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Journal of Southern Medical University logoLink to Journal of Southern Medical University
. 2022 Jan 20;42(1):45–54. [Article in Chinese] doi: 10.12122/j.issn.1673-4254.2022.01.05

肝细胞癌中促癌miRNA调控网络分析与验证

Identification of onco-miRNAs in hepatocellular carcinoma and analysis of their regulatory network

Jingjing YE 1,2, Wenqin XU 1,2, Tianbing CHEN 1,2,*
PMCID: PMC8901393  PMID: 35249869

Abstract

Objective

To construct the regulatory network of survival-related onco-miRNAs and their target genes in hepatocellular carcinoma (HCC) and verify the interactions between the key miRNAs and their targets.

Methods

We screened survival-related miRNAs in HCC in OncomiR and Oncolnc databases, predicted their target genes using miRNet, and conducted survival and expression analysis using GEPIA2 and Ualcan, respectively. The miRNA-target gene co-expression analysis was performed and the miRNA-target network was constructed. Enrichment analysis was performed in Enrichr and protein-protein interaction analysis in STRING database. We tested the effects of transfection with the mimic or inhibitor of hsa-miR-1226-3p or hsa-miR-221-5p on proliferation of HepG2 cells using CCK8 assay and examined the changes in the expressions of the target genes using RT-qPCR. The effect of transfection with hsa-miR-221-5p mimic or inhibitor on protein expressions of the target genes was examined using Western blotting in. A dual luciferase reporter assay was used to test the interaction between hsa-miR-221-5p and its potential target gene GCDH. We further examined the effect of transfection with hsa-miR-221-5p mimic and pEGFP N1-GCDH, alone or in combination, on proliferation, migration and invasion of HepG2 cells.

Results

We identified 223 survival-related miRNAs in HCC from OncomiR and 146 miRNAs from Oncolnc with an intersection of 131 miRNAs, and 48 miRNAs were identified as onco-miRNAs in HCC after survival and expression analysis. Twenty-seven eligible target genes were identified after miRNA-mRNA co-expression analysis. The constructed miRNA-target gene network consisted of 25 miRNAs and 27 target genes. The most enriched term was fatty acid metabolism for the target genes. In HepG2 cells, transfection with the mimic or inhibitor of hsa-miR-1226-3p or hsa-miR-221-5p caused significant changes of the mRNA and protein levels of their respective target genes (P < 0.05). The results of dual luciferase reporter assay confirmed the targeting relationship between hsa-miR-221-5p and GCDH gene (P < 0.05). Transfection with hsa-miR-221-5p mimic significantly suppressed the proliferation, migration and invasion of HepG2 cells, but this effect was obviously relieved by co-transformation with pEGFP N1-GCDH (P < 0.05).

Conclusion

Fatty acid metabolism might be one of the most crucial pathways that mediate the effect of the oncomiRNAs in HCC, and the hsa-miR-221-5p/GCDH axis is an important molecular mechanism for HCC progression.

Keywords: hepatocellular carcinoma, onco-miRNA, regulatory network, bioinformatic analysis

肝细胞癌（LIHC）是最常见的癌症之一，每年新增确诊病例约60万人[1-3]。尽管过去几十年治疗手段一直在发展，肝癌病人整体的预后情况仍然较差，5年期存活率仍不足20%[4-5]。miRNAs是一类小的非编码RNA分子，是近年来众多研究领域中的研究热点，在许多生物学进程中扮演重要角色，包括调控细胞分化、增殖和细胞死亡等[6-7]。已有大量的研究显示miRNAs及其靶标基因的表达失调，在许多癌症的发生和发展进程中发挥重要的功能[8-10]。近年来，随着肿瘤相关的二级数据库的出现和日益完善，使得研究者能够对特定癌种中的表达失调的miRNA及其调控靶标进行系统的挖掘研究[11-15]。然而，围绕肝细胞癌中生存相关的促癌miRNA的系统性研究还未见报道。本项研究旨在鉴定肝细胞癌中表达失调并且与生存相关的促癌miRNA，鉴定和分析它们的靶标基因并构建相应的miRNA-mRNA调控网络，并对关键miRNA-靶基因进行验证，以期可以为促癌miRNA在肝癌的发生和进展中的功能和机制提供新的线索或提示。

