Salem A; Alotaibi, Naif; Moisheer, A S Al-; Moni, Mohammod Ali
    Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors globally, significantly affecting liver functions, thus necessitating the identification of biomarkers and effective therapeutics to improve HCC-based disabilities. This study aimed to identify prognostic biomarkers, signaling cascades, and candidate drugs for the treatment of HCC through integrated bioinformatics approaches such as functional enrichment analysis, survival analysis, molecular docking, and simulation. Differential expression and functional enrichment analyses revealed 176 common differentially expressed genes from two microarray datasets, GSE29721 and GSE49515, significantly involved in HCC development and progression. Topological analyses revealed 12 hub genes exhibiting elevated expression in patients with higher tumor stages and grades. Survival analyses indicated that 11 hub genes (CCNB1, AURKA, RACGAP1, CEP55, SMC4, RRM2, PRC1, CKAP2, SMC2, UHRF1, and FANCI) and three transcription factors (E2F1, CREB1, and NFYA) are strongly linked to poor patient survival. Finally, molecular docking and simulation identified seven candidate drugs with stable complexes to their target proteins: tozasertib (-9.8 kcal/mol), tamatinib (-9.6 kcal/mol), ilorasertib (-9.5 kcal/mol), hesperidin (-9.5 kcal/mol), PF-562271 (-9.3 kcal/mol), coumestrol (-8.4 kcal/mol), and clofarabine (-7.7 kcal/mol). These findings suggest that the identified hub genes and TFs could serve as valuable prognostic biomarkers and therapeutic targets for HCC-based disabilities.
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    Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities
    (2024-10-14) Esha, Jannatul Ferdous; Islam, Tahmidul; Pranto, Md. Appel Mahmud; Borno, Abrar Siam; Faruqui, Nuruzzaman; Abu Yousuf, Mohammad; Azad, AKM; Al-Moisheer, Asmaa Soliman; Alotaibi, Naif; Alyami, Salem A.; Ali Moni, Mohammad
    The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model’s performance. The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality.