Development and Validation of Prognostic Nomogram Based on Negative Lymph Node Count for Patients with Gastric Signet Ring Cell Carcinoma
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摘要:目的
探讨阴性淋巴结数目(NLNC)对胃印戒细胞癌(GSRC)患者预后的影响及构建GSRC患者的预后预测模型。
方法基于SEER数据库收集GSRC患者2101例，随机分为建模组和验证组，检验临床病理特征与GSRC预后的关系。多因素Cox比例风险回归模型分析影响总生存的独立危险因素并建立预后预测模型。一致性指数(C-index)、校准曲线、净分类指数(NRI)、综合判别指数(IDI)和临床决策曲线(DCA)对列线图进行准确性和临床适用性评估。
结果所有患者按照7:3比例划分，建模组1473例，验证组628例。NLNC > 10是GSRC患者预后的保护因素(HR=0.578, 95%CI: 0.504~0.662)，根据多因素Cox比例风险回归模型筛选的变量建立Nomogram图，建模组和验证组的C-index分别为0.737(95%CI: 0.720~0.753)和0.724(95%CI: 0.699~0.749)，区分度良好，校准曲线显示模型的一致性较高。NRI=17.77%，连续NRI=36.34%，IDI=4.2%，表明该模型较传统模型是正向收益，DCA决策曲线远离基准线表明模型临床适用性好。
结论NLNC增加是GSRC患者预后的有利因素。本研究建立的列线图相对准确，可预测GSRC患者的预后。
Abstract:ObjectiveTo explore the influence of negative lymph node count (NLNC) on the prognosis of patients with gastric signet ring cell carcinoma (GSRC) and develop a prognostic nomogram based on NLNC.
MethodsOn the basis of the SEER database, 2 101 patients diagnosed with GSRC were collected and randomly divided into the modeling group and validation group to test the relationship between clinicopathological characteristics and the prognosis of GSRC. The multivariate Cox proportional hazard regression model was used to analyze the independent risk factors affecting overall survival and establish a prognostic prediction model. The consistency index (C-index), calibration curve, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to evaluate the accuracy and clinical applicability of the nomogram.
ResultsAll patients were divided according to the ratio of 7:3, with 1 473 in the modeling group and 628 in the validation group. NLNC > 10 (HR=0.578, 95%CI: 0.504-0.662, P < 0.001) was a protective factor for the prognosis of patients with GSRC, and the nomogram model was established based on multivariate Cox proportional hazards model. The C-index values of the nomogram were 0.737 (95%CI: 0.720-0.753) and 0.724 (95%CI: 0.699-0.749) in the modeling and validation groups, respectively, showing good discrimination. The calibration curves showed high consistency of the model. NRI=17.77%, continuous NRI=36.34%, and IDI=4.2% indicated that the model had positive returns compared with the traditional model. The DCA was far from the baseline, indicating that the model had good clinical applicability.
ConclusionThe increase in NLNC is a favorable factor for the prognosis of patients with GSRC, and a relatively accurate nomogram was established to predict the prognosis of patients with GSRC and help clinicians conduct individualized prognostic evaluations.