1. 材料和方法

1.1. 促癌miRNA的鉴定

从OncoLnc数据库中下载肝细胞癌（Liver hepatocellular carcinoma，LIHC）中的miRNA的数据信息（<a href="http://www.oncolnc.org/download/" target="_blank">http://www.oncolnc.org/download/</a>），提取其中生存相关的miRNA（Raw <italic>P</italic>-value < 0.05）形成数集A；在OncomiR数据库中选择Liver hepatocellular carcinoma，设置<italic>P</italic> < 0.05，提取生存相关的miRNA形成数集B（<a href="http://www.oncomir.org/oncomir/search_cancer_surv_miR.html" target="_blank">http://www.oncomir.org/oncomir/search_cancer_surv_miR.html</a>）；数集A和数集B取交集，形成数集C作进一步分析；数集C中的每个miRNA依次在OncoLnc中检测生存曲线（<a href="http://www.oncolnc.org/" target="_blank">http://www.oncolnc.org/</a>），在OncomiR中作表达分析（<a href="http://www.oncomir.org/oncomir/search_miR_tumor.html" target="_blank">http://www.oncomir.org/oncomir/search_miR_tumor.html</a>），最后我们将在肝细胞癌中表达量较癌旁组织升高的，并且高表达提示预后更差的miRNA鉴定为LIHC中的促癌miRNA。

1.2. 生存相关靶基因的分析与鉴定

在miRnet中设置好选项：Organism，<italic>H. sapiens</italic>（human）；ID type，miRBase ID；Tissue，Not specified；Targets，genes（miRTarbase v8.0+TarBase v8.0+ miRecords）。将促癌miRNA的list直接粘贴到miRnet数据库（<a href="https://www.mirnet.ca/miRNet/upload/MirUploadView.xhtml" target="_blank">https://www.mirnet.ca/miRNet/upload/MirUploadView.xhtml</a>）中，点击“Submit”，然后“Proceed”，得到所有预测的miRNA-靶基因对信息。

将靶基因按200个一组粘贴到GEPIA2中Survival Map工具中的Gene or Transcript框中；Active Datasets选择LIHC，点击“Plot”键进行批量的生存相关性分析，结果中蓝色框表示该基因与生存期相关，并且高表达提示更好的预后（<a href="http://gepia2.cancer-pku.cn/#survival" target="_blank">http://gepia2.cancer-pku.cn/#survival</a>）；经GEPIA2生存分析筛选得到的靶基因进一步利用Ualcan中的“Scan my genes”工具进行批量的表达分析（<a href="http://ualcan.path.uab.edu/analysis.html" target="_blank">http://ualcan.path.uab.edu/analysis.html</a>）；经GEPIA2和Ualcan双重分析后筛选到的靶基因与其对应的miRNA在Starbase数据库中作miRNA-mRNA的共表达系数分析（<a href="http://starbase.sysu.edu.cn/panMirCoExp.php" target="_blank">http://starbase.sysu.edu.cn/panMirCoExp.php</a>），<italic>P</italic> < 0.05被设定为具有统计学上的意义，对应的mRNA才被认定为对应肝细胞癌中促癌miRNA的生存相关靶基因。

1.3. miRNA-mRNA调控网络的构建

将鉴定出的促癌miRNA的生存相关靶基因和miRNA一一对应形成一个矩阵文件，将其导入Cytoscape软件形成可视化相互关联网络图，再作进一步的调整和处理形成清晰的miRNA-mRNA调控网络图。

1.4. 靶基因富集分析

将鉴定出的生存相关靶基因列表拷贝到Enrichr数据库中的粘贴框中，点击“submit”进行富集分析（<a href="https://maayanlab.cloud/Enrichr/" target="_blank">https://maayanlab.cloud/Enrichr/</a>），在“pathways”菜单栏下可查询KEGG富集的结果信息。

1.5. 蛋白互作网络分析及关键基因鉴定

将鉴定出的生存相关靶基因列表拷贝到STRING数据库的粘贴框（<a href="https://www.string-db.org/cgi/input?sessionId=bMa3IQdZVRH6&amp;input_page_active_form=multiple_identifiers" target="_blank">https://www.string-db.org/cgi/input?sessionId=bMa3IQdZVRH6&amp;input_page_active_form=multiple_identifiers</a>），点击“search”，“continue”得到蛋白互作网络图；下载对应的TSV格式文件，将矩阵数据信息导入Cytoscape软件，利用cytohubba插件进行关键基因的分析和鉴定。

