CN113502330A - m6A相关lncRNA在制备预测结直肠癌预后产品中的应用 - Google Patents
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Abstract
本发明公开了m6A相关lncRNA在制备预测结直肠癌预后产品中的应用。本发明采用m6A相关lncRNA：AC087277.2、AC007991.4、AC008494.3、AC254629.1和AL121583.1作为检测靶标，构建的模型预测性能良好；在临床治疗中，患者诊断明确后，构建的模型可快速判断高风险早期结直肠癌患者对化疗的敏感性，为早期结直肠癌患者术后化疗提供新的治疗方案，具有重要的临床意义和应用前景。
Description
技术领域
本发明涉及生物医学技术领域，尤其涉及m6A相关lncRNA在制备预测结直肠癌预后产品中的应用。
背景技术
结直肠癌(CRC)是最常见的胃肠道恶性肿瘤之一，也是癌症相关死亡的主要原因之一。虽然早期CRC(I期和II期)术后的5年生存率可达90％，但I期和II期CRC患者仍分别有5％和20％复发的风险。目前，早期CRC患者是否需要化疗主要取决于已知的临床和病理危险因素，如微卫星不稳定状态、肠梗阻等。然而，这些高危因素并不能区分预后差的患者和使化疗的患者获益。因此，迫切需要一种可靠的分子标志物来识别早期CRC患者的高危人群，优化治疗策略。
存在于大多数真核生物中的信使RNA(mRNAs)、非编码RNA(lncRNAs)和microRNAs(miRNAs)中的m6A，是哺乳动物RNA最常见的表观甲基化修饰。m6A的修饰受到一系列蛋白质因子的调控，包括甲基转移酶、信号转导酶和去甲基酶。有研究发现，m6A的异常修饰在CRC、肝细胞癌、乳腺癌、胶质母细胞瘤和肺癌等多种肿瘤的发生发展中起着重要作用。与mRNA相似，lncRNA也受m6A的调控。而且，越来越多的证据表明，m6A相关lncRNAs在多种肿瘤的发生和发展中起着至关重要的作用，能可靠地预测转移性皮肤黑色素瘤、肾上腺皮质癌、低级别胶质瘤、肺腺癌和胃癌等的预后。如m6A相关lncRNA(NEAT1-1)是一种新的骨转移瘤的特异性标志物，并与不良预后相关；m6A相关lncRNA(METL3和METL14)在头颈部鳞状细胞癌具有促进肿瘤发生发展的作用。最近，有研究报道CRC肿瘤部位与邻近正常部位的lncRNA的甲基化水平存在较大的差异，提示m6A相关lncRNA可能是早期CRC患者预后的可靠预测指标。
因此，我们旨在基于m6A相关的lncRNA建立一个可靠的预测早期CRC患者预后的模型，进而在临床上指导早期CRC患者的预后，提高患者的生存率。
发明内容
本发明的首要目的在于克服现有技术的缺点与不足，提供m6A相关lncRNA在制备预测结直肠癌预后产品中的应用。
本发明的目的通过下述技术方案实现：m6A相关lncRNA在制备预测结直肠癌预后产品中的应用。
所述结直肠癌为TNM分期为I期或II期的早期结直肠癌。
所述m6A相关lncRNA为AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1中的一种或几种。
所述AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1的序列如SEQID No.1-5所示。
所述应用是通过采用检测所述m6A相关lncRNA的表达水平，从而预测结直肠癌预后。
所述检测采用的试剂包括特异性扩增m6A相关lncRNA的引物或者特异性识别m6A相关lncRNA的探针。
所述产品包括试剂盒和芯片。
所述特异性扩增m6A相关lncRNA：AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1的引物分别如SEQ ID No.6-15所示：
一种预测结直肠癌预后的产品，包括检测离体结直肠癌组织样本中m6A相关lncRNA：AC087277.2、AC007991.4、AC008494.3、AC254629.1或AL121583.1表达水平的试剂。
采用所述产品检测m6A相关lncRNA的表达水平之后，使用风险评分公式
进行风险评分；其中，k为m6A相关lncRNA的数量，βi为每个m6A相关lncRNA的系数，Si为每个m6A相关lncRNA表达水平。
所述m6A相关lncRNA：AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1的系数分别为–0.828、0.989、–5.396、–0.151、–1.435。
预测结直肠癌预后情况时，样本的风险评分高于界值0.0481则预后不良，风险评分低于界值0.0481则预后良好。
所述结直肠癌为早期结直肠癌。
一种预测结直肠癌预后模型的构建方法，包括如下步骤：
(1)单因素Cox回归分析筛选获得与早期结直肠癌患者总生存期(OS)显著相关的m6A相关lncRNAs；
(2)将患者分为训练组和验证组，Lasson回归分析每个OS显著相关的m6A相关lncRNAs对生存期的贡献，建立风险评分模型；
(3)使用验证组对风险评分模型进行验证，检验所建模型的预测准确度。
采用上述构建方法获得的预测早期结直肠癌预后模型为：风险评分＝–0.828×AC087277.2的表达水平+0.989×AC007991.4表达水平–5.396×AC008494.3的表达水平–0.151×AC254629.1的表达水平–1.435×AL121583.1的表达水平；当风险评分大于0.0481时，患者预后不良；当风险评分小于0.0481时，患者预后良好。
