Establishing a metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database

Purpose Existing biomarkers for diagnosing and predicting metastasis of lung adenocarcinoma (LUAD) may not meet the demands of clinical practice. Risk prediction models with multiple markers may provide better prognostic factors for accurate diagnosis and prediction of metastatic LUAD. Methods An an...

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Published in:Journal of cancer research and clinical oncology 2023-02, Vol.149 (2), p.885-899
Main Authors: Shao, Fanggui, Ling, Liqun, Li, Changhong, Huang, Xiaolu, Ye, Yincai, Zhang, Meijuan, Huang, Kate, Pan, Jingye, Chen, Jie, Wang, Yumin
Format: Article
Language:English
Subjects:
Adenocarcinoma
Adenocarcinoma of Lung - genetics
Animal models
Animals
Apoptosis
Cancer
Cancer Research
Cell migration
Cell proliferation
Cholecystokinin
Clustered Regularly Interspaced Short Palindromic Repeats
CRISPR
Diagnosis
Gene expression
Hematology
Internal Medicine
Lung cancer
Lung Neoplasms - genetics
Medical prognosis
Medicine
Medicine & Public Health
Metastases
Metastasis
Neural networks
Oncology
Prediction models
Prognosis
Risk groups
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Summary:Purpose Existing biomarkers for diagnosing and predicting metastasis of lung adenocarcinoma (LUAD) may not meet the demands of clinical practice. Risk prediction models with 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 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 differentially expressed genes were subjected to survival and Cox regression analyses to identify the specific genes involved in metastasis for constructing a prediction model. The biological function of RFLNA was verified by analyzing CCK-8, migration, invasion, and apoptosis in LUAD cell lines. Results We identified 108 differentially expressed genes related to metastasis and classified LUAD samples into two subtypes according to gene expression. Subsequently, a prediction model composed of eight metastasis-related genes ( RHOBTB2 , KIAA1524 , CENPW , DEPDC1 , RFLNA , COL7A1 , MMP12 , and HOXB9 ) was constructed. The areas under the curves of the logistic regression and neural network were 0.946 and 0.856, respectively. The model effectively classified patients into low- and high-risk groups. The low-risk group had a better prognosis in both the training and test cohorts, indicating that the prediction model had good diagnostic and predictive power. Upregulation of RFLNA 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 be useful in clinical applications.
ISSN:0171-5216
1432-1335
DOI:10.1007/s00432-022-04495-z