应用加权基因共表达网络分析探索喉癌标志物（英文）

张峰煜; 佘笠; 黄东海

doi:10.11817/j.issn.1672-7347.2023.220630

您当前的位置：

首页 >

文章列表页 >

应用加权基因共表达网络分析探索喉癌标志物（英文）

扫描看全文

文章被引用时，请邮件提醒。

提交

论著 | 更新时间：2023-10-24

应用加权基因共表达网络分析探索喉癌标志物（英文）
Identification of biomarkers in laryngeal cancer by weighted gene co-expression network analysis
中南大学学报(医学版) 中南大学学报(医学版) 2023年48卷第8期页码：1136-1151
作者机构：

1.中南大学湘雅医院耳鼻咽喉头颈外科，长沙 410008
2.耳鼻咽喉重大疾病湖南省重点实验室，长沙 410008
作者简介：

ZHANG Fengyu, Email: damaozfy@163.com, ORCID: 0000-0002-7895-0693
HUANG Donghai, Email: huang3301@126.com, ORCID: 0000-0002-2251-7188
基金信息：

the Natural Science Foundation of Hunan Province, China(2019JJ40481)
DOI：10.11817/j.issn.1672-7347.2023.220630
中图分类号：
CSTR：

张峰煜, 佘笠, 黄东海. 应用加权基因共表达网络分析探索喉癌标志物（英文）[J]. 中南大学学报(医学版), 2023,48(8):1136-1151.

ZHANG Fengyu, SHE Li, HUANG Donghai. Identification of biomarkers in laryngeal cancer by weighted gene co-expression network analysis[J]. Journal of Central South University. Medical Science, 2023,48(8):1136-1151.
张峰煜, 佘笠, 黄东海. 应用加权基因共表达网络分析探索喉癌标志物（英文）[J]. 中南大学学报(医学版), 2023,48(8):1136-1151. DOI： 10.11817/j.issn.1672-7347.2023.220630.

ZHANG Fengyu, SHE Li, HUANG Donghai. Identification of biomarkers in laryngeal cancer by weighted gene co-expression network analysis[J]. Journal of Central South University. Medical Science, 2023,48(8):1136-1151. DOI： 10.11817/j.issn.1672-7347.2023.220630.

摘要

目的,2,喉癌(laryngeal cancer，LC)是全球最常见的头颈部肿瘤之一，世界范围内的死亡率和发病率都很高。尽管付出了巨大的努力，但人们对喉癌发生恶性进展的机制仍然认识不足。本研究旨在通过深度生物信息学分析，寻找可作为喉癌生物标志物或治疗靶点的关键基因标志物。,方法,2,从美国肿瘤基因组图谱(the Cancer Genome Atlas，TCGA)数据库中收集117个喉癌患者样本，16 746个喉癌基因RNA测序数据和9个临床特征，采用加权基因共表达网络分析(weighted gene coexpression network analysis，WGCNA)构建多个共表达基因模块。对相关的共表达模块和临床特征进行研究，验证它们之间的相关性，并利用模块之间的关联来探索疾病发生通路中的关键基因，最后运用Kaplan-Meier plotter验证富集后的基因与喉癌预后的相关性。,结果,2,通过WGCNA，共构建出16个喉癌共表达基因模块，并发现其中的4个共表达模块(黄色、洋红、黑色、褐色共表达模块)与3个临床特征(初始病理诊断年龄、癌症状态和病理N分期)有相关性。其中，黄色和洋红基因共表达模块与病理诊断的年龄呈负相关(分别为,r,=-0.23， ,P,<,0.05；,r,=-0.33，,P,<,0.05)；黑色和褐色基因共表达模块与癌症状态呈负相关(分别为,r,=-0.39，,P,<,0.05；,r,=-0.50，,P,<,0.05)。另外，褐色基因共表达模块与病理N分期呈现显著正相关。基因本体(gene ontology，GO)富集分析鉴定出77条相关通路，京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes，KEGG)通路富集分析鉴定出30条相关信号通路，包括钙信号通路、细胞因子-细胞因子受体相互作用、神经活性配体-受体相互作用和脂肪细胞脂肪分解调控等。随后，我们确定了这些模块中的中枢基因。中枢基因与喉癌患者的总生存率显著相关，包括,CHRNB4,，,FOXL2,，,KCNG1,，,LOC440173,，,ADAMTS15,，,BMP2,，,FAP,和,KIAA1644,。,结论,2,通过WGCNA和验证，筛选出8个可作为喉癌潜在基因生物标志物的基因，为喉癌的临床诊断、预后和治疗提供参考。

