PDF(Pubmed)
Abstract：
UNASSIGNED: Inflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs).
UNASSIGNED: Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result.
UNASSIGNED: 4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718.
UNASSIGNED: SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.
UNASSIGNED: Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result.
UNASSIGNED: 4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718.
UNASSIGNED: SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.
摘要：
■炎症在肺结节（PNs）从良性向恶性的转变中起着重要作用。预测PNs的良性和恶性仍然缺乏有效的方法。尽管Mayo或Brock模型已广泛应用于临床实践，其应用条件有限。本研究旨在通过使用炎症相关生物标志物（IRBM）的机器学习来构建PN的诊断模型。
■首先从GSE135304芯片数据中提取炎症相关基因(IRGs)。然后，在恶性肺结节（MN）和良性肺结节（BN）之间筛选差异表达基因（DEGs）和浸润免疫细胞。对DEGs与浸润免疫细胞进行相关性分析。通过一致性聚类分析鉴定IRG的分子模块。随后，通过加权基因共表达网络分析(WGCNA)过滤IRG模块中的IRBM。利用机器学习方法建立了最优诊断模型。最后,使用外部数据集GSE108375验证该结果.
■4个hubIRGs和3个免疫细胞在MN和BN之间显示出显着差异，C1和C2模块，即PRTN3，ELANE，NFKB1和CTLA4，T细胞CD4初始，NK细胞激活和单核细胞。通过WGCNA分析从黑色模块和黄绿色模块中筛选IRBM。支持向量机（SVM）被确定为最佳模型，曲线下面积（AUC）为0.753。基于5个集线器IRBM建立了一个列线图，即HS.137078，KLC3，C13ORF15，STOM和KCTD13。最后,外部数据集GSE108375验证了此结果，AUC为0.718。
■由5个集线器IRBM建立的SVM模型能够有效地识别MN或BN。炎症和免疫功能障碍的积累对BN向MN的转化至关重要。
■首先从GSE135304芯片数据中提取炎症相关基因(IRGs)。然后，在恶性肺结节（MN）和良性肺结节（BN）之间筛选差异表达基因（DEGs）和浸润免疫细胞。对DEGs与浸润免疫细胞进行相关性分析。通过一致性聚类分析鉴定IRG的分子模块。随后，通过加权基因共表达网络分析(WGCNA)过滤IRG模块中的IRBM。利用机器学习方法建立了最优诊断模型。最后,使用外部数据集GSE108375验证该结果.
■4个hubIRGs和3个免疫细胞在MN和BN之间显示出显着差异，C1和C2模块，即PRTN3，ELANE，NFKB1和CTLA4，T细胞CD4初始，NK细胞激活和单核细胞。通过WGCNA分析从黑色模块和黄绿色模块中筛选IRBM。支持向量机（SVM）被确定为最佳模型，曲线下面积（AUC）为0.753。基于5个集线器IRBM建立了一个列线图，即HS.137078，KLC3，C13ORF15，STOM和KCTD13。最后,外部数据集GSE108375验证了此结果，AUC为0.718。
■由5个集线器IRBM建立的SVM模型能够有效地识别MN或BN。炎症和免疫功能障碍的积累对BN向MN的转化至关重要。
