深度学习在预测甲状腺乳头状癌侧颈淋巴结转移中的应用与思考
作者简介:
邵胜利，男，博士，住院医师，主要从事头颈肿瘤外科工作，ORCID: 0009-0006-8706-1915
刘善廷: 主任医师，医学博士，硕导，河南省肿瘤医院头颈甲状腺外科副主任。河南省临床肿瘤学会甲状腺肿瘤专委会主任委员，河南省抗癌协会头颈专业委员会副主任委员，河南省医学会甲状腺专业委员会副主任委员，河南省医师协会甲状腺专业委员会副主任委员，河南省口腔学会常务理事，《中华医学杂志》通讯编委，《肿瘤防治研究》杂志编委。擅长甲状腺肿瘤及头颈部其他恶性肿瘤的综合治疗。近年来发表SCI及中华系列杂志论文30余篇，主持省厅级科研课题4项，获得河南省卫生科技一等奖1项，二等奖3项
。
通讯作者:
Application and Thinking of Deep Learning in Predicting Lateral Cervical Lymph Node Metastasis of Papillary Thyroid Cancer
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摘要:
甲状腺乳头状癌（PTC）早期即可发生侧颈淋巴结转移。侧颈淋巴结转移是影响PTC患者预后的重要因素，是行颈淋巴结清扫术的绝对适应证，也是国内大多数医疗中心选择腔镜手术的相对禁忌证。因此，术前识别侧颈淋巴结转移对手术决策及预后评估等具有重要意义。目前超声、CT、细胞学及患者临床特征均可为侧颈淋巴结转移提供部分信息，但其准确性并不能很好地满足临床需要。深度学习是医学图像识别或特征提取的主要手段，近几年基于深度学习的超声、CT、细胞学、常规临床参数或以上数据联合的图像或多模态模型被陆续报道并有望实现常规应用。未来，随着大型数据集的建立与共享、自动化标注的实现、算法优化与改进及数据安全问题的解决，深度学习有望准确预测PTC侧颈淋巴结转移，融合于电子病例系统实现自动化的实时分析并辅助临床决策。
Abstract:Papillary thyroid carcinoma (PTC) can exhibit lateral neck lymph node metastasis at an early stage. Lateral neck lymph node metastasis is a crucial factor affecting the prognosis of PTC and is an absolute indication for neck lymph node dissection surgery. Additionally, it is a relative contraindication of endoscopic surgery for most medical centers. Therefore, the preoperative identification of lateral neck lymph node metastasis is vital for surgical decision-making and prognosis assessment. Ultrasound, CT, cytology, and clinical features can provide some information on lateral neck lymph node metastasis, but their accuracy does not fully meet clinical needs. Deep learning is a primary method for medical image recognition or feature extraction. In recent years, deep learning-based ultrasound, CT, cytology, conventional clinical parameters, or multimodal models combining these data have been developed and are expected to achieve routine clinical application. With the establishment and sharing of large datasets, automated annotation, algorithm optimization, and resolution of data security issues, deep learning is expected to accurately predict lateral neck lymph node metastasis in PTC. Furthermore, it can be integrated into electronic medical record systems for automated real-time analysis and assist clinical decision-making.
