Development of a coagulation‑related gene model for prognostication, immune response and treatment prediction in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most prevalent form of lung cancer worldwide. Due to the lack of clinically useful molecular biomarkers, the diagnosis and prognosis of patients with LUAD remain poor. Patients with LUAD often exhibit abnormalities in the levels of coagulation factors. Therefore, the objective of the present study was to develop a model based on coagulation-related factors in LUAD. Gene expression data and clinical information from 582 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA). A set of 138 coagulation-related genes (CRGs) was retrieved from The Molecular Signatures Database, and their expression levels were examined in TCGA dataset to identify differentially expressed CRGs. Predictive models were constructed using least absolute shrinkage and selection operator-Cox regression. The risk score from the model was used to establish high- and low-risk patient groups. Additionally, Kaplan-Meier analyses were performed to evaluate the differences in overall survival (OS) and progression-free survival between the two groups. The accuracy of the model was verified through receiver operating characteristic and principal component analysis. In addition, the tumor immune dysfunction and exclusion algorithm was used to assess immune escape and immunotherapy responses in relation to the CRGs. A predictive model comprising four genes, namely matrix metalloproteinase (MMP) 1, MMP10, cathepsin V and thrombin, was established to estimate the survival rate of patients with LUAD. The OS rates of patients in the high-risk group were lower compared with those in the low-risk group. Furthermore, a combination of high-risk score and low tumor mutation burden was associated with the poorest survival in patients with LUAD. Patients in different risk groups exhibited different drug sensitivities based on their risk scores. In conclusion, the four-gene based prognostic model served as an independent predictor of survival rates in patients with LUAD and may offer a novel approach for prognosis and treatment.