Abstract
目的
针对结直肠癌(colorectal cancer，CRC)预后的肿瘤标志物尚缺乏可靠的准确度和灵敏度。本研究旨在采用生物信息学方法，筛选并验证一组与CRC诊断与预后相关的基因。
方法
从NCBI-GEO数据库中选取关于CRC和正常结直肠黏膜(colorectal mucosa，CRM)组织标本全RNA组测序数据集GSE31905、GSE35279和GSE41657，并分析其中的差异表达基因(differentially expressed genes，DEGs)，先通过Venn图获取这3个数据集的共同DEGs，然后用STRING网络系统和Cytoscape软件进一步富集上述基因，最后在Kaplan-Meier plotter网站上验证富集后的基因与CRC预后的相关性。
结果
通过NCBI-GEO数据库中自带的GEO2R工具分别筛选出3个数据集中CRC与CRM的DEGs(|log2FC|>2和P<0.05)。用Venn图分析软件，将3个数据集中的上调/下调基因分别取交集，得出3个样本共105个共有的上调基因和140个共有的下调基因。将上述上调/下调基因导入STRING网络系统，得出相互作用的基因。将相互作用的基因集导入Cytoscape软件，用MCODE(Molecular Complex Detection)插件查找到61个上调基因。最后通过Kaplan-Meier plotter网站，得到EPHB2、KLK8、DIAPH3、STC2、OXTR、MMP7、MET、KRT85、KRT6B、KRT23、KLK10等11个在CRC中高表达的基因，且与预后相关。
结论
通过生物信息学分析和验证，上述11个基因可在一定程度上预测CRC的不良预后，为CRC的诊断、治疗和预后提供可能的靶标。
Keywords: 生物信息学分析, RNA测序, 结直肠癌, 差异表达基因
Abstract
Objective
The biomarkers targeting colorectal cancer (CRC) prognosis are short of high accuracy and sensitivity in clinic. Through bioinformatics analysis, we aim to identify and confirm a series of key genes referred to the diagnosis and prognosis of CRC.
Methods
GSE31905, GSE35279, and GSE41657 were selected as complete RNA sequencing data sets of CRC and colorectal mucosa (CRM) tissues from the NCBI-GEO database, and the differentially expressed genes (DEGs) were analyzed. The common DEGs in these 3 data sets were obtained by Venn map, and enriched by STRING network system and Cytoscape software. The Kaplan-Meier plotter website was used to verify the correlation between the enriched genes and the prognosis of CRC.
Results
For the whole RNA sequencing data sets of CRC and normal intestinal mucosa samples, the DEGs of CRC and CRM in the 3 data sets (|log2FC|>2 and P<0.05) were screened by GEO2R tool in NCBI-GEO database. By using Venn graph analysis software, the intersection of up-regulated/down-regulated genes in 3 GSE datasets was obtained, and a total 105 up-regulated genes and 140 down-regulated genes were found in the 3 samples. The up-regulated/down-regulated genes were introduced into the STRING network system to obtain the interacting genes. The interacting gene sets were introduced into Cytoscape software, and 61 up-regulated genes were found by Molecular Complex Detection (MCODE) plug-in. Through the Kaplan-Meier plotter website, we found that EPHB2, KLK8, DIAPH3, STC2, OXTR, MMP7, MET, KRT85, KRT6B, KRT23, and KLK10 genes were highly expressed in CRC, and were related to the prognosis.
Conclusion
The above 11 genes verified by bioinformatics retrieval and analysis can predict the poor prognosis of CRC to a certain extent, and they provide a possible target for the diagnosis and treatment of CRC.
