Design of Therapeutic Vaccines Based on Antigen Epitopes of α-9 HPV E6 in Sichuan

Jiaoyu He & Xianping Ding et al. · 2026-04-01

Persistent infections with high-risk human papillomavirus (HR-HPV) are the primary cause of vaginal/cervical squamous intraepithelial neoplasia (VAIN/CIN) and cervical cancer (CC), with the prevalence of infection escalating alongside disease severity. Notably, the α-9 HPV species was responsible for 58.20% of all HR-HPV infections in our study. Given the absence of therapies to eradicate established infections, developing effective therapeutic vaccines is a critical priority. The HPV E6 oncoprotein represents an ideal target for such immunotherapies. In this study, we used a comprehensive in silico approach to identify and characterize potential T-lymphocyte epitopes from the E6 proteins of α-9 HR-HPV, which was predominant in our study cohort. Our integrated bioinformatics pipeline encompassed sequence analysis for conservation, followed by rigorous prediction of antigenicity, allergenicity, proteasomal processing, TAP transport efficiency, and immunogenicity. Through this systematic screening, we identified a panel of epitope candidates predicted to have a high potential for eliciting a robust and specific immune response. While these predictions provide a powerful theoretical foundation, it must be stressed that they constitute computational hypotheses requiring mandatory experimental validation. Our findings do not constitute functional epitopes but rather offer a prioritized, evidence-based roadmap for future laboratory investigations. This work significantly accelerates the rational design of HPV therapeutic vaccines by narrowing the focus to the most viable candidates, thereby conserving substantial time and resources in the downstream experimental verification process.

TL;DR

This work significantly accelerates the rational design of HPV therapeutic vaccines by narrowing the focus to the most viable candidates, thereby conserving substantial time and resources in the downstream experimental verification process.

AI-generated by Semantic Scholar

Authors
Jiaoyu He, Chunlan Cheng, Yishan Ding, Ning Li, Tianjun Li, Chengyue Wang, Bo Wei, Xianping Ding