Screening of characteristic genes of salivary gland adenoid cystic carcinoma based on weighted co-expression network and machine learning

doi:10.19439/j.sjos.2024.06.006

Abstract

Abstract: PURPOSE: To identify potential biomarkers of salivary gland adenoid cystic carcinoma to further understand the potential pathogenesis of adenoid cystic carcinoma. METHODS: Two microarray datasets (GSE59701, GSE88804) were downloaded from NCBI GEO database. LIMMA software package was used to screen SACC differentially expressed genes. WGCNAs were used to find the important module genes that were most associated with SACC. Two machine learning methods(LASSO and SVM-RFE) were used to identify Hub genes. Subsequently, ROC curve used to predict SACC was developed to determine the diagnostic effect. R4.2.1 software was used for statistical analysis. RESULTS: Three hub genes(GABBR1, EN1 and LINC01296) were identified, and a ROC curve with high predictive performance (AUC, 1.000-1.000) was established. CONCLUSIONS: Three hub genes(GABBR1, EN1 and LINC01296) were obtained by WGCNA, LASSO, SVM-RFE as potential biomarkers of SACC, and the findings of this study provide a foothold for future research on potential key genes of SACC, and a target basis for the early diagnosis and treatment of SACC.

Key words: Salivary gland adenoid cystic carcinoma, Machine learning, Weighted analysis, Bioinformatics

CLC Number:

R739.8

BU Wen-chao, CHEN Shi-xin, JIANG Yin-hua, CAO Ming-guo, WU Xin-ru, GUAN Yun-qian, XIE Si-yuan. Screening of characteristic genes of salivary gland adenoid cystic carcinoma based on weighted co-expression network and machine learning[J]. Shanghai Journal of Stomatology, 2024, 33(6): 600-607.

