Acylation modification mediated post-translational modifications learning signature reveals ZDHHC18 promotes progression of lung adenocarcinoma by attenuating immunocyte activation.
Although the acylation modification (AM) significantly influences the development of lung adenocarcinoma (LUAD), the specific mechanisms of acylation modification in this context have not been widely researched. This investigation sought to discover novel therapeutic avenues related to acylation modification for the precision treatment of LUAD.
Multiomics consensus clustering was generated for 12 types of acylation modifications, including crotonylation, lactylation, succinylation, benzoylation, acetylation, malonylation, glutarylation, 2-hydroxyisobutyrylation, β-hydroxybutyrylation, palmitoylation, myristoylation, and prenylation. Then, we established a learning signature referred to as AM.score using machine learning from 1,720 LUAD patients, and was validated across 8 cohorts and in-house cohort. We conducted a thorough evaluation involving 9 distinct immunotherapy cohorts to assess the effectiveness of the AM.score in predicting responses to immunotherapy treatments. Finally, the function of ZDHHC18 in LUAD was conducted both in vivo and in vitro.
The developed AM.score demonstrated remarkable predictive capabilities in forecasting outcomes for LUAD, and outperformed currently utilized prognostic indicators specific to LUAD. Furthermore, the predictive power of AM.score was not confined to LUAD alone; it was validated across various categories of malignancies, showcasing its broad applicability in evaluating responses to immunotherapy. From a biological standpoint, the analysis revealed significant correlations between AM.score and various immunological parameters. Notably, higher levels of AM.score were associated with induced immune responses, which in turn were linked to characteristics commonly associated with immunologically hot tumors-those that can elicit a robust immune response. Comprehensive scRNA-seq and spatial transcriptomics analyses demonstrated that elevated AM.score was associated with increased cell malignancy. Then, ZDHHC18 has been recognized as an essential molecular element in the framework, and higher expression levels of ZDHHC18 was associated with poorer clinical outcomes in LUAD. Mechanistically, knockout ZDHHC18 simultaneously enhance strong immune responses that hinder LUAD progression.
AM.score functions as a crucial predictive biomarker, providing valuable insights into both prognosis and the suitability of immunotherapy for individual patients. Furthermore, the focus on targeting ZDHHC18 presents a promising avenue for improving the effectiveness of immunotherapy.
Multiomics consensus clustering was generated for 12 types of acylation modifications, including crotonylation, lactylation, succinylation, benzoylation, acetylation, malonylation, glutarylation, 2-hydroxyisobutyrylation, β-hydroxybutyrylation, palmitoylation, myristoylation, and prenylation. Then, we established a learning signature referred to as AM.score using machine learning from 1,720 LUAD patients, and was validated across 8 cohorts and in-house cohort. We conducted a thorough evaluation involving 9 distinct immunotherapy cohorts to assess the effectiveness of the AM.score in predicting responses to immunotherapy treatments. Finally, the function of ZDHHC18 in LUAD was conducted both in vivo and in vitro.
The developed AM.score demonstrated remarkable predictive capabilities in forecasting outcomes for LUAD, and outperformed currently utilized prognostic indicators specific to LUAD. Furthermore, the predictive power of AM.score was not confined to LUAD alone; it was validated across various categories of malignancies, showcasing its broad applicability in evaluating responses to immunotherapy. From a biological standpoint, the analysis revealed significant correlations between AM.score and various immunological parameters. Notably, higher levels of AM.score were associated with induced immune responses, which in turn were linked to characteristics commonly associated with immunologically hot tumors-those that can elicit a robust immune response. Comprehensive scRNA-seq and spatial transcriptomics analyses demonstrated that elevated AM.score was associated with increased cell malignancy. Then, ZDHHC18 has been recognized as an essential molecular element in the framework, and higher expression levels of ZDHHC18 was associated with poorer clinical outcomes in LUAD. Mechanistically, knockout ZDHHC18 simultaneously enhance strong immune responses that hinder LUAD progression.
AM.score functions as a crucial predictive biomarker, providing valuable insights into both prognosis and the suitability of immunotherapy for individual patients. Furthermore, the focus on targeting ZDHHC18 presents a promising avenue for improving the effectiveness of immunotherapy.