Keywords: bioinformatics analysis, RNA sequencing, colorectal cancer, different expressed genes
《2018年全球肿瘤年鉴》指出，全球新发结直肠癌(colorectal cancer，CRC)180万例，死亡88万余人。CRC在肿瘤发病率中位居第3，在肿瘤病死率中位居第2[1]。近20年来，随着经济的发展和居民生活水平的逐步提升，我国的CRC发病率逐年攀升，已经赶超欧美日韩等发达国家[2]。CRC起病隐匿，发展迅速，患者在首次就诊时多已出现远处转移。在CRC中，多数癌基因处于活化状态，特别是与肿瘤细胞增殖相关的信号通路处于高度活跃的状态[3]。虽然对CRC患者诊断、转移及预后等的预测因子的研究越来越多，但是其在CRC的敏感度和特异度上尚缺乏可信的资料[4]。
基因测序技术的应用已经超过10年。其主要特点是将组织或细胞中的所有RNA和/或DNA按照不同的基因采用半定量方法测定出来，为后续基因表达量的研究提供了可能[5]。到目前为止，大量的CRC细胞的基因测序结果在GEO(Gene Expression Omnibus)数据库中可被共享。本研究从中选择了GSE31905、GSE35279和GSE41657数据集，得到差异表达基因(differentially expressed genes，DEGs)，然后通过DVIAD (Database for Annotation, Visualization and Integrated Discovery)软件，筛选出了与细胞功能、细胞组件、生物学过程及京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes，KEGG)通路富集相关的基因，检验其蛋白质相互作用(protein-protein interaction，PPI)，并用MCODE(Molecular Complex Detection)插件筛选PPI图中的核心基因，明确上述核心基因在CRC中的表达及与预后的关系。
1. 材料与方法
1.1. GEO数据库的筛选
NCBI-GEO是对科研工作者免费开放的数据库，可在NCBI的PubMed界面(https://www.ncbi.nlm.nih.gov/pubmed/)选项栏中选择GEO datasets，进入GEO数据库，在搜索框中输入检索关键词“colorectal cancer”和“expression profiling by array”，筛选条件设置为“Homo sapiens”，查找符合条件的所有研究结果。为减少数据集之间因为测序方法和基因命名产生的误差，本课题组于同一数据平台GPL6480，选取关于CRC和正常结直肠黏膜组织标本中全RNA组测序数据库GSE31905、GSE35279和GSE41657(Agilent-014850 Whole Human Genome Microarray)，其中GSE31905数据集包含55个CRC标本和7个正常结直肠黏膜标本，GSE35279数据集包含74个CRC标本和5个正常结直肠黏膜标本，GSE41657数据集包含51个CRC标本和12个正常结直肠黏膜标本。
1.2. 候选基因的筛选与富集
1.2.1. DEGs的遴选
利用GEO数据库自带的GEO2R工具可获得CRC与肠黏膜的DEGs，包括上调和下调的基因[6]。将遴选后的基因按“|log2FC|>2”和“P<0.05”条件，通过在线分析网站(http://bioinfogp.cnb.csic.es/tools/venny/index.html)制作Venn图，获得3个数据集的交集，作为共有的DEGs用于后续分析。
1.2.2. 基因本体分析和信号通路富集
本研究采用基因本体(gene ontology，GO)分析[7]、KEGG分析[8]和DAVID线数据库[9]归纳和富集DEGs。具体步骤：在DAVID6.7版本数据库界面(https:// david-d.ncifcrf.gov/)选择“Functional Annotation”分析工具，在“Enter Gene List”中批量输入通过Venn图获得的DEGs，“Select Identifier”设置为“OFFICIAL_GENE_SYMBOL”,“List Type”选择“Gene List”，提交数据后再将分析物种设置为“Homo sapiens”，通过DAVID数据库，将DEGs所转录的RNA或翻译的蛋白质按照功能分类，分别形成GO富集和KEGG功能注释，并将基因按照生理功能、结构组分、信号通路、物质代谢等分类。选择GO富集和KEGG功能注释中显著的DEGs进行进一步分析。
1.2.3. PPI网络和STRING分析
将DEGs导入STRING程序(http://string-db.org)中，选择“Multiple proteins”一项，在“List of Names”输入DEGs，在“Organism”项中选择“Homo sapiens”，点击“Search”获得共有DEGs编码蛋白质的网络作用图。然后在“Exports”选项中将网络作用图的TSV文件下载并导入Cytoscape软件，STRING在线工具与Cytoscape软件互为印证，界定DEGs蛋白质间的PPI潜在联系的相关性[10-11]。通过STRING分析，选择PPI网络结构中的核心基因进行下一步分析。
1.3. 核心基因在CRC中的表达和预后分析
Kaplan-Meier plotter是常用的检测基因与肿瘤患者预后相关性的在线检索网站。其主要数据来自EGA和TCGA数据库[12]。TNMplot数据库(https://www.tnmplot.com/)同样是该平台近期开放的在肿瘤中表达基因的在线检索工具，选择“RNA-seq Start KM plotter for pan-cancer”，将每一个核心基因输入，勾选“Colorectal cancer”进行Kaplan-Meier生存分析，下载Kaplan-Meier plotter图。选择“Compare normal and tumor”，将上述基因逐一输入，再选择“Colorectal cancer”检索分析。其特点是将局限性肿瘤和转移性肿瘤分开，有利于更加清晰地理解基因与肿瘤增殖和转移的相关性。
2. 结 果
2.1. CRC组织中DEGs的遴选
共选取了GSE31905、GSE35279和GSE41657数据集中180例CRC组织和24例结直肠黏膜组织的基因测序结果。通过GEO2R在线检索工具，分别筛选出1 716、1 735和1 074个DEGs。通过Venn图分析，得到105个共有的上调基因和140个共有的下调基因(|log2FC|>2和P<0.05，图1)。
图1.
