Association of Polymorphisms in the 3' UTR of Genes in the ERK1/2 Signaling Pathway with Non-small Cell Lung Cancer
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摘要:
目的 探究4个ERK1/2信号通路的基因3'UTR区域的单核苷酸多态性(single nucleotide polymorphism,SNP)位点(MAPK1基因中的rs9340,NRAS基因中的rs14804,KRAS基因中的rs712和rs7973450)与非小细胞肺癌(non-small cell lung cancer,NSCLC)的相关性。 方法 纳入了478名NSCLC患者及480名健康对照者,利用TaqMan探针法对其进行基因分型检测,并分析上述4个SNP与NSCLC的相关性。 结果 rs9340位点的等位基因在对照组与非小细胞鳞状细胞癌组( squamous cell carcinoma,SCC)中分布频率的差异具有统计学意义(P = 0.009),该结果表明rs9340 位点的G等位基因可能是非小细胞肺鳞癌的保护性因素(OR = 0.67,95% CI: 0.50~0.91)。同时,在 < 50岁年龄组中,rs9340位点的等位基因在对照组和NSCLC组中的分布频率差异具有统计学意义(P = 5.07 × 10-4),该结果表明rs9340等位基因G可能是NSCLC的保护性因素(OR = 0.46,95% CI: 0.29~0.72)。 结论 MAPK1基因SNP位点rs9340可能与NSCLC的发生风险相关。 -
关键词:
- 非小细胞肺癌 /
- ERK1/2信号通路 /
- 单核苷酸多态性 /
- 3'UTR /
- MAPK1
Abstract:Objective To investigate the association between four single nucleotide polymorphisms(SNP)(rs9340 in MAPK1, rs14804 in NRAS, rs712 and rs7973450 in KRAS) in the 3'UTR of ERK1/2 signaling pathway-related genes and non-small cell lung cancer(NSCLC). Methods A total of 478 NSCLC patients and 480 healthy controls were enrolled in this study. Four SNPs were genotyped by using TaqMan assays. The association between the four SNPs and NSCLC was analyzed. Results The distribution frequency difference of the allele of rs9340 was statistically significant between the control group and the non-small cell squamous cell carcinoma(SCC) group(P = 0.009), suggesting that the G allele of rs9340 may be a protective factor for non-small cell lung squamous cell carcinoma(OR = 0.67, 95%CI: 0.50~0.91). In addition, in the < 50 years age group, the distribution frequency difference of the allele of rs9340 was statistically significant between the control group and the NSCLC group(P = 5.07 × 10-4), indicating that the G allele of rs9340 may be a protective factor for NSCLC(OR = 0.46, 95%CI: 0.29~0.72). Conclusion The SNP rs9340 in MAPK1 may be associated with the risk of NSCLC. -
Key words:
- Non-small cell lung cancer /
- ERK1/2 signaling pathway /
- SNP /
- 3'UTR /
- MAPK1
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急性缺血性脑卒中(acute ischemic stroke,AIS)是一种常见的致残率和死亡率较高的神经系统疾病,好发于中老年人。根据TOAST分型可将AIS分为大动脉粥样硬化(large-artery atherosclerosis,LAA)、心源性栓塞(cardio-embolism,CE)、小动脉闭塞(small-artery occlusion,SAO)、其他明确病因(stroke of other determined,ODC)[1]。根据CISS分型可将大动脉粥样硬化缺血性脑卒中分为动脉到动脉栓塞、原位血栓形成、灌注不足和穿支动脉粥样斑块[2]。大动脉粥样硬化型脑梗死(acute ischemic stroke with large-artery atherosclerosis,AIS-LAA)是亚洲人群中最常见的一种脑梗死亚型,该型临床症状较重,病情进展快,预后差[3]。出血转化(hemorrhagic transformation,HT)为脑梗死后继发颅内出血,发生率约为10%,常常提示预后不佳[3]。众所周知,炎症反应和免疫状态是影响脑梗死发展的重要因素,中性粒细胞与淋巴细胞比值(neutrophil-to-lymphocyte ratio,NLR)和衍生中性粒细胞与淋巴细胞比值(derived neutrophil-to-lymphocyte ratio,dNLR)作为新型炎症标志物,目前尚无NLR和dNLR与AIS-LAA患者HT的研究报道,本研究旨在探讨NLR和dNLR对AIS-LAA患者HT的预测价值,可以有效、及时采取合理干预措施,避免HT发生。现报道如下。
1. 资料与方法
1.1 研究对象