Abstract

Objective,2,Laryngeal cancer (LC) is a globally prevalent and highly lethal tumor. Despite extensive efforts, the underlying mechanisms of LC remain inadequately understood. This study aims to conduct an innovative bioinformatic analysis to identify hub genes that could potentially serve as biomarkers or therapeutic targets in LC.,Methods,2,We acquired a dataset consisting of 117 LC patient samples, 16 746 LC gene RNA sequencing data points, and 9 clinical features from the Cancer Genome Atlas (TCGA) database in the United States. We employed weighted gene co-expression network analysis (WGCNA) to construct multiple co-expression gene modules. Subsequently, we assessed the correlations between these co-expression modules and clinical features to validate their associations. We also explored the interplay between modules to identify pivotal genes within disease pathways. Finally, we used the Kaplan-Meier plotter to validate the correlation between enriched genes and LC prognosis.,Results,2,WGCNA analysis led to the creation of a total of 16 co-expression gene modules related to LC. Four of these modules (designated as the yellow, magenta, black, and brown modules) exhibited significant correlations with 3 clinical features: The age of initial pathological diagnosis, cancer status, and pathological N stage. Specifically, the yellow and magenta gene modules displayed negative correlations with the age of pathological diagnosis (,r,=-0.23,P,<,0.05; ,r,=-0.33,P,<,0.05), while the black and brown gene modules demonstrated negative associations with cancer status (,r,=-0.39,P,<,0.05; ,r,=-0.50,P,<,0.05). The brown gene module displayed a positive correlation with pathological N stage. Gene Ontology (GO) enrichment analysis identified 77 items, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified 30 related signaling pathways, including the calcium signaling pathway, cytokine-cytokine receptor interaction, neuro active ligand-receptor interaction, and regulation of lipolysis in adipocytes, etc. Consequently, central genes within these modules that were significantly linked to the overall survival rate of LC patients were identified. Central genes included ,CHRNB4,FOXL2,KCNG1,LOC440173,ADAMTS15,BMP2,FAP, and ,KIAA1644,.,Conclusion,2,This study, utilizing WGCNA and subsequent validation, pinpointed 8 genes with potential as gene biomarkers for LC. These findings offer valuable references for the clinical diagnosis, prognosis, and treatment of LC.

关键词

喉癌共表达模块加权基因共表达网络分析生物标志物

Keywords

laryngeal cancerco-expression gene modulesweighted gene co-expression network analysisgene biomarker

references

Steuer CE, EL-deiry M, Parks JR, et al. An update on larynx cancer[J]. CA Cancer J Clin, 2017, 67(1): 31-50. https://doi.org/10.3322/caac.21386https://doi.org/10.3322/caac.21386.

He J, Tian Y, Wu P, et al. Heterogeneous bovine acellular dermal matrix for mucosal repair in reconstructive surgery for laryngeal and hypopharyngeal carcinoma[J]. Oncol Res Treat, 2015, 38(6): 282-284. https://doi.org/10.1159/000430898https://doi.org/10.1159/000430898.

Li L, Luo Z. Dysregulated miR-27a-3p promotes nasopharyngeal carcinoma cell proliferation and migration by targeting Mapk10[J]. Oncol Rep, 2017, 37(5): 2679-2687. https:// doi.org/10.3892/or.2017.5544https://doi.org/10.3892/or.2017.5544.

Cheng T, Zhan X. Pattern recognition for predictive, preventive, and personalized medicine in cancer[J]. Epma J, 2017, 8(1): 51-60. https://doi.org/10.1007/s13167-017-0083-9https://doi.org/10.1007/s13167-017-0083-9.

Pei G, Chen L, Zhang W. WGCNA application to proteomic and metabolomic data analysis[J]. Methods Enzymol, 2017, 585: 135-158. https://doi.org/10.1016/bs.mie. 2016.09.016https://doi.org/10.1016/bs.mie.2016.09.016.