1.6. 关键基因的表达和生存分析

单个基因的生存分析在GEPIA2中完成，在“survival analysis”界面输入基因名，选择癌种，点击“Plot”键即可生成生存曲线图；单基因的表达分析在Ualcan中完成，在“Enter gene symbol”框中输入基因名，选择癌种（TCGA dataset），点击“Explore”键后在生成的界面点击“Expression”即可生成表达的箱线图。

1.7. 关键基因与miRNA调控关系及细胞功能验证

人肝癌细胞株HepG2用RPMI 1640培养基（含10%胎牛血清）于恒温培养箱（5% CO2、37 ℃）中培养。hsa-miR-1226-3p，hsa-miR-221-5p的mimic和inhibitor，对照的NC，以及荧光素酶报告质粒（p-GCDH-WT和p-GCDH-MUT），过表达质粒pEGFP N1-GCDH均由公司合成而得。Q-PCR、Western blot及Transwell实验均按常规标准操作步骤进行。荧光素酶活性检测按试剂盒说明书操作。EHHADH上游引物：TGAGGAAA TGAGCCTGAAGA，下游引物：ATAACAGTGGCAA TGGTAGTG；GCDH上游引物：GGGTGGACAGTGG CTACAGG，下游引物：TAGGGTGCATGACGAGGG AG；内参基因ACTB上游引物：ACTCTTCCAGCCTT CCTTCC，下游引物：TGTTGGCGTACAGGTCTTTG。GCDH抗体（Proteintech，1∶1000），β-Actin抗体（CST，1∶5000）。

2. 结果

2.1. 肝细胞癌中促癌miRNA及其生存相关靶基因的鉴定

促癌miRNA的鉴定流程如图 1A。从OncoLnc数据库中下载到486条miRNA的数据信息，其中生存相关的miRNA为146个（P < 0.05）；OncomiR数据库中，提取生存相关的miRNA为223个（P < 0.05）；两者的交集为131个；在OncoLnc检测miRNA的预后价值，发现120个miRNA的高表达提示预后结果更差；进一步在OncomiR中逐个作miRNA的表达分析后发现有48个miRNA在肝细胞癌组织中的表达量较癌旁组织升高。依据分析结果我们将这48个miRNA定义为肝细胞癌中的促癌miRNA（表 1）。

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肝细胞癌中促癌miRNA及其生存相关靶基因的鉴定流程图

Identification chart of Onco-miRNA and its survival related target genes in LIHC.A: Identification process of Onco-miRNA in LIHC. B: Identification process of survival related target genes in LIHC.

1.

