摘要
结肠腺癌(COAD)是癌症相关死亡的主要原因之一,准确预测COAD患者预后,评估COAD生存风险因素尤为重要。微小RNA(miRNA)通过靶向下游mRNA广泛参与肿瘤生物学行为调控,已成为具有应用研究前景的标志物。本研究旨在通过生物信息学方法鉴定COAD预后miRNA并构建预后预测模型,为COAD预后判断和制订个体化治疗方案提供参考。
从TCGA数据库中下载COAD患者的临床信息以及miRNA-seq数据,获取差异的miRNA。利用单变量和多变量Cox比例风险回归模型获得关键预后miRNA,用多因素Cox回归模型构建风险评分计算公式。利用Kaplan-Meier方法分析高、低风险评分患者的生存状态;ROC曲线评估风险评分的敏感度及特异度,并且从样本中随机抽取50%的病例做内部验证。采用预后风险模型列线图模型确定COAD患者临床病理参数及风险评分。使用Targetscan及miRDB数据库对预后miRNA模型进行靶基因预测以及利用String数据库进行蛋白与蛋白互作网络分析。
差异表达分析获得320个miRNA,其中167个上调,153个下调。利用单变量和多变量Cox比例风险回归对差异的miRNA进行分析,发现miR-503-5p、miR-335-3p、miR-185-5p、miR-4436b-5p、miR-125b-2-3p为COAD患者关键预后miRNA。结合风险评分的生存分析结果显示,高风险评分患者预后明显差于低风险评分患者(P=0.005 6),在随机抽取的内部验证组中也得到验证(P=0.014)。1、3、5年风险评分模型ROC曲线下面积(AUC)分别为0.666、0.724、0.707,内部验证组分别为0.681、0.699、0.703。Cox回归分析显示,建立用于预测COAD患者预后预测列线图的一致系数为0.836。单因素和多因素Cox分析显示,在建模组及内部验证组中风险评分是COAD的独立预后因素(均P<0.01)。miRNA靶基因预测获得87个靶基因。蛋白与蛋白互作网络分析获得10个蛋白质互作的关键基因。
结肠腺癌(colon adenocarcinoma,COAD)是结肠恶性肿瘤中最常见的组织学类型,为第五大常见癌症,是癌症相关死亡的主要原因。截至2020年,全球结肠癌发病率为6.0%(1 148 515例),病死率约为5.8%(576 858例),呈逐年上升趋
本研究旨在开发COAD miRNA生物标志物,建立COAD预后miRNA模型以及基于年龄、美国癌症分期联合委员会(American Joint Committee on Cancer,AJCC)分期、T分期、放化疗以及风险评分等因素构建的列线图将较准确地预测COAD的风险,对鉴定高或低风险患者、精准预测预后及评估患者生存风险提供理论基础。
通过R(4.1.0)软件的“TCGAbiolinks”包从癌症基因组图谱(the cancer genome atlas,TCGA)数据库(https://cancergenome.nih.gov)中获取COAD患者临床信息及miRNA表达数据,该数据包括444个癌组织及8个癌旁正常组织样品。患者临床信息包括生存时间、生存状态、性别、年龄、TNM分期、T分期、N分期及放化疗信息等。筛选随访时间>30 d及临床信息不完整病例,最终获得359例患者的临床病理信息。
利用R(4.1.0)软件的“limma”包设定差异基因筛选标准为:校正P≤0.05且|log2倍数变化|≥2进行差异分析,获得的差异miRNA。对差异表达的miRNA进行单因素Cox风险回归分析,获得与患者总体生存(overall survival,OS)时间相关的miRNA,并进行Lasso回归分析,获得与患者OS时间显著相关的miRNA。将以上miRNA纳入多因素Cox风险回归分析中,并构建准确预测预后的风险评分模型。使用风险评分中位值作为截断点,将其分为高风险组和低风险组。采用对数秩检验对该模型进行Kaplan-Meier分析。使用随时间变化的受试者操作特征(receiver operating curve,ROC)曲线,测量了1、3、5年OS时间的预后风险模型的预测性能。利用R(4.1.0)软件的“caret”包从359个COAD样本中,随机抽取180个样本,进行内部验证组。为了进一步验证miRNA风险评分能否预测COAD的患者预后,加入TNM分期、年龄、性别、T分期及N分期等临床参数进行单因素和多因素Cox风险回归分析。利用Cox回归分析构建COAD患者预后生存风险列线图。通过TargetScan(https://www.targetscan.org)及miRDB(http://mirdb.org)数据库分析miRNA靶基因(为了提高预测精度,选择了两个数据库中重叠的基因作为目标,并且设定权重≤-0.40及分值>85)。使用检索相互作用基因的搜索工具String(https://cn.string-db.org)数据库获得目标基因的蛋白-蛋白互作(protein-protein interaction,PPI)信息,进一步使用Cytoscape软件对PPI网络进行可视化,使用CytosHubba插件通过EPC算法获得hub基因。
所有统计分析均使用R(4.1.0)(www.r-project.org)进行,所有结果均以P<0.05为差异具有统计学意义。
使用R(4.1.0)软件“limma”包分析444个癌和8个癌旁样本,获得的320个差异miRNA,其包括167个上调的miRNA和153个下调的miRNA(
图1 差异表达的miRNAs A :热图(癌和癌旁组织中差异表达的miRNA);B:火山图(红点为上调的miRNAs;蓝点为下调的miRNAs;黑点为无差异miRNAs)
Figure 1 Differentially expressed miRNAs A: Heat map (miRNAs differentially expressed in cancer and adjacent tissues); B: Volcano plot (red dots representing upregulated miRNAs; blue dots representing downregulated miRNAs; black dots representing miRNAs with no significant difference)
