Abstract
目的
探讨m7G-lncRNAs能否作为结肠癌患者预后及肿瘤微环境的生物标志物。
方法
TCGA数据库筛选m7G-lncRNAs（|Pearson R|>0.4，P < 0.001），多因素Cox分析构建m7G-lncRNAs风险模型。使用ROC和C-index曲线对风险模型进行验证。构建诺莫图和诺莫图的校准曲线用于预测结肠癌患者的预后。点柱图和K-M生存曲线评估风险打分对患者临床分期和预后的影响。CIBERSORT和ESTIMATE探究高低风险组患者肿瘤微环境和免疫细胞浸润程度的联系，同时分析风险打分对结肠癌患者微卫星不稳定性，干细胞指数和免疫检查点表达的影响。使用相互作用基因搜索工具（STRING）构建蛋白质-蛋白质相互作用网络，挖掘m7G-lncRNAs调控的关键靶点。最后，使用蛋白印迹实验在4对结肠癌组织与癌旁正常组织中验证这些关键靶点的表达。
结果
从TCGA数据库鉴别出1722个m7G-lncRNAs。多因素Cox分析筛选出12个lncRNAs用于构建风险模型，其中AC003101.2、AC005014.2、AC008760.1、AC092944.1、AL1161729.4、AL301422.4、AP001619.1、AP003355.1和ZEB1-AS1为高风险lncRNAs，AC025171.4、AC073957.3及TNFRSF10A-AS1为低风险lncRNAs。ROC曲线显示风险模型对患者1年、3年、5年生存预测的AUC值分别为0.727、0.747、0.794。诺莫图预测患者预后的AUC值为0.794，校准曲线显示诺莫图对患者生存的预测与患者实际的生存基本一致。高风险组患者的T分期（T1~T2 vs T3~T4：P=0.034）、N分期（N0 vs N2：P=7.8e-08；N1 vs N2：P=0.00081）以及M分期（M0 vs M1：P=0.007）均高于低风险组患者。低风险组患者常伴随高微卫星不稳定状态（MSS vs MSI-H：P=0.034）。肿瘤干性指数与风险得分呈负相关（r=-0.19；P=7.3e-05）。高风险组患者基质细胞打分（P=0.0028）以及总打分（P=0.007）明显高于低风险组患者较高，激活的肥大细胞（r=-0.11；P=0.045）和静息CD4+T细胞（r=-0.14；P=0.01）的表达也较低。多数免疫检查点在高风险患者中高表达（P < 0.05）。蛋白印迹实验表明m7G-lncRNAs调控的关键靶点ATXN2（P=0.006）and G3BP1（P=0.007）在4对结肠癌组织中表达均高于配对的癌旁正常组织。
结论
12个m7G-lncRNAs构建的风险模型对结肠癌具有重要的预后价值，同时也能反映结肠癌患者肿瘤微环境及免疫治疗的疗效。
Keywords: 结肠癌, m7G, lncRNAs, 预后模型, 肿瘤微环境
Abstract
Objective
To assess the value of m7G-lncRNAs in predicting the prognosis and microenvironment of colorectal cancer (CRC).
Methods
We screened m7G-lncRNAs from TCGA to construct an m7G-lncRNAs risk model using multivariate Cox analysis, which was validated using ROC and C-index curves. Calibration and nomogram were used to predict the prognosis of CRC patients. Point-bar charts and K-M survival curves were used to assess the correlation of risk scores with the patients' clinical staging and prognosis. CIBERSORT and ESTIMATE were used to explore the association between the tumor microenvironment and immune cell infiltration in patients in high and low risk groups and the correlation of risk scores with microsatellite instability, stem cell index and immune checkpoint expression. A protein-protein interaction network was constructed, and the key targets regulated by m7G-lncRNAs were identified and validated in paired samples of CRC and adjacent tissues by immunoblotting.
