Chapter 4 Disease analysis
DOSE(Yu et al. 2015) supports Disease Ontology (DO) Semantic and Enrichment analysis. The enrichDO function is very useful for identifying disease association of interesting genes, and function gseDO function is designed for gene set enrichment analysis of DO.
In addition, DOSE also supports enrichment analysis of Network of Cancer Gene (NCG)(A. et al. 2016) and Disease Gene Network(Janet et al. 2015), please refer to the DOSE vignettes.
4.1 enrichDO function
In the following example, we selected fold change above 1.5 as the differential genes and analyzing their disease association.
## [1] "4312" "8318" "10874" "55143" "55388" "991"x <- enrichDO(gene = gene,
ont = "DO",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe = names(geneList),
minGSSize = 5,
maxGSSize = 500,
qvalueCutoff = 0.05,
readable = FALSE)
head(x)## ID Description
## DOID:170 DOID:170 endocrine gland cancer
## DOID:10283 DOID:10283 prostate cancer
## DOID:3459 DOID:3459 breast carcinoma
## DOID:3856 DOID:3856 male reproductive organ cancer
## DOID:824 DOID:824 periodontitis
## DOID:3905 DOID:3905 lung carcinoma
## GeneRatio BgRatio pvalue p.adjust
## DOID:170 48/331 472/6268 5.662129e-06 0.004784499
## DOID:10283 40/331 394/6268 3.859157e-05 0.013921739
## DOID:3459 37/331 357/6268 4.942629e-05 0.013921739
## DOID:3856 40/331 404/6268 6.821467e-05 0.014410349
## DOID:824 16/331 109/6268 1.699304e-04 0.018859464
## DOID:3905 43/331 465/6268 1.749754e-04 0.018859464
## qvalue
## DOID:170 0.003826407
## DOID:10283 0.011133923
## DOID:3459 0.011133923
## DOID:3856 0.011524689
## DOID:824 0.015082872
## DOID:3905 0.015082872
## geneID
## DOID:170 10874/7153/1381/6241/11065/10232/332/6286/2146/10112/891/9232/4171/993/5347/4318/3576/1515/4821/8836/3159/7980/5888/333/898/9768/4288/3551/2152/9590/185/7043/3357/2952/5327/3667/1634/1287/4582/7122/3479/4680/6424/80310/652/8839/9547/1524
## DOID:10283 4312/6280/6279/597/3627/332/6286/2146/4321/4521/891/5347/4102/4318/701/3576/79852/10321/6352/4288/3551/2152/247/2952/3487/367/3667/4128/4582/563/3679/4117/7031/3479/6424/10451/80310/652/4036/10551
## DOID:3459 4312/6280/6279/7153/4751/890/4085/332/6286/6790/891/9232/10855/4171/5347/4318/701/2633/3576/9636/898/8792/4288/2952/4982/4128/4582/7031/3479/771/4250/2066/3169/10647/5304/5241/10551
## DOID:3856 4312/6280/6279/597/3627/332/6286/2146/4321/4521/891/5347/4102/4318/701/3576/79852/10321/6352/4288/3551/2152/247/2952/3487/367/3667/4128/4582/563/3679/4117/7031/3479/6424/10451/80310/652/4036/10551
## DOID:824 4312/6279/820/7850/4321/3595/4318/4069/3576/1493/6352/8842/185/2952/5327/4982
## DOID:3905 4312/6280/2305/9133/6279/7153/6278/6241/55165/11065/8140/10232/332/6286/3002/9212/4521/891/4171/9928/8061/4318/3576/1978/1894/7980/7083/898/6352/8842/4288/2152/2697/2952/3572/4582/7049/563/3479/1846/3117/2532/2922
## Count
## DOID:170 48
## DOID:10283 40
## DOID:3459 37
## DOID:3856 40
## DOID:824 16
## DOID:3905 43The enrichDO function requires an entrezgene ID vector as input, mostly is the differential gene list of gene expression profile studies. If user needs to convert other gene ID type to entrezgene ID, we recommend using bitr function provided by clusterProfiler.
The ont parameter can be “DO” or “DOLite”, DOLite(Du et al. 2009) was constructed to aggregate the redundant DO terms. The DOLite data is not updated, we recommend user use ont="DO". pvalueCutoff setting the cutoff value of p value and p value adjust; pAdjustMethod setting the p value correction methods, include the Bonferroni correction (“bonferroni”), Holm (“holm”), Hochberg (“hochberg”), Hommel (“hommel”), Benjamini & Hochberg (“BH”) and Benjamini & Yekutieli (“BY”) while qvalueCutoff is used to control q-values.
