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

While human papillomavirus (HPV) is the primary driver of cervical cancer (CC), host immune-related genetic variations are thought to influence clinical heterogeneity. The role of combined immune-related single nucleotide polymorphisms (SNPs) in defining patient subgroups remains underexplored in focused, candidate-gene studies.

We genotyped nine functional SNPs across TNF-α (rs361525, rs1800629), *IL-1β* (rs16944), IFN-γ (rs2430561), *IL-1RN* (rs2234663), *IL-10* (rs3024490, rs1800872, rs1800871), and *IL-6* (rs1474348) in a cohort of 130 Tunisian CC patients. Principal component analysis (PCA), multi-dimensional scaling (MDS), K-means clustering, and random forest modeling were used to explore SNP-based patient subgroups and identify genetic profiles associated with survival.

A high-risk genetic profile, comprising seven SNPs, was identified in 20% of patients. PCA indicated that *IL-10* and TNF-α variants accounted for 38.5% of the observed genetic variance. Unsupervised clustering suggested three distinct SNP-based subgroups with differing genetic architectures. The TNF-α -238 A allele was associated with borderline higher odds of adenocarcinoma (OR 4.57, 95% CI: 0.95-21.95, p=0.050), while the *IL-1β* -511 T allele appeared protective (OR 0.45, 95% CI: 0.19-1.07, p=0.049). Random forest analysis identified the IFN-γ rs2430561 variant as the top predictor of advanced FIGO stage. A nine-SNP polygenic risk score (PRS) was significantly associated with reduced overall survival (HR 2.45, log-rank p<.001) and remained an independent prognostic factor in multivariable analysis. Pathway analysis implicated TNF-α signaling, IL-10 anti-inflammatory, and IL-1 cytokine pathways.

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.