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Key words:
- Deep learning /
- Papillary thyroid carcinoma /
- Lymph node metastasis /
- Multimodal data
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0 引言
胶质母细胞瘤（Glioblastoma, GBM）被认为起源于神经胶质干细胞或祖细胞[1]，是级别较高的恶性胶质瘤（Ⅳ级），也是中枢神经系统（Central nervous system, CNS）最常见的原发性恶性肿瘤，占所有CNS肿瘤的14.5%，占CNS恶性肿瘤的48.6%。GBM患者的中位总生存期（Overall survival, OS）仅为15个月[2-3]。GBM是高度血管化的肿瘤，其生长依赖于新生血管的形成。血管新生是血管内皮细胞（Endothelial cells, ECs）在特定信号刺激下增殖、迁移和分化的复杂过程[4]。内皮细胞除了参与血管生成，进一步为肿瘤提供氧气和营养物质外，还参与肿瘤血管内侵袭，使肿瘤细胞转移到血管腔内，促进肿瘤的侵袭和转移[5]。因此，内皮细胞在GBM的发生、发展中发挥着重要的作用。相较于肿瘤细胞标志物，内皮细胞相关标志物或可以更全面地评估肿瘤进展、反映肿瘤的预后。
寻找可靠的生物标志物来预测GBM患者的预后，并开发新的分子靶向治疗策略，对于目前GBM的治疗至关重要。然而，GBM目前尚缺乏有效的预后标志物。本研究旨在通过多维度生物信息学分析方法，构建GBM内皮细胞风险评分，探讨并验证内皮细胞在GBM中的预后价值。
1 资料与方法
1.1 GBM转录组数据获取与标准化
GBM转录组数据以及相关的临床资料获取于癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库（TCGA-GBM，包含174名GBM患者），其中转录组数据被统一标准化为每百万条reads的转录本（Transcripts per million reads, TPM）格式，并进行log2转化。中国脑胶质瘤基因组图谱（Chinese Glioma Genome Atlas, CGGA）中GBM相关数据（CGGA-GBM，包含388名GBM患者）获取于GlioVis数据库（http://gliovis.bioinfo.cnio.es）。
1.2 GBM单细胞测序分析
GBM单细胞数据（GSE103224）获取于开源单细胞数据库TSCH2数据库（http://tisch.comp-genomics.org/），相关细胞分群结果直接获取于该数据库。内皮细胞相关基因标志物按照如下标准筛选：在满足P.adj<0.05条件下，按照log2FC的绝对值排序在上调（Up-regulated）和下调（Down-regulated）基因中各取前100进行后续分析。
1.3 单因素Cox回归分析
单因素Cox回归分析获取TCGA-GBM预后相关基因，相关筛选标准：风险比（Hazard ratio, HR）>1且P.adj<0.05或HR<1且P.adj<0.05。
1.4 内皮细胞预后相关标志物获取
使用韦恩图对上述四种标志物取交集，获取的内皮细胞预后相关标志物满足条件：HR>1且为上调基因或HR<1且为下调基因。使用森林图展示相关标志物及其预后相关信息。
1.5 LASSO回归分析
基于TCGA-GBM转录组数据，使用LASSO回归分析进一步筛选内皮细胞预后相关标志物，并构建内皮细胞相关预后风险评分，选取交叉验证误差均值最小对应的lambda值（lambdamin），并获取风险评分中各基因的系数，构成风险评分（Risk score）公式：
$$ Risk\;score = \sum\nolimits_{i = 1}^n \beta i*Expi $$ 其中Exp为基因的表达量，β为基因所对应的系数。
1.6 qPCR实验验证预后相关标志物表达差异
选取在解放军总医院第一医学中心收集的GBM样本组织及瘤周正常脑组织样本各3例进行定量PCR实验，验证用于构建风险评分的4个目的基因（DUSP6、STC1、VWA1和TM4SF1）在GBM组织及瘤周正常脑组织中的表达差异，qPCR引物见表1。收集患者临床病理信息。所有患者及家属均签署知情同意书，本研究符合2013年修订的《赫尔辛基宣言》要求。根据公式整理和计算qPCR实验数据，采用两独立样本t检验进行统计{stats[4.2.1]包以及car[3.1-0]包}，用ggplot2[3.3.6]包对数据进行可视化。
