As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an important role in performing a specific treatment. Here, we developed a framework that combines k-means clustering, t-test, sensitivity analysis, self-organizing map (SOM) neural network, and hierarchical clustering methods to classify LUAD into four subtypes. We determined that 24 differentially expressed genes could be used as therapeutic targets, and five genes (i.e., RTKN2, ADAM6, SPINK1, COL3A1, and COL1A2) could be potential novel markers for LUAD. Multivariate analysis showed that the four subtypes could serve as prognostic subtypes. Representative genes of each subtype were also identified, which could be potentially targetable markers for the different subtypes. The function and pathway enrichment analyses of these representative genes showed that the four subtypes have different pathological mechanisms. Mutations associated with the subtypes, e.g., epidermal growth factor receptor (EGFR) mutations in subtype 4 and tumor protein p53 (TP53) mutations in subtypes 1 and 2, could serve as potential markers for drug development. The four subtypes provide a foundation for subtype-specific therapy of LUAD.