CDC6 may serve as an indicator of lung adenocarcinoma prognosis and progression based on TCGA and GEO data mining and...

Lung adenocarcinoma (LUAD) is one of the most lethal types of cancer worldwide, and accurately predicting patient prognosis is an important challenge. Gene prediction models, which are known for their simplicity and efficiency, have the potential to be used for prognostic predictions. However, the availability of models with true clinical value is limited. The present study integrated tissue sequencing and the clinical information of patients with LUAD from The Cancer Genome Atlas and Gene Expression Omnibus databases using bioinformatics. This comprehensive approach enabled the identification of 252 differentially expressed genes. Subsequently, univariate and multivariate Cox analyses were performed using these genes, and 14 and 3 genes [including cell division cycle 6 (CDC6), hyaluronan mediated motility receptor and STIL centriolar assembly protein] were selected for the construction of two prognostic models. Notably, the 3‑gene prognostic model exhibited a comparable predictive ability to that of the 14‑gene model. Functionally, pathway enrichment analysis revealed that CDC6 played a role in regulating the cell cycle and promoting tumor staging. To further investigate the relevance of CDC6, in vitro experiments involving the downregulation of CDC6 expression were conducted, which resulted in significant inhibition of tumor cell migration, invasion and proliferation. Moreover, in vivo experiments demonstrated that downregulating CDC6 expression significantly reduced the burden and metastasis of in situ lung tumors in mice. These findings suggested that CDC6 may be a critical gene involved in the development and prognosis of LUAD. In summary, the present study successfully constructed a simple yet accurate prognostic prediction model consisting of 3 genes. Additionally, the functional importance of CDC6 as a key gene in the model was identified. These findings lay a crucial foundation for further exploration of prognostic prediction models and a deeper understanding of the functional mechanisms of CDC6. Notably, these results have potential clinical implications for improving personalized treatment and prognosis evaluation for patients with LUAD.