表 1 qPCR实验所用引物Table 1 Primers used in qPCR experimentsGene name Gene ID Forward primer Reverse primer DUSP6 1 848 5′-GAACTGTGGTGTCTTGGTACATT-3′ 5′-GTTCATCGACAGATTGAGCTTCT-3′ STC1 6781 5′-CACGAGCTGACTTCAACAGGA-3′ 5′-GGATGTGCGTTTGATGTGGG-3′ VWA1 64856 5′-GCAGACTCGGGCTACTATGTG-3′ 5′-CACGTTGGACTCAGGCACTA-3′ TM4SF1 4071 5′-TGTGGCAAACGATGTGCGA-3′ 5′-TGACACAGTAGCCAGATCCTG-3′ 1.7 生存分析
基于内皮细胞相关预后风险评分，获取每例GBM患者的风险评分数值，根据该数值的中位数，将患者分为高风险和低风险组。使用Kaplan-Meier法构建生存曲线以鉴定该预后风险评分的预后效能。上述分析及数据可视化基于软件R（4.2.1），相关R包见表2。
表 2 分析及数据可视化所用R包Table 2 R packages for analysis and data visualizationAnalysis item R package used LASSO regression
analysisglmnet (4.1.7) Single factor Cox
regression analysissurvival (3.3.1) and rms (6.3-0) Survival analysis survival (3.3.1), survminer and
ggplot2 (3.3.6)Venn diagram ggplot2 (3.3.6), VennDiagram (1.7.3) Forest plot ggplot2 (3.3.6) Co-expression heat map ggplot2 (3.3.6) 2 结果
2.1 GBM预后相关基因
单因素Cox回归分析共筛选获得2 115个GBM预后相关基因，其中1 494个满足HR>1且P.adj<0.05，621个满足HR<1且P.adj<0.05。
2.2 GBM单细胞测序分析
基于TSCH2数据库对GBM单细胞数据（GSE103224）进行解析，降维后共获得7群细胞，见图1A，分别为AC样肿瘤细胞（AC-like malignant）、内皮细胞（Endothelial）、单核/巨噬细胞（Mono/Macro）、NB样肿瘤细胞（NB-like malignant）、神经元（Neuron）、OC样肿瘤细胞（OC-like malignant）以及OPC样肿瘤细胞（OPC-like malignant）。相关细胞群标志物如图1B所示。根据内皮细胞差异基因及相应筛选标准，共获取200个内皮细胞相关基因标志物，其中100个为上调基因（CLND5、ITM2A、IGFBP7、IFI27以及ESAM等），100个为下调基因（CRYAB、CRYGS、CDK4、S100A6以及APOE等）。
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图 1 GBM单细胞测序分析及内皮细胞预后相关标志物分析Figure 1 GBM single-cell sequencing analysis and endothelial cell prognostic marker analysisA: seven groups of cells obtained by analyzing GBM single-cell data (GSE103224); B: expression of seven cell-population marker genes; C: six endothelial cell prognostic markers obtained after Venn diagram analysis; D: forest plot showing the prognostic information related to the prognostic markers of endothelial cells; E: quantitative analysis of the violin plot showing that the six markers were significantly highly expressed in endothelial cells. GBM: glioblastoma.2.3 内皮细胞预后相关标志物
韦恩图分析共获取6个满足条件的交集基因，即内皮细胞预后相关标志物，分别为PLXND1、DUSP6、STC1、ESM1、TM4SF1以及VWA1基因，见图1C。森林图显示了内皮细胞预后相关标志物相关（PLXND1、DUSP6、STC1、ESM1、TM4SF以及VWA1）的预后信息，见图1D。定量分析小提琴图显示上述标志物均在内皮细胞中显著高表达，见图1E。
2.4 内皮细胞相关预后风险评分