Venn图显示GSE31905、GSE35279和GSE41657数据集中共有的差异表达基因交集
Figure 1 Authentication of common differentially expressed genes in the 3 datasets (GSE31905, GSE35279, and GSE41657) through Venn diagram software A: Up-regulated genes; B: Down-regulated genes.
2.2. CRC组织中DEGs的GO分析和KEGG通路富集
将所有入选的245个基因(详见附表https://doi.org/10.11817/j.issn.1672-7347.2021.200952T1)导入DAVID软件进行GO分析，得到GO富集通路，包括：1)生物学过程(biological processes，BP)。上调的基因主要纳入细胞周期、蛋白质水解、药物反应、细胞增殖等通路，而下调的基因主要纳入小分子化合物的转运和蛋白质水解等通路。2)细胞组件(cell component，CC)。上调的基因主要纳入细胞外基质、细胞外结构组成、顶端细胞膜结构组分等通路，而下调的基因主要纳入细胞外泌体、细胞外结构、细胞间膜蛋白结构等通路。3)分子功能(molecular function，MF)。上调的基因主要纳入DNA结合蛋白、生长因子激活和RNA聚合酶启动子结构蛋白等通路，下调的基因主要归入锌指蛋白结合蛋白和激素激活体系(表1)。
表1.
结直肠癌的差异表达基因在GO信号通路中的富集
Table 1 Gene ontology analysis of differentially expressed genes in colorectal cancer
| Classify | GO signaling pathway | Function | Gene | P | False discovery rate | |
|---|---|---|---|---|---|---|
| No. | Proportion/% | |||||
|
Up- regulated gene |
BP_DIRECT | GO:0008283~cell proliferation | 7 | 6.730 7 | 0.011 0 | 0.716 2 |
| BP_DIRECT | GO:0006508~proteolysis | 7 | 6.730 7 | 0.043 0 | 1.000 0 | |
| BP_DIRECT | GO:0001525~angiogenesis | 6 | 5.769 2 | 0.006 0 | 0.716 2 | |
| BP_DIRECT | GO:0042493~response to drug | 6 | 5.769 2 | 0.020 0 | 0.914 8 | |
| BP_DIRECT | GO:0007050~cell cycle arrest | 5 | 4.807 6 | 0.006 0 | 0.716 2 | |
| BP_DIRECT | GO:0007010~cytoskeleton organization | 5 | 4.807 6 | 0.009 0 | 0.716 2 | |
| CC_DIRECT | GO:0005615~extracellular space | 19 | 18.269 0 | <0.000 1 | 0.014 2 | |
| CC_DIRECT | GO:0005576~extracellular region | 19 | 18.269 0 | 0.001 2 | 0.060 7 | |
| CC_DIRECT | GO:0016324~apical plasma membrane | 6 | 5.769 2 | 0.016 7 | 0.578 0 | |
| MF_DIRECT | GO:0043565~sequence-specific DNA binding | 10 | 9.615 3 | 0.001 0 | 0.139 9 | |
| MF_DIRECT | GO:0008083~growth factor activity | 7 | 6.730 7 | 0.000 2 | 0.037 6 | |
| MF_DIRECT | GO:0000978~RNA polymerase II core promoter proximal region sequence-specific DNA binding | 7 | 6.730 7 | 0.010 0 | 0.400 9 | |
|
Down- regulated gene |
BP_DIRECT | GO:0006508~proteolysis | 9 | 6.521 7 | 0.020 6 | 1.000 0 |
| BP_DIRECT | GO:0015701~bicarbonate transport | 8 | 5.797 1 | <0.000 1 | <0.000 1 | |
| BP_DIRECT | GO:1902476~chloride transmembrane transport | 6 | 4.347 8 | <0.000 1 | 0.094 5 | |
| CC_DIRECT | GO:0005886~plasma membrane | 42 | 30.434 0 | 0.010 2 | 0.180 5 | |
| CC_DIRECT | GO:0070062~extracellular exosome | 34 | 24.637 0 | 0.001 8 | 0.079 6 | |
| CC_DIRECT | GO:0005615~extracellular space | 26 | 18.840 0 | <0.000 1 | <0.000 1 | |
| CC_DIRECT | GO:0005576~extracellular region | 22 | 15.942 0 | 0.004 4 | 0.120 1 | |
