通过 CRISPR 文库和 TCGA 数据库建立肺腺癌转移相关诊断和预后模型。

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

机构信息

Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Department of Clinical Laboratory, Key Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

出版信息

J Cancer Res Clin Oncol. 2023 Feb;149(2):885-899. doi: 10.1007/s00432-022-04495-z. Epub 2022 Dec 27.

DOI:10.1007/s00432-022-04495-z

PMID:36574046

Abstract

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.

摘要

目的

现有的用于诊断和预测肺腺癌（LUAD）转移的生物标志物可能无法满足临床实践的需求。具有多个标志物的风险预测模型可为转移性 LUAD 的准确诊断和预测提供更好的预后因素。

方法

使用 CRISPR 技术构建 LUAD 转移的动物模型，通过正常组织和转移组织的 mRNA 测序筛选与 LUAD 转移相关的基因。分析不同亚型的免疫特征，对差异表达基因进行生存和 Cox 回归分析，以确定与转移相关的特定基因，从而构建预测模型。通过分析 LUAD 细胞系中的 CCK-8、迁移、侵袭和凋亡来验证 RFLNA 的生物学功能。

结果

我们鉴定出 108 个与转移相关的差异表达基因，并根据基因表达将 LUAD 样本分为两个亚型。随后，构建了一个由 8 个转移相关基因（RHOBTB2、KIAA1524、CENPW、DEPDC1、RFLNA、COL7A1、MMP12 和 HOXB9）组成的预测模型。逻辑回归和神经网络的曲线下面积分别为 0.946 和 0.856。该模型能够有效地将患者分为低风险和高风险组。在训练和测试队列中，低风险组的预后均更好，表明该预测模型具有良好的诊断和预测能力。上调 RFLNA 成功促进了细胞增殖、迁移、侵袭，并减弱了细胞凋亡，表明 RFLNA 可促进 LUAD 的发展和转移。