Screening and establishing metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database

Abstract

Abstract Purpose: Existing biomarkers for diagnosing and predicting the metastasis of lung adenocarcinoma (LUAD) may not meet the demands of clinical practice. Risk prediction models based on multiple markers may provide better prognostic factors for accurate diagnosis and prediction of metastatic LUAD. Methods: An animal model of LUAD metastasis was constructed using CRISPR library technology, and genes related to LUAD metastasis were screened by mRNA sequencing of normal and metastatic tissues. The immune characteristics of different subtypes were analyzed, and the differential genes were subjected to survival and Cox regression analysis to identify the specific genes for metastasis. The biological function of RFLNA was first verified by analyzing cck-8, migration, invasion and apoptosis in LUAD cell lines. Results: We identified 108 differential genes related to metastasis, and classified LUAD samples into two subtypes according to their expression levels. Subsequently, a prediction model composed of 8 metastasis-related genes (RHOBTB2, KIAA1524, CENPW, DEPDC1, RFLNA, COL7A1, MMP12 and HOXB9) was constructed. The AUC values of the logistic regression and neural network were 0.946 and 0.856, respectively. Moreover, the model can effectively classify patients into low- and high-risk groups. We found a better prognosis in the low-risk group both in the training cohort and test cohort, indicating that the prediction model has good diagnosis and predictive power. Up-regulation of RFLNA expression successfully promoted cell proliferation, migration, invasion, and attenuated apoptosis, suggesting that RFLNA plays a role in promoting LUAD development and metastasis. Conclusion: The model has important diagnostic and prognostic value for metastatic LUAD, and may serve as a novel biomarker for LUAD patients in clinic.