Liu J, Jing L, Tu X. Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease[J]. BMC Cardiovasc Disord, 2016, 16: 54. https://doi.org/10.1186/s12872-016-0217-3https://doi.org/10.1186/s12872-016-0217-3.

Luo Y, Coskun V, Liang A, et al. Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells[J]. Cell, 2015, 161(5): 1175-1186. https://doi.org/10.1016/j.cell.2015. 04.001https://doi.org/10.1016/j.cell.2015.04.001.

Li XT. Identification of key genes for laryngeal squamous cell carcinoma using weighted co-expression network analysis[J]. Oncol Lett, 2016, 11(5): 3327-3331. https://doi.org/10.3892/ol. 2016.4378https://doi.org/10.3892/ol.2016.4378.

Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis[J]. BMC Bioinformatics, 2008, 9559. https://doi.org/10.1186/1471-2105-9-559https://doi.org/10.1186/1471-2105-9-559.

Dutta R, Husain Q, Kam D, et al. Laryngeal papillary squamous cell carcinoma: A population-based analysis of incidence and survival[J]. Otolaryngol Head Neck Surg, 2015, 153(1): 54-59. https://doi.org/10.1177/0194599815581613https://doi.org/10.1177/0194599815581613.

Peng F, Zhang H, Du Y, et al. Cetuximab enhances cisplatin-induced endoplasmic reticulum stress-associated apoptosis in laryngeal squamous cell carcinoma cells by inhibiting expression of TXNDC5[J]. Mol Med Rep, 2018, 17(3): 4767-4776. https://doi.org/10.3892/mmr.2018.8376https://doi.org/10.3892/mmr.2018.8376.

Luo S, Xie C, Wu P, et al. Annexin A2 is an independent prognostic biomarker for evaluating the malignant progression of laryngeal cancer[J]. Exp Ther Med, 2017, 14(6):6113-6118. https://doi.org/10.3892/etm.2017.5298https://doi.org/10.3892/etm.2017.5298.

Zhu Z, Jin Z, Deng Y, et al. Co-expression network analysis identifies four hub genes associated with prognosis in soft tissue sarcoma[J]. Front Genet, 2019, 10: 37. https://doi.org/10.3389/fgene.2019.00037https://doi.org/10.3389/fgene.2019.00037.

Gong C, Hu Y, Zhou M, et al. Identification of specific modules and hub genes associated with the progression of gastric cancer[J]. Carcinogenesis, 2019, 40(10): 1269-1277. https://doi.org/10.1093/carcin/bgz040https://doi.org/10.1093/carcin/bgz040.

Kurian CJ, Leader AE, Thong MSY, et al. Examining relationships between age at diagnosis and health-related quality of life outcomes in prostate cancer survivors[J]. BMC Public Health, 2018, 18(1): 1060. https://doi.org/10.1186/s12889-018-5976-6https://doi.org/10.1186/s12889-018-5976-6.

Meng W, Cui W, Zhao L, et al. Aberrant methylation and downregulation of ZNF667-AS1 and ZNF667 promote the malignant progression of laryngeal squamous cell carcinoma[J]. 2019, 26(1): 13. https://doi.org/10.1186/s12929-019-0506-0https://doi.org/10.1186/s12929-019-0506-0.

Xiao S, Yang M, Yang H, et al. miR-330-5p targets SPRY2 to promote hepatocellular carcinoma progression via MAPK/ERK signaling[J]. Oncogenesis, 2018, 7(11): 90. https://doi.org/10.1038/s41389-018-0097-8https://doi.org/10.1038/s41389-018-0097-8.

Wang JL, Yang MY, Xiao S, et al. Downregulation of castor zinc finger 1 predicts poor prognosis and facilitates hepatocellular carcinoma progression via MAPK/ERK signaling[J]. J Exp Clin Cancer Res, 2018, 37(1): 45. https://doi.org/10.1186/s13046-018-0720-8https://doi.org/10.1186/s13046-018-0720-8.

