Exploration of the screening and regulatory mechanisms of biomarkers related to ac4C modification in laryngeal squamous cell carcinoma patients based on single-cell analysis and machine learning

BackgroundN4-acetylcytidine (ac4C) modification plays a critical role in cancer development. Exploring ac4C modification in laryngeal squamous cell carcinoma (LSCC) may help elucidate its pathogenesis. MethodsLSCC-related datasets were obtained from GEO. After preprocessing and annotating single-cell data, malignant cells were identified by CNV scoring and further divided into subpopulations. Malignant epithelial cells (MECs) were identified and subclustered based on ac4C-related gene activity. Prognostic genes were screened using Cox regression and machine-learning approaches, followed by validation in clinical samples using qPCR. The biological and immunological relevance of these genes was further explored through immune infiltration, immunotherapy response, and mutation analyses. ResultsThe 14,465 identified MECs were classified into five subgroups (MEC1-5), among which MEC3 showed the strongest association with the ac4C gene set. Machine-learning analysis of MEC3-derived genes yielded seven prognostic markers, including BARX1, FHL2, NXPH4, PKMYT1, TNFAIP8L1, CRLF1, and CENPP. qPCR confirmed their differential expression between tumor and adjacent normal tissues. These genes were significantly associated with alterations in the tumor immune microenvironment, with high-risk patients showing increased immune infiltration and immune activity. ConclusionSeven ac4C-related prognostic genes were identified that may contribute to LSCC progression by modulating the tumor immune microenvironment, providing potential therapeutic insights.