基于生物信息学筛选早发性乳腺癌差异表达基因

doi:10.3877/cma.j.issn.1674-0807.2022.01.002

目的

筛选并分析与早发性乳腺癌发生、发展相关的靶基因。

方法

(1)在美国国立生物技术信息中心的公共基因芯片数据库(GEO)中检索早发性乳腺癌样本及非早发性乳腺癌样本相关基因芯片数据。对上述数据使用GEO2R、R4.1.2及Venn软件筛选出相关差异表达基因(DEGs)，并运用在线分析工具(Web Gestalt)对DEGs，进行相关功能和信号通路富集分析。(2)同时，通过String在线数据库构建DEGs编码的蛋白质-蛋白质相互作用(PPI)网络，并利用Cytohubba插件对该网络中的基因进行评分，筛选出枢纽基因。将枢纽基因导入Kaplan-Meier生存分析工具(Kaplan-Meier Plotter)，评估枢纽基因在早发性乳腺癌的预后价值。(3)将肿瘤基因组图谱(TCGA)数据库中的肿瘤组织以年龄为标准进行分组，分析枢纽基因在各年龄组中的表达，并与正常组织中的表达进行比较，对得到的枢纽基因进行验证。DEGs表达量的多组间比较使用Kruskal-Wallis H检验，使用Bonferroni法进行两两比较。

结果

(1)筛选出编号为GSE89116、GSE109169、GSE36295的基因芯片数据集，共得到80个差异表达基因，其中上调差异表达基因17个，下调差异表达基因63个。富集分析显示：DEGs主要富集在脂质代谢和氧化还原过程以及PPAR信号通路、AMPK信号通路上。(2)在PPI中发现主要的关键基因为PPARG、ADIPOQ、LIPE、PCK1、PDK4、ACACB、PLIN1、CAV1、CD36、ANGPTL4。ACACB、ADIPOQ、CAV1、LIPE、PLIN1、PPARG基因的低表达与乳腺癌患者的不良OS相关(HR＝0.69、0.84、0.76、0.88、0.78、0.82；95%CI：0.59~0.80、0.76~0.93、0.67~0.83、0.79~0.97、0.70~0.86、0.73~0.90；P均<0.050)。(3)ACACB、ADIPOQ、LIPE、PLIN1、CAV1及PPARG这6个与预后相关的基因在正常组织中的表达量均远高于各年龄组肿瘤组织中的表达量(χ²＝104.03、179.57、161.85、189.87、118.56、103.62，P均<0.001)，早发性乳腺癌组(21~40岁)的LIPE、PLIN1表达量低于41~60岁、61~80岁年龄组，差异具有统计学意义(LIPE: Z＝21.07、23.12, P均<0.050; PLIN1：Z＝16.89、18.76, P均<0.050)。

结论

早发性乳腺癌与非早发性乳腺癌存在差异基因表达谱，LIPE、PLIN1可能是早发性乳腺癌发生、发展的关键基因。

关键词: 乳腺肿瘤, 生物信息学, 差异表达基因

Objective

To search for and analyze the target genes associated with the occurrence and development of early-onset breast cancer.

Methods

(1)The related gene microarray data on early-onset breast cancer and non-early-onset breast cancer were retrieved from the Gene Expression Omnibus (GEO) database of the U. S.National Center for Biotechnology Information (NCBI). The relevant differentially expressed genes(DEGs) were screened for using GEO2R, R4.1.2 and Venn softwares, and the online analysis tool(Web Gestalt) was used to conduct the enrichment analysis of DEGs-related functions and signal pathways. (2)Meanwhile, a protein-protein interaction (PPI) network was constructed from the String online database and Cytohubba plug-in was used to score the genes in the network and screen out the key genes. Prognostic value of key genes was assessed using the Kaplan-Meier online plotter. (3)Tumor tissues in the Cancer Genome Atlas (TCGA) database were grouped according to patient age, and the expression of keys genes in each age group was analyzed and compared with the expression in normal tissues to verify the obtained key genes. The multi-group comparison of DEGs expression was performed using the Kruskal-Wallis H test, and Bonferroni method was used for pairwise comparison.