References

[1] Chen AM, Bucci MK, Weinberg V, et al.Adenoid cystic carcinoma of the head and neck treated by surgery with or without postoperative radiation therapy: prognostic features of recurrence[J]. Int J Radiat Oncol Biol Phys, 2006, 66(1): 152-159.
[2] Papaspyrou G, Hoch S, Rinaldo A, et al.Chemotherapy and targeted therapy in adenoid cystic carcinoma of the head and neck: a review[J]. Head Neck, 2011, 33(6): 905-911.
[3] Dillon PM, Chakraborty S, Moskaluk CA, et al.Adenoid cystic carcinoma: a review of recent advances, molecular targets, and clinical trials[J]. Head Neck, 2016, 38(4): 620-627.
[4] Kokemueller H, Eckardt A, Brachvogel P, et al.Adenoid cystic carcinoma of the head and neck-a 20 years experience[J]. Int J Oral Maxillofac Surg, 2004, 33(1): 25-31.
[5] Coca-Pelaz A, Rodrigo JP, Bradley PJ, et al.Adenoid cystic carcinoma of the head and neck-an update[J]. Oral Oncol, 2015, 51(7): 652-661.
[6] Kumar P, Kumawat RK, Uttam V, et al.The imminent role of microRNAs in salivary adenoid cystic carcinoma[J].Transl Oncol, 2023, 27: 101573.
[7] Clough E, Barrett T.The gene expression omnibus database[J]. Methods Mol Biol, 2016, 1418: 93-110.
[8] Song X, Du R, Gui H, et al.Identification of potential hub genes related to the progression and prognosis of hepatocellular carcinoma through integrated bioinformatics analysis[J]. Oncol Rep, 2020, 43(1): 133-146.
[9] Li Z, Feng J, Zhong J, et al.Screening of the key genes and signalling pathways for diabetic nephropathy using bioinformatics analysis[J]. Front Endocrinol (Lausanne), 2022, 13: 864407.
[10] Liu Z, Gao J, Yang Y, et al.Potential targets identified in adenoid cystic carcinoma point out new directions for further research[J]. Am J Transl Res, 2021, 13(3): 1085-1108.
[11] Panni S, Lovering RC, Porras P, et al.Non-coding RNA regulatory networks[J]. Biochim Biophys Acta Gene Regul Mech, 2020, 1863(6): 194417.
[12] Jiang M, Liu X, Zhang C, et al.Bioinformatics identification of the candidate microRNAs and construction of a competing endogenous RNA regulatory network in lacrimal gland adenoid cystic carcinoma high-grade transformation[J]. Oncol Lett, 2021, 21(5): 360-372.
[13] Jiang Y, Wang C, Zhou S.Targeting tumor microenvironment in ovarian cancer: premise and promise[J]. Biochim Biophys Acta Rev Cancer, 2020, 1873(2): 188361.
[14] Guo X, Xiao H, Guo S, et al.Identification of breast cancer mechanism based on weighted gene coexpression network analysis[J]. Cancer Gene Ther, 2017, 24(8): 333-341.
[15] Liu C, Zhou Y, Zhou Y, et al.Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning[J]. Comput Biol Med, 2023, 152: 106388.
[16] Feng S, Xu Y, Dai Z, et al.Integrative analysis from multicenter studies identifies a WGCNA-derived cancer-associated fibroblast signature for ovarian cancer[J]. Front Immunol, 2022, 13: 951582.
[17] Yu R, Zhang J, Zhuo Y, et al.Identification of diagnostic signatures and immune cell infiltration characteristics in rheumatoid arthritis by integrating bioinformatic analysis and machine-learning strategies[J]. Front Immunol, 2021, 12: 724934.
[18] Zheng PF, Chen LZ, Liu P, et al.Identification of immune-related key genes in the peripheral blood of ischaemic stroke patients using a weighted gene coexpression network analysis and machine learning[J]. J Transl Med, 2022, 20(1): 1-17.
[19] Ritchie ME, Phipson B, Wu D, et al.limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015, 43(7): e47.
[20] Yao S, Liu T.Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database[J]. Oncol Lett, 2018, 15(6): 8319-8324.
[21] Langfelder P, Horvath S, Langfelder P, et al.WGCNA: an R package for weighted correlation network analysis[J]. BMC Bioinforms, 2008, 9: 559.
[22] Yu G, Wang LG, Han Y, et al.clusterProfiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287.
[23] Breiman L.Random forests[J]. Mach Learn, 2001, 45(1): 5-32.
[24] Alanni R, Hou J, Azzawi H, et al.A novel gene selection algorithm for cancer classification using microarray datasets[J]. BMC Medical Genomics, 2019, 12(1): 10-22.
[25] Engebretsen S, Bohlin J.Statistical predictions with glmnet[J]. Clin Epigenetics, 2019, 11(1): 123-126.
[26] Huang X, Zeng J, Zhou L, et al.A new strategy for analyzing time-series data using dynamic networks: identifying prospective biomarkers of hepatocellular carcinoma[J]. Sci Rep, 2016, 6(1): 32448.
[27] Wang Y, Zhuang H, Jiang XH, et al.Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis[J]. Front Immunol, 2023, 14: 1162458.
[28] Zhang Q, Jiao Y, Ma N, et al.Identification of endoplasmic reticulum stress-related biomarkers of periodontitis based on machine learning: a bioinformatics analysis[J]. Dis Markers, 2022: 8611755.
[29] Ho AS, Kannan K, Roy DM, et al.The mutational landscape of adenoid cystic carcinoma[J]. Nat Genet, 2013, 45(7): 791-798.
[30] Peters HC, Kämmer G, Volz A, et al.Mapping, genomic structure, and polymorphisms of the human GABABR1 receptor gene: evaluation of its involvement in idiopathic generalized epilepsy[J]. Neurogenetics, 1998, 2(1): 47-54.
[31] Shao L, Elujoba-Bridenstine A, Zink KE, et al.The neurotransmitter receptor Gabbr1 regulates proliferation and function of hematopoietic stem and progenitor cells[J]. Blood, 2021, 137(6): 775-787.
[32] Longqiu Y, Pengcheng L, Xuejie F, et al.A miRNAs panel promotes the proliferation and invasion of colorectal cancer cells by targeting GABBR1[J]. Cancer Med, 2016, 5(8): 2022-2031.
[33] Liang KY, Chun-Yu Ho D, Yang HP, et al. LINC01296 promotes cancer stemness traits in oral carcinomas by sponging miR-143[J]. J Dent Sci, 2023, 18(2): 814-821.
[34] Bell D, Bell A, Roberts D, et al.Developmental transcription factor EN1-a novel biomarker in human salivary gland adenoid cystic carcinoma[J].Cancer, 2012, 118(5): 1288-1292.