This study aimed to characterize the key survival-specific genes for lung adenocarcinoma (LUAD) using machine-based learning approaches. Gene expression profiles were download from gene expression omnibus to analyze differentially expressed genes (DEGs) in LUAD tissues versus healthy lung tissue and to construct protein-protein interaction (PPI) networks. Using high-dimensional datasets of cancer specimens from clinical patients in the cancer genome atlas, gene set enrichment analysis was employed to assess the independent effect of meiotic nuclear divisions 1 (MND1) expression on survival status, and univariate and multivariate Cox regression analyses were applied to determine the associations of clinic-pathologic characteristics and MND1 expression with overall survival (OS). A set of 495 DEGs (145 upregulated and 350 downregulated) was detected, including 63 hub genes with ≥ 10 nodes in the PPI network. Among them, MND1 was participated in several important pathways by connecting with other genes via 17 nodes in lung cancer, and more frequently expressed in LUAD patients with advancing stage (OR = 1.68 for stage III vs. stage I). Univariate and multivariate Cox analyses demonstrated that the expression level of MND1 was significantly and negatively correlated with OS. Therefore, MND1 is a promising diagnostic and therapeutic target for LUAD.