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Key words:
- Gastric signet ring cell carcinoma /
- Negative lymph node count /
- Prognosis /
- Nomogram
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0 引言
TET（ten-eleven translocation）家族包括3个成员：TET1、TET2和TET3，此家族通过介导DNA去甲基化的表观遗传调控重新激活沉默基因，在胚胎发育、机体造血、造血系统疾病及肿瘤的发生发展中均起重要作用[1-4]。TET2基因突变多见于骨髓恶性肿瘤，TET3与肿瘤的关系至今研究尚少[3-6]。目前研究提示，TET1参与肿瘤的增殖及侵袭转移过程[6-8]，但其具体作用及相关机制尚未完全清楚。为此，本研究应用慢病毒载体构建稳定转染的TET1过表达细胞株，通过体外实验研究TET1对乳腺癌细胞增殖、迁移及侵袭的影响，并初步探究其相关机制，旨在为临床肿瘤诊治寻找新的靶点。
1 材料与方法
1.1 材料
人乳腺癌MDA-MB-231和人胚肾HEK 293T慢病毒包装细胞均购自中国科学院上海细胞库；大肠杆菌菌株DH5α购自北京安赞诺公司。RPMI 1640培养液和胎牛血清（FBS）购自美国Gibco公司；TrizolTMReagent核酸分离试剂购自日本Takara公司；FastQuant RT Kit（with gDNase）（KR106）和SuperReal PreMix Plus（SYBR Green）（FP205）均购自北京TIANGEN公司；TET1鼠抗人单克隆抗体（GT1462）购自美国GeneTex公司；GAPDH兔抗人多克隆抗体（AP0063）、兔抗E-cadherin、N-cadherin、Vimentin、β-catenin一抗均购自美国Bioworld公司；羊抗兔荧光二抗、羊抗鼠荧光二抗均购自美国ROCKLAND公司；免疫荧光二抗购自美国KPL公司；Matrigel基质胶购自美国BD公司；Transwell小室购自美国Costar公司；CCK-8细胞增殖-毒性检测试剂盒购自上海贝博公司。
1.2 方法
1.2.1 质粒的构建及制备
委托广州复能基因公司构建EX-E2856-Lv201表达载体及EX-NEG-Lv201表达载体。两质粒均具备嘌呤霉素耐药基因，可用嘌呤霉素筛选；将连接产物转化DH5α大肠杆菌，挑单克隆菌种扩大化培养；抽提质粒，测定所提质粒浓度，并进行双向测序验证。
1.2.2 慢病毒感染及筛选
在进行转染前，提前48 h将293T包装细胞铺于10 cm细胞培养皿中，按照病毒包装试剂盒说明进行，转染293T细胞72 h后，收集培养液上清液，即慢病毒液；将其感染靶细胞（MDA-MB-231），同时加入终浓度为8 μg/ml的聚凝胺，并做感染无关序列的对照。以1 μg/ml终浓度的嘌呤霉素进行加压筛选，命名为MDA-MB-231-TET1细胞及阴性对照MDA-MB-231-NC细胞；未经感染的细胞命名为空白对照MDA-MB-231细胞。
1.2.3 qRT-PCR检测TET1的表达
按照说明书使用TRIzol提取各组细胞总RNA，并将其反转录成cDNA，实时荧光定量PCR仪检测TET1的过表达情况。GAPDH为内参，上游引物为：5' -CAAGGCTGAGAACGGGAA-3' ，下游引物为：5' -GCATCGCCCCACTTGATTTT-3' ；TET1上游引物为：5' -CCCGAATCAAGCGGAAGAATA-3' ，下游引物为：5' -TACTTCAGGTTGCACGGT-3' 。反应条件为：95℃ 15 min，95℃ 10 s，63℃ 32 s，42个循环。采用2-ΔΔCT法分析所得qRT-PCR数据，并进行统计学分析。
1.2.4 Western blot检测TET1的表达
收集各组细胞，采用RIPA强效裂解液提取细胞总蛋白，BCA蛋白定量检测试剂盒进行蛋白定量，处理后的蛋白进行SDS-PAGE、转膜；加入相应一抗，4℃孵育过夜，洗膜，加入相应荧光二抗，室温避光孵育1 h，洗膜，在Odyssey红外显像系统中成像进行分析，目的基因相对表达量用目的基因灰度值与GAPDH灰度值的比值表示，并进行统计学分析。
1.2.5 细胞增殖实验（CCK-8法）
取对数生长期的各组细胞，以每孔1×103个细胞加入96孔板内，每组设6复孔；设最初加入CCK-8的时间为0 h，分别在细胞培养的0、24、48和72 h，以10微升/孔加入CCK-8试剂，于37℃、5%CO2的细胞培养箱中继续培养；1~4 h后取出96孔板，酶标仪检测各孔450 nm处的OD值；实验至少重复3次，并对数据进行统计学分析。
1.2.6 细胞周期检测
取对数生长期的各组细胞，常规消化、离心后制备成2×106个细胞悬液，加入70%无水乙醇4℃放置过夜，次日用PBS清洗细胞2次，加核糖核酸酶A（10 mg/ml）2.5 μl及PI（1 mg/ml）25 μl，50 μm尼龙网过滤，上机检测。