所述结直肠癌为早期结直肠癌。
与现有技术相比，本发明具有以下有益效果：
本发明将m6A相关lncRNAlncRNA：AC087277.2、AC007991.4、AC008494.3、AC254629.1和AL121583.1作为检测靶标，构建的模型预测性能良好；在临床治疗中，患者诊断明确后，构建的模型可快速判断高风险早期结直肠癌患者对化疗的敏感性，为早期结直肠癌患者术后化疗提供新的治疗方案，具有重要的临床意义和应用前景。
附图说明
图1是早期结直肠癌患者人口统计学和临床特征表图。
图2是总生存期(OS)显著相关的m6A相关lncRNAs森林图。
图3是预后模型的开发和验证结果图；其中，A为训练组中构建OS显著相关m6A相关lncRNA预后模型的系数曲线图，B为训练组中构建OS显著相关m6A相关lncRNA预后模型的部分似然偏差图，C为训练组中高风险组和低风险组的生存率比较图，D为验证组中高风险组和低风险组的生存率比较图，E为训练组1年、2年和3年早期结直肠癌患者OS的AUC曲线面积检测结果图，F为验证组1年、2年和3年早期结直肠癌患者OS的AUC检测结果图。
图4是训练组和验证组风险评分曲线和生存状态图；其中，A为训练组风险评分曲线图，B为验证组风险评分曲线图，C为训练组生存期图，D为验证组生存期图，E为训练组预后模型中OS显著相关m6A相关lncRNA表达热图，F为验证组预后模型中OS显著相关m6A相关lncRNA表达热图。
图5是早期结直肠癌患者预后的单多因素Cox回归分析；其中，A为训练组单因素Cox回归森林图，B为验证组单因素Cox回归森林图，C为训练组多因素Cox回归森林图，D为验证组多因素Cox回归森林图。
图6是列线图的构建与验证图；其中，A为联合常见的临床病理特征和训练组的风险评分建立列线图，B为1年、2年和3年的早期结直肠癌患者OS的AUC曲线面积检测结果图，C从左至右为1年、2年和3年OS的校准曲线。
图7是早期结直肠癌患者化疗药物敏感性检测结果图；其中，A为喜树碱，B为顺铂，C为甲氨蝶呤，D为雷帕霉素。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例1
(一)患者信息获取：从the Cancer Genome Atlas(TCGA)网站下载早期结直肠癌RNA测序数据(FPKM值)和相应的临床资料，包括年龄、性别、分期、总体生存时间和生存状态。为了减少统计偏移，没有生存信息或生存时间小于3个月的患者被排除在进一步的评估中。经过筛选，TCGA数据库中的251例早期结直肠癌患者最终纳入分析(见图1)。
(二)预后相关的m6A相关lncRNA的获取：利用Ensembl的GTF文件区别早期结直肠癌RNA测序数据中的mRNA和lncRNA。接下来，使用R软件提取已鉴定的23个m6A基因(METTTL3、METTTL14、METTTL16、WTAP、VIRMA、ZC3H13、RBM15、RBM15B、ALKBH5、FTO、YTHDC1、YTHDC2、YTHDF1、YTHDF2、YTHDF3、HNRNPC、FMR1、LRPPRC、HNRNPA2B1、IGFBP1、IGFBP2、IGFBP3、RBMX)的表达数据。m6A基因与所有lncRNA进行Pearson相关分析。将绝对相关系数r≥0.4和P值＜0.001的lncRNA定义为m6A相关的lncRNA。采用单因素Cox回归分析筛选获得7个与总生存期(OS)显著相关(P<0.01)的m6A相关lncRNAs，分别为EPS15-AS1、LINC00562、AC087277.2、AC007991.4、AC008494.3、AC254629.1和AL121583.1(图2)，以上lncRNAs的序列如SEQ ID No.1-5所示。
(三)预后模型的开发和验证：将纳入完整临床资料的早期结直肠癌患者做预后分析。使用“caret”包将早期结直肠癌患者按1:1的比例随机分为训练组(n＝127)和测试组(n＝124)。基于训练组的数据建立预后预测模型。首先，采用最小绝对收缩和选择算子(LASSO)Cox回归分析7个与总生存期(OS)显著相关的m6A相关lncRNAs来确定最佳候选的m6A相关的lncRNA特征，以构建预后特征。采用十倍交叉验证来防止过拟合。获得了包含5个m6A相关lncRNA的最优模型(AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1)(图3A、B)。其次，根据该模型中各m6A相关lncRNA的系数和表达量(表达量采用荧光定量PCR获得，以GAPDH作为内参；引物序列如SEQ ID No.6-15所示)，计算每个患者的风险评分。公式如下：
(k：预后签名中m6A相关lncRNA的数量；βi：每个m6A相关lncRNA的系数；Si:m6A相关lncRNA表达水平)。具体计算方法为风险评分＝–0.828×AC087277.2+0.989×AC007991.4–5.396×AC008494.3–0.151×AC254629.1–1.435×AL121583.1。然后，以训练组风险评分中位数为界值(0.0481)，将训练组患者分为高风险组和低风险组。采用Kaplan-Meier(K-M)和log-rank检验比较两组患者之间的生存率，K-M生存曲线显示，高风险组的OS比低风险组差(p＝0.001)(图3C)。用R包“survival ROC”进行ROC(受试者工作特征曲线)分析曲线下面积(AUC)，评价风险评分预测1、2、3年早期结直肠癌患者OS的特异性和敏感性，结果显示1年、2年和3年的早期结直肠癌患者OS的AUC分别为0.929、0.954和0.841(图3E)。此外，验证组也分为高低风险组，高风险组的OS显著短于低风险组(图3D)，ROC结果显示1年、2年和3年的早期结直肠癌患者OS的AUC分别为0.664、0.760和0.754(图2F)。最后，训练组和验证组风险评分曲线和生存状态图显示，高危组患者预后更差，死亡更多，长期生存期更短(图4)。