Results
We identified a total of 1722 m7G-lncRNAs from TCGA database, from which 12 lncRNAs were screened to construct the risk model. The AUCs of the risk model for predicting survival outcomes at 1, 3 and 5 years were 0.727, 0.747 and 0.794, respectively. The AUC of the nomogram for predicting prognosis was 0.794, and the predicted results were consistent with actual survival outcomes of the patients. The patients in the high-risk group showed more advanced tumor stages and a greater likelihood of high microsatellite instability than those in the low-risk group (P < 0.05). The tumor stemness index was negatively correlated with the risk score (r=-0.19; P=7.3e-05). Patients in the high-risk group had higher stromal cell scores (P=0.0028) and higher total scores (P=0.007) with lowered expressions of activated mast cells (r=-0.11; P=0.045) and resting CD4+ T cells (r=-0.14; P=0.01) and increased expressions of most immune checkpoints (P < 0.05). ATXN2 (P= 0.006) and G3BP1 (P=0.007) were identified as the key targets regulated by m7G-lncRNAs, and their expressions were both higher in CRC than in adjacent tissues.
Conclusion
The risk model based on 12 m7G-lncRNAs has important prognostic value for CRC and can reflect the microenvironment and the efficacy of immunotherapy in the patients.
Keywords: colon cancer, m7G, long non-coding RNAs, prognostic model, tumor microenvironment
全球癌症报告显示，结直肠癌的发病率和死亡率均位列前3，发病人群还有年轻化的趋势[1]。结直肠癌患者主要的死亡原因是继发的转移，研究报道转移性结直肠癌患者5年生存率不足20%[2]。因此，找到对结肠癌具有预后预测价值的生物标志物是研究的重点。
RNA甲基化在癌症的发展过程中起着十分重要的地位[3]。随着RNA测序技术的发展，许多RNA修饰不断被发现，包括5-甲基胞嘧啶、N1-甲基腺苷、N6甲基腺苷、N7-甲基腺苷等[4]。甲基化在结肠癌的适应性免疫中起重要调节作用，甲基化状态的改变能影响结肠癌患者对免疫检查点抑制剂治疗的反应性[5]。此外，结肠癌的甲基化状态受肿瘤内部缺氧的环境影响发生改变，这种变化促进了转移的发生[6]。以上研究表明甲基化影响结肠癌的发生以及结肠癌的肿瘤微环境。
lncRNA的甲基化修饰对肿瘤的发展具有十分重要的影响，研究表明ALKBH5，一种去甲基化酶，通过去甲基化lncRNA NEAT1来促进胃癌的侵袭和转移[7]。异常的甲基化导致HNF1A-AS1的减少可以通过促进上皮间质转化行为，导致喉鳞状细胞癌的恶性进展[8]。体内和体外实验表明METTL14的敲减能大幅抑制lncRNAXIST的甲基化，从而增强了结肠癌转移能力[9]。以上研究反映甲基化对lncRNAs的调控对肿瘤的进程起到重要调节作用。
m7G是一种最常发生在tRNA上的甲基化修饰，有助于维持tRNA的稳定性[10, 11]。近来，研究表明m7G也影响其他类型的RNA的稳定性[12, 13]。然而m7G对lncRNAs的影响以及其在结肠癌中发挥的功能尚无研究报道。本研究根据TCGA数据库构建了m7G-lncRNAs风险模型。随后评估风险模型对患者临床病理特征和预后的关系。最后探究风险模型对结肠癌患者微卫星不稳定性、免疫微环境及免疫检查点表达情况的影响，旨在为转移性结肠癌患者的治疗和管理提供一定的指导意义。
1. 材料和方法
1.1. 结肠组织及实验试剂
配对的结肠癌及癌旁正常组织来源于在蚌埠医学院第一附属医院接受治疗的患者（表 1）。ATXN2、G3BP1及GAPDH一抗（武汉三鹰生物技术），二抗（武汉爱博泰科生物技术）。蛋白上样缓冲液、蛋白分子量标准、RIPA裂解液（强）及蛋白酶抑制剂混合物（碧云天生物技术），PAGE凝胶快速制备试剂盒（10%）（上海雅酶生物医药）。所有实验均通过伦理审查（伦科批字【2020】第238号）。
1.