The universe setting the background gene universe for testing. If user do not explicitly setting this parameter, enrichDO will set the universe to all human genes that have DO annotation.
The minGSSize (and maxGSSize) indicates that only those DO terms that have more than minGSSize (and less than maxGSSize) genes annotated will be tested.
The readable is a logical parameter, indicates whether the entrezgene IDs will mapping to gene symbols or not.
We also implement setReadable function that helps the user to convert entrezgene IDs to gene symbols.
## ID Description
## DOID:170 DOID:170 endocrine gland cancer
## DOID:10283 DOID:10283 prostate cancer
## DOID:3459 DOID:3459 breast carcinoma
## DOID:3856 DOID:3856 male reproductive organ cancer
## DOID:824 DOID:824 periodontitis
## DOID:3905 DOID:3905 lung carcinoma
## GeneRatio BgRatio pvalue p.adjust
## DOID:170 48/331 472/6268 5.662129e-06 0.004784499
## DOID:10283 40/331 394/6268 3.859157e-05 0.013921739
## DOID:3459 37/331 357/6268 4.942629e-05 0.013921739
## DOID:3856 40/331 404/6268 6.821467e-05 0.014410349
## DOID:824 16/331 109/6268 1.699304e-04 0.018859464
## DOID:3905 43/331 465/6268 1.749754e-04 0.018859464
## qvalue
## DOID:170 0.003826407
## DOID:10283 0.011133923
## DOID:3459 0.011133923
## DOID:3856 0.011524689
## DOID:824 0.015082872
## DOID:3905 0.015082872
## geneID
## DOID:170 NMU/TOP2A/CRABP1/RRM2/UBE2C/MSLN/BIRC5/S100P/EZH2/KIF20A/CCNB1/PTTG1/MCM2/CDC25A/PLK1/MMP9/CXCL8/CTSV/NKX2-2/GGH/HMGA1/TFPI2/RAD51/APLP1/CCNE1/PCLAF/MKI67/IKBKB/F3/AKAP12/AGTR1/TGFB3/HTR2B/GSTT1/PLAT/IRS1/DCN/COL4A5/MUC1/CLDN5/IGF1/CEACAM6/SFRP4/PDGFD/BMP4/CCN5/CXCL14/CX3CR1
## DOID:10283 MMP1/S100A9/S100A8/BCL2A1/CXCL10/BIRC5/S100P/EZH2/MMP12/NUDT1/CCNB1/PLK1/MAGEA3/MMP9/BUB1B/CXCL8/EPHX3/CRISP3/CCL5/MKI67/IKBKB/F3/ALOX15B/GSTT1/IGFBP4/AR/IRS1/MAOA/MUC1/AZGP1/ITGA7/MAK/TFF1/IGF1/SFRP4/VAV3/PDGFD/BMP4/LRP2/AGR2
## DOID:3459 MMP1/S100A9/S100A8/TOP2A/NEK2/CCNA2/MAD2L1/BIRC5/S100P/AURKA/CCNB1/PTTG1/HPSE/MCM2/PLK1/MMP9/BUB1B/GBP1/CXCL8/ISG15/CCNE1/TNFRSF11A/MKI67/GSTT1/TNFRSF11B/MAOA/MUC1/TFF1/IGF1/CA12/SCGB2A2/ERBB4/FOXA1/SCGB1D2/PIP/PGR/AGR2
## DOID:3856 MMP1/S100A9/S100A8/BCL2A1/CXCL10/BIRC5/S100P/EZH2/MMP12/NUDT1/CCNB1/PLK1/MAGEA3/MMP9/BUB1B/CXCL8/EPHX3/CRISP3/CCL5/MKI67/IKBKB/F3/ALOX15B/GSTT1/IGFBP4/AR/IRS1/MAOA/MUC1/AZGP1/ITGA7/MAK/TFF1/IGF1/SFRP4/VAV3/PDGFD/BMP4/LRP2/AGR2
## DOID:824 MMP1/S100A8/CAMP/IL1R2/MMP12/IL12RB2/MMP9/LYZ/CXCL8/CTLA4/CCL5/PROM1/AGTR1/GSTT1/PLAT/TNFRSF11B
## DOID:3905 MMP1/S100A9/FOXM1/CCNB2/S100A8/TOP2A/S100A7/RRM2/CEP55/UBE2C/SLC7A5/MSLN/BIRC5/S100P/GZMB/AURKB/NUDT1/CCNB1/MCM2/KIF14/FOSL1/MMP9/CXCL8/EIF4EBP1/ECT2/TFPI2/TK1/CCNE1/CCL5/PROM1/MKI67/F3/GJA1/GSTT1/IL6ST/MUC1/TGFBR3/AZGP1/IGF1/DUSP4/HLA-DQA1/ACKR1/GRP
## Count
## DOID:170 48
## DOID:10283 40
## DOID:3459 37
## DOID:3856 40
## DOID:824 16
## DOID:3905 434.2 enrichNCG function
Network of Cancer Gene (NCG)(A. et al. 2016) is a manually curated repository of cancer genes. NCG release 5.0 (Aug. 2015) collects 1,571 cancer genes from 175 published studies. DOSE supports analyzing gene list and determine whether they are enriched in genes known to be mutated in a given cancer type.