Results

(1)Three gene microarray datasets (GSE89116, GSE109169 and GSE36295) were selected to obtain 80 DEGs, including 17 up-regulated genes and 63 down-regulated genes. Enrichment analysis showed that DEGs were mainly enriched in lipid metabolism, redox process, PPAR signal pathway and AMPK signal pathway. (2)The main key genes found in PPI included PPARG, ADIPOQ, LIPE, PCK1, PDK4, ACACB, PLIN1, CAV1, CD36 and ANGPTL4. The low expressions of ACACB, ADIPOQ, CAV1, LIPE, PLIN1 and PPARG genes were associated with lower OS in breast cancer patients(HR=0.69, 0.84, 0.76, 0.88, 0.78, 0.82; 95%CI: 0.59-0.80, 0.76-0.93, 0.67-0.83, 0.79-0.97, 0.70-0.86, 0.73-0.90; all P<0.050). (3)The expressions of six genes related to the prognosis (ACACB, ADIPOQ, LIPE, PLIN1, CAV1 and PPARG) in normal tissues were significantly higher than those in tumor tissues of all age groups(χ²=104.03, 179.57, 161.85, 189.87, 118.56, 103.62; all P<0.001). The expressions of LIPE and PLIN1 in the early-onset breast cancer patients (21-40 years) were significantly lower than those in other age groups (41-60 years and 61-80 years)(LIPE: Z=21.07, 23.12, both P<0.050; PLIN1: Z=16.89, 18.76, both P<0.050).

Conclusions

There is an expression profile of DEGs between early-onset breast cancer and non-early-onset breast cancer. LIPE and PLIN1 may be the key genes for the occurrence and development of early-onset breast cancer.

Key words: Breast neoplasms, Bioinformatics, Differentially expressed genes

图1 乳腺癌差异表达基因韦恩图　a图所示上调差异表达基因；b图所示下调差异表达基因

图2 乳腺癌组织中上调差异表达基因的功能富集分析　a、b、c图分别所示主要生物学过程、细胞成分及分子功能的差异表达基因数量

图3 乳腺癌组织中下调差异表达基因的功能富集分析　a、b、c图分别所示主要生物学过程、细胞成分及分子功能的差异表达基因数量

图4 乳腺癌组织中上调差异表达基因的通路富集分析注：FDR为错误发现率

图5 乳腺癌组织中下调差异表达基因的通路富集分析注：FDR为错误发现率，通过对显著性差异P值校正所得，FDR≤0.050视为结果假阳性率为0

图6 乳腺癌差异表达基因的蛋白质-蛋白质相互作用网络

图7 乳腺癌蛋白质-蛋白质相互作用网络中的核心基因注：方框颜色越深表示在相互作用网络中的连接度越高

图8 不同关键基因表达的乳腺癌患者总生存曲线比较　a、b、c、d、e、f图分别表示不同ACACB、ADIPOQ、CAV1、LIPE、PLIN1、PPARG基因表达的乳腺癌患者总生存曲线注：ACACB、ADIPOQ、CAV1、LIPE、PLIN1、PPARG基因低表达组与高表达组比较，HR＝0.69、0.84、0.76、0.88、0.78、0.82，95%CI：0.59~0.80、0.76~0.93、0.67~0.83、0.79~0.97、0.70~0.86、0.73~0.90，P均<0.050

表1 正常乳腺组织和不同年龄组乳腺癌组织中ACACB、ADIPOQ、LIPE、PLIN1、CAV1、PPARG表达量的比较

组别		例数	ACACB	ADIPOQ	LIPE	PLIN1	CAV1	PPARG
正常组织		114	41.99(20.52，97.24)	230.06(64.34，592.06)	85.84(25.33，267.71)	331.72(109.61，914.44)	416.22(238.42，720.29)	55.28(25.59，106.66)
乳腺癌组织
	21~40岁	97	3.70(1.75，5.91)^a	1.35(0.09，7.99)^a	2.58(1.38，5.79)^a	1.62(0.52，5.80)^a	44.26(26.90，64.32)^a	6.20(3.42，10.09)^a
	41~60岁	505	4.45(2.26，7.72)^a	1.68(0.33，8.82)^a	4.46(2.19，7.86)^ab	2.40(0.65，9.16)^ab	41.18(25.77，67.16)^a	5.82(3.45，9.86)^a
	61~80岁	431	4.10(2.41，7.74)^a	1.59(0.14，8.11)^a	4.53(2.23，7.84)^ab	2.72(0.71，9.17)^ab	37.12(23.76，55.92)^a	5.17(3.28，9.08)^a
	81~100岁	54	4.11(1.94，6.49)^a	0.47(0.04，5.32)^a	4.64(1.71，7.98)^a	1.09(0.29，5.08)^a	27.89(16.21，40.34)^a	4.00(2.11，7.80)^a
χ²值			104.03	179.57	161.85	189.87	118.56	103.62
P值			<0.001	<0.001	<0.001	<0.001	<0.001	<0.001