选取2020年10月至2021年2月首都医科大学附属北京友谊医院神经内科住院的AIS-LAA患者151例,根据HT发生的情况分为HT组19例和non-HT组132例。
1.2 纳入与排除标准
纳入标准:(1)AIS诊断依据2018年中国急性缺血性脑卒中诊治指南的相关标准,并经影像学证实[4];(2)患者自症状出现3 d内就诊,入院后即刻行头CT、血常规、血生化等检查,入院3 d内完善头MRI,头颈MRA或CTA检查,若突发神经系统症状改变,即刻完善头CT。
排除标准:(1)首诊头CT提示脑出血;(2)感染、自身免疫性疾病、肿瘤、肝功能异常、肾功能异常、血液系统疾病。根据HT将所有患者分为HT组19例,non-HT组132例。本研究经首都医科大学附属北京友谊医院伦理委员会审核批准。
1.3 方法
收集临床资料及实验室检查指标,观察指标比较两组患者一般资料(年龄、性别、高血压病史、糖尿病病史、高脂血症病史、脑卒中病史、吸烟史、饮酒史、入院时NIHSS评分、入院时收缩压)、入院后治疗(抗血小板治疗、抗凝治疗、溶栓治疗)、LAA机制分型、实验室检查指标(白细胞计数、中性粒细胞计数、淋巴细胞计数、NLR、dNLR)。其中NLR = 中性粒细胞计数/淋巴细胞计数;dNLR = 中性粒细胞计数/(白细胞计数 - 中性粒细胞计数)。用多因素Logistic回归分析及ROC曲线评价NLR、dNLR对AIS-LAA患者HT的预测价值。
1.4 统计学处理
应用SPSS 23.0统计学软件进行数据分析,符合正态分布的连续变量以平均数±标准差(
$\bar x\pm s $ )表示,通过独立t检验分析;非正态分布的连续性变量在表格中通常以中位数(四分位数间距)表示;计数资料分析采用χ2检验;多因素Logistic回归分析AIS-LAA患者HT影响因素;ROC曲线评价NLR、dNLR对AIS-LAA患者HT的预测价值。以P < 0.05为差异有统计学意义。2. 结果
2.1 2组患者一般资料和实验室检查指标比较
HT组患者入院时NHISS评分、入院时收缩压高于non-HT组,HT组接受溶栓治疗的患者多于non-HT组,HT组淋巴细胞计数、NLR及dNLR高于non-HT组(P < 0.05),见表1。
表 1 2组患者基线资料和实验室检查数据[($\bar x \pm s $ )/n(%)/[M(P25,P75)]Table 1. Baseline characteristics and laboratory examination data in 2 groups of patients [($\bar x \pm s $ )/n(%)/[M(P25,P75)]基线信息 总例数(n = 151) 非 HT组(n = 132) HT组(n = 19) t/χ2/z P 年龄(岁) 64±10.8 64±10.4 65±11.2 0.163 0.684 男性 107(72.6) 93(70.5) 14(73.7) 0.084 0.774 高血压病史 81(53.6) 70(53.0) 11(57.9) 0.158 0.699 糖尿病病史 61(40.4) 53(40.2) 8(42.1) 0.026 0.876 高脂血症病史 17(11.3) 14(10.6) 3(15.8) 0.447 0.507 脑卒中病史 12(7.9) 10(7.6) 2(10.5) 0.198 0.657 吸烟史 55(36.4) 47(35.6) 8(42.1) 0.303 0.582 饮酒史 89(58.9) 77(58.3) 12(63.2) 0.160 0.689 入院时NIHSS评分,
中位数4[2,9] 4[2,7] 7[4,11] 4.768 0.022* 入院时收缩压(mmHg) 140.5±16.5 138.6±16.2 153.4±15.7 8.833 0.003* 抗血小板治疗 149(98.7) 130(98.5) 19(100) 0.292 0.589 抗凝治疗 6(4.0) 5(3.8) 2(10.6) 1.706 0.192 溶栓治疗 7(4.6) 4(3.0) 3(15.8) 6.100 0.013* 机制 动脉到动脉栓塞 73(48.3) 63(47.7) 10(52.6) 0.160 0.689 原位血栓形成 40(26.5) 36(27.3) 4(21.1) 0.330 0.566 灌注不足 12(7.9) 10(7.6) 2(10.5) 0.198 0.657 穿支动脉粥样斑块 26(17.2) 23(17.4) 3(15.8) 0.031 0.860 实验室检查结果 白细胞 (109/L) 6.93[5.64,8.33] 6.81[5.36,7.89] 8.3[5.22,10.47] 3.874 0.052 中性粒细胞(109/L) 4.67[3.54,6.66] 4.47[3.54,6.13] 6.98[3.21,9.30] 3.531 0.058 淋巴细胞(109/L) 1.73[1.39,2.07] 1.75[1.53,2.11] 1.38[1.04,1.49] 8.826 0.003* NLR 2.57[1.88,4.46] 2.49[1.69,3.82] 4.91[2.10,7.89] 10.218 < 0.001* dNLR 1.91[1.18,2.90] 1.75[1.13,2.63] 3.63[1.78,5.48] 10.872 < 0.001* *P < 0.05。 2.2 多因素Logistic分析
因变量为是否发生脑梗死出血转化,0 = 未发生脑梗死出血转化,1 = 发生脑梗死出血转化。自变量纳入准则为:根据纳入样本的基线信息(表1), 2组具有统计学差异的因素,即入院时收缩压、入院时NIHSS评分、是否接受溶栓治疗。自变量赋值:0 = 未接受溶栓治疗,1 = 接受溶栓治疗。以上结果提示,NLR和dNLR可能是AIS-LAA患者HT的独立影响因素(P < 0.05),见表2。
表 2 多因素Logistic回归分析NLR、dNLR和大动脉粥样硬化所致脑梗死后出血转化的相关性Table 2. Multivariate analysis for the association between NLR,dNLR and HT in AIS-LAA检测变量§ β Wald Adjusted OR(95% CI) P NLR 1.365 10.444 1,441(1.154-1.798) 0.001* dNLR 1.938 12.405 1.505(1.163-2.165) < 0.001* §多因素Logistic回归分析校正了入院时收缩压、入院时NIHSS评分、溶栓治疗。*P < 0.05。 2.3 ROC曲线分析
