Introduction: Posttranslational modification (PTM) plays an important role in protein regulation and may influence tumor initiation and progression. However, the role of PTM-related programs in lung adenocarcinoma (LUAD) remains incompletely understood. Materials and Methods: Single-cell RNA sequencing (scRNA-seq) data were analyzed to quantify the activity of PTM-related gene set using the AUCell algorithm and to characterize intercellular communication using CellChat. Molecular subtypes and differentially expressed genes (DEGs) were identified using ConsensusClusterPlus and limma packages, respectively, followed by functional enrichment analysis. Univariate Cox regression, LASSO regression with 10-fold cross-validation, and stepwise multivariable Cox regression based on the Akaike information criterion (AIC) were used to construct a prognostic model. Immune infiltration was evaluated using ESTIMATE, CIBERSORT, MCP-counter, and TIMER algorithms, and drug sensitivity was predicted using oncoPredict package. The IMvigor210 cohort was used for exploratory assessment of immunotherapy response. Quantitative real-time reverse transcription PCR (qRT-PCR) was used to measure the expressions of the model genes. CCK-8, wound-healing, and Transwell assays were performed to evaluate the effects of Results: Comparison between two PTM groups revealed that receptor ligands such as MIF-(CD74+CXCR4) and MIF-(CD74+CD44) had higher communication probabilities in the high-PTM-score group. Two molecular subtypes were identified, and a six-gene risk model for LUAD was established based on Conclusion: The PTM-related six-gene model showed potential for prognostic stratification and characterization of immune-related features in LUAD. However, further prospective and experimental validation is required before direct clinical application.