单因素Cox风险回归分析,获得38个与患者OS时间相关的miRNA(均P<0.05)。基于单因素Cox分析结果,对38个差异的Lasso回归分析,采用交叉验证法进行迭代分析,结果显示当变量数为19时,模型均方根误差最小,其对应的是λ=0.018(
图2 Lasso回归分析 A:Lasso筛选变量动态过程图;B:交叉验证过程参数λ的筛选过程
Figure 2 Lasso regression analysis A: Dynamic process diagram of Lasso variable selection; B: Selection process of cross-validation process parameter λ
ID | coef | HR(95% CI) | P |
---|---|---|---|
hsa-miR-503-5p | 0.497 | 1.643(1.242 3~2.173) | 0.001 |
hsa-miR-335-3p | 0.620 | 1.858(1.163~2.970) | 0.010 |
hsa-miR-185-5p | 0.853 | 2.345(1.195~4.602) | 0.013 |
hsa-miR-4436b-5p | 0.792 | 2.208(1.172~4.160) | 0.014 |
hsa-miR-125b-2-3p | 0.311 | 1.365(1.015~1.837) | 0.040 |
利用5个miRNA的表达量创建与OS时间相关风险评分公式:风险评分=(0.497)×miR-503-5p表达值+(0.620)×miR-335-3p表达值+(0.853)×miR-185-5p表达值+(0.792)×miR-4436b-5p表达值+(0.311)×miR-125b-2-3p表达值。使用风险评分中位值作为截断点,将患者分为高风险组和低风险组。每例患者的风险评分,患者生存时间及生存状况及免疫基因表达的热图如
图3 高风险评分与低风险评分COAD患者预后风险模型 A:高风险(红色)和低风险(蓝色)患者风险评分分布、生存状态(蓝色表示存活患者;红色代表死亡患者)及miRNA表达的热图(红色代表免疫基因高表达;黑色代表免疫基因低表达);B:高风险评分与低风险评分患者生存曲线(黄色代表高风险评分患者;蓝色代表低风险评分患者);C:miRNA预后模型时间依赖性ROC曲线显示患者1、3、5年OS时间的AUC
Figure 3 Prognostic risk model high-risk and low-risk score COAD patients A: Distribution of risk scores (red representing high-risk patients, blue representing low-risk patients), survival status (blue representing surviving patients, and red representing deceased patients), and heatmap of miRNA expression (red representing high expression of immune genes, black represents low expression of immune genes); B: Survival curves for high-risk and low-risk score patients (yellow representing high-risk score patients, blue representing low-risk score patients); C: Time-dependent ROC curve of the miRNA prognostic model showing the AUC values for 1-, 3-, and 5-year OS
每例内部验证组患者的风险评分分布和生存时间、生存状况及免疫基因表达热图如
图4 内部验证高风险评分与低风险评分COAD患者预后风险模型 A:高风险(红色)和低风险(蓝色)患者风险评分分布、生存状态(蓝色表示存活患者;红色代表死亡患者)及miRNA表达的热图(红色代表免疫基因高表达;黑色代表免疫基因低表达);B:高风险评分与低风险评分患者生存曲线(黄色代表高风险评分患者;蓝色代表低风险评分患者);C:miRNA预后模型时间依赖性ROC曲线显示患者1、3、5年OS时间的AUC
Figure 4 Internal validation of the prognostic risk model for high-risk and low-risk COAD patients A: Distribution of risk scores (red representing high-risk patients, blue representing low-risk patients), survival status (blue representing surviving patients, and red representing deceased patients), and heatmap of miRNA expression (red representing high expression of immune genes, black represents low expression of immune genes); B: Survival curves for high-risk and low-risk score patients (yellow representing high-risk score patients, blue representing low-risk score patients); C: Time-dependent ROC curve of the miRNA prognostic model showing the AUC values for 1-, 3-, and 5-year OS