1.2.7 划痕实验
取对数生长期的各组细胞，以每孔5×105个细胞接种于6孔板，培养24 h后细胞融合度达80%~90%时，用200 μl枪头垂直行“十”字划痕，加入无血清RMPI 1640培养液，倒置光学显微镜下随机取6个固定点观察、照相，此时记为0 h；继续培养24 h后取出6孔板，在同一观察点处观察划痕愈合情况，并于倒置显微镜下拍照。计算划痕愈合率（%）=（0 h划痕宽度-24 h划痕宽度）/0 h划痕宽度×100%。
1.2.8 细胞侵袭能力的测定
取对数生长期的各组细胞，制备成无血清细胞悬液，浓度为5×105个/毫升，向涂布基质胶的上室加入200 μl细胞悬液；下室加入600 μl含有5%FBS的培养液，培养20~24 h；取出小室，擦去膜上未迁移的细胞，10%多聚甲醛固定细胞，结晶紫染色，洗去染料，于倒置显微镜下观察（200倍），随机选取膜上、下、左、右、中各2个视野拍照，计数，计算平均值。
1.2.9 免疫荧光染色检测
取对数生长期的各组细胞，常规消化、离心后铺入24孔板中，待细胞融合度达50%~60%时，进行染色，PBS洗3次，每次5 min，应用4%多聚甲醛固定细胞20 min，室温；弃4%多聚甲醛，PBS洗3次，每次5 min；0.5% Triton-X-100透膜10 min（若为膜蛋白，此步可省），PBS漂洗细胞，3次，每次5 min；10%BSA室温封闭1 h；弃10% BSA，一抗200 μl，4℃孵育过夜；加入二抗200 μl，37℃避光孵育1.5~2 h；PBS漂洗3次，每次5 min，5 μg/ml DAPI染色10 min，室温；PBS漂洗3次，每次5 min，于荧光显微镜下观察、拍照。
1.3 统计学方法
采用SPSS13.0软件进行数据分析，组间差异分析采用t检验或方差分析，P < 0.05为差异有统计学意义。
2 结果
2.1 稳定过表达TET1的MDA-MB-231细胞系的建立
将收集的病毒液感染靶细胞MDA-MB-231，感染72 h后，经嘌呤霉素筛选，感染效率明显提高，MDA-MB-231-NC组和MDA-MB-231-TET1组的95%以上细胞表达绿色荧光蛋白（eGFP），见图 1。
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图 1 稳定表达eGFP及TET1 MDA-MB-231细胞系的建立Figure 1 Establishment of MDA-MB-231 cell lines with stable expression of eGFP and TET1The figures were taken at the same location under fluorescent field and bright field; A: the eGFP-negative control was stably transfected into MDA-MB-231 cells (×100); B: the TET1 plasmid was stably transfected into MDA-MB-231 cells (×100)2.2 稳定转染MDA-MB-231细胞中TET1 mRNA及蛋白的表达
与阴性对照组和空白对照组细胞比较，MDA-MB-231-TET1细胞TET1 mRNA（P=0.003）及蛋白（P=0.03）表达水平明显升高，差异均具有统计学意义，见图 2。
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图 2 慢病毒感染前后MDA-MB-231细胞中TET1 mRNA及蛋白的表达Figure 2 Expression of TET1 mRNA and protein in MDA-MB-231 cells before and after lentiviral transfection tested by qPCR and Western blot1: MDA-MB-231; 2: MDA-MB-231-NC; 3: MDA-MB-231-TET1; A: qPCR analysis was performed to detect TET1 mRNA levels after transfection and it showed that cell strain of stable overexpression of TET1 was effectively established; B: the expression of TET1 protein in different cell lines were detected by Western blot. C: the histograms were made by Prism 6 according to figure B; n=32.3 TET1对MDA-MB-231细胞增殖的影响
与阴性对照组和空白对照组细胞相比，MDA-MB-231-TET1细胞增殖速度显著降低，差异有统计学意义（P < 0.001），见图 3。