肝细胞癌中的促癌miRNA

Onco-miRNAs in HCC

No miRNA Name Upregulated in/Prognosis Expression(Log2 Mean) miRNA Name Upregulated in/Prognosis Expression(Log2 Mean)
Tumor Normal P Tumor Normal P
1 hsa-miR-106b-5p tumor/poor 7.76 7.33 0.0000485 25 hsa-miR-3615 tumor/poor 2.84 2.38 0.00427
2 hsa-miR-10b-5p tumor/poor 13.96 10.54 4.24E-16 26 hsa-miR-361-5p tumor/poor 8.29 8.07 0.0218
3 hsa-miR-1180-3p tumor/poor 4.49 3.17 1.58E-09 27 hsa-miR-3677-3p tumor/poor 2.12 0.56 6.03E-13
4 hsa-miR-1226-3p tumor/poor 0.35 0.02 0.000649 28 hsa-miR-3682-3p tumor/poor 0.91 0.15 0.0000125
5 hsa-miR-1266-5p tumor/poor 2.46 0.83 4.8E-08 29 hsa-miR-3928-3p tumor/poor 0.92 0.34 0.000184
6 hsa-miR-1269a tumor/poor 6.04 2.39 2.03E-08 30 hsa-miR-421 tumor/poor 1.71 0.55 2.91E-08
7 hsa-miR-1270 tumor/poor 0.79 0.05 0.0000335 31 hsa-miR-425-5p tumor/poor 7.15 6.79 0.0103
8 hsa-miR-1301-3p tumor/poor 2.93 1.96 0.00000354 32 hsa-miR-452-3p tumor/poor 2.52 1.15 0.0000146
9 hsa-miR-1307-3p tumor/poor 10.4 9.7 0.0000107 33 hsa-miR-452-5p tumor/poor 7.22 5.51 1.15E-07
10 hsa-miR-132-3p tumor/poor 6.25 5.75 0.0011 34 hsa-miR-500a-3p tumor/poor 8.01 7.24 7.37E-08
11 hsa-miR-151a-3p tumor/poor 11.67 10.9 1.12E-10 35 hsa-miR-500a-5p tumor/poor 1.87 1.52 0.00754
12 hsa-miR-183-5p tumor/poor 10.69 8.05 1.06E-09 36 hsa-miR-500b-5p tumor/poor 1.87 1.52 0.00711
13 hsa-miR-185-3p tumor/poor 2.72 2.42 0.0225 37 hsa-miR-501-3p tumor/poor 5.41 4.78 8.15E-07
14 hsa-miR-188-5p tumor/poor 1.54 0.58 4.71E-08 38 hsa-miR-532-3p tumor/poor 5.6 5.34 0.0423
15 hsa-miR-221-5p tumor/poor 1.35 0.65 0.0000216 39 hsa-miR-550a-5p tumor/poor 2.01 1.51 0.00409
16 hsa-miR-222-3p tumor/poor 4.54 3.59 0.00000275 40 hsa-miR-589-5p tumor/poor 6.31 5.35 8.49E-11
17 hsa-miR-25-3p tumor/poor 13.19 12.58 0.0000018 41 hsa-miR-652-3p tumor/poor 4.28 3.93 0.0352
18 hsa-miR-3127-5p tumor/poor 1.45 0.66 0.0000136 42 hsa-miR-769-5p tumor/poor 3.69 3.37 0.0016
19 hsa-miR-3200-3p tumor/poor 0.91 0.18 0.000023 43 hsa-miR-877-5p tumor/poor 0.53 0.08 0.0000231
20 hsa-miR-32-3p tumor/poor 0.51 0.27 0.0458 44 hsa-miR-937-3p tumor/poor 1.13 0.35 0.000323
21 hsa-miR-324-3p tumor/poor 4.17 3.71 0.000115 45 hsa-miR-9-3p tumor/poor 1.13 0.07 0.000271
22 hsa-miR-330-5p tumor/poor 4.06 3.21 0.00000117 46 hsa-miR-940 tumor/poor 0.94 0.38 0.00221
23 hsa-miR-331-5p tumor/poor 1.49 1.09 0.000981 47 hsa-miR-9-5p tumor/poor 8.43 6.78 0.000118
24 hsa-miR-339-3p tumor/poor 2.78 2.35 0.00186 48 hsa-miR-99b-3p tumor/poor 4.43 3.95 0.00657
22 hsa-miR-330-5p tumor/poor 4.06 3.21 0.00000117 46 hsa-miR-940 tumor/poor 0.94 0.38 0.00221
23 hsa-miR-331-5p tumor/poor 1.49 1.09 0.000981 47 hsa-miR-9-5p tumor/poor 8.43 6.78 0.000118
24 hsa-miR-339-3p tumor/poor 2.78 2.35 0.00186 48 hsa-miR-99b-3p tumor/poor 4.43 3.95 0.00657
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促癌miRNA的生存相关靶基因的鉴定流程如图 1B所示。miRnet数据库中预测出10 278个基因为上述促癌miRNA的靶基因；经GEPIA2数据库的表达分析后有129个符合预期；再经Ualcan生存分析后有44个符合预期；Starbase数据库中作miRNA-mRNA的共表达系数分析后，有27个mRNA与其对应的miRNA的共表达关系符合预期的负相关关系，形成45个miRNA-mRNA对（表 2）。这45个mRNA即为这些促癌miRNA的生存相关靶基因。

2.