单因素及多因素Cox回归分析结果显示,年龄、TNM分期及风险评分与患者OS率明显相关(均P<0.05),即风险评分是COAD的独立预后指标(
变量 | 单因素 | 多因素 | ||
---|---|---|---|---|
HR(95% CI) | P | HR(95% CI) | P | |
年龄 | 1.043(1.013~1.075) | 0.005 | 1.058(1.024~1.093) | 0.000 6 |
性别 | 0.657(0.796~2.911) | 0.204 | 0.808(0.405~1.613) | 0.546 |
T分期 | 8.996(1.231~65.76) | 0.030 | 5.287(0.703~39.790) | 0.106 |
N分期 | 2.234(1.186~4.208) | 0.013 | 0.169(0.020~1.407) | 0.100 1 |
TNM分期 | 2.298(1.184~4.461) | 0.014 | 14.405(1.636~126.838) | 0.016 |
风险评分 | 1 510(71.39~3 194) | 2.59e-06 | 1.721(1.281~2.312) | 0.000 3 |
列线图结果
图5 COAD患者OS时间列线图
Figure 5 Nomogram of OS time in COAD patients
分析结果表明,钙调蛋白1(calmodulin1,CALM1),蛋白质磷酸酶2催化亚基α(protein phosphatase 2 catalytic subunit α,PPP2CA),细胞分裂周期42(cell division cycle 42,CDC42),Akt丝氨酸/苏氨酸激酶3(Akt serine/threonine kinase 3,Akt3),异质核核糖核蛋白H1(heterogeneous nuclear ribonucleoprotein H1,HNRNPH1),圆盘大Maguk脚手架蛋白2(discs large MAGUK scaffold protein 2,DLG2),相关联的细胞分裂周期4(cell division cycle associated 4,CDCA4),肌动蛋白相关蛋白3(actin related protein 3,ACTR3),ADP核糖基化因子如GTP酶2(ADP ribosylation factor like GTPase 2,ARL2),细胞周期蛋白2(cyclin D2,CCND2)等10个基因为蛋白质互作的关键基因(
图6 miRNA模型的PPI网络图
Figure 6 PPI network diagram of miRNAs model
本研究通过生物信息学方法,分析COAD和癌旁样本芯片数据,获得差异表达的320个miRNA,基于差异基因的表达数据及临床数据,利用单因素和多因素Cox比例风险回归模型,鉴定出5个与患者OS高度相关的miRNA(miR-503-5p、miR-335-3p、miR-185-5p、miR-4436b-5p、miR-125b-2-3p),并建立miRNA预后风险模型。miRNA预后风险模型在COAD癌组织及癌旁正常组织中差异表达将有利于COAD的诊断及筛选。Kakan
本研究建立的miRNA预后风险模型具有良好的预测预后性能,并且5个miRNA分子均为高危型与患者的预后呈负相关。它不仅体现在实验组及训练组中高低风险患者生存状态具有明显的差异,即高风险患者风险评分及表达量较高,生存人数少,低风险患者则相反,还表现在随时间变化ROC曲线及生存分析曲线研究中,AUC均>0.6,具有较高的敏感度及特异度,并且高风险患者较低风险患者生存时间更短,具有预测预后的价值。此外,本研究还进一步利用单因素及多因素Cox回归分析风险评分和患者临床特征对预后的价值,结果表明风险评分与患者预后相关,列线图模型也进一步证实。因此,我们有理由相信miRNA预后风险模型具有评估患者临床进展及预后能力以及成为评估是否将患者加入早期诊断治疗和个体化治疗的指标。
众所周知,miRNA通过靶向下游mRNA发挥生物学,当靶向致癌途径中的负调控因子时具有致癌性,而靶向癌基因时发挥抑癌作
综上所述,本研究通过生物信息学方法,确定了miR-503-5p、miR-335-3p、miR-185-5p、miR-4436b-5p、miR-125b-2-3p 5个miRNA在COAD中高表达,并构建了以上分子的预后风险模型。此外,本研究发现5个miRNA分子均为高危型与患者的预后呈负相关,具有较好的预测预后的价值。本研究不足之处在于较多的实验研究围绕在线数据展开,未来需要收集更多的临床数据加以验证。本研究将为COAD诊断和筛查、评估患者临床进展、预后能力以及成为评估是否将患者加入早期诊断治疗和个体化治疗的指标提供了新的思路及理论依据。
作者贡献声明
向瑶、黄美园负责研究内容的设计;王俊普、周伟弘、幸雯雯、任建强、陈栋良负责数据收集、整理及分析;向瑶负责文章写作;所有作者均参与并同意对工作的各个方面的负责。
利益冲突
所有作者均声明不存在利益冲突。
参考文献
Sung H, Ferlay J, Siegel RL, 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. [百度学术]
Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021[J]. CA Cancer J Clin, 2021, 71(1):7-33. doi: 10.3322/caac.21654. [百度学术]
