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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Integrated Multi-Omics Data Analysis Identifies a Novel Genetics-Risk Gene of IRF4 Associated with Prognosis of Oral Cavity Cancer

Author(s): Yan Lv, Xuejun Xu, Zhiwei Wang, Yukuan Huang, Yunlong Ma* and Mengjie Wu*

Volume 17, Issue 8, 2022

Published on: 23 August, 2022

Page: [744 - 758] Pages: 15

DOI: 10.2174/1574893617666220524122040

Price: $65

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Abstract

Background: Oral cavity cancer (OCC) is one of the most common carcinoma diseases. Recent genome-wide association studies (GWAS) have reported numerous genetic variants associated with OCC susceptibility. However, the regulatory mechanisms of these genetic variants underlying OCC remain largely unclear.

Objective: This study aimed to identify OCC-related genetics risk genes contributing to the prognosis of OCC.

Methods: By combining GWAS summary statistics (N = 4,151) with expression quantitative trait loci (eQTL) across 49 different tissues from the GTEx database, we performed an integrative genomics analysis to uncover novel risk genes associated with OCC. By leveraging various computational methods based on multi-omics data, we prioritized some of these risk genes as promising candidate genes for drug repurposing in OCC.

Results: Using two independent computational algorithms, we found that 14 risk genes whose geneticsmodulated expressions showed a notable association with OCC. Among them, nine genes were newly identified, such as IRF4 (P = 2.5×10-9 and P = 1.06×10-4), TNS3 (P = 1.44×10-6 and P = 4.45×10-3), ZFP90 (P = 2.37×10-6 and P = 2.93×10-4), and DRD2 (P = 2.0×10-5 and P = 6.12×10-3), by using MAGMA and S-MultiXcan methods. These 14 genes were significantly overrepresented in several cancer- related terms (FDR < 0.05), and 10 of 14 genes were enriched in 10 potential druggable gene categories. Based on differential gene expression analysis, the majority of these genes (71.43%) showed remarkable differential expressions between OCC patients and paracancerous controls. By integration of multi-omics-based evidence from genetics, eQTL, and gene expression, we identified that the novel risk gene of IRF4 exhibited the highest ranked risk score for OCC (score = 4). Survival analysis showed that dysregulation of IRF4 expression was significantly associated with cancer patients’ outcomes (P = 8.1×10-5).

Conclusion: Based on multiple omics data, we constructed a computational framework to pinpoint risk genes for OCC, and we prioritized 14 risk genes associated with OCC. There were nine novel risk genes, including the IRF4 gene, which is significantly associated with the prognosis of OCC. These identified genes provide a drug repurposing resource to develop therapeutic drugs for treating patients, thereby contributing to the personalized prognostic management of OCC patients.

Keywords: Oral cavity cancer, GWAS, SNP, susceptibility genes, protein-protein interaction, multiple omics data.