(四)风险评分与临床病理特征的关系：为了进一步评估m6A相关lncRNA模型在预测预后中的作用，我们将风险评分和一些常见的临床病理特征年龄、性别、TNM分期纳入预后相关分析。单变量和多变量Cox分析结果(图5)显示，无论是训练组还是验证组，风险评分是影响早期结直肠癌患者预后的独立预后因素(训练组：危险比(HR)＝2.489，95％可信区间(CI)＝1.475–4.201，p＜0.001)；验证组：HR＝1.924，95％CI＝1.284–2.883，p＝0.002)。
(五)构建列线图：为了开发一种定量方法来预测临床环境中早期结直肠癌患者的预后，我们联合常见的临床病理特征年龄、性别、TNM分期和训练组的风险评分建立了一个列线图(图6A)。其中，危险评分对预后的影响最大，其次依次是年龄、性别、TNM分期。1年、2年和3年的AUC曲线的面积分别为0.860，0.873，0.842(图6B)，一致性指数(C指数)为0.779，此外，1年、2年和3年OS的校准曲线接近标准曲线(图6C)，表明模型性能良好。
实施例2检测化疗药物敏感性
已知癌症药物敏感性基因组学(GDSC)数据库(https://cancerrxgene.org)可进行大规模药物筛查。结合基因组分析，可系统地识别药物反应生物标志物。基于该数据库，发明人使用“pRRophetic”包来预测早期结直肠癌患者不同风险组中常用化疗药物(喜树碱、顺铂、雷帕霉素、甲氨蝶呤)的半数最大抑制浓度(IC50)。然后采用Wilcoxon符号秩检验比较实施例1中的高风险组和低风险组中不同化疗药物的IC50差异，筛选出组间差异显著的药物，进而用于指导临床上实施案例1模型中高风险早期结直肠癌患者术后的化疗。IC50越低，代表对该种药物更敏感。如图7所示，我们发现高风险的早期结直肠癌患者对喜树碱和顺铂更为敏感，这可能为早期结直肠癌患者术后化疗提供新的治疗方案。
以上所述是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也视为本发明的保护范围。
序列表
<110> 中山大学附属第六医院
<120> m6A相关lncRNA在制备预测结直肠癌预后产品中的应用
<160> 15
<170> SIPOSequenceListing 1.0
<210> 1
<211> 151
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC087277.2
<400> 1
gactccttgc ttgaggggca gagtcctctt gctctaagaa aagaaattgg ttcttccttt 60
catctacttt tcctgccagt gtttatttgc tagcccatat gttcctgcac agggtcactc 120
ctattgtgaa ataaatgctg aataaataaa a 151
<210> 2
<211> 605
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC007991.4
<400> 2
tcaccaaggt gggtttatac tgatgatatt taccttagtt tttccatctt tttttttttt 60
ttgagatgga gtcttgttct gtcacccagg ctggaatgca gtggcgtgat ttccgctcac 120
tgcaacctcc acctcctggg ttcaattgat tctcctgcct cagcctcctg agtagctgag 180
attacaggcg cccgccacca ggcccggcta attttttgta tttttagtag agacgaggtt 240
tcaccatgtt gcccagataa aatacaggat gaaaaatgaa gtaaaagact aaaaatataa 300
taatgataac agtggacatg tcaagccagg gcatattttc tttatcattt tgtcgtgtta 360
tctaaacttt gcatgatgca gttataatgc ttttagaaaa tttttggaaa gttaaatgta 420
aattagatga aaatctgtca catagcataa gaaatctgag aaaatctcca aaccttacgg 480
acatctccat gacctttgcc ccacacatat gccatggtga tgcatcccag aactagacgt 540
gcaaggcgct gtgacttgtg gtctgtgaga tgatcaatgc tgagcatgtt taactgaaaa 600
caaat 605
<210> 3
<211> 944