## ID
## soft_tissue_sarcomas soft_tissue_sarcomas
## bladder bladder
## glioma glioma
## Description GeneRatio BgRatio
## soft_tissue_sarcomas soft_tissue_sarcomas 28/1172 28/1571
## bladder bladder 61/1172 67/1571
## glioma glioma 68/1172 76/1571
## pvalue p.adjust qvalue
## soft_tissue_sarcomas 0.0002517511 0.008056037 0.006360029
## bladder 0.0005108168 0.008173069 0.006452423
## glioma 0.0008511747 0.009079196 0.007167787
## geneID
## soft_tissue_sarcomas 1029/999/6850/4914/4342/2185/55294/2041/4851/23512/2044/4058/5290/8726/4486/5297/5728/3815/2324/7403/5925/4763/1499/7157/5159/2045/3667/2066
## bladder 9700/2175/9603/1029/8997/688/1026/896/677/6256/55294/8085/4851/3265/1999/3845/8243/10605/8295/4854/5290/2033/4780/23224/23217/2064/23385/55252/10735/4853/387/288/30849/9794/7403/287/463/472/4297/2065/2262/8289/9611/5925/2068/4763/7157/2186/1387/3910/2261/7248/23037/23345/7832/79633/10628/22906/388/4036/3169
## glioma 4603/4609/1029/3418/8877/1019/7027/4613/1030/1956/1106/2264/3417/6597/4914/55359/896/894/2321/3954/5335/5781/8439/673/9444/4851/8087/2050/8493/3845/3482/667/56999/5290/2033/4233/577/5894/5156/80036/9407/3020/1021/5598/5728/8621/1828/63035/23592/8880/2260/54880/4916/2263/1639/90/546/8289/4763/7157/23152/5295/4602/595/2261/6938/4915/26137
## Count
## soft_tissue_sarcomas 28
## bladder 61
## glioma 684.3 enrichDGN and enrichDGNv functions
DisGeNET(Janet et al. 2015) is an integrative and comprehensive resources of gene-disease associations from several public data sources and the literature. It contains gene-disease associations and snp-gene-disease associations.
The enrichment analysis of disease-gene associations is supported by the enrichDGN function and analysis of snp-gene-disease associations is supported by the enrichDGNv function.
## ID
## umls:C1134719 umls:C1134719
## umls:C0032460 umls:C0032460
## umls:C0206698 umls:C0206698
## umls:C0007138 umls:C0007138
## umls:C0031099 umls:C0031099
## umls:C0005695 umls:C0005695
## Description GeneRatio
## umls:C1134719 Invasive Ductal Breast Carcinoma 28/476
## umls:C0032460 Polycystic Ovary Syndrome 38/476
## umls:C0206698 Cholangiocarcinoma 36/476
## umls:C0007138 Carcinoma, Transitional Cell 35/476
## umls:C0031099 Periodontitis 28/476
## umls:C0005695 Bladder Neoplasm 36/476
## BgRatio pvalue p.adjust
## umls:C1134719 231/17381 4.312190e-11 1.225524e-07
## umls:C0032460 434/17381 2.819624e-10 3.521620e-07
## umls:C0206698 399/17381 3.717403e-10 3.521620e-07
## umls:C0007138 389/17381 7.093837e-10 5.040171e-07
## umls:C0031099 270/17381 1.634417e-09 9.290027e-07
## umls:C0005695 442/17381 5.871618e-09 2.781190e-06
## qvalue
## umls:C1134719 9.164539e-08
## umls:C0032460 2.633487e-07
## umls:C0206698 2.633487e-07
## umls:C0007138 3.769068e-07
## umls:C0031099 6.947133e-07
## umls:C0005695 2.079789e-06
## geneID
## umls:C1134719 9133/7153/6241/55165/11065/51203/22974/4751/5080/332/2568/3902/6790/891/24137/9232/10855/79801/4318/55635/5888/1493/9768/3070/4288/367/4582/5241
## umls:C0032460 4312/6280/6279/7153/259266/6241/55165/55872/4085/6286/7272/366/891/4171/7941/1164/3161/4603/990/29127/4318/53335/3294/3070/2952/5327/367/3667/4582/563/27324/3479/114899/9370/2167/652/5346/5241
## umls:C0206698 