« Previous Next »
[1]
Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin 2015; 65(2): 87-108.
[http://dx.doi.org/10.3322/caac.21262] [PMID: 25651787]
[2]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020; 70(1): 7-30.
[http://dx.doi.org/10.3322/caac.21590] [PMID: 31912902]
[3]
Montero PH, Patel SG. Cancer of the oral cavity. Surg Oncol Clin N Am 2015; 24(3): 491-508.
[http://dx.doi.org/10.1016/j.soc.2015.03.006] [PMID: 25979396]
[4]
Kumar M, Nanavati R, Modi TG, Dobariya C. Oral cancer: Etiology and risk factors: A review. J Cancer Res Ther 2016; 12(2): 458-63.
[http://dx.doi.org/10.4103/0973-1482.186696] [PMID: 27461593]
[5]
Lesseur C, Diergaarde B, Olshan AF, et al. Genome-wide association analyses identify new susceptibility loci for oral cavity and pharyngeal cancer. Nat Genet 2016; 48(12): 1544-50.
[http://dx.doi.org/10.1038/ng.3685] [PMID: 27749845]
[6]
Ferreiro-Iglesias A, McKay JD, Brenner N, et al. Germline determinants of humoral immune response to HPV-16 protect against oropharyngeal cancer. Nat Commun 2021; 12(1): 5945.
[http://dx.doi.org/10.1038/s41467-021-26151-9] [PMID: 34642315]
[7]
Graff RE, Cavazos TB, Thai KK, et al. Cross-cancer evaluation of polygenic risk scores for 16 cancer types in two large cohorts. Nat Commun 2021; 12(1): 970.
[http://dx.doi.org/10.1038/s41467-021-21288-z] [PMID: 33579919]
[8]
Hashibe M, McKay JD, Curado MP, et al. Multiple ADH genes are associated with upper aerodigestive cancers. Nat Genet 2008; 40(6): 707-9.
[http://dx.doi.org/10.1038/ng.151] [PMID: 18500343]
[9]
McKay JD, Truong T, Gaborieau V, et al. A genome-wide association study of upper aerodigestive tract cancers conducted within the INHANCE consortium. PLoS Genet 2011; 7(3): e1001333.
[http://dx.doi.org/10.1371/journal.pgen.1001333] [PMID: 21437268]
[10]
Xu M, Li J, Xiao Z, Lou J, Pan X, Ma Y. Integrative genomics analysis identifies promising SNPs and genes implicated in tuberculosis risk based on multiple omics datasets. Aging (Albany NY) 2020; 12(19): 19173-220.
[http://dx.doi.org/10.18632/aging.103744] [PMID: 33051402]
[11]
Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet 2012; 90(1): 7-24.
[http://dx.doi.org/10.1016/j.ajhg.2011.11.029] [PMID: 22243964]
[12]
Canela-Xandri O, Rawlik K, Tenesa A. An atlas of genetic associations in UK Biobank. Nat Genet 2018; 50(11): 1593-9.
[http://dx.doi.org/10.1038/s41588-018-0248-z] [PMID: 30349118]
[13]
Ma Y, Huang Y, Zhao S, et al. Integrative genomics analysis reveals a 21q22.11 locus contributing risk to COVID-19. Hum Mol Genet 2021; 30(13): 1247-58.
[http://dx.doi.org/10.1093/hmg/ddab125] [PMID: 33949668]
[14]
Pairo-Castineira E, Clohisey S, Klaric L, et al. Genetic mechanisms of critical illness in COVID-19. Nature 2021; 591(7848): 92-8.
[http://dx.doi.org/10.1038/s41586-020-03065-y] [PMID: 33307546]
[15]
Buniello A, MacArthur JAL, Cerezo M, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 2019; 47(D1): D1005-12.
[http://dx.doi.org/10.1093/nar/gky1120] [PMID: 30445434]
[16]
Shete S, Liu H, Wang J, et al. A genome-wide association study identifies two novel susceptible regions for squamous cell carcinoma of the head and neck. Cancer Res 2020; 80(12): 2451-60.
[http://dx.doi.org/10.1158/0008-5472.CAN-19-2360] [PMID: 32276964]
[17]
Chai AWY, Lim KP, Cheong SC. Translational genomics and recent advances in oral squamous cell carcinoma. Semin Cancer Biol 2020; 61: 71-83.
