Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier.
Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier.
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DOI：
10.21037/tlcr-23-473
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发表时间：
2023-10-31
影响因子：
4
通讯作者：
中科院分区：
文献类型：
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免疫微环境在癌症从发病到复发中起着关键作用。机器学习（ML）算法可以促进实验室和临床数据的分析，以预测肺癌复发。及时发现和干预对于肺癌复发的长期生存至关重要。我们的研究旨在通过比较四种ML模型的预测准确性来评估肺癌复发的临床和基因组预测因子。共纳入了41例2007年6月至2014年10月在纽约大学Langone医学中心接受手术的早期肺癌患者（复发，n=16;无复发，n=25）。所有患者在切除时均采集肿瘤组织和血沉棕黄层。CIBERSORT算法定量肿瘤浸润免疫细胞（TIIC）。进行蛋白质-蛋白质相互作用（PPI）网络和京都基因和基因组百科全书（KEGG）途径分析，以发掘肿瘤进展的潜在分子驱动因素。数据被分为训练集（75%）和验证集（25%）。使用优化的临床和基因组特征开发了嵌入式线性核支持向量机（SVM）ML模型来预测肿瘤复发。活化的自然杀伤（NK）细胞、M0巨噬细胞和M1巨噬细胞与进展呈正相关。相反，T CD 4+记忆静息细胞呈负相关。在PPI网络中，TNF和IL 6成为突出的枢纽基因。整合临床病理预后因素、肿瘤基因表达（45个基因）和血沉棕黄层基因表达（47个基因）的预测模型产生了不同的受试者工作特征（ROC）-曲线下面积（AUC）：训练集分别为62.7%、65.4%和59.7%，验证集分别为58.3%、83.3%和75.0%。值得注意的是，在线性SVM模型中将基因表达与临床数据合并导致了显着的准确性提高，训练中的AUC为92.0%，验证中为91.7%。利用最大似然算法，肿瘤组织和血沉棕黄层的免疫基因表达数据可以提高肺癌复发预测的精度。
Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse. Our study aimed to evaluate the clinical and genomic prognosticators for lung cancer recurrence by comparing the predictive accuracy of four ML models. A total of 41 early-stage lung cancer patients who underwent surgery between June 2007 and October 2014 at New York University Langone Medical Center were included (with recurrence, n=16; without recurrence, n=25). All patients had tumor tissue and buffy coat collected at the time of resection. The CIBERSORT algorithm quantified tumor-infiltrating immune cells (TIICs). Protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to unearth potential molecular drivers of tumor progression. The data was split into training (75%) and validation sets (25%). Ensemble linear kernel support vector machine (SVM) ML models were developed using optimized clinical and genomic features to predict tumor recurrence. Activated natural killer (NK) cells, M0 macrophages, and M1 macrophages showed a positive correlation with progression. Conversely, T CD4+ memory resting cells were negatively correlated. In the PPI network, TNF and IL6 emerged as prominent hub genes. Prediction models integrating clinicopathological prognostic factors, tumor gene expression (45 genes), and buffy coat gene expression (47 genes) yielded varying receiver operating characteristic (ROC)-area under the curves (AUCs): 62.7%, 65.4%, and 59.7% in the training set, 58.3%, 83.3%, and 75.0% in the validation set, respectively. Notably, merging gene expression with clinical data in a linear SVM model led to a significant accuracy boost, with an AUC of 92.0% in training and 91.7% in validation. Using ML algorithm, immune gene expression data from tumor tissue and buffy coat may enhance the precision of lung cancer recurrence prediction.
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影响因子：
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作者：
Song W;Wang Z;Kan P;Ma Z;Wang Y;Wu Q;Yao X;Zhang B
通讯作者：
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影响因子：
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作者：
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DOI：
10.1016/j.jtho.2017.10.006
发表时间：
2018-01
期刊：
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子：
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作者：
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影响因子：
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通讯作者：
Sanchez-Garcia, Isidro