School of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.
Hunan Engineering Technology Research Center of Optoelectronic Health Detection, Changsha, China.
Ann Med. 2022 Dec;54(1):790-802. doi: 10.1080/07853890.2022.2048064.
The clinical application of lung cancer detection based on breath test is still challenging due to lack of predictive molecular markers in exhaled breath. This study explored potential lung cancer biomarkers and their related pathways using a typical process for metabolomics investigation.
Breath samples from 60 lung cancer patients and 176 healthy people were analyzed by GC-MS. The original data were GC-MS peak intensity removing background signal. Differential metabolites were selected after univariate statistical analysis and multivariate statistical analysis based on OPLS-DA and Spearman rank correlation analysis. A multivariate PLS-DA model was established based on differential metabolites for pattern recognition. Subsequently, pathway enrichment analysis was performed on differential metabolites.
The discriminant capability was assessed by ROC curve of whom the average AUC and average accuracy in 100-fold cross validations were 0.871 and 0.787, respectively. Eight potential biomarkers were involved in a total of 18 metabolic pathways. Among them, 11 metabolic pathways have -value smaller than .1.
Some pathways among them are related to risk factors or therapies of lung cancer. However, more of them are dysregulated pathways of lung cancer reported in studies based on genome or transcriptome data.
We believe that it opens the possibility of using metabolomics methods to analyze data of exhaled breath and promotes involvement of knowledge dataset to cover more volatile metabolites.
Although a series of related research reported diagnostic models with highly sensitive and specific prediction, the clinical application of lung cancer detection based on breath test is still challenging due to disease heterogeneity and lack of predictive molecular markers in exhaled breath. This study may promote the clinical application of this technique which is suitable for large-scale screening thanks to its low-cost and non-invasiveness. As a result, the mortality of lung cancer may be decreased in future.Key messagesIn the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer.We found that it is possible to apply metabolomics methods in analysis of data from breath test, which is meaningful to discover convinced volatile markers with definite pathological and histological significance.
由于呼气中缺乏预测性的分子标志物，基于呼气检测的肺癌临床应用仍然具有挑战性。本研究采用典型的代谢组学研究方法，探索潜在的肺癌生物标志物及其相关通路。
采用 GC-MS 分析 60 例肺癌患者和 176 例健康人的呼气样本。原始数据为 GC-MS 峰强度减去背景信号。基于 OPLS-DA 和 Spearman 秩相关分析的单变量统计分析和多变量统计分析，选择差异代谢物。基于差异代谢物建立用于模式识别的多元 PLS-DA 模型。随后，对差异代谢物进行通路富集分析。
通过 100 倍交叉验证的 ROC 曲线评估判别能力，平均 AUC 和平均准确率分别为 0.871 和 0.787。8 个潜在的生物标志物涉及 18 个代谢途径。其中，有 11 条代谢途径的 P 值小于 0.1。
其中一些途径与肺癌的风险因素或治疗方法有关。然而，更多的是基于基因组或转录组数据研究中报道的肺癌失调途径。
我们相信，这为使用代谢组学方法分析呼气数据开辟了可能性，并促进了知识数据集的参与，以涵盖更多的挥发性代谢物。
尽管一系列相关研究报告了具有高灵敏度和特异性预测的诊断模型，但由于疾病异质性和呼气中缺乏预测性分子标志物，基于呼气检测的肺癌临床应用仍然具有挑战性。由于该技术具有低成本和非侵入性的特点，本研究可能会促进该技术的临床应用，适合大规模筛查。因此，未来肺癌的死亡率可能会降低。
在本研究中，发现了 11 条涉及 8 个潜在生物标志物的通路，这些通路是肺癌的失调通路。我们发现，应用代谢组学方法分析呼气试验数据是有可能的，这对于发现具有明确病理和组织学意义的可靠挥发性标志物具有重要意义。
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