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图 3 TET1过表达对MDA-MB-231细胞增殖能力的影响TET1 inhibited the proliferation of MDA-MB-231 in vitro. CCK-8 assay showed that the overexpression of TET1 suppressed MDA-MB-231 cell growth; Cells (1×103) were seeded into 96-well plates and the viable cell number was evaluated as the value of absorbance at 450nm; n=6, ***: P < 0.001, compared with control groupsFigure 3 Effects of TET1 overexpression on proliferation ability of MDA-MB-231 cells2.4 TET1对MDA-MB-231细胞周期的影响
MDA-MB-231-TET1细胞G2/M期延长，阴性对照组细胞G2/M期占（4.30±1.36）%，而MDA-MB-231-TET1细胞G2/M期占（13.24±3.64）%，差异有统计学意义（P=0.002），见图 4。提示MDA-MB-231-TET1细胞G2/M期阻滞。
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图 4 TET1过表达对MDA-MB-231细胞细胞周期的影响Figure 4 Effects of TET1 overexpression on MDA-MB-231 cell proliferationA: the overexpression of TET1 inhibited cell cycle progression and DNA synthesis in MDA-MB-231 cells. The overexpression of TET1 arrested cell cycle progression by accumulating cells in G2/M phase; B: the histogram was made by Prism 6; n=3; **: P < 0.012.5 TET1对MDA-MB-231细胞迁移的影响
与阴性对照组和空白对照组细胞相比，MDA-MB-231-TET1迁移能力显著降低，统计学分析显示，MDA-MB-231-TET1与阴性及空白对照组的迁移能力比较，差异具有统计学意义（P < 0.001），见图 5。
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图 5 TET1过表达对MDA-MB-231细胞迁移的影响Figure 5 Effects of TET1 overexpression on migration ability of MDA-MB-231 cellsA:the migration ability of MDA-MB-231 cells and MDA-MB-231 cells transfected with TET1 or not were tested by wound healing assay (SP ×100); B: the histogram was made by Prism 6; n=32.6 TET1对MDA-MB-231细胞侵袭的影响
与阴性对照组和空白对照组细胞相比，MDA-MB-231-TET1侵袭能力显著降低，差异有统计学意义（P < 0.001），见图 6。
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图 6 TET1过表达对MDA-MB-231细胞侵袭的影响Figure 6 Effects of TET1 overexpression on invasion ability of MDA-MB-231 cellsA: the effect of TET1 expression on the migration of MDA-MB-231 cells; transwell assay was performed on MDA-MB-231 cells transfected with TET1 or control; B: the histogram was made by Prism 6; n=3; ***: P < 0.001, compared with control groups2.7 TET1对MDA-MB-231细胞间充质转化（EMT）的影响