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC008494.3
<400> 3
gccacagccc tcgacctaca gccttccaga ccttcccacc gtacggcggg agagtggggg 60
taacctcaag cccacttgaa accagtcagt gccaactggg cggccgcgca gcccccttcc 120
ggcgcccagt ttgcggtccg actccgccgc gcctttccgc tggagcccgg gccttgtgcc 180
agcctggctg actgtgccac gagttacctg cttgtctcga gtgaagaata gcgctccggc 240
aaaacctgga ggcagcgctg gaaaaatcgc acgtgccgat cccgtaagaa atccagccgc 300
tctccctcac cgctccctgc tagcctctca tcctcagtgg ccgccatgct gctccggaag 360
cgacgtccgc cgcgacccgg aatcagtgcc cgcggagaga aagaaccctc ctgtgctgaa 420
ccacagggtg ccgggtgagg ggacctgcgc ccgccgcaga ctaagccagt atttgaaacc 480
gctatcatct gggtcctgtg cagcacttcc tctctccgat atctccctag ctagcgcatg 540
accctacctt ttctactcaa agccctcgag gcttgtaaac acctcgcagg ccttgtgaaa 600
atcaagtcca caaatccctc cgtcagaatt tttatcttgg gaagcgcaag gtcaaaagca 660
tcaagtacgc tttgagacaa ttctggctct gtagaacctg aggatggtat atctgaaaac 720
gaggatggat cagtataaac ttgctaacag attcattttt ggagggccag ttttaagccg 780
aacggttagg gaaacagaaa aacaaaccag tggagataaa tgtttattga cactacaaaa 840
gctcagagac tttcctaatc ttaatccttt ctggatacca ggaatcactt aaaaatctgt 900
gtataatgcc cccaaacata tacaacatgc atattcatac ctat 944
<210> 4
<211> 1955
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC254629.1
<400> 4
agaacgaggg gccggggctc taccctaggt gagggggcgg gattccacct tagatcaagt 60
gggcagggct ccaccctaga aggagggggc ggggttcaac cctagaacga ggaggcgggg 120
ccccacccta gaatgagtgg gcggggttct ctcccagaat taggggacag gactcaaccc 180
catcaaagga gcagagctcc agggcagggc tccactgtag ggcaacgggg atgggcttgg 240
agcggcactc tggggacgag gctccactgt aggcccaggg cgcggagccg gggcggagca 300
tggggcaggg cttcatccta gaatgagtgg gaggggctcg gctgatgccg atggtgagcg 360
gggccttacc gacacgtggg accgctcgtc tccagaacgc actggccctg gtgacagtcg 420
atggcgttgt ccacccccga cgtgcatttg gtgacacagc ggagccgggt ggcctccacc 480
aaggggaagt agaactcttc atagcccgtg ggagcggcgc ggcggcagat ggctgggggt 540
tggggagggg gatgcacaga gacaaggacc tcatccattc tattcagggg cctgataacg 600
gtgcctctga atatgcgcga ggaggattcc atttccgagg tgggatgctg tggggtagac 660
cctgggattc gatgagggga ggccacccgc agcaccctca ccctctcctc gtggcccctg 720
ctgtcctcag gtggaagcca ctaaccagcg ccaggaaggg ccgccctggg ccacatgact 780
tgcctccccc aaaggtctct ccctgcccac aggcttcaat gggcaaatgt ccctggtagc 840
ccggatcata gccgcgggag accactcagc cctttacccc acccttacct gccggggtca 900
gctctgtctt gctgttgttg ttcaccttga tggagtcagg cttaaaacac agggctgcgg 960