| CC_DIRECT | GO:0005887~integral component of plasma membrane | 18 | 13.043 0 | 0.021 9 | 0.233 9 | |
| CC_DIRECT | GO:0009986~cell surface | 9 | 6.521 7 | 0.038 9 | 0.334 0 | |
| CC_DIRECT | GO:0016324~apical plasma membrane | 8 | 5.797 1 | 0.004 7 | 0.120 1 | |
| CC_DIRECT | GO:0005578~proteinaceous extracellular matrix | 7 | 5.072 4 | 0.012 1 | 0.180 5 | |
| MF_DIRECT | GO:0008270~zinc ion binding | 17 | 12.318 0 | 0.005 2 | 0.230 0 | |
| MF_DIRECT | GO:0005179~hormone activity | 8 | 5.797 1 | <0.000 1 | <0.000 1 | |
对DEGs进行KEGG分析，结果发现：上调基因主要富集于TGF-β信号通路、Wnt信号通路及CO2代谢通路等，而下调基因主要富集于氮的代谢、药物代谢、微量分泌、胰腺的分泌蛋白等信号通路(P<0.05，表2)。
表2.
差异表达基因在KEGG信号通路中的富集
Table 2 KEGG pathway analysis of differentially expressed genes in colorectal cancer
| Classify | GO signaling pathway | Function | Gene | P |
False discovery rate |
|
|---|---|---|---|---|---|---|
| No. | Proportion/% | |||||
|
Up- regulated gene |
KEGG_PATHWAY | hsa04350: TGF-beta signaling pathway | 4 | 3.846 154 | 0.011 7 | 0.711 4 |
| KEGG_PATHWAY | hsa05202: Transcriptional misregulation in cancer | 5 | 4.807 692 | 0.014 2 | 0.711 4 | |
| KEGG_PATHWAY | hsa04310: Wnt signaling pathway | 4 | 3.846 154 | 0.042 7 | 1.000 0 | |
| KEGG_PATHWAY | hsa05230: Central carbon metabolism in cancer | 3 | 2.884 615 | 0.050 7 | 1.000 0 | |
|
Down- regulated gene |
KEGG_PATHWAY | hsa00910: Nitrogen metabolism | 5 | 3.623 188 | <0.000 1 | <0.000 1 |
| KEGG_PATHWAY | hsa00982: Drug metabolism - cytochrome P450 | 7 | 5.072 464 | <0.000 1 | <0.000 1 | |
| KEGG_PATHWAY | hsa00980: Metabolism of xenobiotics by cytochrome P450 | 7 | 5.072 464 | <0.000 1 | <0.000 1 | |
| KEGG_PATHWAY | hsa05204: Chemical carcinogenesis | 7 | 5.072 464 | <0.000 1 | <0.000 1 | |
| KEGG_PATHWAY | hsa04972: Pancreatic secretion | 7 | 5.072 464 | <0.000 1 | 0.001 2 | |
| KEGG_PATHWAY | hsa04964: Proximal tubule bicarbonate reclamation | 4 | 2.898 551 | <0.000 1 | 0.009 6 | |
| KEGG_PATHWAY | hsa04978: Mineral absorption | 4 | 2.898 551 | 0.004 6 | 0.054 9 | |
| KEGG_PATHWAY | hsa00830: Retinol metabolism | 4 | 2.898 551 | 0.012 9 | 0.135 7 | |
2.3. PPI网络图和节点分子分析
将245个DEGs导入STRING在线工具，生成PPI网络图及网络连接数据集(图2)，并将此数据导入Cytoscape软件，用MCODE插件系统分析245个基因所构成的PPI网络图中的关键基因结点，共得到61个关键信号基因(分别为SEMA6A、EFNA5、EPHA2、EPHA3、EPHA4、MET、EPHB2、CLCA1、MS4A12、ZG16、GUCA2B、SLC26A3、CLCA4、GSTA2、UGT1A8、ADH1C、GSTA5、GSTA1、ADH1APPARGC1A、MMP7、ANLN、TPX2、AURKA、MYC、HGF、PCK1、MMP3、MLXIPL、DIAPH3、PPARG、ERCC6L、TNFRSF17、IGJ、MZB1、POU2AF1、FUT1、NEU4、ST6GALNAC6、ST6GALNAC1、EDN3、OXTR、P2RY1、PYY、SST、INSL5、SALL4、TDGF1、KLF4、TMEM132A、SCG3、STC2、SAA2、APOBR、SAA1、KRT80、KRT23、KRT6B、SPINK5、KLK8、KLK10)。
图 2.
