A bioinformatics analysis of KIF transcriptomic subtyping reveals prognostic heterogeneity and differential therapeutic responses in cervical cancer.

Kinesin family genes (KIFs), a group of microtubule-associated motor proteins, have emerged as potential novel biomarkers in cervical cancer (CC). In the present study, a comprehensive bioinformatics analysis of KIF expression profiles was conducted using The Cancer Genome Atlas CC dataset and 23 KIFs with prognostic significance were identified. Non-negative matrix factorization based on their expression patterns revealed three distinct molecular subtypes of CC: C1, C2 and C3. To facilitate subtype prediction, a neural network model trained on KIF expression data was developed and validated in independent Mexican and Korean cohorts. Multi-omics characterization of the subtypes revealed distinct biological features: C1 was associated with downregulated oncogenic signaling; C2 exhibited activation of Hippo-YAP and VEGFR pathways; and C3 was characterized by Wnt signaling activation and an immune-silent phenotype. Predicted immunotherapy responses also varied across subtypes, with C1 patients anticipated to have the most favorable outcomes. Notably, KIF4A and KIF1A were identified as novel biomarker candidates specific to subtypes C2 and C3, respectively, and their expression patterns were validated in a Chinese CC cohort via immunohistochemistry, supporting their potential utility in prognostication and patient stratification. Overall, these findings provide new insights into the molecular heterogeneity of CC and highlight KIF genes as promising biomarkers for guiding personalized therapeutic strategies.