Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Thorac Cancer. 2024 Jun;15(16):1312-1319. doi: 10.1111/1759-7714.15315. Epub 2024 Apr 29.
Lung adenocarcinoma (LUAD) is a common pathological type of lung cancer. The presence of lymph node metastasis plays a crucial role in determining the overall treatment approach and long-term prognosis for early LUAD, therefore accurate prediction of lymph node metastasis is essential to guide treatment decisions and ultimately improve patient outcomes.
We performed transcriptome sequencing on T1 LUAD patients with positive or negative lymph node metastases and combined this data with The Cancer Genome Atlas Program cohort to identify potential risk molecules at the tissue level. Subsequently, by detecting the expression of these risk molecules by real-time quantitative PCR in serum samples, we developed a model to predict the risk of lymph node metastasis from a training cohort of 96 patients and a validation cohort of 158 patients.
Through transcriptome sequencing analysis of tissue samples, we identified 11 RNA (miR-412, miR-219, miR-371, FOXC1, ID1, MMP13, COL11A1, PODXL2, CXCL13, SPOCK1 and MECOM) associated with positive lymph node metastases in T1 LUAD. As the expression of FOXC1 and COL11A1 was not detected in serum, we constructed a predictive model that accurately identifies patients with positive lymph node metastases using the remaining nine RNA molecules in the serum of T1 LUAD patients. In the training set, the model achieved an area under the curve (AUC) of 0.89, and in the validation set, the AUC was 0.91.
We have established a new risk prediction model using serum samples from T1 LUAD patients, enabling noninvasive identification of those with positive lymph node metastases.
肺腺癌（LUAD）是一种常见的肺癌病理类型。淋巴结转移的存在对早期 LUAD 的整体治疗方法和长期预后起着至关重要的作用，因此准确预测淋巴结转移对于指导治疗决策并最终改善患者预后至关重要。
我们对有或无淋巴结转移的 T1 LUAD 患者进行了转录组测序，并将这些数据与癌症基因组图谱计划队列相结合，以在组织水平上识别潜在的风险分子。随后，通过实时定量 PCR 检测这些风险分子在血清样本中的表达，我们在一个由 96 例患者组成的训练队列和一个由 158 例患者组成的验证队列中开发了一种预测淋巴结转移风险的模型。
通过组织样本的转录组测序分析，我们确定了 11 种与 T1 LUAD 中阳性淋巴结转移相关的 RNA（miR-412、miR-219、miR-371、FOXC1、ID1、MMP13、COL11A1、PODXL2、CXCL13、SPOCK1 和 MECOM）。由于 FOXC1 和 COL11A1 在血清中未检测到表达，我们构建了一个预测模型，该模型使用 T1 LUAD 患者血清中剩余的 9 种 RNA 分子准确识别出阳性淋巴结转移的患者。在训练集中，该模型的 AUC 为 0.89，在验证集中，AUC 为 0.91。
我们使用 T1 LUAD 患者的血清样本建立了一个新的风险预测模型，能够无创识别出阳性淋巴结转移的患者。