Western blot结果显示：与阴性对照组和空白对照组细胞相比，MDA-MB-231-TET1组细胞E-cadherin表达明显升高（P < 0.001），N-cadherin（P=0.003）、Vimentin表达降低（P=0.041），黏附蛋白的辅助因子β-catenin表达降低（P < 0.001），见图 7。免疫荧光染色检测结果显示：与阴性对照组和空白对照组细胞相比，MDA-MB-231-TET1组细胞E-cadherin表达明显升高，N-cadherin、Vimentin表达降低，MDA-MB-231-TET1组、阴性对照组和空白对照组细胞的细胞质和细胞核均有β-catenin表达，但相较于MDA-MB-231-TET1组细胞，阴性对照组和空白对照组细胞的细胞核内β-catenin表达升高，提示其向核内转移，见图 8。
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图 7 TET1过表达对EMT相关分子标志物表达的影响Figure 7 Effects of TET1 overexpression on expression of EMT-related molecular markers1: MDA-MB-231; 2: MDA-MB-231-NC; 3: MDA-MB-231-TET1; A: the effects of TET1 overexpression on the expression of EMT-related molecular markers in MDA-MB-231 cells were detected by Western blot; B: the histograms were made by Prism 6; n=3; ***: P < 0.001; **: P < 0.01; *: P < 0.05![]()
图 8 TET1过表达对MDA-MB-231细胞EMT相关分子标志物表达的影响Figure 8 Effects of TET1 overexpression on expression of EMT-related molecular markers in MDA-MB-231 cellsA, B, C, D: the expression and location of EMT markers in MDA-MB-231 cells before and after TET1 transfection detected by immunofluorescence; n=33 讨论
TET家族是一种α-酮戊二酸和Fe2+依赖的双加氧酶，能够将5-甲基胞嘧啶（5mC）转化为5-羟甲基胞嘧啶（5hmC），并进一步转化为5-甲酰胞嘧啶（5fC）和5-羧基胞嘧啶（5caC），启动DNA去甲基化程序[1-3]。研究证实，TET家族蛋白能使DNA甲基化介导的基因沉默重新激活，参与肿瘤的增殖及侵袭转移过程[6-8]。相较于正常组织，TET蛋白在乳腺癌及肝癌组织中的表达均有不同程度地降低，但TET1的表达最低[5]。Neri等[9]发现，通过沉默TET1在正常结肠上皮细胞中的表达，可使细胞周期进程加快，然而重新激活TET1的表达后，结肠细胞的增殖则被抑制；而在肾癌786-O细胞中，下调TET1的表达明显抑制细胞增殖，并使细胞阻滞于G0/G1期[10]。因此，TET1对肿瘤细胞增殖能力的影响作用不一，本研究结果发现TET1过表达可明显降低乳腺癌MDA-MB-231细胞增殖速度，TET1过表达可促使细胞周期中的G2/M期阻滞，表明TET1可通过阻滞G2/M期抑制乳腺癌细胞增殖。
肿瘤的转移是一个多基因、多步骤、多因素的过程，经历脱离原发肿瘤、进入血液循环、趋化“归巢”及黏附“定居”等多个阶段后，最终形成新的转移灶[11-12]。上皮间质转化可诱导肿瘤细胞脱离原发部位，通过血液循环或淋巴循环转移至远处。在此过程中，肿瘤细胞的上皮型标志物E-cadherin表达降低，间质型标志物Vimentin及N-cadherin表达增加，使肿瘤细胞获得间质细胞所具有的迁移能力[13-15]。EMT的发生受精密的信号通路网络调控，其中Wnt/β-catenin信号通路被认为是促进EMT发生的重要信号通路之一，分泌型Wnt蛋白是该通路的启动因子，其进入细胞后可抑制由Axin、APC和GSK-3β组成的复合物的活性，导致β-catenin无法被GSK-3β磷酸化而在细胞质内聚集并向核内转移，入核后直接结合转录因子TCF/LEF并激活其活性，启动下游靶基因的表达，进而促使EMT的发生[16-18]。在正常情况下，成熟细胞内的β-catenin大多数与E-cadherin结合于细胞膜上表达，细胞质内游离的β-catenin保持在较低水平，故目前研究学者把β-catenin的核内移现象作为Wnt/β-catenin信号通路开通的标志[19-20]。最近研究显示，Liu等[21]在研究肺癌A549细胞EMT过程时发现，TET1表达明显上调时，DNA甲基转移酶（DNMT）1、3a及3b表达明显降低。然而，Tsai等[22]提出，TET1能作为乏氧诱导因子HIF-1α和HIF-2α的转录共激活因子，上调乏氧应答基因的表达来促进乏氧诱导EMT的发生。这均提示TET1可能参与到肿瘤的EMT过程中。因此，本研究首先检测TET1对乳腺癌MDA-MB-231细胞迁移、侵袭能力的影响，结果发现过表达TET1的细胞迁移速度明显慢于阴性对照组及空白对照组，同时，其侵袭能力也明显低于阴性对照组及空白对照组。