肝细胞癌中促癌miRNA及其生存相关靶基因

Onco-miRNAs and their survival-related target genes in HCC

No Target miRNA R P Target miRNA R P
1 ABAT hsa-mir-106b-5p -0.278 5.66E-08 24 GADD45A hsa-mir-331-5p -0.248 1.43E-06
2 ABAT hsa-mir-183-5p -0.26 4.05E-07 25 GCDH hsa-mir-221-5p -0.186 0.000334
3 ACAT1 hsa-mir-9-5p -0.148 0.00445 26 IGF2 hsa-mir-32-3p 0.105 0.0443
4 ADH1C hsa-mir-106b-5p -0.385 1.62E-14 27 IGF2 hsa-mir-9-3p -0.205 0.0000715
5 APOC3 hsa-mir-769-5p -0.128 0.0135 28 IVD hsa-mir-589-5p -0.126 0.0155
6 CLU hsa-mir-425-5p -0.157 0.00251 29 IVD hsa-mir-9-3p -0.223 0.0000155
7 CPEB3 hsa-mir-1301-3p -0.369 2.31E-13 30 LIPC hsa-mir-940 -0.103 0.0486
8 CPEB3 hsa-mir-151a-3p -0.237 0.00000419 31 MYOM2 hsa-mir-940 -0.17 0.001
9 CPEB3 hsa-mir-25-3p -0.151 0.00371 32 PPARGC1A hsa-mir-421 -0.262 3.17E-07
10 CPEB3 hsa-mir-500b-5p -0.314 6.73E-10 33 QDPR hsa-mir-132-3p -0.201 0.000101
11 CPEB3 hsa-mir-9-3p -0.103 0.0484 34 QDPR hsa-mir-940 -0.215 3.09E-05
12 CPEB3 hsa-mir-9-5p -0.187 0.000289 35 RCAN1 hsa-mir-106b-5p -0.238 3.65E-06
13 CPEB3 hsa-mir-99b-3p -0.177 0.000646 36 RCAN1 hsa-mir-421 -0.253 8.47E-07
14 CPS1 hsa-mir-106b-5p -0.253 8.47E-07 37 RNF125 hsa-mir-151a-3p -0.226 0.000011
15 CPS1 hsa-mir-324-3p -0.209 0.0000507 38 RNF125 hsa-mir-32-3p -0.193 0.000187
16 CXCL2 hsa-mir-769-5p -0.189 0.000265 39 RNF125 hsa-mir-940 -0.168 0.00118
17 CYP4A11 hsa-mir-3127-5p -0.202 0.0000912 40 SLC43A2 hsa-mir-1307-3p 0.103 0.0474
18 CYP4A11 hsa-mir-940 -0.118 0.0229 41 STARD5 hsa-mir-3682-3p -0.175 0.000698
19 CYP8B1 hsa-mir-940 -0.173 0.000839 42 UGP2 hsa-mir-106b-5p -0.243 2.32E-06
20 DHRS1 hsa-mir-10b-5p -0.126 0.015 43 UGP2 hsa-mir-132-3p -0.198 0.000123
21 DMGDH hsa-mir-132-3p -0.291 1.23E-08 44 UGP2 hsa-mir-3200-3p -0.167 0.00124
22 DNMT3L hsa-mir-9-5p -0.156 0.00265 45 UGP2 hsa-mir-425-5p -0.113 0.0291
23 EHHADH hsa-mir-1226-3p -0.286 2.25E-08
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2.2. miRNA-mRNA调控网络的构建

基于上述分析鉴定的结果，我们构建了肝细胞癌中促癌miRNA及其生存相关靶基因的调控网络（图 2）。该调控网络由25个促癌miRNA和27个生存相关靶基因组成，其中hsa-mir-940靶向最多的基因（6个），CPEB3可被最多的miRNA靶向（7个）（图 2）。

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肝细胞癌中促癌miRNA与其生存相关靶基因的调控网络

Regulatory network of onco-miRNAs (triangles) and their survival-related target genes (circle) in HCC. Red triangles represent up-regulated miRNAs in HCC, and green circles represent down-regulated target genes.

2.3. 靶基因富集分析及关键基因鉴定

为了分析生存相关的靶基因所参与的细胞内信号通路，我们进行了富集分析，结果显示富集程度最高的为脂肪酸代谢信号通路（图 3A），富集在该通路的基因包括EHHADH，GCDH，ACAT1，ADH1C和CYP4A11（图 3B）。

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靶基因富集分析

Enrichment analysis of the target genes.

进一步用STRING数据库的分析得到生存相关靶基因的蛋白互作网络（图 4A）；将该网络导入Cytoscape软件，利用Cytohubba插件中的Closness，Degree，DMNC，EPC，MCC及MNC算法得到排名前9的基因（图 4B）；在此基础上，再取交集得到排名前三的关键基因为EHHADH，GCDH和ACAT1。

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蛋白互作分析及关键基因鉴定

Protein and protein interaction analysis and identification of the hub genes. A: Protein interaction network of survival-related target genes. B: Hub genes identified by different methods through Cytohubba.