结肠癌患者的临床信息
Clinical information of colon cancer patients
| Case No. | Gender | Age (year) | T-stage | N-stage | M-stage |
| 1 | Male | 74 | T2 | N1 | M0 |
| 2 | Male | 67 | T2 | N0 | M0 |
| 3 | Male | 61 | T3 | N1 | M0 |
| 4 | Female | 26 | T2 | N0 | M0 |
1.2. 数据来源及筛选m7G-lncRNAs
结合文献[<xref ref-type="bibr" rid="b10">10</xref>]和GSEA数据库（<a href="http://www.gseamsigdb.org/gsea/index.jsp" target="_blank">http://www.gseamsigdb.org/gsea/index.jsp</a>）的GOMF_M7G_5_PPPN_ DIPHOSPHATASE_ACTIVITY、GOMF_RNA_7MET HYLGUANOSINE_CAP_BINDING、GOMF_RNA_ CAP_BINDING基因集，获取m<sup>7</sup>G基因。下载TCGA数据库（<a href="https://portal.gdc.cancer.gov/" target="_blank">https://portal.gdc.cancer.gov/</a>）中结肠癌患者的转录组数据和临床数据，使用Perl脚本区分lncRNA和mRNA。以|Pearson R| >0.4，<italic>P</italic> < 0.001筛选m<sup>7</sup>G-lncRNAs，使用“ggalluvial”包绘制共表达桑基图。
1.3. 构建m7G-lncRNAs风险模型
单因素及多因素Cox筛选独立预后的m7G-lncRNAs。根据多因素Cox结果构建风险模型，风险得分=（风险指数1*lncRNA1的表达量）+（风险指数2*lncRNA2的表达量）+（风险指数3*lncRNA3的表达量）+……+（风险指数n*lncRNAn的表达量）。
1.4. 验证m7G-lncRNAs风险模型
根据风险评分的中位值，将患者分为高危和低危两组。K-M生存曲线确定两组间总生存的差异。使用“pheatmap”包绘制风险基因在风险模型中的表达量。使用“SurvivalROC”、“Survival”包绘制ROC曲线、Cindex及诺莫图对风险模型进行验证。同时，分析风险得分与结肠癌患者临床病理特征的关系，并使用“Survival”包分析风险模型对不同临床亚组患者预后的预测意义。
1.5. 风险模型与结肠癌微卫星不稳定性和免疫浸润的联系
比较风险得分与结肠癌患者的微卫星不稳定性及细胞干性指数的关系。使用“limma”，“org.Hs.eg.db”，“clusterProfiler”和“enrichplot”包分析高低风险组患者在分子水平上的功能差异。使用ESTIMATE分别计算结肠癌患者的基质细胞打分，免疫细胞打分以及总打分[14]。使用CIBERSORT分析结肠癌患者组织中免疫细胞含量[15]。最后使用Pearson相关性分析探究风险得分与免疫细胞含量的相关性。
1.6. 模型lncRNAs调控的分子靶点
以|Pearson R| >0.6，<italic>P</italic> < 0.001筛选出与模型lncRNAs具有共表达关系的分子作为模型lncRNAs参与调节的靶分子。使用Metascape在线网站（<a href="https://metascape.org/gp/index.html#/main/step1" target="_blank">https://metascape.org/gp/index.html#/main/step1</a>）绘制靶分子的蛋白-蛋白互作网络。使用Cytoscape的cytoHubba插件以Degree值排名前十位的分子作为模型lncRNAs调节的核心分子。使用TCGA数据库、临床组织样本及HPA数据库（<a href="https://www.proteinatlas.org/" target="_blank">https://www.proteinatlas.org/</a>）分析这些核心分子在结肠癌组织及癌旁正常组织的表达情况。
1.7. 蛋白印迹实验
使用组织剪剪碎病人的组织，PBS清洗2遍。使用RIPA裂解液充分裂解患者组织，离心获取上清即为蛋白样品。使用酶标仪计算每个患者的蛋白样品浓度，并使用RIPA按照浓度最低的样品统一所有样品的浓度。按说明书要求加入适量蛋白上样缓冲液，在沸水中使蛋白变性，然后放入-20 ℃冰箱保存，之后每次使用时重新沸水浴10 min。按说明书配置分离胶和浓缩胶，加入蛋白Marker和蛋白样品，120 V电泳，随后200 mA转膜2 h，TBST清洗3次后使用5%脱脂奶粉室温封闭2 h，TBST洗膜3次，一抗封闭过夜。TBST清洗3次后二抗室温孵育1.5 h，TBST再次洗膜后上机显影。使用ImageJ软件计算ATXN2、G3BP1和GAPDH的灰度值，使用GAPDH对ATXN2、G3BP1的表达量进行校正，使用配对t检验比较癌旁组织与癌组织中ATXN2、G3BP1相对灰度值的差异。
