Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Immunol. 2024 Jan 12;14:1334886. doi: 10.3389/fimmu.2023.1334886. eCollection 2023.
Natural killer (NK) cells are crucial for tumor prognosis; however, their role in non-small-cell lung cancer (NSCLC) remains unclear. The current detection methods for NSCLC are inefficient and costly. Therefore, radiomics represent a promising alternative.
We analyzed the radiogenomics datasets to extract clinical, radiological, and transcriptome data. The effect of NK cells on the prognosis of NSCLC was assessed. Tumors were delineated using a 3D Slicer, and features were extracted using pyradiomics. A radiomics model was developed and validated using five-fold cross-validation. A nomogram model was constructed using the selected clinical variables and a radiomic score (RS). The CIBERSORTx database and gene set enrichment analysis were used to explore the correlations of NK cell infiltration and molecular mechanisms.
Higher infiltration of NK cells was correlated with better overall survival (OS) ( 0.002). The radiomic model showed an area under the curve of 0.731, with 0.726 post-validation. The RS differed significantly between high and low infiltration of NK cells ( 0.01). The nomogram, using RS and clinical variables, effectively predicted 3-year OS. NK cell infiltration was correlated with the ICOS and BTLA genes ( 0.001) and macrophage M0/M2 levels. The key pathways included TNF-α signaling via NF-κB and Wnt/β-catenin signaling.
Our radiomic model accurately predicted NK cell infiltration in NSCLC. Combined with clinical characteristics, it can predict the prognosis of patients with NSCLC. Bioinformatic analysis revealed the gene expression and pathways underlying NK cell infiltration in NSCLC.
自然杀伤 (NK) 细胞对肿瘤预后至关重要；然而，它们在非小细胞肺癌 (NSCLC) 中的作用仍不清楚。目前 NSCLC 的检测方法效率低下且成本高昂。因此，放射组学代表了一种很有前途的替代方法。
我们分析了放射基因组数据集，以提取临床、放射学和转录组数据。评估了 NK 细胞对 NSCLC 预后的影响。使用 3D Slicer 对肿瘤进行描绘，并使用 pyradiomics 提取特征。使用五重交叉验证开发和验证放射组学模型。使用选定的临床变量和放射组学评分 (RS) 构建列线图模型。使用 CIBERSORTx 数据库和基因集富集分析探索 NK 细胞浸润的相关性和分子机制。
NK 细胞浸润较高与总生存期 (OS) 较好相关 ( 0.002)。放射组学模型的曲线下面积为 0.731，验证后为 0.726。NK 细胞浸润高和低的 RS 差异有统计学意义 ( 0.01)。使用 RS 和临床变量的列线图有效预测了 3 年 OS。NK 细胞浸润与 ICOS 和 BTLA 基因 ( 0.001) 和巨噬细胞 M0/M2 水平相关。关键途径包括 TNF-α 信号通过 NF-κB 和 Wnt/β-catenin 信号。
我们的放射组学模型准确预测了 NSCLC 中 NK 细胞的浸润。结合临床特征，可预测 NSCLC 患者的预后。生物信息学分析揭示了 NSCLC 中 NK 细胞浸润的基因表达和途径。
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