Authors
Zainab Tabassum, Kabeer Hadi, Akkiraju Sudheer, Sainitin Donakonda
Published in
Computational biology and chemistry. Volume 126. Issue Pt 1. Pages 109447. Sep 29, 2026. Epub Sep 29, 2026.
Abstract
Cervical cancer, the fourth most frequent malignancy in women worldwide, continues to be a major public health concern. Around 90% of high-grade cervical cancer cases are linked to infection with human papillomavirus (HPV). This phenotype, histologically, includes two major subtypes of cervical cancer: squamous cell carcinoma and adenocarcinoma. Drug resistance poses a major hurdle to treatment because it targets the apoptotic pathway. To overcome this, there is an urgent need for alternative routes to a curative strategy. Here, we focused on identifying non-apoptotic cell death pathways as a potential therapeutic option. Bulk RNA sequencing data for cervical cancer samples were obtained from the TCGA-CESC dataset. We combined gene set enrichment analysis, co-expression network analysis, and machine learning, which revealed ferroptosis as a key vulnerability at the bulk RNA level. Single-cell RNA sequencing data further confirmed the expression of ferroptosis-related components, including SCC and ADC. Subsequent analyses focused on identifying key regulators of the ferroptosis pathway. Further dissection of ferroptosis regulators and PSAT1 was prioritized as a prime translational target in adenocarcinoma compared with squamous cell carcinoma. To advance therapeutic translation, we applied AI-driven de novo compound generation to design a candidate drug targeting PSAT1. Additionally, molecular docking and molecular dynamics simulations identified GEN_42 as a putative PSAT1-binding compound. These findings nominate PSAT1 as a therapeutic vulnerability in cervical adenocarcinoma and GEN_42 as a computationally generated lead compound for further biological, cellular, and in vivo analyses to elucidate ferroptosis-induced cell death through modulation of PSAT1 function.
PMID:
42828974
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 8
- Comments 0