利用Ualcan做表达分析的结果显示，EHHADH，GCDH和ACAT1在肝细胞癌组织中的表达水平较正常组织显著升高（图 5A~C）；GEPIA数据库得到的生存分析结果显示，EHHADH，GCDH和ACAT1高表达的患者比低表达的患者总体生存率更高（图 5D~F）。

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关键基因的表达和生存分析

Expression and survival analysis of the key genes.

我们选取了EHHADH，GCDH两个关键基因和其对应的miRNA进行了实验验证。Q-PCR结果显示，向HepG2细胞中分别转染hsa-miR-1226-3p和hsa-miR- 221-5p的mimics后，EHHADH和GCDH的表达量对应的发生显著下调（图 6A）。而向HepG2细胞中分别转染hsa-miR-1226-3p和hsa-miR-221-5p的inhibitor后，EHHADH和GCDH的表达量相应的上升（图 6B）。CCK8结果显示，转染hsa-miR-1226-3p的mimics和inhibitor后，细胞增殖活力的改变并不明显（图 6C）。而转染hsa-miR-221-5p的mimics和inhibitor后，可显著地促进或抑制细胞的增殖活力（图 6D）。

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关键miRNA-基因对的验证

Validation of key miRNA-gene pairs. A: Detection of target gene mRNA level after transfection of HEG2 cells with miRNA mimics. B: Detection of target gene mRNA level after transfection of HEG2 cells with miRNA inhibitors. C: CCK8 kit was used to detect the changes of cell viability after transfection with hsa-mir-1226-3p mimics and inhibitor. D: Changes of cell viability after transfection with hsa-mir-221-5p mimics and inhibitor. *P < 0.05, **P < 0.01 vs control.

我们选择了hsa-miR-221-5p/GCDH进行了进一步的验证。Western blot实验结果显示，转染hsa-miR- 221-5p的mimics和inhibitor后，GCDH的蛋白水平发生相应的下调或增加（图 7A）。序列分析鉴定出了GCDH与hsa-miR-221-5p的结合位点（图 7B）；利用结合位点附近的序列我们构建了正常序列的（WT）和突变序列的（MUT）荧光素酶报告质粒（图 7B）；将hsa-miR- 221-5p或NC的mimics与两种质粒分别组合，共转染HEG2细胞，检测结果显示hsa-miR-221-5p的mimic可以显著降低正常序列质粒（GCDH-WH）组的荧光素酶活性（图 7C）。

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miR-221-5p/GCDH靶向关系验证

Validation of the targeting relationship between miR-221-5p and GCDH. A: Expression level of GCDH protein detected by Western blotting in HepG2 cells transfected with hsa-miR-221-5p mimics or inhibitor. B: Binding site analysis of miR-221-5p/GCDH. C: Luciferase reporter experiment for verifying the targeting relationship of miR-221-5p/GCDH. **P < 0.01 vs control.

CCK8结果显示，转染pEGFP N1-GCDH可抑制细胞的增殖活力，并且可以拯救由hsa-miR-221-5p的mimics带来的增殖促进效应（图 8A）。Transwell实验结果与CCK8结果保持一致，过表达hsa-miR-221-5p可增加细胞的迁移和侵袭能力，而过表达GCDH可抑制细胞的迁移和侵袭能力；并且GCDH可恢复由hsamiR-221-5p过表达形成的迁移和侵袭能力的增强（图 8B~E）。

8.

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miR-221-5p/GCDH轴的功能验证

Functional verification of the miR-221-5p/GCDH axis. A: CCK8 was used to detect the proliferation activity of cells transfected with NC、miR-1226-3p mimics、pEGFP N1-GCDH、miR-1226-3p mimics+ pEGFP N1-GCDH. B-C: Transwell detected the migration ability of cells transfected with NC、miR-1226- 3p mimics、pEGFP N1-GCDH、miR-1226-3p mimics+ pEGFP N1-GCDH. D-E: Transwell detected the invasion ability of cells transfected with NC、miR-1226-3p mimics、pEGFP N1-GCDH、miR-1226-3p mimics+ pEGFP N1-GCDH. (B, D: Original magnification: ×200).**P < 0.01, ***P < 0.001.

