ArticleJournal of cancer research and clinical oncology2023
Establishing a metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Biobank of genetically defined murine prostate cancer tumoroids uncovers oncogenic pathways and drug vulnerabilities driven by PTEN-loss.Cell reports methods · 2026Article
- DEP domain‑containing 1 is a prognostic biomarker associated with the cell cycle in gliomas.Oncology letters · 2025Article
- A Novel KIF4A-related Model for Predicting Immunotherapy Response and Prognosis in Kidney Renal Clear Cell Carcinoma.Combinatorial chemistry & high throughput screening · 2025Article
- Roles of DEPDC1 in various types of cancer (Review).Oncology letters · 2024Review
- Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters.OncoTargets and therapy · 2024Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
purposeExisting 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.
methodsAn 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.
resultsWe 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.
conclusionThe model has important diagnostic and prognostic value for metastatic LUAD and may be useful in clinical applications.
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