A plasma miRNA-based classifier for small cell lung cancer diagnosis [miRNA-Seq]. A plasma miRNA-based classifier for small cell lung cancer diagnosis [miRNA-Seq]

Objectives: Small cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based biomarker classifier to assist in SCLC diagnoses. Materials and Methods: We profiled deregulated circulating cell-free miRNA in the plasma of SCLC patients. We tested selected miRs on a training cohort and created a classifier by integrating miRNA expression and patient clinical data. Finally, we applied the classifier on a validation dataset. Results: We determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group. Conclusion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis. Overall design: A total of 38 samples were used for miRNA extraction from plasma. The cohort comprises 10 high-risk smokers that were used as control samples, 10 adenocarcinomas, 10 squamous cell carcinomas, and 8 SCLC patients. The RNA was purified from 200 uL of plasma and NGS for small RNA was performed.

研究目的：小细胞肺癌（Small Cell Lung Cancer，SCLC）预后不佳且诊断难度大。针对高危吸烟者的肺癌筛查可降低肺癌死亡率，但当前筛查工作主要聚焦于非小细胞肺癌（Non-Small Cell Lung Cancer，NSCLC）。小细胞肺癌的诊断与监测仍面临重大挑战。循环微RNA（Circulating MicroRNAs，miRNAs/miRs）的异常表达在多种肿瘤中均有报道，可为肿瘤发生发展的发病机制提供研究思路。本研究对小细胞肺癌患者的循环微RNA进行全面评估，旨在开发基于微RNA的生物标志物分类器，以辅助小细胞肺癌的诊断。
材料与方法：本研究对小细胞肺癌患者血浆中失调的循环游离微RNA进行了表达谱分析。我们在训练队列中对筛选出的微RNA进行验证，并整合微RNA表达数据与患者临床信息构建分类器。最终，将该分类器应用于验证数据集进行性能评估。
研究结果：本研究证实，miR-375-3p可区分小细胞肺癌与非小细胞肺癌患者，以及小细胞肺癌与鳞状细胞癌（Squamous Cell Carcinoma）患者。此外，本研究发现，整合miR-375-3p、miR-320b及miR-144-3p的表达数据，并结合种族与年龄信息，可将转移性小细胞肺癌与对照组人群进行有效区分。
研究结论：本研究提出了一种基于微RNA的小细胞肺癌生物标志物分类器，该分类器整合了临床人口统计学特征与特定临界值，可为小细胞肺癌的诊断提供参考依据。
整体实验设计：本研究共收集38份血浆样本用于微RNA提取。研究队列包含10名高危吸烟者（作为对照样本）、10例腺癌患者、10例鳞状细胞癌患者及8例小细胞肺癌患者。研究人员从200微升血浆中纯化得到RNA，并开展小RNA下一代测序（Next-Generation Sequencing，NGS）。