Bioinformatics analysis of potential targets influencing the prognosis of OSCC

doi:10.12439/kqhm.1005-4979.2024.03.005

Abstract

Abstract:

Objective: To apply bioinformatics technology to screen differentially expressed genes (DEGs) in oral squamous cell carcinoma (OSCC) microarray data to further predict potential targets and prognostic genes. Methods: GSE23558 and GSE138206 datasets were downloaded from gene expression omnibus (GEO) database to analyze DEGs of OSCC and normal oral mucosa tissues online with the help of GEO2R; the associated pathways, protein-protein interaction (PPI) network and key network nodes were analyzed, and the top 25 hub genes were screened and verified through multiple external databases. The Timer website was used to analyze the relationship between hub genes and immune cell infiltration and immune checkpoints. Based on Lasso-Cox algorithm, a prognostic risk model of related genes was constructed. A nomogram containing prognostic risk model and multiple clinicopathological factors was constructed. Results: Integrin α3 (ITGA3) and secreted phosphoprotein 1 (SPP1) genes were highly expressed in head and neck squamous cell carcinoma (HNSCC) tissues and were correlated with poor prognosis of patients (P<0.05). ITGA3 was considered to be a potential immunotherapy target with future clinical significance. The prognostic model based on SPP1 and ITGA3 genes can effectively predict the prognosis of OSCC patients. Conclusion: ITGA3 and SPP1 may be biomarkers for the prognosis of OSCC patients. These results provide new clues for exploring the molecular mechanism and targeted therapy of OSCC.

Key words: oral squamous cell carcinoma, differentially expressed genes, target gene, ITGA3, prognostic risk signature

摘要：

目的: 应用生物信息学技术对口腔鳞状细胞癌（oral squamous cell carcinoma，OSCC）基因芯片数据进行差异表达基因（differentially expressed genes，DEGs）筛选，进一步预测潜在靶点及预后基因。方法: 利用基因表达综合数据库（gene expression omnibus，GEO）下载基因芯片数据集（GSE23558和GSE138206），联合GEO2R工具在线分析OSCC与正常口腔黏膜组织的DEGs；并对其进行相关通路、蛋白质相互作用（protein-protein interaction，PPI）网络及关键网络节点分析，筛选前25个枢纽基因，通过多个外部数据库对其进行验证。应用Timer网站进一步分析枢纽基因与免疫细胞浸润及免疫检查点的关系，基于Lasso-Cox算法，构建相关基因的预后风险模型。构建包含预后风险模型和多个临床病理因素的列线图。结果: 分泌磷酸蛋白1 （secreted phosphoprotein 1，SPP1）、整合素α3 (integrin α3，ITGA3)基因在头颈部鳞状细胞癌（head and neck squamous cell carcinoma，HNSCC）中的表达水平与患者的不良预后相关（P<0.05）。ITGA3被认为是具有未来临床意义的潜在免疫治疗靶点。基于SPP1和ITGA3基因构建的预后模型，可以有效预测OSCC患者的预后。结论: ITGA3、SPP1可能为OSCC患者的治疗靶点及预后的生物标志物，并为探索OSCC的分子机制提供了理论依据。

关键词: 口腔鳞状细胞癌, 差异表达基因, 靶基因, ITGA3, 预后风险模型

CLC Number:

R782

ZOU Xian, SONG Tao. Bioinformatics analysis of potential targets influencing the prognosis of OSCC[J]. 《Journal of Oral and Maxillofacial Surgery》, 2024, 34(3): 193-201.

邹宪, 宋涛. 影响口腔鳞状细胞癌预后潜在靶点的生物信息学分析[J]. 《口腔颌面外科杂志》, 2024, 34(3): 193-201.