Guo Y, Yuan J, Yin S, et al. MAP2K6-FP enhances the sensitiveness of paclitaxel for ovarian cancer via inducing autophagy[J]. Int J Gynecol Cancer, 2017, 27(6): 1082-1087. https://doi.org/10.1097/igc.0000000000001003https://doi.org/10.1097/igc.0000000000001003.

Khotskaya YB, Holla VR, Farago AF, et al. Targeting TRK family proteins in cancer[J]. Pharmacol Ther, 2017, 173: 58-66. https://doi.org/10.1016/j.pharmthera.2017.02.006https://doi.org/10.1016/j.pharmthera.2017.02.006.

da Veiga Moreira J, Hamraz M, Abolhassani M, et al. Metabolic therapies inhibit tumor growth in vivo and in silico[J]. 2019, 9(1): 3153. https://doi.org/10.1038/s41598-019-39109-1https://doi.org/10.1038/s41598-019-39109-1.

Gao C, Takezaki T, Wu J, et al. Interaction between cytochrome P-450 2E1 polymorphisms and environmental factors with risk of esophageal and stomach cancers in Chinese[J]. Cancer Epidemiol Biomarkers Prev, 2002, 11(1): 29-34.

Liu J, Wen G, Cao D. Aldo-keto reductase family 1 member B1 inhibitors: old drugs with new perspectives[J]. Recent Pat Anticancer Drug Discov, 2009, 4(3): 246-253.

Rahmani B, Hamediasl D, Naserpour Farivar T, et al. Omega-3 PUFA alters the expression level but not the methylation pattern of the WIF1 gene promoter in a pancreatic cancer cell line (MIA PaCa-2)[J]. Biochem Genet, 2019, 57(4): 477-486. https://doi.org/10.1007/s10528-018-9895-0https://doi.org/10.1007/s10528-018-9895-0.

黄佳祎, 郭丹. 抑制硬化蛋白基因表达有利于诱导成骨细胞的分化: 体外乳腺癌骨转移模型[J]. 南方医科大学学报, 2017, 37(8): 1035-1039. https://doi.org/10.3969/j.issn.1673-4254.2017.08.06https://doi.org/10.3969/j.issn.1673-4254.2017.08.06.

HUANG Jiayi, GUO Dan. SOST knockdown promotes differentiation of osteoblasts MG63 and mesenchymal stem cells C3H10 in an in vitro model of bone metastasis of breast cancer[J]. Journal of Southern Medical University, 2017, 37(8): 1035-1039. https://doi.org/10.3969/j.issn.1673-4254.2017.08.06https://doi.org/10.3969/j.issn.1673-4254.2017.08.06.

Henen MA, Mahlawat P, Zwieb C, et al. TGF-beta2 uses the concave surface of its extended finger region to bind betaglycan’s ZP domain via three residues specific to TGF-β and inhibin-α[J]. J Biol Chem, 2019, 294(9): 3065-3080. https:// doi.org/10.1074/jbc.RA118.005210https://doi.org/10.1074/jbc.RA118.005210.

Sand JP, Kokorina NA, Zakharkin SO, et al. BMP-2 expression correlates with local failure in head and neck squamous cell carcinoma[J]. Otolaryngol Head Neck Surg, 2014, 150(2): 245-250. https://doi.org/10.1177/0194599813513003https://doi.org/10.1177/0194599813513003.

Hamson EJ, Keane FM, Tholen S, et al. Understanding fibroblast activation protein (FAP): substrates, activities, expression and targeting for cancer therapy[J]. Proteomics Clin Appl, 2014, 8(5/6): 454-463. https://doi.org/10.1002/prca. 201300095https://doi.org/10.1002/prca.201300095.

Du HD, Wu CP, Zhou L, et al. Separation and cultivation of laryngeal carcinoma-associated fibroblasts and biological influence on a laryngeal carcinoma cell line[J]. Acta Otolaryngol, 2013, 133(7): 755-760. https://doi.org/10.3109/00016489.2013.766925https://doi.org/10.3109/00016489.2013.766925.

Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes[J]. 2018, 9(1): 77-102. https://doi.org/10.1007/s13167-018-0128-8https://doi.org/10.1007/s13167-018-0128-8.

浏览量

下载量

CSCD

工具集

关联资源

Comparative molecular analysis of oral submucous fibrosis and other organ fibrosis based on weighted gene co-expression network analysis