Abstract
Protein phosphorylation is a dynamic and reversible modification involved in almost all cellular processes. Numerous investigations have shown that protein phosphorylation modification enzymes (PPMEs) that regulate protein phosphorylation play an important role in the occurrence and treatment of tumors. However, there is still a lack of effective insights into the value of PPMEs in the classification and treatment of patients with lung adenocarcinoma (LUAD). Here, four topological algorithms identified 15 hub PPMEs from a protein-protein interaction (PPI) network. This PPI network was constructed using 124 PPMEs significantly correlated with 35 cancer hallmark-related pathways. Our study illustrates that these hub PPMEs can affect the survival of patients with LUAD in the form of somatic mutation or expression perturbation. Consistency clustering based on hub PPMEs recognized two phosphorylation modification subtypes (namely cluster1 and cluster2) from LUAD. Compared with patients in cluster1, the survival prognosis of patients in cluster2 is worse. This disparity is probably attributed to the higher tumor mutation burden, the higher male proportion, and the more significant expression disturbance in patients in cluster2. Moreover, phosphorylation modification subtypes also have different characteristics in terms of immune activity, immune infiltration level, immunotherapy response, and drug sensitivity. We constructed a PSig scoring system by using a principal component analysis algorithm to estimate the level of phosphorylation modification in individual LUAD patients. Patients in the high and low PSig score groups demonstrated different characteristics in terms of survival rate, tumor mutation burden, somatic gene mutation rate, immune cell abundance, and sensitivity to immunotherapy and drug treatment. This work reveals that phosphorylation plays a non-negligible role in the tumor microenvironment and immunotherapy of LUAD. Evaluating the phosphorylation status of individual LUAD patients by the PSig score can contribute to enhancing our cognition of the tumor microenvironment and guiding the formulation of more effective personalized treatment strategies.