NLR预测AIS-LAA患者HT的AUC为0.71 [95% CI (0.63-0.78),P = 0.004],NLR的cut-off值为4.61,灵敏度63.16%,特异度80.3%;dNLR预测AIS-LAA患者HT的AUC为0.75 [95% CI (0.67-0.82),P < 0.001],dNLR的cut-off值为2.78,灵敏度63.16%,特异度83.3%,见图1、表3。
表 3 NLR和dNLR对大动脉粥样硬化所致脑梗死出血转化的预测价值Table 3. Comparison of predictive power between dNLR and NLR检测变量 Cut-off值 特异性 敏感度 曲线下面积 (95%CI) z P NLR 4.61 80.3% 63.16% 0.71(0.63-0.78) 2.915 0.004* dNLR 2.78 83.3% 63.16% 0.75(0.67-0.82) 4.423 < 0.001* *P < 0.05。 3. 讨论
急性脑梗死出血转化是急性脑梗死患者的常见并发症,也是脑梗死预后不良的重要因素,其发生率为30.0%~76.1%[5]。亚洲人脑梗死后出血转化发生率显著高于西方人群且西方人群中心源性栓塞所致脑梗死占比更高[3]。当AIS发生时,外周免疫及炎性细胞被激活并进入缺血脑组织对脑梗死预后发挥双重作用[6-7]。本研究通过中性粒细胞和淋巴细胞计数寻找大动脉粥样硬化所致脑梗死后出血转化的预测因素,为改善脑梗死预后提供线索。
本研究为回顾性临床研究,结果提示:伴随入院时NIHSS评分和收缩压升高,出现脑梗死后出血转化更易发生。其原因可能是AIS后局部脑组织血氧供应不足,大量神经细胞死亡,血管扩张,加重脑水肿,使AIS症状加重,增高HT的发生风险。与此同时,当AIS发生时,机体反应性致血压升高形成脑缺血组织内高灌注趋势,血脑屏障功能受到进一步损伤,加重脑梗死后出血转化。一项Meta研究表明,心房颤动所致栓子脱落堵塞血管是AIS患者发生出血转化的独立影响因素,NLR是心源性栓塞所致脑梗死患者出血转化的独立影响因素[8]。同时,AIS后接受溶栓治疗和血管内治疗的AIS患者较易出现脑梗死后出血转化,溶栓使缺血性脑组织受到再灌注损伤,进一步加重炎性反应及血脑屏障功能障碍,红细胞外渗,引起血管源性水肿导致出血转化[7]。然而,中国急性脑梗死患者中大动脉粥样硬化是导致AIS的主要因素,炎症反应是大动脉粥样硬化的病理基础[9],所以研究炎症反应对大动脉粥样硬化所致脑梗死后出血转化尤为重要。
本研究结果显示:HT组淋巴细胞计数低于non-HT组,HT组NLR及dNLR高于non-HT组且具有统计学差异,NLR和dNLR是AIS-LAA后HT的独立影响因素,表明炎症反应参与大动脉粥样硬化所致急性脑梗死后出血转化的发生发展。一项脑梗死后出血转化的尸检研究发现,中性粒细胞在脑梗死区域大量募集,局部基质金属蛋白酶9(matrix metalloproteinase-9,MMP-9)增加,基底层IV型胶原降解,血脑屏障破坏导致出血转化[10]。动脉粥样硬化可以使颅内血管发生慢性的炎症反应,当AIS-LAA发生时,炎症反应加剧,中性粒细胞合成并释放细胞因子MMP-9,这些因子通过降解血管周围脊膜蛋白影响血管基底膜,破坏血脑屏障完整性导致HT发生[11]。此外脑梗死可诱导中性粒细胞活化产生弹性蛋白酶、髓过氧化物酶,与组蛋白、DNA形成中性粒细胞胞外网状陷阱(NETs),从而破坏血脑屏障[12- 13]。有研究表明,卒中后血小板活化,血小板p-选择素集合T淋巴细胞的p-选择素糖蛋白配体或CD162因子使炎症细胞粘附于细胞内皮上促进血栓炎症反应,破坏血管通透性诱发出血转化[14]。当AIS发生时,皮质醇激素水平升高导致淋巴细胞减少,加剧神经细胞死亡[15]。
NLR和dNLR可反映机体免疫功能和炎性状态,是新型炎症治疗。NLR已广泛应用于脑梗死、心肌梗死及肿瘤的生存期预测中,dNLR仅应用于肿瘤患者生存时间的预测,目前尚没有两者在大动脉粥样硬化所致脑梗死出血转化的研究。本研究结果显示,dNLR预测AIS-LAA患者HT的AUC为0.75,dNLR的cut-off值为2.78,灵敏度63.16%,特异度83.3%;NLR预测AIS-LAA患者HT的AUC为0.71,NLR的cut-off值为4.61,灵敏度63.16%,特异度80.3%。本研究结果与既往研究结果一致。有大量研究表明,急性缺血性脑卒中患者NLR越高,提示HT发生风险越高[16-18]。然而笔者的研究表明,虽然NLR和dNLR可能为大动脉粥样硬化所致急性缺血性脑梗死出血转化的独立影响因素,但dNLR具有更高的灵敏度和特异度,且数据可取性更为便捷。
综上所述,NLR和dNLR可能是AIS-LAA患者HT的独立影响因素,且dNLR具有更高的灵敏度和特异度。通过二者的预测,可积极预防高风险患者,从而有助于降低AIS-LAA患者致残率和致死率。本研究存在一定的局限性:首先,本研究是单中心回顾性分析且样本量较小,统计时存在偏倚,笔者将在今后的工作中进一步验证结果;其次,本研究仅分析入院时NLR和dNLR指标,需动态观察两者变化,更客观的阐述二者与HT之间的关系。
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表 1 所选SNP位点信息
Table 1. The information of selected SNPs in the current study
SNPs 基因 功能 位置 等位基因 中国南方汉族人群MAF rs9340 MAPK1 3'UTR突变 Chr 22:21761064 G > A 0.21 rs14804 NRAS 3'UTR突变 Chr 1:114707222 G > A 0.04 rs712 KRAS 3'UTR突变 Chr 12:25209618 A > C 0.48 rs7973450 KRAS 3'UTR突变 Chr 12:25208208 A > G 0.09 SNP,单核苷酸多态性;MAF,次要等位基因频率;Chr,染色体。 表 2 受试者临床信息[$ x \pm s $/n(%)]
Table 2. The clinical characteristics of the subjects enrolled in the current study [$ x \pm s $/n(%)]