Jonas S, Izaurralde E. Towards a molecular understanding of microRNA-mediated gene silencing[J]. Nat Rev Genet, 2015, 16(7):421-433. doi: 10.1038/nrg3965. [百度学术]
Bovell L, Shanmugam C, Katkoori VR, et al. miRNAs are stable in colorectal cancer archival tissue blocks[J]. Front Biosci (Elite Ed), 2012, 4(5):1937-1940. doi: 10.2741/514. [百度学术]
Xue WJ, Wang YX, Xie YW, et al. miRNA-based signature associated with tumor mutational burden in colon adenocarcinoma[J]. Front Oncol, 2021, 11:634841. doi: 10.3389/fonc.2021.634841. [百度学术]
Zheng GL, Zhang GJ, Zhao Y, et al. Screening of miRNAs as prognostic biomarkers for colon adenocarcinoma and biological function analysis of their target genes[J]. Front Oncol, 2021, 11:560136. doi: 10.3389/fonc.2021.560136. [百度学术]
Corrao G, Zaffaroni M, Bergamaschi L, et al. Exploring miRNA signature and other potential biomarkers for oligometastatic prostate cancer characterization: the biological challenge behind clinical practice. A narrative review[J]. Cancers (Basel), 2021, 13(13):3278. doi: 10.3390/cancers13133278. [百度学术]
Karadag A, Ozen A, Ozkurt M, et al. Identification of miRNA signatures and their therapeutic potentials in prostate cancer[J]. Mol Biol Rep, 2021, 48(7):5531-5539. doi: 10.1007/s11033-021-06568-7. [百度学术]
Zhang L, Tao HS, Li J, et al. Comprehensive analysis of the competing endogenous circRNA-lncRNA-miRNA-mRNA network and identification of a novel potential biomarker for hepatocellular carcinoma[J]. Aging, 2021, 13(12):15990-16008. doi: 10.18632/aging.203056. [百度学术]
Sorop A, Constantinescu D, Cojocaru F, et al. Exosomal microRNAs as biomarkers and therapeutic targets for hepatocellular carcinoma[J]. Int J Mol Sci, 2021, 22(9):4997. doi: 10.3390/ijms22094997. [百度学术]
Aftab M, Poojary SS, Seshan V, et al. Urine miRNA signature as a potential non-invasive diagnostic and prognostic biomarker in cervical cancer[J]. Sci Rep, 2021, 11(1):10323. doi: 10.1038/s41598-021-89388-w. [百度学术]
Wang J, Zhang C. Identification and validation of potential mRNA- microRNA- long-noncoding RNA (mRNA-miRNA-lncRNA) prognostic signature for cervical cancer[J]. Bioengineered, 2021, 12(1):898-913. doi: 10.1080/21655979.2021.1890377. [百度学术]
徐文迪, 田那科·沙帕尔, 刘奎杰, 等. 预测结直肠癌预后免疫相关基因对标志模型的构建及验证[J]. 中国普通外科杂志, 2021, 30(4):449-463. doi: 10.7659/j.issn.1005-6947.2021.04.010. [百度学术]
Xu WD, Tiannake·SPR, Liu KJ, et al. Development and validation of immune-related gene pairs signature for prognostic prediction of colorectal cancer[J]. China Journal of General Surgery, 2021, 30(4):449-463. doi: 10.7659/j.issn.1005-6947.2021.04.010. [百度学术]
