Nucleotide metabolism and cervical cancer: A comprehensive study of molecular subtypes and therapeutic targets

Cervical cancer (CC) is among the most prevalent malignancies in women worldwide. Tumor progression involves profound metabolic reprogramming, in which nucleotide metabolism sustains deoxyribonucleic acid/ribonucleic acid synthesis and proliferation; however, its role in CC remains unclear. Transcriptomic and clinical data of CC patients were obtained from The Cancer Genome Atlas. Nonnegative matrix factorization classified patients into molecular subtypes based on nucleotide metabolism-related genes and compared their survival, immune infiltration, somatic mutations, and drug sensitivity. Mfuzz soft clustering identified subtype-associated expression patterns. Gene set variation analysis scoring, differential expression analysis, and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment of differentially expressed genes were then performed. Prognostic models were constructed using multiple machine learning algorithms, and the model with the highest concordance index (C-index) was selected. Patients were stratified into high- and low-risk groups and assessed using multivariate Cox regression, Kaplan-Meier, and time-dependent receiver operating characteristic analyses. Immune checkpoint inhibitor response was predicted using the tumor immune dysfunction and exclusion algorithm, and molecular docking was performed between core genes and candidate compounds. Nonnegative matrix factorization revealed 5 subtypes with distinct outcomes: cluster 2 showed the worst prognosis, whereas clusters 3 and 4 had favorable survival. TTN, KMT2C, HUWE1, and PIK3CA were frequently mutated across subtypes. B cells, regulatory T cells, CD8+ T cells, and M2 macrophages were enriched in clusters 3 and 4. VX-680, CIL70, and BRD-K01737880 exhibited significantly lower IC50 values in the poor-prognosis group. Seven key genes (SERPINE1, CAV1, SOD2, SLC16A1, XDH, CYP27B1, and NAGS) were identified by Mfuzz clustering. Forty-two differentially expressed genes between the high- and low-score groups were predominantly enriched in xenobiotic response, nutrient sensing, small-molecule catabolism, inflammatory response, tumor necrosis factor signaling, apoptosis, and hypoxia. The generalized boosted regression model achieved the highest C-index, with 1-, 3-, and 5-year area under the curves of 0.62, 0.67, and 0.61, indicating modest discrimination. Tumor immune dysfunction and exclusion analysis indicated reduced immune checkpoint inhibitor responsiveness in the high-risk group. Molecular docking predicted favorable binding between core genes and candidate compounds, providing hypotheses for experimental validation. This study delineates the associations of nucleotide metabolism-related genes with molecular classification and prognosis in CC and identifies potential biomarkers, candidate therapeutic agents, and a prognostic model providing complementary assessment value.