/ / Recommend / Download Citations

URL: https://journal06.magtech.org.cn/Jweb_joms/EN/10.12439/kqhm.1005-4979.2024.03.005

https://journal06.magtech.org.cn/Jweb_joms/EN/Y2024/V34/I3/193

Figures/Tables 8

Figure 1 Volcano and Wayne diagrams of DEGs in GSE23558 and GSE138206 datasets

Figure 2 GO and KEGG enrichment analysis of differential genes

Figure 3 PPI network and hub genes

Table 1 The top 25 hub genes

排名	名称	得分
1	ANLN	36
1	AURKA	36
1	NCAPG	36
1	KIAA0101	36
1	HMMR	36
6	DLGAP5	34
6	CDCA5	34
6	BIRC5	34
6	NEK2	34
6	CDC20	34
6	CCNB2	34
6	MELK	34
6	SPP1	34
14	KIF4A	32
15	MMP3	30
15	SHCBP1	30
15	KIF14	30
15	CXCL10	30
19	WDHD1	28
19	UHRF1	28
21	HGF	26
21	E2F7	26
21	STAT1	26
24	MMP1	24
25	ITGA3	20

Figure 4 Survival curve of hub genes

Figure 5 ITGA3、SPP1 expression levels in HNSCC and normal tissues

Figure 6 Tumor immune correlation analysis

Figure 7 Lasso-Cox regression for screening prognostic risk models

References 28

[1]	Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019[J]. CA Cancer J Clin, 2019, 69(1): 7-34.
[2]	Cramer JD, Burtness B, Le QT, et al. The changing therapeutic landscape of head and neck cancer[J]. Nat Rev Clin Oncol, 2019, 16: 669-683. doi: 10.1038/s41571-019-0227-z pmid: 31189965
[3]	Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424.
[4]	Ho SY, Wu WS, Lin LC, et al. Cordycepin enhances radiosensitivity in oral squamous carcinoma cells by inducing autophagy and apoptosis through cell cycle arrest[J]. Int J Mol Sci, 2019, 20(21): 5366.
[5]	Li ZN, Liu FY, Kirkwood KL. The p38/MKP-1 signaling axis in oral cancer: Impact of tumor-associated macrophages[J]. Oral Oncol, 2020, 103: 104591.
[6]	Zhu LS, Wang YL, Li R, et al. Circ_BICD2 acts as a ceRNA to promote tumor progression and Warburg effect in oral squamous cell carcinoma by sponging miR-107 to enhance HK2[J]. Am J Transl Res, 2020, 12(7): 3489-3500.
[7]	Ambatipudi S, Gerstung M, Pandey M, et al. Genome-wide expression and copy number analysis identifies driver genes in gingivobuccal cancers[J]. Genes Chromosomes Cancer, 2012, 51(2): 161-173.
[8]	Li JF, Miao BB, Wang SX, et al. Hiplot: A comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization[J]. Brief Bioinform, 2022, 23(4): bbac261.
[9]	Li TW, Fan JY, Wang BB, et al. Timer: A web server for comprehensive analysis of tumor-infiltrating immune cells[J]. Cancer Res, 2017, 77(21): e108-e110.
[10]	Blanco JL, Porto-Pazos AB, Pazos A, et al. Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection[J]. Sci Rep, 2018, 8(1): 15688. doi: 10.1038/s41598-018-33911-z pmid: 30356060
[11]	Chen WL, Wang XK, Wu W. Identification of ITGA3 as an oncogene in human tongue cancer via integrated bioinformatics analysis[J]. Curr Med Sci, 2018, 38(4): 714-720. doi: 10.1007/s11596-018-1935-9 pmid: 30128883
[12]	Yadav M, Pradhan D, Singh RP. Integrated analysis and identification of nine-gene signature associated to oral squamous cell carcinoma pathogenesis[J]. 3 Biotech, 2021, 11(5): 215. doi: 10.1007/s13205-021-02737-4 pmid: 33928003
[13]	Zou B, Li J, Xu K, et al. Identification of key candidate genes and pathways in oral squamous cell carcinoma by integrated Bioinformatics analysis[J]. Exp Ther Med, 2019, 17(5): 4089-4099. doi: 10.3892/etm.2019.7442 pmid: 31007745
