Hossein Azizi

Integrated Analysis, Machine Learning, Molecular Docking and Dynamics of CDK1 Inhibitors in Epithelial Ovarian Cancer: A Multifaceted Approach Towards Targeted Therapy

Epithelial ovarian cancer (EOC) remains one of the deadliest gynecologic malignancies, largely due to late diagnosis and treatment resistance. The main objective of this study is to identify and validate CDK1 as a high-confidence therapeutic target in EOC and to assess the dual-target inhibitory potential of the natural compound Naringin against both CDK1 and its regulator WEE1. This study employed an integrative pipeline combining transcriptomic profiling, protein–protein interaction network analysis, machine learning, and molecular simulations to identify key oncogenic regulators in EOC. CDK1 emerged as a central hub gene, exhibiting strong association with poor prognosis and signaling convergence. CDK1 overexpression correlated with adverse survival outcomes and robust involvement in critical oncogenic pathways. Molecular docking and dynamics simulations assessed the binding efficacy of seven compounds with CDK1 and WEE1, with Naringin showing high-affinity binding, stable complex formation, and minimal predicted toxicity. This study underscores the power of computational-experimental integration in accelerating oncology drug discovery, providing visual and quantitative evidence that systematically connect the study’s aim to its findings.

The involvement of ribonucleoproteins in promoting epithelial ovarian cancer through a systems biology approach

Abstract Background Epithelial ovarian cancer (EOC), originating from the ovarian epithelial cells, represents approximately 90% of all ovarian cancer cases and includes several subtypes. Aim This study investigates the role of hub ribonucleoprotein genes in EOC progression. Methods Microarray datasets GSE28799 and GSE54388 from GEO were analyzed using Transcriptome Analysis Console (TAC) software for differential expression ( p  ≤ 0.05, LogFC ≥4). Upregulated RNP genes, including FBL and HNRNPC, were identified. Protein–protein interactions were analyzed using STRING and visualized in Cytoscape. Clustering was performed with Gephi software. Gene expression and alterations were validated using the HPA and cBioPortal databases. Results FBL and HNRNPC were significantly upregulated in EOC, playing key roles in tumor progression. Network analysis showed close interactions within the same gene cluster. Pathway enrichment linked them to spliceosome and ribosome biogenesis, affecting gene regulation and cellular function. Samples with FBL and HNRNPC deletions showed lower mRNA expression. Survival analysis indicated that their upregulation negatively affects patient survival, suggesting that disrupting these genes could slow cancer progression. Conclusion This study highlights the crucial role of FBL and HNRNPC in EOC. These genes are significantly upregulated and actively contribute to key cellular processes like spliceosome function and ribosome biogenesis, which help sustain tumor growth. Through network and pathway analyses, researchers have uncovered their strong functional connection, further emphasizing their importance in cancer biology. Notably, higher expression levels of these genes are linked to poorer patient survival. Targeting and disrupting their expression may provide new strategies to slow tumor progression and improve patient outcomes.

HPV-Driven Immune Evasion in Cervical Cancer: Transcriptomic Identification of Downregulated Hub Genes and Suppressed Leukocyte Migration Pathways

Cervical cancer progression, particularly in the context of HPV infection, is driven by complex transcriptional alterations within the tumor microenvironment. Understanding the molecular mechanisms underlying HPV-induced immune evasion is crucial for developing effective therapeutic strategies. Transcriptomic analyses were performed using three independent datasets (GSE127265, GSE166466, and GSE218460) to identify differentially expressed genes (DEGs) between HPV-positive and HPV-negative cervical cancer samples. Protein–protein interaction networks were constructed using Cytoscape and STRING, and immune infiltration was assessed via the TIMER database. A total of 572 DEGs were commonly identified between tumor and normal tissues, with HPV-positive samples showing distinct transcriptional profiles. Several downregulated hub genes were associated with immune regulation and receptor tyrosine kinase signaling. Immune infiltration analysis revealed altered dendritic cell and T cell patterns, indicating HPV-mediated immune modulation. Pathway enrichment identified the leukocyte transendothelial migration pathway as a key mechanism impaired by HPV infection. These findings highlight the critical role of immune-related hub genes in HPV-driven cervical cancer progression and suggest potential therapeutic targets to counteract HPV-induced immune suppression.