aaacacaggc acagatggaa tgggcggggc agatcaaggg gttaaggtca aggggttagc 1020
gccaggaggc aggataggca cagggagggc agtgagggct ctctccgtct gggagagccc 1080
tctcccagac ggagagagcc acaggcgaag actcctgttg cctgcctcat ctcccctgaa 1140
taatccgagg tcttcagcca tccgggaaat gctgaccatg acaatcaggg ggtctctttc 1200
ccagtaccga ggtcacgggg agaccctccc tagccccacc cttgacaccg ttgtgtgtgg 1260
acttattccc cttctcccaa cctgccagag gtaccacagg gctggcacct gagcccagtg 1320
gccccctggc tgagctggag tgggctgggg tgacacctca ggccctggcc cctccctcca 1380
gcctgggctc actctgggag tcctggcagc tgttcacatc ctggctggcg ttctgcagcc 1440
cctccttcag cgtggtcttc acctgctcat actcgctctc cagctggggg ctgaagggca 1500
tctccagcag gaccagggat gggttctcac tatgttgccc aggctggtct tgaactgctt 1560
tcaggtgatc cacccgccta gcctacccaa agtgctagga ttgcaagtgt gagccaccac 1620
atctggcctg acgttctctt cttgttgcat caagccatca tccaccgggc gcccctagac 1680
gtgcgttctc tccctctctc tctttctctc tctctctttc taatgctgac ccactcagta 1740
gagcaggggc cagccagagg gggacaaagt catcaatcca ggcaccacct tcccacatgt 1800
ccctgcaact tctctccttc cagtgtgctg aagaagagag gtgccacccc tcctccagat 1860
aaatggcccc agaaaacgat ccgaaaaaat acccagggag tgattcctta tgtaccccct 1920
ccacccctct ttataaaatc tgatgattac aaaag 1955
<210> 5
<211> 682
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AL121583.1
<400> 5
ctatgtaccc tgctgtccaa atgacggcac tgagtcagtg agaggtttca taacttgccc 60
aaggccacag aactggatgg agaaagactg cagagattca aacccaggcc tgctgacatg 120
cagagccaat gagaatatat tatcttgcag tgcagtttat gggatatatg tgtccctttg 180
ggaattcctg ggtgtctgct ggtggcctgt taacttggtt cttgaatatg cactttgctt 240
ataactgtgc agtttgaaaa gtgactcatc cccaaactag gcagggttga gtgtctgtcc 300
tgtttatctt ccacatgcca cctgcactaa cttctgaaga taatctataa taaaagcact 360
gtgattaaaa aacagaaaaa cacacaccaa aaatgtatag gtttgtctgt tggtgcttct 420
gacaagaaaa taaactcatg aatctcatta actttaaaaa aatcacatct acttaaggtt 480
taatgactaa agccaacttt ctaacatatc tgtaccgttc tctgaacata ctgttttcac 540
acttaactac gatgttgtca cttgtttctg tctgattctg caagggcaga gaccttgaaa 600
tattcaaatc tggattcccc catggtttga cccagctgaa gtcaggcagt gttcgctgaa 660
agaaataaag gtgtttccat aa 682
<210> 6
<211> 25
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC087277.2-F
<400> 6
ggttcttcct ttcatctact tttcc 25
<210> 7
<211> 26
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC087277.2-R
<400> 7
tttatttatt cagcatttat ttcaca 26