[http://dx.doi.org/10.1016/j.semcancer.2019.09.011] [PMID: 31542510]
[18]
Lopes-Santos G, Bernabé DG, Miyahara GI, Tjioe KC. Beta-adrenergic pathway activation enhances aggressiveness and inhibits stemness in head and neck cancer. Transl Oncol 2021; 14(8): 101117.
[http://dx.doi.org/10.1016/j.tranon.2021.101117] [PMID: 33993095]
[19]
Maruyama S, Cheng J, Yamazaki M, et al. Metastasis-associated genes in oral squamous cell carcinoma and salivary adenoid cystic carcinoma: A differential DNA chip analysis between metastatic and nonmetastatic cell systems. Cancer Genet Cytogenet 2010; 196(1): 14-22.
[http://dx.doi.org/10.1016/j.cancergencyto.2009.08.002] [PMID: 19963131]
[20]
Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet 2019; 20(8): 467-84.
[http://dx.doi.org/10.1038/s41576-019-0127-1] [PMID: 31068683]
[21]
Hindorff LA, Sethupathy P, Junkins HA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 2009; 106(23): 9362-7.
[http://dx.doi.org/10.1073/pnas.0903103106] [PMID: 19474294]
[22]
Li MJ, Liu Z, Wang P, et al. GWASdb v2: An update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res 2016; 44(D1): D869-76.
[http://dx.doi.org/10.1093/nar/gkv1317] [PMID: 26615194]
[23]
Dong Z, Ma Y, Zhou H, et al. Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets. BMC Pulm Med 2020; 20(1): 270.
[http://dx.doi.org/10.1186/s12890-020-01303-7] [PMID: 33066754]
[24]
Ma X, Wang P, Xu G, Yu F, Ma Y. Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma. BMC Med Genomics 2020; 13(1): 123.
[http://dx.doi.org/10.1186/s12920-020-00768-z] [PMID: 32867763]
[25]
Sun H, Zhang J, Ma Y, Liu J. Integrative genomics analysis identifies five promising genes implicated in insomnia risk based on multiple omics datasets. Biosci Rep 2020; 40(9): BSR20201084.
[http://dx.doi.org/10.1042/BSR20201084] [PMID: 32830860]
[26]
Ma Y, Qiu F, Deng C, et al. Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19. Genome Med 2022; 14(1): 16.
[http://dx.doi.org/10.1186/s13073-022-01021-1] [PMID: 35172892]
[27]
Xiang B, Deng C, Qiu F, et al. Single cell sequencing analysis identifies genetics-modulated ORMDL3+ cholangiocytes having higher metabolic effects on primary biliary cholangitis. J Nanobiotechnology 2021; 19(1): 406.
[http://dx.doi.org/10.1186/s12951-021-01154-2] [PMID: 34872583]
[28]
Wainberg M, Sinnott-Armstrong N, Mancuso N, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet 2019; 51(4): 592-9.
[http://dx.doi.org/10.1038/s41588-019-0385-z] [PMID: 30926968]
[29]
Zeng B, Bendl J, Kosoy R, Fullard JF, Hoffman GE, Roussos P. Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits. Nat Genet 2022; 54(2): 161-9.
[http://dx.doi.org/10.1038/s41588-021-00987-9] [PMID: 35058635]
[30]
He X, Fuller CK, Song Y, et al. Sherlock: Detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am J Hum Genet 2013; 92(5): 667-80.
[http://dx.doi.org/10.1016/j.ajhg.2013.03.022] [PMID: 23643380]
[31]
Luo XJ, Mattheisen M, Li M, et al. Systematic integration of brain eQTL and GWAS identifies ZNF323 as a novel schizophrenia risk gene and suggests recent positive selection based on compensatory advantage on pulmonary function. Schizophr Bull 2015; 41(6): 1294-308.
[http://dx.doi.org/10.1093/schbul/sbv017] [PMID: 25759474]
[32]
Yang CP, Li X, Wu Y, et al. Comprehensive integrative analyses identify GLT8D1 and CSNK2B as schizophrenia risk genes. Nat Commun 2018; 9(1): 838.
