Metabolic profiles in laryngeal cancer defined two distinct molecular subtypes with divergent prognoses.

Background: Laryngeal cancer (LCA) is the second most common type of head and neck malignancy, characterized by high recurrence rates and poor overall survival (OS). However, progress in curing LCA through molecular-targeted diagnostics and therapies is slow and limited. The occurrence and progression of cancer are closely associated with metabolic reprogramming. Therefore, this study aimed to identify metabolism-related LCA subtypes through a comprehensive analysis of transcriptomic, mutational, methylation, and single-cell RNA sequencing, in hopes of finding factors which influences the prognosis of LCA. Methods: First, to identify metabolism-related LCA subtypes, data from 114 patients with LCA from The Cancer Genome Atlas (TCGA) dataset were collected for an unsupervised clustering analysis, which focused on the expression characteristics of survival-related metabolic genes. Subsequently, prognostic and diagnostic models have been developed using machine learning techniques. Specifically, the prognostic model utilized the least absolute shrinkage and selection operator (LASSO) Cox regression, whereas the diagnostic model was built using the Random Forest (RF) algorithm. Furthermore, to ensure the reproducibility, the results of the subtypes and models were validated using three independent bulk RNA datasets and a scRNA-seq dataset. Results: Two robust subtypes were identified and independently validated. Each subtype has a distinct prognostic outcomes and molecular features. Specifically, the Conclusion: This study offers new perspectives for patient stratification and presents opportunities for therapeutic development in LCA. Furthermore, we explored the potentials of several key tumor markers for both diagnosis and prognosis prediction.