项目 肺癌组 对照组 t/χ2 P 样本数 478 480 年龄(岁) 55.94 ± 10.79 55.16 ± 11.35 1.09 0.275 性别 男 305(63.8) 277(57.7) 3.74 0.053 女 173(36.2) 203(42.3) 病理类型 腺癌 327(68.4) 8.00 < 0.001* 鳞状细胞癌 151(31.6) 临床分期 Ⅰ期 126(26.3) 26.45 < 0.001* Ⅱ期 74(15.5) Ⅲ期 151(31.6) Ⅳ期 127(26.6) 表 3 在健康对照组和NSCLC组中4个SNP位点等位基因及基因型频率分布结果[n(%)]
Table 3. Allele and genotype frequencies of four SNPs between the control group and NSCLC group [n(%)]
SNPs 等位基因/基因型 对照组 肺癌组 对照组HWE 肺癌组 vs 对照组 χ2 P OR (95%CI) χ2 P rs9340 G 773(80.5) 728(76.2) 0.77(0.62~0.96) 5.39 0.020 A 187(19.5) 228(23.8) G/G 308(64.2) 273(57.1) 0.87 0.350 5.63 0.060 G/A 157(32.7) 182(38.1) A/A 15(3.1) 23(4.8) rs14804 G 936(97.5) 935(97.8) 1.14(0.63~2.06) 0.19 0.661 A 24(2.5) 21(2.2) G/G 456(95.0) 458(95.8) 0.32 0.574 1.58 0.454 G/A 24(5.0) 19(4.0) A/A 0 1(0.2) rs712 A 197(20.5) 202(21.1) 1.04(0.83~1.29) 1.04 0.743 C 763(79.5) 754(78.9) A/A 21(4.4) 29(6.1) 0.05 0.826 1.68 0.431 A/C 155(32.3) 144(30.1) C/C 304(63.3) 305(63.8) rs7973450 A 873(90.9) 876(91.6) 1.09(0.79~1.50) 1.09 0.590 G 87(9.1) 80(8.4) A/A 399(83.1) 401(83.9) 1.30 0.254 1.01 0.604 A/G 75(15.6) 74(15.5) G/G 6(1.3) 3(0.6) HWE,哈迪温伯格平衡。*P < 0.012(经Bonferroni校正,n = 4)。 表 4 在健康对照组和不同病理类型肺癌组中4个SNP位点等位基因及基因型频率分布结果[n(%)]
Table 4. Allele and genotype frequencies of four SNPs between different pathological types of NSCLC [n(%)]
SNPs 等位基因/基因型 对照组 AC组 SCC组 AC组 vs 对照组 SCC组 vs 对照组 SCC组 vs AC组 OR(95%CI) χ2 P OR(95%CI) χ2 P OR(95%CI) χ2 P rs9340 G 773(80.5) 506(77.4) 222(73.5) 0.83(0.65~1.05) 2.35 0.125 0.67(0.50~0.91) 6.77 0.009* 0.81(0.59~1.11) 1.70 0.193 A 187(19.5) 148(22.6) 80(26.5) G/G 308(64.2) 192(58.7) 81(53.7) 2.53 0.282 7.26 0.027 2.13 0.345 G/A 157(32.7) 122(37.3) 60(39.7) A/A 15(3.1) 13(4.0) 10(6.6) rs14804 G 936(97.5) 640(97.9) 295(97.7) 1.17(0.60~2.28) 0.22 0.640 1.08(0.46~2.53) 0.03 0.858 0.92(0.37~2.31) 0.03 0.862 A 24(2.5) 14(2.1) 7(2.3) G/G 456(95.0) 313(95.7) 145(96.0) 0.22 0.636 3.90 0.142 2.41 0.300 G/A 24(5.0) 14(4.3) 5(3.3) A/A 0 0 1(0.7) rs712 A 197(20.5) 140(21.4) 62(20.5) 1.05(0.83~1.35) 0.18 0.667 1.00(0.73~1.38) 1.10 × 10−5 0.997 0.95(0.68~1.33) 0.09 0.758 C 763(79.5) 514(78.6) 240(79.5) A/A 21(4.4) 20(6.1) 9(6.0) 1.34 0.512 1.01 0.602 0.12 0.943 A/C 155(32.3) 100(30.6) 44(29.1) C/C 304(63.3) 207(63.3) 98(64.9) rs7973450 A 873(90.9) 597(91.3) 279(92.4) 1.04(0.74~1.48) 0.06 0.810 1.21(0.75~1.95) 0.60 0.437 1.16(0.70~1.92) 0.32 0.568 G 87(9.1) 57(8.7) 23(7.6) A/A 399(83.1) 273(83.5) 128(84.8) 0.20 0.907 1.94 0.380 1.41 0.493 A/G 75(15.6) 51(15.6) 23(15.2) G/G 6(1.3) 3(0.9) 0 AC,肺腺癌;SCC,肺鳞状细胞癌。*P < 0.012(经Bonferroni校正,n = 4)。 表 5 健康对照组和不同分期肺癌组中4个SNP位点等位基因及基因型频率分布结果[n(%)]
Table 5. Allele and genotype frequencies of four SNPs between different stages of NSCLC [n(%)]