3. 讨论

肿瘤发病分子机制的研究有助于新的诊断和预后标志物的开发，以及可能的新的治疗靶点的发现。随着生物数据量的急速增加和生物信息学的快速发展，近年来，肿瘤相关的二级数据库逐渐出现并且功能也日益完善。这使得许多研究者能够方便地对特定癌种中的基因表达异常进行系统的研究[16-20]。然而，围绕肝细胞癌中生存相关的促癌miRNA的系统性研究还未见报道。本项研究中，我们分别从OncomiR和Oncolnc数据库中下载了肝细胞癌中生存相关的miRNA，再取两者的交集，提高了筛选结果的可靠性。通过表达和生存分析明确有48个miRNA为促癌miRNA。其中部分miRNA在肝细胞癌中的功能及其作用机制已有较为细致的报道。如hsa-miR-183-5p[21]，hsa-miR-222-3p[22]，hsa-miR- 421[23]，hsa-miR-940[24]和hsa-miR-9-5p[25]等。还有部分促癌miRNA在肝细胞癌中的功能还未见文献报道，因此，这些miRNA也值得未来进一步研究。为了探究促癌miRNA促进肝细胞癌发生、发展的分子机制，我们通过miRnet数据库预测了促癌miRNA的靶基因，并通过GEPIA2的生存分析和Ualcan的表达分析，以及Starbase中的miRNA-mRNA的共表达分析，最终鉴定出了27个与肝细胞癌病人生存相关的靶基因。通过靶基因和miRNA的对应关系，我们构建了肝细胞癌中促癌miRNA和生存相关靶基因的调控网络，由25个miRNA和27个靶基因组成。该网络可能部分揭示了促癌miRNA导致肝细胞癌发生和发展，以及影响病人预后的分子机制。该网络中少数miRNA-mRNA的调控关系已经有了实验性的报道给予了支持。例如，有研究报道hsa-miR-9-5p可靶向ACAT1 mRNA的3’端非转录区，降低其蛋白表达水平[26]。然而，更多的调控关系则需要以后进一步的研究和验证。

为了研究生存相关的靶基因可能集中作用的信号通路，我们做了富集分析，结果显示有5个生存相关靶基因（EHHADH，GCDH，ACAT1，ADH1C和CYP4A11）富集在脂肪酸代谢信号通路中。以往的研究报道表明，脂肪酸参与合成细胞质膜及相关信号分子，在肿瘤进展中发挥重要作用[27]。进一步通过蛋白互作网络的构建及关键基因分析，鉴定出EHHADH，GCDH和ACAT1为这些生存相关靶基因中的关键基因。这三个基因在我们构建的miRNA-mRNA网络中分别与hsa-miR-1226-3p，hsa-miR-221-5p和hsa-miR- 9-5p形成靶向关系。ACAT1在不同肿瘤中的功能已经有报道[28-30]。hsa-miR-9-5p与ACAT1的靶向关系也有实验数据的支持[25]。但是，hsa-miR-1226-3p/EHHADH和hsa-miR-221-5p/GCDH的调控关系目前还未见报道。在本研究中，Q-PCR结果支持了它们与靶基因的特异性调控关系。然而CCK8结果仅支持hsa-miR- 221-5p在肝癌细胞中有功能。为此，我们通过Western和荧光酶报告实验进一步证实了hsa-miR-221-5p/ GCDH的靶向关系。细胞功能实验证实，hsa-miR-221- 5p具有促进细胞增殖和侵袭、迁移的能力，而GCDH则发挥的是抑制的功能；过表达GCDH可恢复由hsamiR-221-5p表达上调导致的细胞增殖能力及侵袭、迁移能力的增强。这些结果表明hsa-miR-221-5p/GCDH轴在肝细胞癌的中发挥重要的调节功能。同时，验证实验也表明本研究中构建的促癌miRNA和生存相关靶基因的调控网络具有较高的可信度；然而，更多分子的调控关系和功能仍需要以后进一步的研究。

总之，本研究基于数据库对肝细胞癌中生存相关的促癌miRNA进行了系统性的挖掘和研究，并对鉴定到的关键miRNA/靶基因对进行了实验验证，这些结果有望为肝细胞癌的发病机制和治疗提供新的线索。

Biography

叶静静，检验技师，E-mail：Y1995_211@126.com

Funding Statement

国家自然科学基金（81901519）；安徽省自然科学基金（1908085QH380）

Supported by National Natural Science Foundation of China(81901519)

Contributor Information

叶 静静 (Jingjing YE), Email: Y1995_211@126.com.

陈 天兵 (Tianbing CHEN), Email: ctb0410021@126.com.

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