结直肠癌差异表达基因的PPI网络
Figure 2 PPI network of differentially expressed genes constructed by STRING online database and module analysis
2.4. Kaplan-Meier plotter在线工具分析结果
用Kaplan-Meier plotter在线工具分析上述61个基因在CRC中的表达，最终筛选出EPHB2、KLK8、DIAPH3、STC2、OXTR、MMP7、MET、KRT85、KRT6B、KRT23、KLK10共11个表达上调的基因(P<0.05，图3)。
图3.
11个在结直肠癌中表达上调的基因在正常结直肠黏膜、原位CRC和转移性CRC中的表达
Figure 3 Expression of 11 up-regulated genes of colorectal cancer in normal colorectal tissue, colorectal cancer in situ, and metastatic colorectal cancer
上述11个基因与CRC患者总生存率(overall survival，OS)的Kaplan-Meier生存分析结果示：EPHB2(Cutoff值：414)，KLK8(Cutoff值：40)，DIAPH3(Cutoff值：3)，STC2(Cutoff值：38)，OXTR(Cutoff值：122)，MMP7(Cutoff值：1 453)，MET(Cutoff值：1 400)，KRT85(Cutoff值：68)，KRT6B(Cutoff值：162)，KRT23(Cutoff值：99)，KLK10(Cutoff值：280)与患者OS呈负相关(P<0.05，图4)。
图4.
结直肠癌患者11个表达上调的基因的生存曲线
Figure 4 Survival curves of the 11 up-regulated genes in patients with colorectal cancer
3. 讨 论
本研究首先从NCBI-GEO中选取3个检测CRC和正常黏膜组织的数据库(GSE31905、GSE35279和GSE41657)，并通过一系列的富集和检验工具，最终发现11个在CRC中高表达的基因，这些基因的表达与CRC患者预后呈负相关。在180例CRC组织和24例结直肠黏膜组织的基因测序中发现105个上调基因和140个下调基因(|log2FC|>2和P<0.05)。通过DAVID在线检索工具，本研究发现DEGs在生物学进程相关通路中，主要富集于细胞周期调节、蛋白质水解等；在细胞组分结构中，主要参与细胞外基质和细胞膜结构的组成；而在分子功能中，主要参与DNA复制、转录与翻译的调节，以及生长因子的激活等。在KEGG信号通路中，DEGs主要集中于TGF-β信号通路、Wnt信号通路、药物代谢及氮的代谢等。以上信号通路、细胞结构及分子机制等的结构归类均提示DEGs集中参与细胞水平的增殖、侵袭与转移的调控，在分子水平上主要参与肿瘤增殖信号通路激活和细胞膜、细胞外基质等信号分子构成。这充分体现了基于生物信息学的基因检索与富集的准确性和敏感性。
通过KEGG信号通路的富集及STRING软件PPI网络图的分析，本研究得到11个在CRC中高表达的基因，且这些基因与CRC患者预后呈负相关，其中KLK8、DIAPH3、STC2、OXTR、MMP7、KRT23、KRT6B等7个基因在正常组织、原发性CRC组织和转移性CRC组织中的表达呈梯度增高。这两项检测互相印证了基因在预测预后中的准确性。
KLK8在CRC[13]、卵巢癌[14]、肺癌[15]、宫颈癌[16]和乳腺癌[17]中均呈高表达，且与预后不良相关。Guo等[18]在肿瘤细胞的研究中发现DIAPH3正向调控细胞变形虫样变，而这种细胞形变往往提示细胞的侵袭、转移能力增强。Calvo等[19]在肿瘤相关成纤维细胞的研究中发现：DIAPH3作为细胞骨架蛋白的调节因子，参与YAP1依赖的机械力传导机制，促进肿瘤细胞的增殖。KRT23也在各种实体恶性肿瘤中高表达[20-22]，其中，在结肠癌中，KRT23通过促进端粒酶反转录酶表达，促进肿瘤细胞端粒的修复，增强肿瘤细胞的增殖，从而使其永生化[22]。
目前，对于肿瘤分子诊断学和预后的研究已日臻成熟。单一基因的表达或突变远远不能解释肿瘤的发生或预测肿瘤患者的预后。基于高通量的测序结果和信息统计学的归因研究，为系统寻找肿瘤预后的预测因子提供了可能。因此，本研究通过一系列生物信息学的统计和分析，预测并在一定程度上证实了一组与CRC的预后呈负相关的基因。在后续的研究中，本研究将通过体内外细胞生物实验，探索和证实上述基因介导肿瘤预后的可能机制。
附录.
