March 3, 2026Open Access

A machine learning approach to a nine-SNP immunogenetic score for prognostic stratification in cervical cancer

Key Points

Survival outcomes vary by identified SNP-based subgroups in cervical cancer patients, highlighting a potential approach for personalized treatment.
The nine-SNP polygenic risk score correlates with survival, showing promise for improved prognostic stratification.
Observational analysis identifies associations based on a limited single-cohort SNP panel, emphasizing the need for broader validation.
Future external validation is crucial to confirm findings from this limited cohort and explore their implications in wider datasets.

Abstract

This focused, candidate-gene analysis identifies prognostic SNP-based subgroups and a nine-SNP polygenic risk score associated with survival in cervical cancer. While this work provides a foundation for immunogenetic risk stratification, the findings are derived from a limited SNP panel in a single cohort. Future validation in larger, independent cohorts with genome-wide data is required to confirm these preliminary genetic associations and to determine their relationship to broader molecular subgroups.

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Discussion

Authors

Sabrina Zidi

Besma Yacoubi-Loueslati

Boutheina Ben Abdelmoumen Mardassi

Journals

SHILAP Revista de lepidopterología

Frontiers in Immunology

Actions

Institutions

Tunis El Manar University

Institut Pasteur de Tunis

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

A machine learning approach to a nine-SNP immunogenetic score for prognostic stratification in cervical cancer

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study