As an important element in regulating the tumor microenvironment (TME), integrin plays a key role in tumor progression. This study aimed to establish prognostic signatures to predict the overall survival and identify the immune landscape of patients with lung adenocarcinoma based on integrins. The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus datasets were used to obtain information on mRNA levels and clinical factors (GSE72094). The least absolute shrinkage and selection operator (LASSO) model was used to create a prediction model that included six integrin genes. The nomogram, risk score, and time-dependent receiver operating characteristic analysis all revealed that the signatures had a good prognostic value. The gene signatures may be linked to carcinogenesis and TME, according to a gene set enrichment analysis. The immunological and stromal scores were computed using the ESTIMATE algorithm, and the data revealed, the low-risk group had a higher score. We discovered that the B lymphocytes, plasma, CD4+ T, dendritic, and mast cells were much higher in the group with low-risk using the CiberSort. Inflammatory processes and several HLA family genes were upregulated in the low-risk group. The low-risk group with a better prognosis is more sensitive to immune checkpoint inhibitor medication, according to immunophenoscore (IPS) research. We found that the patients in the high-risk group were more susceptible to chemotherapy than other group patients, according to the prophetic algorithm. The gene signatures could accurately predict the prognosis, identify the immune status of patients with lung adenocarcinoma, and provide guidance for therapy.