肺癌
医学
单变量分析
腺癌
肿瘤科
淋巴结
阶段(地层学)
内科学
癌症
病理
细胞
生存分析
多元分析
癌症研究
生物
古生物学
遗传学
作者
Yuexian Wei,Zubiao Niu,Xinyu Hou,Mengzhe Liu,Yuqi Wang,Yongan Zhou,Chenxi Wang,Qunfeng Ma,Yichao Zhu,Xinyue Gao,Peiyun Li,Shuo Gao,Sibo Zhan,Zi Jian Yang,Yanhong Tai,Q. Shao,Jianlin Ge,Jilei Hua,Lihua Gao,Qiang Sun,Hong Jiang,Hongyan Huang
出处
期刊:PubMed
日期:2023-01-01
卷期号:13 (3): 1091-1102
被引量:2
摘要
Lung cancer is ranked as the leading cause of cancer-related death worldwide, and the development of novel biomarkers is helpful to improve the prognosis of non-small cell lung cancer (NSCLC). Cell-in-cell structures (CICs), a novel functional surrogate of complicated cell behaviors, have shown promise in predicting the prognosis of cancer patients. However, the CIC profiling and its prognostic value remain unclear in NSCLC. In this study, we retrospectively explored the CIC profiling in a cohort of NSCLC tissues by using the "Epithelium-Macrophage-Leukocyte" (EML) method. The distribution of CICs was examined by the Chi-square test, and univariate and multivariate analyses were performed for survival analysis. Four types of CICs were identified in lung cancer tissues, namely, tumor-in-tumor (TiT), tumor-in-macrophage (TiM), lymphocyte-in-tumor (LiT), and macrophage-in-tumor (MiT). Among them, the latter three constituted the heterotypic CICs (heCICs). Overall, CICs were more frequently present in adenocarcinoma than in squamous cell carcinoma (P = 0.009), and LiT was more common in the upper lobe of the lung compared with other lobes (P = 0.020). In univariate analysis, the presence of TiM, heCIC density, TNM stage, T stage, and N stage showed association with the overall survival (OS) of NSCLC patients. Multivariate analysis revealed that heCICs (HR = 2.6, 95% CI 1.25-5.6) and lymph node invasion (HR = 2.6, 95% CI 1.33-5.1) were independent factors associated with the OS of NSCLC. Taken together, we profiled the CIC subtypes in NSCLC for the first time and demonstrated the prognostic value of heCICs, which may serve as a type of novel functional markers along with classical pathological factors in improving prognosis prediction for patients with NSCLC.