1.8. 数据分析
所有的统计分析使用R版本4.1.0进行。使用配对t检验对蛋白印迹结果进行分析；使用Kaplan-Meier曲线比较高低风险组患者的生存；使用多因素Cox分析进行模型的构建及独立预后分析；相关性分析采用Pearson相关性检验。检验水准为α=0.05。
2. 结果
2.1. 风险模型的构建
TCGA数据库中提取28个m7G基因和14 057个lncRNA的表达谱数据。共发现1722个m7G相关的lncRNAs使用桑基图对其进行可视化（图 1A）。单因素Cox分析结果表明43个m7G-lncRNAs与结肠癌患者的生存显著相关。多因素Cox分析进一步筛选出12个lncRNAs并将这些lncRNAs用于风险模型的构建（表 2）。其中AC003101.2、AC005014.2、AC008760.1、AC092944.1、AL1161729.4、AL301422.4、AP001619.1、AP003355.1和ZEB1-AS1为高风险lncRNAs，AC025171.4、AC073957.3及TNFRSF10A-AS1为低风险lncRNAs。根据风险得分的中位值将结肠癌患者分为高低风险两组。K-M曲线显示，高风险组患者的总生存期明显低于低风险组（图 1B）。风险曲线和风险散点图显示，随着风险得分的增高，患者的死亡率也不断增高（图 1C、D）。风险热图显示风险基因的表达与风险得分的关系（图 1E）。最后，使用Cytoscape构建m7G基因与模型lncRNAs的共表达网络，其中红色节点为模型lncRNAs，蓝色节点为m7G基因（图 1F）。使用桑基图对模型lncRNAs对结肠癌患者预后的影响及其与m7G基因的共表达关系进行可视化（图 1G）。
1.
风险模型的构建
Construction of the risk model. A: Sankey diagram of m7G-related lncRNAs. B: Kaplan-Meier survival curves of the overall survival of patients in the high- and low-risk groups. C, D: Different patterns of survival status and survival time between the highand low-risk groups. E: Heatmap showing the expression standards of the 12 prognostic lncRNAs for each patient. F: Co-expression network of m7G genes and model lncRNAs. G: Sankey diagram of the risk model.
2.
用于风险模型构建的lncRNAs及其风险系数
lncRNAs and their risk coefficients used for the construction of model
| Id | Coef | HR |
| ZEB1-AS1 | 0.596950633 | 1.816570955 |
| TNFRSF10A-AS1 | -0.180356411 | 0.834972565 |
| AC003101.2 | 0.783401486 | 2.188905148 |
| AL161729.4 | 0.290928357 | 1.337668746 |
| AL391422.4 | 0.445530157 | 1.56131772 |
| AC005014.2 | 1.034916224 | 2.814870407 |
| AC025171.4 | -1.469641781 | 0.230007864 |
| AP003555.2 | 0.304117941 | 1.355428908 |
| AC073957.3 | -0.227357249 | 0.796636134 |
| AC092944.1 | 1.091958559 | 2.980105073 |
| AC008760.1 | 0.362683981 | 1.437181611 |
| AP001619.1 | 0.584238022 | 1.793623763 |
2.2. 风险模型的验证
单因素Cox回归分析中，风险评分和95% CI的危险比（HR）分别为1.336和1.246~1.433（P < 0.001，图 2A），多因素Cox回归分析的风险评分和95% CI的危险比（HR）分别为1.302和1.198~1.414（P < 0.001，图 2B）。结肠癌患者1年、3年和5年的ROC值分别为0.727, 0.747和0.794（图 2C）。C-index曲线表明，相较于常见的临床指标，如TNM分期，风险模型对结肠癌患者预后的预测具有更高的敏感性和准确性（图 2D）。接着本研究构建了一个预测患者预后的列线图（图 2E），列线图ROC曲线的AUC值为0.794（图 2F），校准曲线显示诺莫图对患者1、3及5年生存时间的预测几乎与实际生存时间一致（图 2G）。
2.