李晓东, 吴钢, 刘永军, 等. 结肠癌患者血清miR-192和miR-23a水平的变化及其临床意义[J]. 中国普通外科杂志, 2018, 27(2):246-251. doi: 10.3978/j.issn.1005-6947.2018.02.019. [百度学术]
Li XD, Wu G, Liu YJ, et al. Changes of serum miR-192 and miR-23a levels in colon cancer patients and the clinical significance[J]. China Journal of General Surgery, 2018, 27(2):246-251. doi:10.3978/j.issn.1005-6947.2018.02.019. [百度学术]
Preethi KA, Selvakumar SC, Ross K, et al. Liquid biopsy: Exosomal microRNAs as novel diagnostic and prognostic biomarkers in cancer[J]. Mol Cancer, 2022, 21(1):54. doi: 10.1186/s12943-022-01525-9. [百度学术]
Zhang Y, Xu H. Serum exosomal miR-378 upregulation is associated with poor prognosis in non-small-cell lung cancer patients[J]. J Clin Lab Anal, 2020, 34(6):e23237. doi: 10.1002/jcla.23237. [百度学术]
Luo R, Liu H, Chen J. Reduced circulating exosomal miR-382 predicts unfavorable outcome in non-small cell lung cancer[J]. Int J Clin Exp Pathol, 2021, 14(4):469-474. [百度学术]
Janpipatkul K, Trachu N, Watcharenwong P, et al. Exosomal microRNAs as potential biomarkers for osimertinib resistance of non-small cell lung cancer patients[J]. Cancer Biomark, 2021, 31(3):281-294. doi: 10.3233/CBM-203075. [百度学术]
Zhao Y, Xu L, Wang X, et al. A novel prognostic mRNA/miRNA signature for esophageal cancer and its immune landscape in cancer progression[J]. Mol Oncol, 2021, 15(4):1088-1109. doi: 10.1002/1878-0261.12902. [百度学术]
Kakan SS, Edman MC, Yao A, et al. Tear miRNAs identified in a murine model of sjögren's syndrome as potential diagnostic biomarkers and indicators of disease mechanism[J]. Front Immunol, 2022, 13:833254. doi: 10.3389/fimmu.2022.833254. [百度学术]
Hildebrandt A, Kirchner B, Meidert AS, et al. Detection of atherosclerosis by small RNA-sequencing analysis of extracellular vesicle enriched serum samples[J]. Front Cell Dev Biol, 2021, 9:729061. doi: 10.3389/fcell.2021.729061. [百度学术]
Zhang K, Wang YY, Xu Y, et al. A two-miRNA signature of upregulated miR-185-5p and miR-362-5p as a blood biomarker for breast cancer[J]. Pathol Res Pract, 2021, 222:153458. doi: 10.1016/j.prp.2021.153458. [百度学术]
Sacchetto C, Mohseni Z, Colpaert RMW, et al. Circulating miR-185-5p as a Potential Biomarker for Arrhythmogenic Right Ventricular Cardiomyopathy[J]. Cells, 2021, 10(10):2578. doi: 10.3390/cells10102578. [百度学术]
Huang HQ, Chen G, Xiong DD, et al. Down-regulation of microRNA-125b-2-3p is a risk factor for a poor prognosis in hepatocellular carcinoma[J]. Bioengineered, 2021, 12(1):1627-1641. doi: 10.1080/21655979.2021.1921549. [百度学术]
Arslan S, Bayyurt B, Engin A, et al. microRNA analysis from acute to convalescence in Crimean Congo hemorrhagic fever[J]. J Med Virol, 2021, 93(8):4729-4737. doi: 10.1002/jmv.26909. [百度学术]