[http://dx.doi.org/10.1038/s41467-018-03247-3] [PMID: 29483533]
[33]
Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet 2019; 15(1): e1007889.
[http://dx.doi.org/10.1371/journal.pgen.1007889] [PMID: 30668570]
[34]
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 2015; 4: 7.
[http://dx.doi.org/10.1186/s13742-015-0047-8] [PMID: 25722852]
[35]
Pruim RJ, Welch RP, Sanna S, et al. LocusZoom: Regional visualization of genome-wide association scan results. Bioinformatics 2010; 26(18): 2336-7.
[http://dx.doi.org/10.1093/bioinformatics/btq419] [PMID: 20634204]
[36]
The Genotype-Tissue Expression (GTEx) project Nat Genet. 2013 45(6): 580-5.
[http://dx.doi.org/10.1038/ng.2653] [PMID: 23715323]
[37]
de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: Generalized gene-set analysis of GWAS data. PLOS Comput Biol 2015; 11(4): e1004219.
[http://dx.doi.org/10.1371/journal.pcbi.1004219] [PMID: 25885710]
[38]
Auton A, Brooks LD, Durbin RM, et al. A global reference for human genetic variation. Nature 2015; 526(7571): 68-74.
[http://dx.doi.org/10.1038/nature15393] [PMID: 26432245]
[39]
Kwong KS, Holland B, Cheung SH. A modified Benjamini–Hochberg multiple comparisons procedure for controlling the false discovery rate. J Stat Plan Inference 2002; 104(2): 351-62.
[http://dx.doi.org/10.1016/S0378-3758(01)00252-X]
[40]
Barbeira AN, Bonazzola R, Gamazon ER, et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol 2021; 22(1): 49.
[http://dx.doi.org/10.1186/s13059-020-02252-4] [PMID: 33499903]
[41]
Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 2010; 11(3): R25.
[http://dx.doi.org/10.1186/gb-2010-11-3-r25] [PMID: 20196867]
[42]
Wang J, Duncan D, Shi Z, Zhang B. WEB-based gene set analysis toolkit (WebGestalt): Update 2013. Nucleic Acids Res 2013; 41(Web Server issue): W77-83.
[43]
Eeles RA, Olama AA, Benlloch S, et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet 2013; 45(4): 385-91.
[http://dx.doi.org/10.1038/ng.2560]
[44]
Zhong Y, Chen L, Li J, et al. Integration of summary data from GWAS and eQTL studies identified novel risk genes for coronary artery disease. Medicine (Baltimore) 2021; 100(11): e24769.
[http://dx.doi.org/10.1097/MD.0000000000024769] [PMID: 33725943]
[45]
von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. STRING: A database of predicted functional associations between proteins. Nucleic Acids Res 2003; 31(1): 258-61.
[http://dx.doi.org/10.1093/nar/gkg034] [PMID: 12519996]
[46]
Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010; 38(Web Server issue): W214-20.
[http://dx.doi.org/10.1093/nar/gkq537]
[47]
Jourquin J, Duncan D, Shi Z, Zhang B. GLAD4U: Deriving and prioritizing gene lists from PubMed literature. BMC Genomics 2012; 13 Suppl 8(Suppl 8): S20.
[48]
Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017; 45(W1): W98-W102.
[http://dx.doi.org/10.1093/nar/gkx247] [PMID: 28407145]
[49]
Rashkin SR, Graff RE, Kachuri L, et al. Pan-cancer study detects genetic risk variants and shared genetic basis in two large cohorts. Nat Commun 2020; 11(1): 4423.
[http://dx.doi.org/10.1038/s41467-020-18246-6] [PMID: 32887889]
[50]
Piñero J, Bravo À, Queralt-Rosinach N, et al. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 2017; 45(D1): D833-9.
[http://dx.doi.org/10.1093/nar/gkw943] [PMID: 27924018]
[51]
Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019; 10(1): 1523.
