Department of Tumor Blood, Chongqing University Jiangjin Hospital, Chongqing, China.
Thoracic Surgery, The Third Medical Center of PLA General Hospital, Beijing, China.
Thorac Cancer. 2023 Jun;14(16):1492-1499. doi: 10.1111/1759-7714.14892. Epub 2023 Apr 25.
The lymph node metastasis stage of lung cancer is an important decisive factor in the need for postoperative adjuvant treatment and the difference between stage IIIa and stage IIIB that is the necessary information to distinguish whether surgery can be performed or not. The specificity of the clinical diagnosis of lung cancer with lymph node metastasis cannot meet the requirements of preoperative evaluation of surgical indications and prediction of surgical removal range in lung cancer.
This was an early experimental laboratory trial. The model identification data included the RNA sequence data of 10 patients from our clinical data and 188 patients with lung cancer from The Cancer Genome Atlas dataset. The model development and validation data consisted of RNA sequence data for 537 cases from the Gene Expression Omnibus dataset. We explore the predictive value of the model on two independent clinical data.
A higher specificity of diagnostic model for patients with lung cancer with lymph node metastases consisted of DDX49, EGFR, and tumor stage (T-stage), which were the independent predictive factors. The area under the curve value, specificity, and sensitivity for predicting lymph node metastases were 0.835, 70.4%, and 78.9% at RNA expression level in the training group, and 0.681, 73.2%, and 75.7% at RNA expression level in the validation group as shown as in result part. To verify the predictive performance of the combined model for lymph node metastases, we downloaded the GSE30219 data set (n = 291) and the GSE31210 data set (n = 246) from the Gene Expression Omnibus (GEO) database as the training group and validation group, respectively. In addition, the model had a higher specificity for predicting lymph node metastases in independent tissue samples.
Determination of DDX49, EGFR, and T-stage could form a novel prediction model to improve the diagnostic efficacy of lymph node metastasis in clinical application.
肺癌的淋巴结转移分期是决定术后辅助治疗和 IIIA 期与 IIIB 期差异的重要决定因素，这些差异是区分是否需要手术的必要信息。淋巴结转移的肺癌临床诊断的特异性不能满足术前评估手术指征和预测肺癌手术切除范围的要求。
这是一项早期的实验性实验室试验。模型鉴定数据包括来自我们临床数据的 10 名患者和来自癌症基因组图谱数据集的 188 名肺癌患者的 RNA 序列数据。模型开发和验证数据包括来自基因表达综合数据集的 537 例病例的 RNA 序列数据。我们在两个独立的临床数据上探索了模型的预测价值。
一个由 DDX49、EGFR 和肿瘤分期（T 分期）组成的、对肺癌伴淋巴结转移患者具有更高特异性的诊断模型是独立的预测因素。在训练组中，RNA 表达水平的曲线下面积、特异性和敏感性分别为 0.835、70.4%和 78.9%，在验证组中分别为 0.681、73.2%和 75.7%。为了验证该联合模型对淋巴结转移的预测性能，我们从基因表达综合数据库（GEO）下载了 GSE30219 数据集（n=291）和 GSE31210 数据集（n=246）作为训练组和验证组。此外，该模型在独立的组织样本中对预测淋巴结转移具有更高的特异性。
DDX49、EGFR 和 T 分期的确定可以形成一种新的预测模型，以提高临床应用中淋巴结转移的诊断效果。