<210> 8
<211> 20
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC007991.4-F
<400> 8
agtggacatg tcaagccagg 20
<210> 9
<211> 20
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC007991.4-R
<400> 9
atggcatatg tgtggggcaa 20
<210> 10
<211> 23
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC008494.3-F
<400> 10
caaggtcaaa agcatcaagt acg 23
<210> 11
<211> 23
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC008494.3-R
<400> 11
tccaaaaatg aatctgttag caa 23
<210> 12
<211> 23
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC254629.1-F
<400> 12
gtgcgttctc tccctctctc tct 23
<210> 13
<211> 23
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AC254629.1-R
<400> 13
aatcactccc tgggtatttt ttc 23
<210> 14
<211> 24
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AL121583.1-F
<400> 14
aaaacagaaa aacacacacc aaaa 24
<210> 15
<211> 24
<212> DNA
<213> 人工序列(Artificial Sequence)
<220>
<223> AL121583.1-R
<400> 15
cagacagaaa caagtgacaa catc 24
Claims (10)
1.m6A相关lncRNA在制备预测结直肠癌预后产品中的应用。
2.根据权利要求1所述应用，其特征在于，
所述m6A相关lncRNA为AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1中的一种或几种；
所述AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1的序列为SEQ IDNo.1-5所示。
3.根据权利要求1所述应用，其特征在于，
所述结直肠癌为TNM分期为I期或II期的早期结直肠癌。
4.根据权利要求1所述应用，其特征在于，所述应用是通过检测所述m6A相关lncRNA的表达水平，从而预测结直肠癌预后。
5.根据权利要求4所述应用，其特征在于，
所述检测采用的试剂包括特异性扩增m6A相关lncRNA的引物或者特异性识别m6A相关lncRNA的探针；
所述产品包括试剂盒和芯片。
6.根据权利要求5所述应用，其特征在于，所述特异性扩增m6A相关lncRNA：AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1的引物分别如SEQ IDNo.6-15所示。
7.一种预测结直肠癌预后的产品，其特征在于，包括检测离体结直肠癌组织样本中m6A相关lncRNA：AC087277.2、AC007991.4、AC008494.3、AC254629.1或AL121583.1表达水平的试剂。
8.根据权利要求7所述预测结直肠癌预后的产品，其特征在于，采用所述产品检测m6A相关lncRNA的表达水平之后，使用风险评分公式
进行风险评分；其中，k为m6A相关lncRNA的数量，βi为每个m6A相关lncRNA的系数，Si为每个m6A相关lncRNA表达水平；
所述m6A相关lncRNA：AC087277.2、AC007991.4、AC008494.3、AC254629.1、AL121583.1的系数分别为–0.828、0.989、–5.396、–0.151、–1.435；
预测结直肠癌预后情况时，样本的风险评分高于界值0.0481则预后不良，风险评分低于界值0.0481则预后良好。
9.一种预测结直肠癌预后模型的构建方法，其特征在于，包括如下步骤：
(1)单因素Cox回归分析筛选获得与早期结直肠癌患者总生存期OS显著相关的m6A相关lncRNAs；
(2)将患者分为训练组和验证组，Lasson回归分析每个OS显著相关的m6A相关lncRNAs对生存期的贡献，建立风险评分模型；
(3)使用验证组对风险评分模型进行验证，检验所建模型的预测准确度。
10.采用权利要求9所述构建方法获得的预测早期结直肠癌预后模型为：风险评分＝–0.828×AC087277.2的表达水平+0.989×AC007991.4表达水平–5.396×AC008494.3的表达水平–0.151×AC254629.1的表达水平–1.435×AL121583.1的表达水平；当风险评分大于0.0481时，患者预后不良；当风险评分小于0.0481时，患者预后良好。
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