附表1.
| Classify | n | Common gene |
|---|---|---|
|
Up- regulated gene |
105 | ACSL6, AJUBA, ANLN, ASCL2, ASIC1, ATP11A, AZGP1, BMP7, C6orf223, CA9, CARD14, CCAT1, CDH3, CEMIP, CGREF1, CHI3L1, CKMT2, CLDN1, CRNDE, CXCL8, DIAPH3, DLX4, DPEP1, ERCC6L, EREG, ESM1, ETV4, FABP6, FAM3B, FGF18, FOXQ1, FUT1, GDPD5, GPT2, GTF2IRD1, HOMER1, HOXB8, IFITM4P, INHBA, IRX5, ITGA2, KIAA1549, KLK10, KLK8, KRT23, KRT6B, KRT80, LEMD1, LINC00659, LRRC8E, MACC1, MET, MEX3A, MLXIPL, MMP11, MMP3, MMP7, MSX1, MSX2P1, MTHFD1L, MYC, NFE2L3, NKD1, NKD2, OXTR, PHLDA1, PITX2, PLEKHS1, PPM1H, PRR7, PSAT1, REG3A, S100A2, SAA1, SAA2, SALL4, SH3TC2, SLC16A1-AS1, SLC22A3, SLC6A20, SLC6A6, SLC7A5, SLCO1B3, SLCO4A1, SP5, SPERT, SRPX2, STC2, STRIP2, SULT2B1, TCN1, TDGF1, TDGF1P3, TESC, TGFBI, TGM2, TMEM132A, TMPRSS3, TPX2, TRIB3, TUBB3, UCA1, WDR72, ZAK |
|
Down- regulated gene |
140 | KCNMA1, SPIB, GSTA1, HSD17B2, ZG16, LDHD, ITLN1, AKR1B10, APOBR, TRPM6, MT1JP, DSCAML1, ANO5, PYY, GSTA2, POU2AF1, B3GNT7, MZB1, SLC26A3, PIGR, DPP10-AS1, FCGBP, CHGA, CAPN9, MT1M, SPINK5, KLF4, C11orf86, CLCA1, PPARGC1A, CRYBA2, MFSD4A, P2RY1, IGHM, GLDN, INSL5, EDN3, SPON1, IGLJ3, UGT1A8, LYPD8, MS4A8, TNFRSF17, CD177, AMPD1, NRAP, B3GALT5-AS1, PCK1, CPM, ADH1C, UGT2A3, SST, SCNN1B, FAM30A, CDKN2B, LAMA1, CWH43, MUC4, SCGB2A1, SLITRK6, KIF5C, MOGAT2, CA12, KLK15, GUCA2A, SEMA6A, ATP2A3, FAM151A, CA7, CPA6, SPINK2, GREM2, LGALS2, CHGB, CCL23, MMP28, DPF3, FOXP2, GUCA2B, EFNA5, CHP2, SCARA5, TTR, CAPN13, CLCA4, MT1E, CA4, CTSG, NXPE4, UNC5C, IL1R2, RAB3B, ASPG, RNF152, SPDEF, NEU4, RGS13, CA2TPO, BCAS1, NEURL1, SLC26A2, ADH1A, CEACAM7, PADI2, WFDC2, ITM2A, ARHGAP44, ADTRP, FDCSP, FAM189A2, RFX6, RHBDL2, ST6GALNAC6, SCG3, BEST2, GCG, CHST5, CLDN8, MS4A12, CDH19, VSIG2, PRAC1, GSTA5, BMX, HEPACAM2, TMEM61, NXPE1, CA1, STMN2, IGHA2///IGHA1///IGH, ROR1, SLC4A4, SI, FMN2, SCN9A, JCHAIN, EYA2, ST6GALNAC1 |
利益冲突声明
作者声称无任何利益冲突。
原文网址
http://xbyxb.csu.edu.cn/xbwk/fileup/PDF/2021101063.pdf
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