Hanan Rizk

Dr. Hanan Rizk

orcid

Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 December 2025 in Cancer Cell International
Verified
Journal Article
Open access
PDF file
HRHanan A. Rizk
Hanan A. Rizk
HRHanan Rizk
Hanan Rizk
ARAbdullah Fathy Radwan
Abdullah Fathy Radwan
MAMai A. Abd-Elmawla
Mai A. Abd-Elmawla
NENourhan Elfar
Nourhan Elfar
ADAhmed S. Doghish
Ahmed S. Doghish
Renal cell carcinoma (RCC), the most common type of kidney cancer, is characterized by a poor prognosis due to its aggressive metastasis and resistance to

3 February 2026 in Tissue and Cell
Verified
Journal Article
Closed access
EFEman Fayad
Eman Fayad
MAMaha A. Aljumaa
Maha A. Aljumaa
HRHanan Rizk
Hanan Rizk
AGAml Ghanem
Aml Ghanem
AHAmira Hathout
Amira Hathout
DEDalia Elebeedy
Dalia Elebeedy

Verified
Journal Article
Open access
PDF file
ARAbdullah Fathy Radwan
Abdullah Fathy Radwan
HRHanan Rizk
Hanan Rizk
MAMona Sami Awed
Mona Sami Awed
MSManar R. Senosi
Manar R. Senosi
AGAhmed Gamal
Ahmed Gamal
GAGhada Ayeldeen
Ghada Ayeldeen
Behçet’s disease (BD) is a chronic, multisystem vasculitis characterized by dysregulated immune responses and prominent Th1/Th17 polarization; however, the upstream epigenetic regulatory mechanisms driving this inflammatory imbalance remain incompletely understood. Here, we investigated the coordinated expression and diagnostic relevance of key long non-coding RNAs (lncRNAs; NEAT1 and MEG3) and microRNAs (miR-124 and miR-146a), and their association with IL-17/IL-6-mediated inflammation in BD. In a case–control study, serum levels of the selected non-coding RNAs were quantified by quantitative real-time PCR, while cytokine concentrations were measured using ELISA, complemented by bioinformatic interaction analysis and integrated statistical and machine learning approaches. Patients with BD exhibited marked downregulation of NEAT1, MEG3, miR-124, and miR-146a (p < 0.0001), accompanied by significantly elevated IL-17 and IL-6 levels. Individually, biomarkers demonstrated strong discriminatory capacity (AUC 0.83–0.92), while combined panels further improved classification performance. Multivariate modeling identified these non-coding RNAs as independent predictors of BD, and machine learning analysis identified miR-146a and IL-17 as the most influential contributors to disease classification. Notably, selected biomarkers showed associations with specific clinical manifestations, supporting their potential clinical relevance. Bioinformatic analyses identified putative interactions between NEAT1 and miR-124/miR-146a, while MEG3 demonstrated independent diagnostic value without evidence of direct interaction with the investigated miRNAs. Collectively, these findings provide a hypothesis-generating framework for future mechanistic investigations and support the potential diagnostic value of the investigated biomarker panel, although validation in larger multicenter cohorts and disease-control populations is warranted.