[http://dx.doi.org/10.1038/s41467-019-09234-6] [PMID: 30944313]
[52]
Lin A, Wang RT, Ahn S, Park CC, Smith DJ. A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes. Genome Res 2010; 20(8): 1122-32.
[http://dx.doi.org/10.1101/gr.104216.109] [PMID: 20508145]
[53]
Dobbin KK, Beer DG, Meyerson M, et al. Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays. Clin Cancer Res 2005; 11(2 Pt 1): 565-72.
[PMID: 15701842]
[54]
Ho T, Wei Q, Sturgis EM. Epidemiology of carcinogen metabolism genes and risk of squamous cell carcinoma of the head and neck. Head Neck 2007; 29(7): 682-99.
[http://dx.doi.org/10.1002/hed.20570] [PMID: 17274053]
[55]
Neumann AS, Sturgis EM, Wei Q. Nucleotide excision repair as a marker for susceptibility to tobacco-related cancers: A review of molecular epidemiological studies. Mol Carcinog 2005; 42(2): 65-92.
[http://dx.doi.org/10.1002/mc.20069] [PMID: 15682379]
[56]
Wei S, Liu Z, Zhao H, et al. A single nucleotide polymorphism in the alcohol dehydrogenase 7 gene (alanine to glycine substitution at amino acid 92) is associated with the risk of squamous cell carcinoma of the head and neck. Cancer 2010; 116(12): 2984-92.
[http://dx.doi.org/10.1002/cncr.25058] [PMID: 20336794]
[57]
Wei Q, Yu D, Liu M, et al. Genome-wide association study identifies three susceptibility loci for laryngeal squamous cell carcinoma in the Chinese population. Nat Genet 2014; 46(10): 1110-4.
[http://dx.doi.org/10.1038/ng.3090] [PMID: 25194280]
[58]
Liyanage UE, Law MH, Han X, et al. Combined analysis of keratinocyte cancers identifies novel genome-wide loci. Hum Mol Genet 2019; 28(18): 3148-60.
[http://dx.doi.org/10.1093/hmg/ddz121] [PMID: 31174203]
[59]
McKay JD, Hung RJ, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet 2017; 49(7): 1126-32.
[http://dx.doi.org/10.1038/ng.3892] [PMID: 28604730]
[60]
Sarin KY, Lin Y, Daneshjou R, et al. Genome-wide meta-analysis identifies eight new susceptibility loci for cutaneous squamous cell carcinoma. Nat Commun 2020; 11(1): 820.
[http://dx.doi.org/10.1038/s41467-020-14594-5] [PMID: 32041948]
[61]
Visconti A, Duffy DL, Liu F, et al. Genome-wide association study in 176,678 Europeans reveals genetic loci for tanning response to sun exposure. Nat Commun 2018; 9(1): 1684.
[http://dx.doi.org/10.1038/s41467-018-04086-y] [PMID: 29739929]
[62]
Figueiredo JC, Hsu L, Hutter CM, et al. Genome-wide diet-gene interaction analyses for risk of colorectal cancer. PLoS Genet 2014; 10(4): e1004228.
[http://dx.doi.org/10.1371/journal.pgen.1004228] [PMID: 24743840]
[63]
Huyghe JR, Bien SA, Harrison TA, et al. Discovery of common and rare genetic risk variants for colorectal cancer. Nat Genet 2019; 51(1): 76-87.
[http://dx.doi.org/10.1038/s41588-018-0286-6] [PMID: 30510241]
[64]
Litchfield K, Levy M, Orlando G, et al. Identification of 19 new risk loci and potential regulatory mechanisms influencing susceptibility to testicular germ cell tumor. Nat Genet 2017; 49(7): 1133-40.
[http://dx.doi.org/10.1038/ng.3896] [PMID: 28604728]
[65]
Alvisi G, Brummelman J, Puccio S, et al. IRF4 instructs effector Treg differentiation and immune suppression in human cancer. J Clin Invest 2020; 130(6): 3137-50.
[http://dx.doi.org/10.1172/JCI130426] [PMID: 32125291]
[66]
Zheng Y, Chaudhry A, Kas A, et al. Regulatory T-cell suppressor program co-opts transcription factor IRF4 to control T(H)2 responses. Nature 2009; 458(7236): 351-6.