[14]	Sim YC, Hwang JH, Ahn KM. Overall and disease-specific survival outcomes following primary surgery for oral squamous cell carcinoma: Analysis of consecutive 67 patients[J]. J Korean Assoc Oral Maxillofac Surg, 2019, 45(2): 83-90. doi: 10.5125/jkaoms.2019.45.2.83 pmid: 31106136
[15]	Gonzalez H, Hagerling C, Werb Z. Roles of the immune system in cancer: From tumor initiation to metastatic progression[J]. Genes Dev, 2018, 32(19-20): 1267-1284.
[16]	Zhang WG, Fan JL, Chen Q, et al. SPP1 and AGER as potential prognostic biomarkers for lung adenocarcinoma[J]. Oncol Lett, 2018, 15(5): 7028-7036. doi: 10.3892/ol.2018.8235 pmid: 29849788
[17]	Choe EK, Yi JW, Chai YJ, et al. Upregulation of the adipokine genes ADIPOR1 and SPP1 is related to poor survival outcomes in colorectal cancer[J]. J Surg Oncol, 2018, 117(8): 1833-1840. doi: 10.1002/jso.25078 pmid: 29761507
[18]	Fudge NJ, Mearow KM. Extracellular matrix-associated gene expression in adult sensory neuron populations cultured on a laminin substrate[J]. BMC Neurosci, 2013, 14: 15. doi: 10.1186/1471-2202-14-15 pmid: 23360524
[19]	Xu CJ, Sun LC, Jiang CH, et al. SPP1, analyzed by bioinformatics methods, promotes the metastasis in colorectal cancer by activating EMT pathway[J]. Biomed Pharmacother, 2017, 91: 1167-1177. doi: S0753-3322(17)31469-5 pmid: 28531945
[20]	Hubbard NE, Chen QJ, Sickafoose LK, et al. Transgenic mammary epithelial osteopontin (spp1) expression induces proliferation and alveologenesis[J]. Genes Cancer, 2013, 4(5-6): 201-212. doi: 10.1177/1947601913496813 pmid: 24069507
[21]	Koshizuka K, Hanazawa T, Kikkawa N, et al. Regulation of ITGA3 by the anti-tumor miR-199 family inhibits cancer cell migration and invasion in head and neck cancer[J]. Cancer Sci, 2017, 108(8): 1681-1692.
[22]	Tang XR, Wen X, He QM, et al. MicroRNA-101 inhibits invasion and angiogenesis through targeting ITGA3 and its systemic delivery inhibits lung metastasis in nasopharyngeal carcinoma[J]. Cell Death Dis, 2017, 8(1): e2566.
[23]	Sa KD, Zhang X, Li XF, et al. A miR-124/ITGA3 axis contributes to colorectal cancer metastasis by regulating anoikis susceptibility[J]. Biochem Biophys Res Commun, 2018, 501(3): 758-764.
[24]	Sakaguchi T, Yoshino H, Yonemori M, et al. Regulation of ITGA3 by the dual-stranded microRNA-199 family as a potential prognostic marker in bladder cancer[J]. Br J Cancer, 2017, 116(8): 1077-1087.
[25]	Kurozumi A, Goto Y, Matsushita R, et al. Tumor-suppressive microRNA-223 inhibits cancer cell migration and invasion by targeting ITGA3/ITGB1 signaling in prostate cancer[J]. Cancer Sci, 2016, 107(1): 84-94.
[26]	陈小坤, 何瑶, 闫美娜, 等. 整合素α3在卵巢上皮细胞癌中的表达及意义[J]. 江苏大学学报(医学版), 2017, 27(4): 356-359.
[27]	O'Connell GC, Treadway MB, Petrone AB, et al. Peripheral blood AKAP7 expression as an early marker for lymphocyte-mediated post-stroke blood brain barrier disruption[J]. Sci Rep, 2017, 7(1): 1172. doi: 10.1038/s41598-017-01178-5 pmid: 28446746
[28]	Li Y, Huang WQ, Chen LL. LncRNA NEAT1 regulates proliferation, migration and invasion of tongue squamous cell carcinoma cells by regulating miR-339-5p/ITGA3 axis[J]. Shanghai Kou Qiang Yi Xue, 2020, 29(3): 267-274.

Please choose a citation manager

Content to export