LASSO回归分析共筛选出4个基因（DUSP6、STC1、VWA1和TM4SF1）用于风险评分的构建，变量系数谱见图2A。风险评分=0.171*DUSP6+0.144*STC1+0.041*VWA1−0.004*TM4SF1。变量轨迹图见图2B。TCGA-GBM发现集生存分析显示，风险评分是GBM患者预后不佳的影响因素（HR=1.56，95%CI：1.10~2.21，P=0.007），见图2C，CGGA-GBM验证集生存分析，风险评分亦是GBM患者预后不佳的影响因素（HR=1.40，95%CI：1.12~1.75，P=0.003）。qPCR实验结果显示DUSP6、STC1、VWA1和TM4SF1在GBM组织与瘤周正常脑组织中的表达均显著上调（P<0.001），见图3。
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图 2 内皮细胞相关预后风险评分Figure 2 Endothelial cell-related prognostic risk scoreA: variable coefficient spectrum of four genes screened by LASSO regression analysis; B: six endothelial cell prognostic markers corresponding with the variable trajectory diagram; C: TCGA-GBM discovery-set survival analysis; D: CGGA-GBM validation-set survival analysis.![]()
图 3 目的基因在GBM组织与瘤周正常脑组织中的表达差异Figure 3 Difference in the expression of the target gene between GBM tissues and normal brain tissues3 讨论
GBM是成人中脑部最常见、侵袭性最强的原发性恶性肿瘤。尽管GBM患者在接受最大限度的手术切除后可以继续联合放化疗，但预后仍很差，其中位生存期仅约15个月，5年生存率<10%[6-8]。因此我们亟需探索更多的治疗方法，以提高疗效。目前尚缺乏十分可靠的生物标志物来预测当前或新疗法的预后。Sareen等[9]进行的一项系统评价和Meta分析结果显示：O6-甲基鸟嘌呤-DNA甲基转移酶（O6 -methylguanine-DNA methyltransferase, MGMT）启动子甲基化和异柠檬酸脱氢酶1（Isocitrate dehydrogenase 1, IDH1）突变与GBM患者较长的总生存期显著相关，合并风险比分别为1.66（95%CI：1.32~2.09；P<
0.0001 ）和2.37（95%CI：1.81~3.12；P<0.00001 ）。另有研究显示MGMT启动子甲基化水平在预测替莫唑胺（Temozolomide, TMZ）治疗反应中具有重大的临床意义[10]。1p/19q共缺失和第10染色体丢失也是TMZ反应的阳性预测指标。另一方面，错配修复系统（Mismatch repair, MMR）缺陷导致的高突变表型可预测GBM对TMZ具有耐药性。编码H3.3组蛋白的基因H3F3A的突变是儿童患者预后不良的重要标志物。MYB、MN1和MAPK通路的改变则是积极的预后因素[11]。新的研究通过“液体活检”技术在血液或脑脊液中识别GBM特异性的外泌体，从而实现对GBM患者预后的微创预测[12]。另外，细胞因子信号转导抑制因子蛋白通过JAK/STAT通路和NF-κB信号通路调控GBM的生物合成，从而发挥抑癌作用。SOCS蛋白表达的上调与GBM更好的预后相关[13]。理论上，有许多长链非编码RNA（Long non-coding RNA, lncRNA），如HOTAIR、H19和NEAT1可作为GBM的诊断和预后指标。遗憾的是，迄今为止尚未有lncRNAs成功应用于临床[14-15]。Jarmuzek等[16]对炎性反应和免疫标志物作为GBM患者预后因素的最新研究进行了全面的综述和荟萃分析，结果发现中性粒细胞/淋巴细胞比值（NLR）或血小板/淋巴细胞比值（PLR）较高的患者预后更差。同样，细胞死亡过程中释放入血的游离DNA（Cell-free DNA, cfDNA）（HR=2.35，95%CI：1.27~4.36，P<0.01）较高的患者预后更差[16]。在GBM中，一些miRNA的水平降低。研究表明，这些miRNA的过度表达可增强细胞凋亡和抑制肿瘤进展，提示预后较好[17]。国内有学者研究发现固有免疫分子CD58在高级别胶质瘤中的表达高于低级别胶质瘤和非瘤脑组织，其表达差异与胶质瘤生存期相关，故可作为判定胶质瘤的恶性程度及预后的一项指标[18]。但上述生物标志物在GBM预后的临床预测中仍存在一定的局限性。目前还需要探索更有效、更实用的GBM预后标志物。鉴于血管生成在肿瘤生长和转移中的关键作用，以及GBM的高度血管化特性[19]，探究GBM血管内皮细胞相关预后标志物用于构建预后体系已成为新的研究方向。同时，抗血管生成药物也已被广泛探索作为GBM治疗的新选择[5]。GBM细胞分泌大量的促血管生成因子，营造出强烈的促血管新生的肿瘤微环境（Tumor microenvironment, TME）。GBM内皮细胞是血肿瘤屏障（Blood-tumour barrier, BTB）的重要结构基础，使化疗药物难以通过BTB到达病灶[20]。这成为GBM药物治疗的难题。通过应用单细胞测序对BTB进行更深入的了解，并通过生物信息分析方法开发新的内皮细胞生物标志物，从而实现靶向调节GBM内皮细胞的生物学行为，调控GBM的血管新生及BTB的通透性，抑制肿瘤的生长和侵袭，将为GBM提供新的治疗策略[21]。