Goodall GJ, Wickramasinghe VO. RNA in cancer[J]. Nat Rev Cancer, 2021, 21(1):22-36. doi: 10.1038/s41568-020-00306-0. [百度学术]
Wen CH, Feng XQ, Yuan HG, et al. Circ_0003266 sponges miR-503-5p to suppress colorectal cancer progression via regulating PDCD4 expression[J]. BMC Cancer, 2021, 21(1):284. doi: 10.1186/s12885-021-07997-0. [百度学术]
Li L, Wan DM, Li L, et al. lncRNA RAET1K promotes the progression of acute myeloid leukemia by targeting miR-503-5p/INPP4B axis[J]. Onco Targets Ther, 2021, 14:531-544. doi: 10.2147/OTT.S291123. [百度学术]
Han L, Cheng J, Li AF. hsa_circ_0072387 suppresses proliferation, metastasis, and glycolysis of oral squamous cell carcinoma cells by downregulating miR-503-5p[J]. Cancer Biother Radiopharm, 2021, 36(1):84-94. doi: 10.1089/cbr.2019.3371. [百度学术]
Wei L, Sun C, Zhang Y, et al. miR-503-5p inhibits colon cancer tumorigenesis, angiogenesis, and lymphangiogenesis by directly downregulating VEGF-A[J]. Gene Ther, 2022, 29(1/2):28-40. doi: 10.1038/s41434-020-0167-3. [百度学术]
Park GB, Kim D. microRNA-503-5p inhibits the CD97-mediated JAK2/STAT3 pathway in metastatic or paclitaxel-resistant ovarian cancer cells[J]. Neoplasia, 2019, 21(2):206-215. doi: 10.1016/j.neo.2018.12.005. [百度学术]
Gao Y, Wang YF, Wang XF, et al. miR-335-5p suppresses gastric cancer progression by targeting MAPK10[J]. Cancer Cell Int, 2021, 21(1):71. doi: 10.1186/s12935-020-01684-z. [百度学术]
Ji YY, Song Y, Wang AN. miR-335-5p inhibits proliferation of Huh-7 liver cancer cells via targeting the Oct4/Akt pathway[J]. Eur Rev Med Pharmacol Sci, 2021, 25(4):1853-1860. doi: 10.26355/eurrev_202102_25080. [百度学术]
Wen HQ, Liu ZY, Tang JJ, et al. miR-185-5p targets RAB35 gene to regulate tumor cell-derived exosomes-mediated proliferation, migration and invasion of non-small cell lung cancer cells[J]. Aging, 2021, 13(17):21435-21450. doi: 10.18632/aging.203483. [百度学术]
Baldi S, Khamgan H, Qian YY, et al. Downregulated ARID1A by miR-185 is associated with poor prognosis and adverse outcomes in colon adenocarcinoma[J]. Front Oncol, 2021, 11:679334. doi: 10.3389/fonc.2021.679334. [百度学术]
Meng XY, Liu KT, Xiang ZF, et al. miR-125b-2-3p associates with prognosis of ccRCC through promoting tumor metastasis via targeting EGR1[J]. Am J Transl Res, 2020, 12(9):5575-5585. [百度学术]
Zeng ZL, Lu JH, Wang Y, et al. The lncRNA XIST/miR-125b-2-3p axis modulates cell proliferation and chemotherapeutic sensitivity via targeting Wee1 in colorectal cancer[J]. Cancer Med, 2021, 10(7):2423-2441. doi: 10.1002/cam4.3777. [百度学术]