Anoshirvan Kazemnejad

In Silico Transcriptomic Analysis for Identification of Potential Diagnostic and Prognostic Biomarkers and Therapeutic Targets in Cervical Cancer using a Hybrid Genetic Algorithm–Support Vector Machine Approach

Background: Cervical cancer is the leading malignancy among women worldwide, posing clinical and public health challenges. This in silico study aims to identify potential diagnostic biomarkers, therapeutic targets, and prognostic markers associated with cervical cancer through integrative bioinformatics approaches. Methods: A hybrid machine learning approach, combining genetic algorithm (GA) and support vector machine (SVM), was applied to high-dimensional gene expression data from publicly available transcriptomic datasets, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A total of 72 Geo samples (Affymetrix, Illumina) served as the primary dataset after normalization. Results: The GA-SVM model achieved about 99% accuracy and AUC with 10-fold cross validation, clearly separating cervical cancer from normal tissues. Eight genes (CXCL9, CTGF, ZNF704, ZEB2, SASH1, PTN, KPNA2, SLC5A1) were identified as diagnostic biomarkers. Protein-protein interaction (PPI) and functional enrichment analyses revealed 42 therapeutic targets (e.g. CDK1, BRCA1, CCNB1, and AURKB) linked to regulating cell cycle, DNA repair, and mitotic processes. Survival analysis identified six genes (CXCL1, DNMT1, MMP1, MYBL2, PCNA, and RRM2) as key prognostic markers. Additionally, transcription factor analysis identified E2F1 and TP63 as major regulators of the prognostic genes, elucidating the molecular mechanisms underlying cervical cancer progression. Conclusion: The identified gene signatures may serve as candidates for hypothesis generation and provide a computational framework to prioritize biomarkers and therapeutic targets in cervical cancer. However, these findings are based on in silico analyses and require experimental and clinical validation before translation into practice.

From screen to screening: a randomized controlled trial on mHealth vs. traditional training on knowledge, attitudes, practice, self-efficacy, and adherence intention to pap smear

Cervical cancer (CC) is a significant global health concern, particularly in low- and middle-income countries, where screening participation remains low. This study aimed to compare the effectiveness of two educational interventions, a mobile application-based program (mHealth) and face-to-face training (F2F), in improving knowledge, attitudes, self-efficacy, practices, and Adherence Intention to Pap smear testing among women in Saveh, Iran. This study was a parallel-group randomized controlled trial (RCT) conducted between 2023 and 2024. The participants were randomly assigned to three groups: (A) control, (B) F2F training, and (C) mHealth-based education (via the PapTest smartphone app). Data were collected at four time points: baseline (pretest), immediately postintervention, four weeks postintervention, and twelve weeks postintervention. The primary outcomes were measured via two validated instruments: the Cervical Cancer Screening Self-Efficacy Scale and the Self-Efficacy Scale for Pap Smear Screening Participation (SES-PSSP). Statistical analyses, including correlation tests, chi-square tests, ANOVAs, and Kruskal‒Wallis tests were performed via SPSS version 24. The intervention phase results revealed significant differences over time and across groups in terms of knowledge, attitudes, self-efficacy, and practices (P  0.05). Both mHealth-based education and F2F training significantly improved knowledge, attitudes, self-efficacy, and practices related to Pap smear screening. However, the mobile app intervention demonstrated greater and more sustained improvements than did face-to-face training. Given their accessibility, cost-effectiveness, and scalability, mHealth interventions represent a promising strategy for enhancing cervical cancer screening Adherence Intention. Future research should explore longer follow-up periods and hybrid education models to optimize screening participation and Adherence Intention in diverse populations. Iranian Clinical Trial Register (IRCT20231130060228N1). Registration date: 2024-02-19, 1402/11/30. URL: https://irct.behdasht.gov.ir/trial/74165 .