表1 正常乳腺组织和不同年龄组乳腺癌组织中ACACB、ADIPOQ、LIPE、PLIN1、CAV1、PPARG表达量的比较

组别		例数	ACACB	ADIPOQ	LIPE	PLIN1	CAV1	PPARG
正常组织		114	41.99(20.52，97.24)	230.06(64.34，592.06)	85.84(25.33，267.71)	331.72(109.61，914.44)	416.22(238.42，720.29)	55.28(25.59，106.66)
乳腺癌组织
	21~40岁	97	3.70(1.75，5.91)^a	1.35(0.09，7.99)^a	2.58(1.38，5.79)^a	1.62(0.52，5.80)^a	44.26(26.90，64.32)^a	6.20(3.42，10.09)^a
	41~60岁	505	4.45(2.26，7.72)^a	1.68(0.33，8.82)^a	4.46(2.19，7.86)^ab	2.40(0.65，9.16)^ab	41.18(25.77，67.16)^a	5.82(3.45，9.86)^a
	61~80岁	431	4.10(2.41，7.74)^a	1.59(0.14，8.11)^a	4.53(2.23，7.84)^ab	2.72(0.71，9.17)^ab	37.12(23.76，55.92)^a	5.17(3.28，9.08)^a
	81~100岁	54	4.11(1.94，6.49)^a	0.47(0.04，5.32)^a	4.64(1.71，7.98)^a	1.09(0.29，5.08)^a	27.89(16.21，40.34)^a	4.00(2.11，7.80)^a
χ²值			104.03	179.57	161.85	189.87	118.56	103.62
P值			<0.001	<0.001	<0.001	<0.001	<0.001	<0.001

[1]	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.
[2]	Anders CK, Hsu DS, Broadwater G, et al. Young age at diagnosis correlates with worse prognosis and defines a subset of breast cancers with shared patterns of gene expression[J]. J Clin Oncol, 2008, 26(20)：3324-3330.
[3]	Gnerlich JL, Deshpande AD, Jeffe DB, et al. Elevated breast cancer mortality in women younger than age 40 years compared with older women is attributed to poorer survival in early-stage disease[J]. J Am Coll Surg, 2009，208(3)：341-347.
[4]	Kolečková M, Kolář Z, Ehrmann J, et al. Age-associated prognostic and predictive biomarkers in patients with breast cancer[J]. Oncol Lett, 2017，13(6)：4201-4207.
[5]	U．S.National Center for Biotechnology Information.Gene Expression Omnibus[DB/OL]. Bethesda：National Center for Biotechnology Information，2000[2021-03-03]． URL
[6]	Ghent University. Venn Diagram [DB/OL]. Ghent: Ghent University, 2012[2021-03-03]． URL
[7]	U．S.National Institute of Health. Web Gestalt[DB/OL]. Bethesda：National Institute of Health，2005[2021-03-03]． URL
[8]	Swiss Institute of Bioinformatics. String [DB/OL]. Lausanne: Swiss Institute of Bioinformatics，2000[2021-03-03]． URL
[9]	Semmelweis University.Kaplan-Meier Plotter [DB/OL]. Budapest：Semmelweis University，2009[2021-03-03]． URL
[10]	Chandrashekar DS, Bashel B, Balasubramanya SAH, et al. UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses[J]. Neoplasia, 2017, 19(8)：649-658.
[11]	Spiegelman BM. PPAR-gamma: adipogenic regulator and thiazolidinedione receptor[J]. Diabetes, 1998，47(4)：507-514.
[12]	Xu YY, Liu H, Su L, et al. PPARγ inhibits breast cancer progression by upregulating PTPRF expression[J]. Eur Rev Med Pharmacol Sci, 2019，23(22)：9965-9977.
[13]	Tang W, Chen Y, Wang Y, et al.Peroxisome proliferator-activated receptor gamma (PPARG) polymorphisms and breast cancer susceptibility: a meta-analysis[J]. Int J Clin Exp Med, 2015, 8(8)：12 226-12 238.
[14]	Zhang X, Zhang CC, Yang H, et al. Anepistatic interaction between Pnpla2 and Lipe reveals new pathways of adipose tissue lipolysis[J]. Cells, 2019，8(5)：395.
[15]	Samuel VT, Shulman GI. The pathogenesis of insulin resistance: integrating signaling pathways and substrate flux[J]. J Clin Invest, 2016，126(1)：12-22.
[16]	Bowers LW, Rossi EL, O’Flanagan CH, et al. The role of the insulin/IGF system in cancer: lessons learned from clinical trials and the energy balance-cancer link[J]. Front Endocrinol (Lausanne), 2015，6：77.
[17]	Parida S, Siddharth S, Sharma D. Adiponectin, obesity, and cancer: clash of the bigwigs in health and disease[J]. Int J Mol Sci, 2019，20(10)：2519.
[18]	Chung SJ, Nagaraju GP, Nagalingam A, et al. ADIPOQ/adiponectin induces cytotoxic autophagy in breast cancer cells through STK11/LKB1-mediated activation of the AMPK-ULK1 axis[J]. Autophagy, 2017，13(8)：1386-1403.
[19]	Wang H, Bell M, Sreenivasan U, et al. Unique regulation of adipose triglyceride lipase (ATGL) by perilipin 5, a lipid droplet-associated protein[J]. J Biol Chem, 2011，286(18)：15 707-15 715.
[20]	Zhou C, Wang M, Zhou L, et al. Prognostic significance of PLIN1 expression in human breast cancer[J]．Oncotarget, 2016，7(34)：54 488-54 502.
[21]	Wu KN, Queenan M, Brody JR, et al. Loss of stromal caveolin-1 expression in malignant melanoma metastases predicts poor survival[J]. Cell Cycle, 2011，10(24)：4250-4255.
[22]	Zhao Z, Han FH, Yang SB, et al. Loss ofstromal caveolin-1 expression in colorectal cancer predicts poor survival[J]. World J Gastroenterol, 2015，21(4)：1140-1147.
[23]	Campbell L, AI-Jayyoussi G, Gutteridge R, et al. Caveolin-1 in renal cell carcinoma promotes tumour cell invasion, and in co-operation with pERK predicts metastases in patients with clinically confined disease[J]. J Transl Med, 2013，11：255.
[24]	Liu WR, Jin L, Tian MX, et al. Caveolin-1 promotes tumor growth and metastasis via autophagy inhibition in hepatocellular carcinoma[J]. Clin Res Hepatol Gastroenterol, 2016，40(2)：169-178.
[25]	Witkiewicz AK, Dasgupta A, Sammons S, et al. Loss of stromal caveolin-1 expression predicts poor clinical outcome in triple negative and basal-like breast cancers[J]. Cancer Biol Ther, 2010，10(2)：135-143.
[26]	Jeon SM, Chandel NS, Hay N. AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress[J]. Nature, 2012, 485(7400): 661-665.