TME中的免疫细胞在肿瘤生长过程中发挥着重要作用。近年来的多项研究表明，TME中的B细胞与改善临床预后相关。浆细胞是终末分化的B细胞，可产生高度特异性的抗体。关于临床结局与抗体产生之间关系以及Stephanie等的研究数据显示，在不分泌抗体的小鼠中，双重免疫检查点抑制剂治疗对乳腺癌无效，这强烈表明抗体的产生在抗肿瘤免疫反应中具有重要意义[22]。越来越多的证据表明，自然杀伤细胞（Natural killer, NK）细胞的频率、浸润和功能提高了GBM患者的生存率。但GBM细胞可表达独特的MHC-Ⅰ类分子与NK细胞表面的受体结合，从而抑制其功能[23]，且NK细胞受体NCR2高表达可能与GBM预后较差有关[24]。T细胞在机体对恶性肿瘤的适应性免疫应答中发挥着重要作用。调节性T细胞（Regulatory T cells, Tregs）是一类独特的T细胞群，通过免疫抑制措施调节机体的整体免疫稳态。其中表达叉头状转录因子3（Forkhead Box P3, FOXP3）转录因子的Tregs尤为重要。该转录因子可下调NFAT和NFκB信号通路，从而下调IL2等重要效应细胞因子的表达。GBM预后较差与Tregs/T效应细胞比例较高有关[23]。
综上所述，内皮细胞基因相关标志物可在一定程度上反映GBM的恶性程度及预后。本研究通过qPCR实验进一步验证了GBM中内皮细胞基因相关标志物（DUSP6、STC1、VWA1 和TM4SF1）在GBM组织中的表达均较瘤周正常脑组织显著上调，并通过采用多基因构建风险评分，且验证集生存分析显示风险评分是GBM患者预后不佳的影响因素，进一步完善了GBM预后分析体系。未来需进一步进行大规模临床分析研究。
Competing interests: The authors declare that they have no competing interests.利益冲突声明：所有作者均声明不存在利益冲突。作者贡献：邵胜利：论文撰写、资料整理、项目负责人王吉恒：资料收集、论文撰写及审阅刘善廷：研究设计、立项负责及审阅 -
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图 2 目前有关深度学习预测PTC侧颈淋巴结转移的相关报道概括
Figure 2 Summary of reports on deep learning in predicting lateral cervical lymph node metastasis in PTC
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图 3 深度学习预测PTC侧颈淋巴结转移未来可能的方向
Figure 3 Possible future of deep learning in predicting lateral cervical lymph node metastasis in PTC
表 1 超声及CT在诊断PTC侧颈淋巴结转移中的表现
Table 1 Ultrasound and CT in diagnosis of lateral cervical lymph node metastasis in PTC
Literature Year Ultrasound CT Sample Sensitivity Specificity Sensitivity Specificity Ahn JE, et al [17] 2008 0.65 0.82 0.79 0.78 181 Kim E, et al[18] 2008 0.64 0.92 0.74 0.95 277 Choi JS, et al[19] 2009 0.94 0.25 0.82 1 352 Lee DW, et al[20] 2013 0.70 0.84 0.82 0.64 558 Lesnik D, et al[21] 2014 0.81 0.87 0.79 0.83 196 Na DK, et al[22] 2014 0.64 0.91 0.71 0.93 352 Lee Y, et al[23] 2018 0.74 0.90 0.82 0.90 764 Yang SY, et al[24] 2019 0.85 0.87 0.85 0.80 453 
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