Table_2_Identification of Transcriptional Pattern Related to Immune Cell Infiltration With Gene Co-Expression Network in Papillary Thyroid Cancer.xlsx

BackgroundA growing body of evidence suggests that immune cell infiltration in cancer is closely related to clinical outcomes. However, there is still a lack of research on papillary thyroid cancer (PTC).
MethodsBased on single-sample gene set enrichment analysis (SSGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) tool, the infiltration level of immune cell and key modules and genes associated with the level of immune cell infiltration were identified using PTC gene expression data from The Cancer Genome Atlas (TCGA) database. In addition, the co-expression network and protein-protein interactions network analysis were used to identify the hub genes. Moreover, the immunological and clinical characteristics of these hub genes were verified in TCGA and GSE35570 datasets and quantitative real-time polymerase chain reaction (PCR). Finally, receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of hub genes.
ResultsActivated B cell, activated dendritic cell, CD56bright natural killer cell, CD56dim natural killer cell, Eosinophil, Gamma delta T cell, Immature dendritic cell, Macrophage, Mast cell, Monocyte, Natural killer cell, Neutrophil and Type 17 T helper cell were significantly changed between PTC and adjacent normal groups. WGCNA results showed that the black model had the highest correlation with the infiltration level of activated dendritic cells. We found 14 hub genes whose expression correlated to the infiltration level of activated dendritic cells in both TCGA and GSE35570 datasets. After validation in the TCGA dataset, the expression level of only 5 genes (C1QA, HCK, HLA-DRA, ITGB2 and TYROBP) in 14 hub genes were differentially expressed between PTC and adjacent normal groups. Meanwhile, the expression levels of these 5 hub genes were successfully validated in GSE35570 dataset. Quantitative real-time PCR results showed the expression of these 4 hub genes (except C1QA) was consistent with the results in TCGA and GSE35570 dataset. Finally, these 4 hub genes had diagnostic value to distinguish PTC and adjacent normal controls.
ConclusionsHCK, HLA-DRA, ITGB2 and TYROBP may be key diagnostic biomarkers and immunotherapy targets in PTC.

研究背景 越来越多的研究证据表明，肿瘤中的免疫细胞浸润与临床结局密切相关。然而，目前针对甲状腺乳头状癌（papillary thyroid cancer, PTC）的相关研究仍较为匮乏。
研究方法 本研究基于单样本基因集富集分析（single-sample gene set enrichment analysis, SSGSEA）算法与加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）工具，利用癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中的甲状腺乳头状癌基因表达数据，分析免疫细胞浸润水平，并筛选与免疫细胞浸润水平相关的关键模块及基因。此外，本研究通过共表达网络与蛋白质相互作用网络分析筛选核心基因。同时，本研究在TCGA与GSE35570数据集以及实时定量聚合酶链反应（quantitative real-time polymerase chain reaction, PCR）实验中验证了这些核心基因的免疫学与临床特征。最后，本研究通过受试者工作特征（receiver operating characteristic, ROC）曲线分析评估核心基因的诊断价值。
研究结果 甲状腺乳头状癌组与癌旁正常组间，活化B细胞、活化树突状细胞、CD56bright自然杀伤细胞、CD56dim自然杀伤细胞、嗜酸性粒细胞、γδ T细胞、未成熟树突状细胞、巨噬细胞、肥大细胞、单核细胞、自然杀伤细胞、中性粒细胞及17型辅助T细胞的浸润水平均存在显著差异。加权基因共表达网络分析结果显示，黑色模块与活化树突状细胞的浸润水平相关性最高。本研究在TCGA与GSE35570数据集中共筛选得到14个与活化树突状细胞浸润水平相关的核心基因。在TCGA数据集验证后，14个核心基因中仅5个基因（C1QA、HCK、HLA-DRA、ITGB2及TYROBP）在甲状腺乳头状癌组与癌旁正常组间存在表达差异。同时，这5个核心基因的表达水平在GSE35570数据集中得到了成功验证。实时定量PCR结果显示，除C1QA外的其余4个核心基因的表达趋势与TCGA及GSE35570数据集的分析结果一致。最后，这4个核心基因具备区分甲状腺乳头状癌与癌旁正常对照的诊断价值。
研究结论 HCK、HLA-DRA、ITGB2及TYROBP或可作为甲状腺乳头状癌的关键诊断生物标志物与免疫治疗靶点。