[1]	郏亚平, 曾书娥. 含鳞状细胞癌成分的乳腺化生性癌的超声与病理特征分析[J]. 中华医学超声杂志（电子版）, 2023, 20(08): 844-848.
[2]	唐玮, 何融泉, 黄素宁. 深度学习在乳腺癌影像诊疗和预后预测中的应用[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 323-328.
[3]	康夏, 田浩, 钱进, 高源, 缪洪明, 齐晓伟. 骨织素抑制破骨细胞分化改善肿瘤骨转移中骨溶解的机制研究[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 329-339.
[4]	衣晓丽, 胡沙沙, 张彦. HER-2低表达对乳腺癌新辅助治疗疗效及预后的影响[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 340-346.
[5]	施杰, 李云涛, 高海燕. 腋窝淋巴结阳性Luminal A型乳腺癌患者新辅助与辅助化疗的预后及影响因素分析[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 353-361.
[6]	伍秋苑, 陈佩贤, 邓裕华, 何添成, 周丹. 肠道微生物在乳腺癌中的研究进展[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 362-365.
[7]	谭巧, 苏小涵, 侯令密, 黎君彦, 邓世山. 乳腺髓样癌的诊治进展[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 366-368.
[8]	周婉丽, 钱铮, 李喆. 槐耳在乳腺癌免疫治疗中的研究进展[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 369-371.
[9]	熊倩, 罗凤. 乳腺癌患者术后康复现状与对策的研究进展[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 372-374.
[10]	杨小菁, 姜瑞瑞, 石玉香, 王静静, 李长天. 乳腺孤立性纤维性肿瘤一例[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 375-377.
[11]	冯雪园, 韩萌萌, 马宁. 乳腺原发上皮样血管内皮瘤一例[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 378-380.
[12]	张圣平, 邓琼, 张颖, 张建文, 梁辉, 王铸. 孤儿核受体HNF4α在肾透明细胞癌中的表达及意义[J]. 中华腔镜泌尿外科杂志(电子版), 2023, 17(06): 627-632.
[13]	张维志, 刘连新. 基于生物信息学分析IPO7在肝癌中的表达及意义[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 694-701.
[14]	陈安, 冯娟, 杨振宇, 杜锡林, 柏强善, 阴继凯, 臧莉, 鲁建国. 基于生物信息学分析CCN4在肝细胞癌中表达及其临床意义[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 702-707.
[15]	王苏贵, 皇立媛, 姜福金, 吴自余, 张先云, 李强, 严大理. 异质性细胞核核糖蛋白A2B1在前列腺癌中的作用及其靶向中药活性成分筛选研究[J]. 中华临床医师杂志(电子版), 2023, 17(06): 731-736.

阅读次数

全文

摘要

选择文件类型/文献管理软件名称

选择包含的内容

Screening for differentially expressed genes in early-onset breast cancer using bioinformatic analysis

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

Screening for differentially expressed genes in early-onset breast cancer using bioinformatic analysis