风险模型的验证
Validation of the risk model. A, B: Univariate analysis and multivariate analysis for validating the independent prognosis value of the model. C, D: ROC curves and C-index curves for assessing the performance of the risk score for predicting patient's survival. E: Nomogram for predicting overall survival time. F, G: Calibration curves and ROC curves for determining the accuracy of the nomogram for overall survival and progression-free survival at 1, 3 and 5 years, respectively
2.3. 风险得分可以预测结肠癌患者的临床病理分期和预后
风险得分的高低与结肠癌患者较高的T分期，N分期以及M分期相关（图 3A、B）。同时风险得分也能预测不同亚组如T3~T4期，所有的N分期以及M0期结肠癌患者的预后（图 3D~G）。
3.
风险得分与不同临床分期亚组患者预后的关系
Correlation of the risk scores with prognosis of the patients in different clinical stages. A-C: Correlation of risk scores with the patients' T-stage (A), N-stage (B) and M-stage (C). D-G: K-M survival curves showing the correlation of the risk scores with the prognosis of colon cancer patients in T3-T4 (D), N0 (E), N1-N2 (F), and M0 (G) stages.
2.4. 风险得分影响患者的微卫星不稳定性和免疫微环境
本研究进一步分析风险得分与结肠癌患者微卫星不稳定状态的联系，结果风险得分较高的患者具有高微卫星不稳定状态（MSS vs MSI-H：P=0.034，图 4A、B）。风险得分与结肠癌患者的干细胞指数呈现负相关关系（图 4C）。GSEA富集分析显示高低风险组患者间多数免疫相关通路存在明显差异（图 4D、E）。结果表明风险得分与激活的肥大细胞（r=-0.11，P=0.045）和静息CD4+T细胞（r=-0.14，P=0.01，图 4F、G）。ESTIMATE结果显示高风险组患者的基质细胞打分和总打分均较高（图 4H）。多数免疫检查点基因在高低风险组患者的表达也存在显著差异（P < 0.05，图 4I）。
4.
风险与结肠癌患者微卫星不稳定性和肿瘤微环境的关系
Correlation of the risk model scores with MSI and microenvironment in colon cancer. A, B: The risk score is negatively correlated with MSI-H. C: The risk score is negatively correlated with the RNAss. D, E: The GSEA in the high- and low-risk groups. F, G: Infiltrating level of immune cells in the high- and low-risk groups. H: Correlation between risk score and TME scores. I: Expression levels of the immune check points in the high- and low-risk groups.
2.5. 模型lncRNAs调控多种致癌分子的表达
本研究首先使用Cytoscape的cytoHubba插件筛选模型lncRNAs调节的核心分子（图 5A）。TCGA数据库表明这些核心分子多数在结肠癌癌旁和癌组织中存在差异表达（图 5B）。Western blot实验结果显示，与癌旁正常组织相比，ATXN2（P=0.006）和G3BP1（P=0.007）在结肠癌组织中的表达量增高（图 5C~E）。
5.
