Cong Jingjing, Wang Anna, Wang Yingjia, Li Xinge, Pi Junjian, Liu Kaijing, Zhang Hongjie, Yan Xiaoyan, Li Hongmei
Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
College of Basic Medical Sciences, Shandong First Medical University, Jinan 250117, China.
Zhongguo Fei Ai Za Zhi. 2024 Dec 20;27(12):919-930. doi: 10.3779/j.issn.1009-3419.2024.102.43.
Lung cancer represents the main cause of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) is the most main subtype. More than half of NSCLC patients have already developed distant metastasis (DM) at the time of diagnosis and have a poor prognosis. Therefore, it is necessary to find new biomarkers for predicting NSCLC DM in order to guide subsequent treatment and thus improve the prognosis of NSCLC patients. Numerous studies have shown that microRNAs (miRNAs) are abnormally expressed in lung cancer tissues and play an important role in tumorigenesis and progression. The aim of this study is to identify differentially expressed miRNAs in lung adenocarcinoma tissues with DM group compared to those with non-distant metastasis (NDM) group, and to construct a miRNA signature for predicting DM of lung adenocarcinoma.
We first obtained miRNA and clinical data for patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Subsequently, bioinformatics analysis, which included different R packages, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a range of online analysis tools, was performed to analyze the data.
A total of 12 differentially expressed miRNAs were identified between the DM and NDM groups, and 8 miRNAs (miR-377-5p, miR-381-5p, miR-490-5p, miR-519d-5p, miR-3136-5p, miR-320e, miR-2355-5p, miR-6784-5p) were screened for constructing a miRNA signature. The efficacy of this miRNA signature in predicting DM was good with an area under the curve (AUC) of 0.831. Logistic regression analysis showed that this miRNA signature was an independent risk factor for DM of lung adenocarcinoma. Next, target genes of the eight miRNAs were predicted, and enrichment analysis showed that these target genes were enriched in a variety of pathways, including pathways in cancer, herpes simplex virus I infection, PI3K-Akt pathway, MAPK pathway, Ras pathway, etc. CONCLUSIONS: This miRNA signature has good efficacy in predicting DM of lung adenocarcinoma and has the potential to be a predictor of DM of lung adenocarcinoma.
肺癌是全球癌症相关死亡的主要原因，非小细胞肺癌（NSCLC）是最主要的亚型。超过一半的NSCLC患者在诊断时已发生远处转移（DM），预后较差。因此，有必要寻找预测NSCLC远处转移的新生物标志物，以指导后续治疗，从而改善NSCLC患者的预后。大量研究表明，微小RNA（miRNA）在肺癌组织中异常表达，在肿瘤发生和进展中起重要作用。本研究的目的是鉴定有远处转移（DM）的肺腺癌组织与无远处转移（NDM）的肺腺癌组织中差异表达的miRNA，并构建一个预测肺腺癌远处转移的miRNA特征。
我们首先从癌症基因组图谱（TCGA）数据库中获取肺腺癌患者的miRNA和临床数据。随后，进行了生物信息学分析，包括不同的R包、Kaplan-Meier分析、受试者工作特征（ROC）曲线以及一系列在线分析工具，以分析数据。
在DM组和NDM组之间共鉴定出12个差异表达的miRNA，并筛选出8个miRNA（miR-377-5p、miR-381-5p、miR-490-5p、miR-519d-5p、miR-3136-5p、miR-320e、miR-2355-5p、miR-6784-5p）用于构建miRNA特征。该miRNA特征预测DM的效能良好，曲线下面积（AUC）为0.831。逻辑回归分析表明，该miRNA特征是肺腺癌远处转移的独立危险因素。接下来，预测了这8个miRNA的靶基因，富集分析表明这些靶基因富集在多种途径中，包括癌症途径、单纯疱疹病毒I感染途径、PI3K-Akt途径、MAPK途径、Ras途径等。结论：该miRNA特征在预测肺腺癌远处转移方面具有良好的效能，有潜力成为肺腺癌远处转移的预测指标。
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