[http://dx.doi.org/10.1038/nature07674] [PMID: 19182775]
[67]
Agnarelli A, Chevassut T, Mancini EJ. IRF4 in multiple myeloma-Biology, disease and therapeutic target. Leuk Res 2018; 72: 52-8.
[http://dx.doi.org/10.1016/j.leukres.2018.07.025] [PMID: 30098518]
[68]
Akimova T, Zhang T, Negorev D, et al. Human lung tumor FOXP3+ Tregs upregulate four “Treg-locking” transcription factors. JCI Insight 2017; 2(16): 94075.
[http://dx.doi.org/10.1172/jci.insight.94075] [PMID: 28814673]
[69]
Vernier M, McGuirk S, Dufour CR, et al. Inhibition of DNMT1 and ERRα crosstalk suppresses breast cancer via derepression of IRF4. Oncogene 2020; 39(41): 6406-20.
[http://dx.doi.org/10.1038/s41388-020-01438-1] [PMID: 32855526]
[70]
Ramis-Zaldivar JE, Gonzalez-Farré B, Balagué O, et al. Distinct molecular profile of IRF4-rearranged large B-cell lymphoma. Blood 2020; 135(4): 274-86.
[http://dx.doi.org/10.1182/blood.2019002699] [PMID: 31738823]
[71]
Arruga F, Bracciamà V, Vitale N, et al. Bidirectional linkage between the B-cell receptor and NOTCH1 in chronic lymphocytic leukemia and in Richter’s syndrome: Therapeutic implications. Leukemia 2020; 34(2): 462-77.
[http://dx.doi.org/10.1038/s41375-019-0571-0] [PMID: 31467429]
[72]
Nadeu F, Delgado J, Royo C, et al. Clinical impact of clonal and subclonal TP53, SF3B1, BIRC3, NOTCH1, and ATM mutations in chronic lymphocytic leukemia. Blood 2016; 127(17): 2122-30.
[http://dx.doi.org/10.1182/blood-2015-07-659144] [PMID: 26837699]
[73]
Geng Y, Fan J, Chen L, et al. A notch-dependent inflammatory feedback circuit between macrophages and cancer cells regulates pancreatic cancer metastasis. Cancer Res 2021; 81(1): 64-76.
[PMID: 33172931]
[74]
Stransky N, Egloff AM, Tward AD, et al. The mutational landscape of head and neck squamous cell carcinoma. Science 2011; 333(6046): 1157-60.
[http://dx.doi.org/10.1126/science.1208130] [PMID: 21798893]
[75]
Agrawal N, Frederick MJ, Pickering CR, et al. Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1. Science 2011; 333(6046): 1154-7.
[http://dx.doi.org/10.1126/science.1206923] [PMID: 21798897]
[76]
Aref S, Rizk R, El Agder M, Fakhry W, El Zafarany M, Sabry M. NOTCH-1 gene mutations influence survival in acute myeloid leukemia patients. Asian Pac J Cancer Prev 2020; 21(7): 1987-92.
[http://dx.doi.org/10.31557/APJCP.2020.21.7.1987] [PMID: 32711424]
[77]
Gan RH, Wei H, Xie J, et al. Notch1 regulates tongue cancer cells proliferation, apoptosis and invasion. Cell Cycle 2018; 17(2): 216-24.
[http://dx.doi.org/10.1080/15384101.2017.1395534] [PMID: 29117785]
[78]
Kujan O, Huang G, Ravindran A, Vijayan M, Farah CS. CDK4, CDK6, cyclin D1 and Notch1 immunocytochemical expression of oral brush liquid-based cytology for the diagnosis of oral leukoplakia and oral cancer. J Oral Pathol Med 2019; 48(7): 566-73.
[http://dx.doi.org/10.1111/jop.12902] [PMID: 31172614]
[79]
Ma Y, Li MD. Establishment of a strong link between smoking and cancer pathogenesis through DNA methylation analysis. Sci Rep 2017; 7(1): 1811.
[80]
Ferrarotto R, Eckhardt G, Patnaik A, et al. A phase I dose-escalation and dose-expansion study of brontictuzumab in subjects with selected solid tumors. Ann Oncol 2018; 29(7): 1561-8.
[http://dx.doi.org/10.1093/annonc/mdy171] [PMID: 29726923]
[81]
