Integrative profiling of diverse post-translational modifications for prognostic stratification and personalized therapy in papillary thyroid cancer
TL;DR
The PTMI proposed in this study holds strong potential as a prognostic biomarker and therapeutic predictor, offering valuable insights for personalized management of PTC patients.
Abstract
Post-translational modifications (PTMs) are pivotal in tumor biology, yet their role in papillary thyroid cancer (PTC) remains unclear. We integrated bulk and single-cell transcriptomes with clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to analyze 17 PTMs and construct a prognostic model using 12 machine learning algorithms for predicting the thyroid cancer-free interval (TCFi). Enrichment analysis, single-cell analysis, and immune-related analysis were performed to elucidate the biological role of PTMs. Therapeutic responses of PTC patients were predicted based on the model. We validated the expression of model genes and identified the key signature associated with the malignant phenotypes of PTC. We filtered out 12 genes to construct a post-translational modification index (PTMI) and identified three molecular clusters of PTC. Shapley additive explanations (SHAP) and nomogram models confirmed the predictive efficacy of PTMI. Integrated analyses revealed significant associations between PTMI and immune features. High-PTMI patients showed sensitivity to FDA-approved drugs and chemotherapeutics but resistance to radioactive iodine therapy. Notably, we identified TYMS as a key functional PTMI signature. The PTMI proposed in this study holds strong potential as a prognostic biomarker and therapeutic predictor, offering valuable insights for personalized management of PTC patients.