SNPs 等位基因/基因型 对照组 I+II期组 III+IV期组 I+II期组 vs 对照组 III+IV期组 vs 对照组 III+IV期组 vs I+II期组 OR(95%CI) χ2 P OR(95%CI) χ2 P OR(95%CI) χ2 P rs9340 G 773(80.5) 303(75.7) 425(76.4) 0.76(0.57~1.00) 3.89 0.049 0.78(0.61~1.01) 3.54 0.060 1.04(0.77~1.40) 0.06 0.805 A 187(19.5) 97(24.3) 131(23.6) G/G 308(64.2) 114(57.0) 159(57.2) 4.20 0.122 3.78 0.151 0.37 0.831 G/A 157(32.7) 75(37.5) 107(38.5) A/A 15(3.1) 11(5.5) 12(4.3) rs14804 G 936(97.5) 394(98.5) 541(97.3) 1.68(0.68~4.15) 1.31 0.253 0.92(0.48~1.78) 0.05 0.815 0.55(0.21~1.43) 1.55 0.213 A 24(2.5) 6(1.5) 15(2.7) G/G 456(95.0) 194(97.0) 264(95.0) 1.34 0.247 1.76 0.414 1.59 0.451 G/A 24(5.0) 6(3.0) 13(4.6) A/A 0 0 1(0.4) rs712 A 197(20.5) 79(19.8) 123(22.1) 0.95(0.71~1.28) 0.10 0.747 1.10(0.85~1.42) 0.54 0.462 1.15(0.84~1.59) 0.79 0.375 C 763(79.5) 321(80.2) 433(77.9) A/A 21(4.4) 9(4.5) 20(7.2) 0.21 0.901 2.92 0.232 1.48 0.476 A/C 155(32.3) 61(30.5) 83(29.9) C/C 304(63.3) 130(65.0) 175(62.9) rs7973450 A 873(90.9) 370(92.5) 506(91.0) 1.23(0.80~1.89) 0.88 0.349 1.01(0.70~1.45) 2.08 × 10−3 0.964 0.82(0.51~1.32) 0.68 0.411 G 87(9.1) 30(7.5) 50(9.0) A/A 399(83.1) 173(86.5) 228(82.0) 1.53 0.465 4.10 0.129 7.14 0.028 A/G 75(15.6) 24(12.0) 50(18.0) G/G 6(1.3) 3(1.5) 0 *P < 0.012(经Bonferroni校正,n = 4)。 表 6 不同年龄组中4个SNP位点等位基因及基因型频率分布结果[n(%)] (1)
Table 6. Allele and genotype frequencies of four SNPs between different age groups [n(%)] (1)
SNPs 等位基因/
基因型< 50岁组 50~65岁组 > 65岁组 < 50岁
对照组vs肺癌组50~65岁
对照组vs肺癌组> 65岁
对照组vs肺癌组对照组 肺癌组 对照组 肺癌组 对照组 肺癌组 OR(95%CI) χ2 P OR(95%CI) χ2 P OR(95%CI) χ2 P rs9340 G 220(85.9) 196
(73.7)387
(79.3)380
(75.4)166(76.9) 152(81.7) 0.46(0.29~0.72) 12.10 5.07 × 10−4* 0.80(0.59~1.08) 2.16 0.142 1.35
(0.83~2.19)1.43 0.231 A 36(14.1) 70
(26.3)101
(20.7)124
(24.6)50
(23.1)34
(18.3)G/G 92(71.9) 70
(52.6)151
(61.9)144
(57.2)65
(60.2)59
(63.4)14.24 8.15 × 10−4 2.98 0.225 6.26 0.044 G/A 36(28.1) 56
(42.1)85
(34.8)92
(36.5)36
(33.3)34
(36.6)A/A 0 7
(5.3)8
(3.3)16
(6.3)7
(6.5)0 rs14804 G 248(96.9) 260
(97.7)475
(97.3)490
(97.2)213
(98.6)185
(99.5)1.40
(0.48~4.09)0.38 0.539 0.96
(0.45~2.06)0.01 0.912 2.61
(0.27~25.27)0.74 0.391 A 8(3.1) 6
(2.3)13
(2.7)14
(2.8)3
(1.4)1
(0.5)G/G 120(93.8) 127
(95.5)231
(94.7)239
(94.8)105
(97.2)92
(98.9)0.39 0.533 1.04 0.592 0.74 0.389 G/A 8(6.2) 6
(4.5)13
(5.3)12
(4.8)3
(2.8)1
(1.1)A/A 0 0 0 1
(0.4)0
(0.0)0 表 6 不同年龄组中4个SNP位点等位基因及基因型频率分布结果[n(%)] (2)
Table 6. Allele and genotype frequencies of four SNPs between different age groups [n(%)] (2)
SNPs 等位基因/
基因型< 50岁组 50~65岁组 > 65岁组 < 50岁
对照组vs肺癌组50~65岁
对照组vs肺癌组> 65岁
对照组vs肺癌组对照组 肺癌组 对照组 肺癌组 对照组 肺癌组 OR(95%CI) χ2 P OR(95%CI) χ2 P OR(95%CI) χ2 P rs712 A 53(20.7) 50
(18.8)95
(19.5)111
(22.0)49
(22.7)41
(22.0)0.89
(0.58~1.36)0.30 0.584 1.17
(0.86~1.59)0.98 0.321 0.96
(0.60~1.54)0.02 0.878 C 203(79.3) 216
(81.2)393
(80.5)393
(78.0)167
(77.3)145
(78.0)A/A 8(6.3) 4
(3.0)6
(2.5)19
(7.5)7
(6.5)6
(6.4)1.65 0.439 7.35 0.025 0.04 0.982 A/C 37(28.9) 42
(31.6)83
(34.0)73
(29.0)35
(32.4)29
(31.2)C/C 83(64.8) 87
(65.4)155
(63.5)160
(63.5)66
(61.1)58
(62.4)rs7973450 A 233(91.0) 246
(92.5)441
(90.4)459
(91.1)199
(92.1)171
(91.9)1.21
(0.65~2.27)0.37 0.543 1.09
(0.71~1.67)0.15 0.703 0.97
(0.47~2.01)5.14 × 10−3 0.943 G 23(9.0) 20