Moore G, Annett S, McClements L, Robson T. Top notch targeting strategies in cancer: A detailed overview of recent insights and current perspectives. Cells 2020; 9(6): E1503.
[http://dx.doi.org/10.3390/cells9061503] [PMID: 32575680]
[82]
Wilzén A, Nilsson S, Sjöberg RM, Kogner P, Martinsson T, Abel F. The Phox2 pathway is differentially expressed in neuroblastoma tumors, but no mutations were found in the candidate tumor suppressor gene PHOX2A. Int J Oncol 2009; 34(3): 697-705.
[PMID: 19212675]
[83]
Ashktorab H, Washington K, Zarnogi S, et al. Determination of distinctive hypomethylated genes in African American colorectal neoplastic lesions. Therap Adv Gastroenterol 2020; 13: 1756284820905482.
[http://dx.doi.org/10.1177/1756284820905482] [PMID: 32547637]
[84]
Eshragh J, Dhruva A, Paul SM, et al. Associations between neurotransmitter genes and fatigue and energy levels in women after breast cancer surgery. J Pain Symptom Manage 2017; 53(1): 67-84.e7.
[http://dx.doi.org/10.1016/j.jpainsymman.2016.08.004] [PMID: 27720787]
[85]
Tsai SC, Sheen MC, Chen BH. Association between HLA-DQA1, HLA-DQB1 and oral cancer. Kaohsiung J Med Sci 2011; 27(10): 441-5.
[http://dx.doi.org/10.1016/j.kjms.2011.06.003] [PMID: 21943816]
[86]
Kozuka R, Enomoto M, Sato-Matsubara M, et al. Association between HLA-DQA1/DRB1 polymorphism and development of hepatocellular carcinoma during entecavir treatment. J Gastroenterol Hepatol 2019; 34(5): 937-46.
[http://dx.doi.org/10.1111/jgh.14454] [PMID: 30160782]
[87]
Shim H, Park B, Shin HJ, et al. Protective association of HLA-DRB1*13:02, HLA-DRB1*04:06, and HLA-DQB1*06:04 alleles with cervical cancer in a Korean population. Hum Immunol 2019; 80(2): 107-11.
[http://dx.doi.org/10.1016/j.humimm.2018.10.013] [PMID: 30352277]
[88]
Garza-González E, Bosques-Padilla FJ, Pérez-Pérez GI, Flores-Gutiérrez JP, Tijerina-Menchaca R. Association of gastric cancer, HLA-DQA1, and infection with Helicobacter pylori CagA+ and VacA+ in a Mexican population. J Gastroenterol 2004; 39(12): 1138-42.
[http://dx.doi.org/10.1007/s00535-004-1462-2] [PMID: 15622476]
[89]
Arrillaga-Romany I, Chi AS, Allen JE, Oster W, Wen PY, Batchelor TT. A phase 2 study of the first imipridone ONC201, a selective DRD2 antagonist for oncology, administered every three weeks in recurrent glioblastoma. Oncotarget 2017; 8(45): 79298-304.
[http://dx.doi.org/10.18632/oncotarget.17837] [PMID: 29108308]
[90]
Li J, Zhu S, Kozono D, et al. Genome-wide shRNA screen revealed integrated mitogenic signaling between dopamine receptor D2 (DRD2) and epidermal growth factor receptor (EGFR) in glioblastoma. Oncotarget 2014; 5(4): 882-93.
[http://dx.doi.org/10.18632/oncotarget.1801] [PMID: 24658464]
[91]
Lin GM, Chen YJ, Kuo DJ, et al. Cancer incidence in patients with schizophrenia or bipolar disorder: A nationwide population-based study in Taiwan, 1997-2009. Schizophr Bull 2013; 39(2): 407-16.
[http://dx.doi.org/10.1093/schbul/sbr162] [PMID: 22045828]
[92]
Tan Y, Sun R, Liu L, et al. Tumor suppressor DRD2 facilitates M1 macrophages and restricts NF-&#954;B signaling to trigger pyroptosis in breast cancer. Theranostics 2021; 11(11): 5214-31.
[http://dx.doi.org/10.7150/thno.58322] [PMID: 33859743]
[93]
Madhukar NS, Khade PK, Huang L, et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun 2019; 10(1): 5221.
[http://dx.doi.org/10.1038/s41467-019-12928-6] [PMID: 31745082]
[94]
