Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer

作者：Na Wang, Cong Yao, Changliang Luo, Shaoping Liu, Long Wu, Weidong Hu, Qian Zhang, Yuan Rong, Chunhui Yuan, Xinghuan Wang · 发表于：Clinical Chemistry and Laboratory Medicine (CCLM) · 年份：2023 · DOI：10.1515/cclm-2023-0291 · 被引用次数：28 · 研究领域：Cancer-related molecular mechanisms research、Ferroptosis and cancer prognosis、RNA modifications and cancer

OBJECTIVES: Non-small cell lung cancer (NSCLC) accounts for more than 80 % of all lung cancers, and its 5-year survival rate can be greatly improved by early diagnosis. However, early diagnosis remains elusive because of the lack of effective biomarkers. In this study, we aimed to develop an effective diagnostic model for NSCLC based on a combination of circulating biomarkers. METHODS: Tissue-deregulated long noncoding RNAs (lncRNAs) in NSCLC were identified in datasets retrieved from the Gene Expression Omnibus (GEO, n=727) and The Cancer Genome Atlas (TCGA, n=1,135) databases, and their differential expression was verified in paired local plasma and exosome samples from NSCLC patients. Subsequently, LASSO regression was used to screen for biomarkers in a large clinical population, and a logistic regression model was used to establish a multi-marker diagnostic model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), clinical impact curves, and integrated discrimination improvement (IDI) were used to evaluate the efficiency of the diagnostic model. RESULTS: Three lncRNAs-PGM5-AS1, SFTA1P, and CTA-384D8.35 were consistently expressed in online tissue datasets, plasma, and exosomes from local patients. LASSO regression identified nine variables (Plasma CTA-384D8.35, Plasma PGM5-AS1, Exosome CTA-384D8.35, Exosome PGM5-AS1, Exosome SFTA1P, Log10CEA, Log10CA125, SCC, and NSE) in clinical samples that were even...