(7.5)47
(9.6)45
(8.9)17
(7.9)15
(8.1)A/A 108(84.4) 114
(85.7)198
(81.2)209
(82.9)93
(86.1)78
(83.9)1.10 0.578 0.69 0.709 2.35 0.309 A/G 17(13.3) 18
(13.5)45
(18.4)41
(16.3)13
(12.0)15
(16.1)G/G 3(2.3) 1
(0.8)1
(0.4)2
(0.8)2
(1.9)0 *P < 0.012(经Bonferroni校正,n = 4)。 -
[1] Sung H,Ferlay J,Siegel R L,et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin,2021,71(3):209-249. doi: 10.3322/caac.21660 [2] Zheng R,Zhang S,Zeng H,et al. Cancer incidence and mortality in China,2016[J]. Journal of the National Cancer Center,2022,2(1):1-9. doi: 10.1016/j.jncc.2022.02.002 [3] Nooreldeen R,Bach H. Current and future development in lung cancer diagnosis[J]. Int J Mol Sci,2021,22(16):8661. doi: 10.3390/ijms22168661 [4] Cao W,Chen H D,Yu Y W,et al. Changing profiles of cancer burden worldwide and in China: A secondary analysis of the global cancer statistics 2020[J]. Chin Med J (Engl),2021,134(7):783-791. doi: 10.1097/CM9.0000000000001474 [5] Saller J J,Boyle T A. Molecular pathology of lung cancer[J]. Cold Spring Harb Perspect Med,2022,12(3):a037812. doi: 10.1101/cshperspect.a037812 [6] Duma N,Santana-Davila R,Molina JR. Non-small cell lung cancer: Epidemiology,screening,diagnosis,and treatment[J]. Mayo Clin Proc,2019,94(8):1623-1640. doi: 10.1016/j.mayocp.2019.01.013 [7] Zhang J,Chen S F,Zhen Y,et al. Multicenter analysis of lung cancer patients younger than 45 years in Shanghai[J]. Cancer,2010,116(15):3656-3662. doi: 10.1002/cncr.25100 [8] Heist R S,Sequist L V,Engelman J A. Genetic changes in squamous cell lung cancer: a review[J]. J Thorac Oncol,2012,7(5):924-933. doi: 10.1097/JTO.0b013e31824cc334 [9] Kim Y,Hammerman P S,Kim J,et al. Integrative and comparative genomic analysis of lung squamous cell carcinomas in East Asian patients[J]. J Clin Oncol,2014,32(2):121-128. doi: 10.1200/JCO.2013.50.8556 [10] Niu Z,Jin R,Zhang Y,et al. Signaling pathways and targeted therapies in lung squamous cell carcinoma: mechanisms and clinical trials[J]. Signal Transduct Target Ther,2022,7(1):353. doi: 10.1038/s41392-022-01200-x [11] Shi Q,Ruan J,Yang Y C,et al. rs66651343 and rs12909095 confer lung cancer risk by regulating CCNDBP1 expression[J]. PLoS One,2023,18(4):e0284347. doi: 10.1371/journal.pone.0284347 [12] Anjum J,Mitra S,Das R,et al. A renewed concept on the MAPK signaling pathway in cancers: polyphenols as a choice of therapeutics[J]. Pharmacol Res,2022,184(2022):106398. [13] Qi M, Elion E A. MAP kinase pathways [J]. J Cell Sci, 2005, 118(Pt 16): 3569-3572. [14] Morrison D K. MAP kinase pathways[J]. Cold Spring Harb Perspect Biol,2012,4(11):a011254. [15] Keshet Y, Seger R. The MAP kinase signaling cascades: A system of hundreds of components regulates a diverse array of physiological functions[M]. //Seger R. MAP kinase signaling protocols. Second Edition. Totowa, NJ: Humana Press, 2010: 3-38. [16] Sinkala M,Nkhoma