Ma Y, Yuan W, Jiang X, Cui WY, Li MD. Updated findings of the association and functional studies of DRD2/ANKK1 variants with addictions. Mol Neurobiol 2015; 51(1): 281-99.
[http://dx.doi.org/10.1007/s12035-014-8826-2] [PMID: 25139281]
[95]
Ma Y, Yuan W, Cui W, Li MD. Meta-analysis reveals significant association of 3′-UTR VNTR in SLC6A3 with smoking cessation in Caucasian populations. Pharmacogenomics J 2016; 16(1): 10-7.
[http://dx.doi.org/10.1038/tpj.2015.44] [PMID: 26149737]
[96]
Ma Y, Wen L, Cui W, et al. Prevalence of cigarette smoking and nicotine dependence in men and women residing in two provinces in China. Front Psychiatry 2017; 8: 254.
[http://dx.doi.org/10.3389/fpsyt.2017.00254] [PMID: 29249991]
[97]
Liu Q, Xu Y, Mao Y, et al. Genetic and epigenetic analysis revealing variants in the NCAM1-TTC12-ANKK1-DRD2 cluster associated significantly with nicotine dependence in chinese han smokers. Nicotine Tob Res 2020; 22(8): 1301-9.
[http://dx.doi.org/10.1093/ntr/ntz240] [PMID: 31867628]
[98]
Bidwell LC, Karoly HC, Thayer RE, et al. DRD2 promoter methylation and measures of alcohol reward: Functional activation of reward circuits and clinical severity. Addict Biol 2019; 24(3): 539-48.
[http://dx.doi.org/10.1111/adb.12614] [PMID: 29464814]
[99]
Clarke TK, Adams MJ, Davies G, et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117). Mol Psychiatry 2017; 22(10): 1376-84.
[http://dx.doi.org/10.1038/mp.2017.153] [PMID: 28937693]
[100]
Ma Y, Fan R, Li MD. Meta-analysis reveals significant association of the 3′-UTR VNTR in SLC6A3 with alcohol dependence. Alcohol Clin Exp Res 2016; 40(7): 1443-53.
[http://dx.doi.org/10.1111/acer.13104] [PMID: 27219321]
[101]
Trynka G, Sandor C, Han B, et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet 2013; 45(2): 124-30.
[http://dx.doi.org/10.1038/ng.2504] [PMID: 23263488]
[102]
Finucane HK, Bulik-Sullivan B, Gusev A, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 2015; 47(11): 1228-35.
[http://dx.doi.org/10.1038/ng.3404] [PMID: 26414678]
[103]
Maurano MT, Humbert R, Rynes E, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 2012; 337(6099): 1190-5.
[http://dx.doi.org/10.1126/science.1222794] [PMID: 22955828]
[104]
Pickrell JK. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am J Hum Genet 2014; 94(4): 559-73.
[http://dx.doi.org/10.1016/j.ajhg.2014.03.004] [PMID: 24702953]
[105]
Ma Y, Li J, Xu Y, et al. Identification of 34 genes conferring genetic and pharmacological risk for the comorbidity of schizophrenia and smoking behaviors. Aging (Albany NY) 2020; 12(3): 2169-225.
[http://dx.doi.org/10.18632/aging.102735] [PMID: 32012119]
[106]
Liu Q, Han H, Wang M, et al. Association and cis-mQTL analysis of variants in CHRNA3-A5, CHRNA7, CHRNB2, and CHRNB4 in relation to nicotine dependence in a Chinese Han population. Transl Psychiatry 2018; 8(1): 83.
[http://dx.doi.org/10.1038/s41398-018-0130-x] [PMID: 29666375]
[107]
Zhang Y, Ma Y, Huang Y, et al. Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data. Comput Struct Biotechnol J 2020; 18: 2953-61.
[http://dx.doi.org/10.1016/j.csbj.2020.10.007] [PMID: 33209207]
[108]
Hu H, Liu R, Zhao C, et al. CITEMOXMBD: A flexible single-cell multimodal omics analysis framework to reveal the heterogeneity of immune cells. RNA Biol 2022; 19(1): 290-304.
[http://dx.doi.org/10.1080/15476286.2022.2027151] [PMID: 5130112]

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