模型lncRNAs调控的靶分子在临床样本中的表达
Expression of the target molecules regulated by the identified lncRNAs in clinical samples. A: PPI network of the core molecules regulated by the lncRNAs in the risk model. B: Expression of the core molecules in TCGA database. C, E: Western blotting showing higher expressions ofATXN2 (P=0.006) and G3BP1 (P=0.007) in colon cancer tissues than in adjacent tissues (n=4).
3. 讨论
本研究发现多数的lncRNAs与m7G基因存在共表达的关系。随后，使用与m7G具有共表达关系的lncRNAs构建了用于预测结肠癌患者预后的风险模型。模型lncRNAs中AC003101.2、AP001619.1和AC008760.1被报道与结肠癌的预后相关[16-18]。此外，ZEB1-AS1也参与乳腺癌、肝癌、胰腺癌及结直肠癌等多种肿瘤的恶性进展和耐药[19-22]。我们使用多种方法进行进一步探究风险模型对结肠癌患者预后的预测作用。K-M曲线显示高风险组患者的五年生存率明显高于低风险患者，ROC曲线、C-index曲线、诺莫图及诺莫图的校准曲线和ROC曲线进一步验证了模型的准确性。风险模型还与结肠癌患者的分期分级相关，且对不同临床亚组结肠癌患者的预后也具有较好的预测作用。
免疫治疗在结肠癌的治疗中大放异彩[23]，本研究探究了高低风险两组患者免疫细胞浸润以及肿瘤微环境打分的差异。我们发现高风险组患者的基质细胞打分和总打分均高于高风险患者，这说明高风险患者的肿瘤纯度较低。低肿瘤纯度与肿瘤的恶性进展，治疗耐药性和预后评估密切相关[24]。这反映了高低风险组患者预后的差异的内在原因可能是两者肿瘤微环境的不同。微卫星是分布人类基因组中包含1~4个碱基对的短串联重复DNA序列，微卫星不稳定被认为是结直肠癌的主要致癌途径之一，与MSS/MSI-L肿瘤的结肠癌患者相比，MSI-H的结肠癌患者显著受益于免疫检查点抑制剂治疗[25, 26]。本研究发现低风险组患者的微卫星不稳定状态更高，同时低风险组患者多种免疫检查点的表达量更低，以上结果表明低危组患者可能比高风险组患者更得益于免疫检查点抑制剂治疗。
为探究模型lncRNAs参与调节的功能，我们使用共表达分析以及蛋白蛋白互作网络筛选出模型lncRNAs调节的核心基因。磷酸酶和张力素同源物（PTEN）是一种肿瘤抑制因子，研究报道其丢失可促进结肠癌的恶性进展[27]。靶向结肠癌中的PTEN是一种重要的结肠癌治疗策略[28]。SOS1也是结肠癌治疗中的重要靶点，研究表明SOS1和MEK的联合抑制可能对KRAS驱动的恶性肿瘤有一定的治疗效果[29]。由于ATXN2和G3BP1在结肠癌中尚无报道，因此我们对ATXN2和G3BP1进行了临床样本的验证，结果表明此两者在结肠癌中表达增高，可能与结肠癌的恶性发展有关。模型的lncRNAs参与多种与结肠癌密切相关的分子的调控，这进一步证实了本研究构建的风险模型在结直肠癌的应用中可能会发挥重要作用。
虽然本研究获得了一定喜人的结果，但本研究也存在一定的不足之处。首先，由于本研究的分析是基于公共数据库的挖掘，因此使用的所有样本都是回顾性数据，故存在一定数据选择的主观性。此外，需要有更进一步的体内体外实验来探究模型lncRNAs与PTEN及SOS1等分子之间的关系。
Biography
陈曙冉，在读硕士研究生，E-mail: 2606772991@qq.com
Funding Statement
安徽省自然科学基金（2108085MH291）；蚌埠医学院512人才培养计划项目（by51201107）；蚌埠医学院自然科学基金（2020byzd153）；蚌埠医学院研究生科研创新计划项目（Byycx21084）
Contributor Information
陈 曙冉 (Shuran CHEN), Email: 2606772991@qq.com.
刘 牧林 (Mulin LIU), Email: liumulin66@aliyun.com.
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