P,Mulder N,et al. Integrated molecular characterisation of the MAPK pathways in human cancers reveals pharmacologically vulnerable mutations and gene dependencies[J]. Commun Biol,2021,4(1):9. doi: 10.1038/s42003-020-01552-6 [17] Brennecke J,Stark A,Russell RB,et al. Principles of microRNA-target recognition[J]. PLoS Biol,2005,3(3):e85. doi: 10.1371/journal.pbio.0030085 [18] Liu C J,Fu X,Xia M,et al. miRNASNP-v3: a comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets[J]. Nucleic Acids Research,2021,49(D1):D1276-D1281. doi: 10.1093/nar/gkaa783 [19] Wu W,Wu L,Zhu M,et al. miRNA mediated noise making of 3'UTR mutations in cancer[J]. Genes (Basel),2018,9(11):545. doi: 10.3390/genes9110545 [20] 中华医学会,中华医学会肿瘤学分会,中华医学会杂志社. 中华医学会肺癌临床诊疗指南(2018版)[J]. 中华肿瘤杂志,2018,40(12):30. [21] 赫捷,李霓,陈万青,等. 中国肺癌筛查与早诊早治指南(2021,北京)[J]. 中国肿瘤,2021,30(02):81-111. [22] 黄鼎智, 李琳, 李旭, 等.老年晚期肺癌内科治疗中国专家共识(2022版)[J].中国肺癌杂志, 2022, 25(06): 363-384. [23] Yang J,Yan Z,Wang Y,et al. Association study of relationships of polymorphisms in the miR-21,miR-26b,miR-221/222 and miR-126 genes with cervical intraepithelial neoplasia and cervical cancer[J]. BMC Cancer,2021,21(1):997. doi: 10.1186/s12885-021-08743-2 [24] Shi Y Y,He L. SHEsis,a powerful software platform for analyses of linkage disequilibrium,haplotype construction,and genetic association at polymorphism loci[J]. Cell Res,2005,15(2):97-98. doi: 10.1038/sj.cr.7290272 [25] Lee J,Son M J,Son C Y,et al. Genetic Variation and Autism: A Field Synopsis and Systematic Meta-Analysis[J]. Brain Sci,2020,10(10):692. doi: 10.3390/brainsci10100692 [26] Lake D,Corrêa S A,Müller J. Negative feedback regulation of the ERK1/2 MAPK pathway[J]. Cell Mol Life Sci,2016,73(23):4397-413. doi: 10.1007/s00018-016-2297-8 [27] Balmanno K,Cook S J. Tumour cell survival signalling by the ERK1/2 pathway[J]. Cell Death & Differentiation,2009,16(3):368-377. [28] Yan Z,Ohuchida K,Fei S,et al. Inhibition of ERK1/2 in cancer-associated pancreatic stellate cells suppresses cancer-stromal interaction and metastasis[J]. BioMed Central,2019,38(1):221. [29] Marampon F,Ciccarelli C,Zani B M. Biological rationale for targeting MEK/ERK pathways in anti-cancer therapy and to potentiate tumour responses to radiation[J]. Int J Mol Sci,2019,20(10):2530. doi: 10.3390/ijms20102530 [30] Zhou B,Lin W,Long Y,et al. Notch signaling pathway: architecture,disease,and therapeutics[J]. Signal Transduct Target Ther,2022,7(1):95. doi: 10.1038/s41392-022-00934-y [31] Pino M S,Chung D C. The chromosomal instability pathway in colon cancer[J]. Gastroenterology,2010,138(6):2059-2072. doi: 10.1053/j.gastro.2009.12.065 [32] Müller M F,Ibrahim A E,Arends M J. Molecular pathological classification of colorectal cancer[J]. Virchows Arch,2016,469(2):125-134. doi: 10.1007/s00428-016-1956-3 [33] Ding L,Getz G,Wheeler D A,et al. Somatic mutations affect key pathways