由此说明，TET1抑制乳腺癌细胞的迁移、侵袭能力。进一步定量检测EMT相关分子标志物在各组细胞中的表达，结果发现，过表达TET1可明显升高上皮样标志物E-cadherin蛋白的表达，抑制间质样标志物N-cadherin及Vimentin蛋白表达，抑制黏附蛋白的辅助分子β-catenin蛋白表达，而β-catenin在过表达TET1的细胞中表达均一，但在阴性对照组及空白对照组细胞的细胞核中β-catenin表达高于胞质，提示β-catenin向核内转移。这均与EMT机制相符，由此说明TET1可以通过调控EMT参与乳腺癌的侵袭及转移，通过促进乳腺癌细胞向上皮样表型转化，升高E-cadherin的表达增强细胞间的黏附能力，降低β-catenin在细胞质内积累进而阻止β-catenin入核，从而抑制EMT发生。类似研究也证实了此推论[9]，TET1可降低β-catenin在结肠癌细胞的细胞核中的表达，通过抑制Wnt/β-catenin通路进而抑制肿瘤细胞的增殖。
关于TET1如何调控肿瘤的增殖、侵袭转移的相关机制尚未完全清楚，近年研究证实，高表达TET1的乳腺癌患者预后较好[23-24]。在乳腺癌细胞中，TET1可通过对金属蛋白酶组织抑制剂（TIMP）基因的启动子去甲基化，维持TIMP2及TIMP3的表达来抑制肿瘤转移[8]。而Sun等[24]却提出，同样在乳腺癌中，高迁移率蛋白（HMGA2）是TET1重要的上游调控因子，促进TET1通过对其自身及HOXA7和HOXA9的启动子进行去甲基化来上调其表达，抑制肿瘤侵袭、转移并改善乳腺癌患者的预后。有学者研究乏氧时发现，TET1还能通过去甲基化上调胰岛素诱导基因1（INSIG1）的表达，促进代谢及EMT[22]。这提示TET1不仅具有去甲基化的活性，还可能在乏氧条件下参与到肿瘤细胞代谢的调控中。
综上所述，上调TET1蛋白表达能够通过阻滞G2/M期来抑制乳腺癌MDA-MB-231细胞的增殖，并且能够抑制乳腺癌MDA-MB-231细胞的迁移、侵袭，其机制可能与通过Wnt/β-catenin通路抑制EMT的发生相关，为研究TET1与肿瘤的关系提供了新的研究思路。深入探讨TET1对肿瘤增殖及侵袭的作用机制可能为癌症治疗提供新的靶点。
Competing interests: The authors declare that they have no competing interests.作者贡献：李金洲：提出研究选题、数据分析及论文撰写汪文杰、姚亚龙、穆彦熹、陈康：设计研究方案、整理数据沈亦敏、王舟、黄泽平：数据分析与修订论文陈晓：研究课题监管与指导、论文审阅与修订 -
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图 2 X-tile计算最佳截断值并绘制OS生存曲线
Figure 2 Optimal cutoff value calculated by X-tile and OS curves
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图 4 建模组和验证组3年(A, B)和5年OS(C, D)列线图的校准曲线
Figure 4 Calibration curves for predicting 3-year(A, B) and 5-year OS(C, D) of the nomogram in the modeling and validation groups
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图 5 模型的IDI图和基于Cox回归的新旧模型的DCA曲线
Figure 5 IDI graph and DCA curves of old and new models based on Cox regression
表 1 建模组和验证组患者的人口统计和病理特征
Table 1 Demographic and clinicopathologic variables in modeling and verification groups
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表 2 建模组1473例GSRC患者生存预后的单因素及多因素分析结果
Table 2 Univariate and multivariate analyses of overall survival of 1473 GSRC patients in modeling group
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表 3 NLNC分组在不同pT、pN分期上的GSRC患者的单因素Cox生存分析
Table 3 Univariate Cox survival analysis of patients with GSRC grouped by NLNC in different pT and pN stages
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