in lung adenocarcinoma[J]. Nature,2008,455(7216):1069-1075. doi: 10.1038/nature07423 [34] Hill M,Tran N. miRNA interplay: mechanisms and consequences in cancer[J]. Dis Model Mech,2021,14(4):dmm047662. doi: 10.1242/dmm.047662 [35] Zhu Z,Zhang F,Hu H,et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets[J]. Nat Genet,2016,48(5):481-487. doi: 10.1038/ng.3538 [36] Fabian M R,Sonenberg N. The mechanics of miRNA-mediated gene silencing: a look under the hood of miRISC[J]. Nat Struct Mol Biol,2012,19(6):586-593. doi: 10.1038/nsmb.2296 [37] Chan J J,Tabatabaeian H,Tay Y. 3'UTR heterogeneity and cancer progression[J]. Trends Cell Biol,2023,33(7):568-582. doi: 10.1016/j.tcb.2022.10.001 [38] Sturgill T W,Ray L B,Erikson E,et al. Insulin-stimulated MAP-2 kinase phosphorylates and activates ribosomal protein S6 kinase II[J]. Nature,1988,334(6184):715-718. doi: 10.1038/334715a0 [39] AACR Project GENIE Consortium. AACR project GENIE: powering precision medicine through an international consortium[J]. Cancer Discov,2017,7(8):818-831. doi: 10.1158/2159-8290.CD-17-0151 [40] Rubio K,Romero-Olmedo A J,Sarvari P,et al. Non-canonical integrin signaling activates EGFR and RAS-MAPK-ERK signaling in small cell lung cancer[J]. Theranostics,2023,13(8):2384-2407. doi: 10.7150/thno.79493 [41] Wang Y,Guo Z,Tian Y,et al. MAPK1 promotes the metastasis and invasion of gastric cancer as a bidirectional transcription factor[J]. BMC Cancer,2023,23(1):959. doi: 10.1186/s12885-023-11480-3 [42] Zhu L,Yang S,Wang J. miR-217 inhibits the migration and invasion of HeLa cells through modulating MAPK1[J]. Int J Mol Med,2019,44(5):1824-1832. [43] Gagliardi M,Pitner M K,Park J,et al. Differential functions of ERK1 and ERK2 in lung metastasis processes in triple-negative breast cancer[J]. Sci Rep,2020,10(1):8537. doi: 10.1038/s41598-020-65250-3 [44] Guo N,Zhang N,Yan L,et al. Correlation between genetic polymorphisms within the MAPK1/HIF-1/HO-1 signaling pathway and risk or prognosis of perimenopausal coronary artery disease[J]. Clin Cardiol,2017,40(8):597-604. doi: 10.1002/clc.22708 [45] Insodaite R,Smalinskiene A,Liutkevicius V,et al. Associations of polymorphisms localized in the 3'UTR regions of the KRAS,NRAS,MAPK1 genes with laryngeal squamous cell carcinoma[J]. Genes (Basel),2021,12(11):1679. doi: 10.3390/genes12111679 [46] Hirsch F R,Scagliotti G V,Mulshine J L,et al. Lung cancer: current therapies and new targeted treatments[J]. Lancet,2017,389(10066):299-311. doi: 10.1016/S0140-6736(16)30958-8 [47] Kulasingam V,Diamandis E P. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies[J]. Nat Clin Pract Oncol,2008,5(10):588-599. doi: 10.1038/ncponc1187 [48] Chen W,Zheng R,Baade P D,et al. Cancer statistics in China,2015[J]. CA Cancer J Clin,2016,66(2):115-132. doi: 10.3322/caac.21338 期刊类型引用(7)
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