4312/2305/55872/4751/8140/10635/10232/5918/332/6286/2146/4521/891/10855/2921/7941/1164/4318/3576/1978/79852/8842/4485/214/65982/6863/1036/6935/4128/3572/4582/7031/7166/4680/80310/9
## umls:C0007138 4312/991/6280/6241/55165/10460/6373/8140/890/10232/4085/332/6286/2146/4171/1033/6364/5347/4318/3576/8836/9700/898/4288/2952/367/8382/2947/3479/9338/23158/2167/2066/2625/9
## umls:C0031099 4312/6279/3669/820/7850/332/4321/6364/3595/4318/3576/3898/8792/1493/4485/10472/185/6863/2205/2952/5327/4982/23261/2200/3572/2006/1308/2625
## umls:C0005695 4312/10874/6280/3868/6279/597/7153/6241/9582/10460/4085/5080/332/2146/6790/10855/4171/5347/4318/3576/8836/9636/9700/898/4288/214/2952/367/2947/4582/3479/6424/9338/2066/1580/9
## Count
## umls:C1134719 28
## umls:C0032460 38
## umls:C0206698 36
## umls:C0007138 35
## umls:C0031099 28
## umls:C0005695 36snp <- c("rs1401296", "rs9315050", "rs5498", "rs1524668", "rs147377392",
"rs841", "rs909253", "rs7193343", "rs3918232", "rs3760396",
"rs2231137", "rs10947803", "rs17222919", "rs386602276", "rs11053646",
"rs1805192", "rs139564723", "rs2230806", "rs20417", "rs966221")
dgnv <- enrichDGNv(snp)
head(dgnv)## ID
## umls:C3272363 umls:C3272363
## umls:C0948008 umls:C0948008
## umls:C0038454 umls:C0038454
## umls:C0027051 umls:C0027051
## umls:C0010054 umls:C0010054
## umls:C0010068 umls:C0010068
## Description GeneRatio
## umls:C3272363 Ischemic Cerebrovascular Accident 20/20
## umls:C0948008 Ischemic stroke 20/20
## umls:C0038454 Cerebrovascular accident 7/20
## umls:C0027051 Myocardial Infarction 6/20
## umls:C0010054 Coronary Arteriosclerosis 6/20
## umls:C0010068 Coronary heart disease 6/20
## BgRatio pvalue p.adjust
## umls:C3272363 141/46589 1.014503e-51 1.379725e-49
## umls:C0948008 148/46589 2.867870e-51 1.950151e-49
## umls:C0038454 243/46589 7.045680e-12 3.194042e-10
## umls:C0027051 163/46589 6.222154e-11 1.889883e-09
## umls:C0010054 166/46589 6.948100e-11 1.889883e-09
## umls:C0010068 314/46589 3.198889e-09 7.250815e-08
## qvalue
## umls:C3272363 1.922217e-50
## umls:C0948008 2.716929e-50
## umls:C0038454 4.449903e-11
## umls:C0027051 2.632964e-10
## umls:C0010054 2.632964e-10
## umls:C0010068 1.010175e-08
## geneID
## umls:C3272363 rs1401296/rs9315050/rs5498/rs1524668/rs147377392/rs841/rs909253/rs7193343/rs3918232/rs3760396/rs2231137/rs10947803/rs17222919/rs386602276/rs11053646/rs1805192/rs139564723/rs2230806/rs20417/rs966221
## umls:C0948008 rs1401296/rs9315050/rs5498/rs1524668/rs147377392/rs841/rs909253/rs7193343/rs3918232/rs3760396/rs2231137/rs10947803/rs17222919/rs386602276/rs11053646/rs1805192/rs139564723/rs2230806/rs20417/rs966221
## umls:C0038454 rs1524668/rs147377392/rs2231137/rs10947803/rs386602276/rs2230806/rs20417
## umls:C0027051 rs5498/rs147377392/rs909253/rs11053646/rs1805192/rs20417
## umls:C0010054 rs5498/rs147377392/rs11053646/rs1805192/rs2230806/rs20417
## umls:C0010068 rs5498/rs147377392/rs11053646/rs1805192/rs2230806/rs20417
## Count
## umls:C3272363 20
## umls:C0948008 20
## umls:C0038454 7
## umls:C0027051 6
## umls:C0010054 6
## umls:C0010068 64.4 gseDO fuction
In the following example, in order to speedup the compilation of this document, only gene sets with size above 120 were tested and only 100 permutations were performed.
library(DOSE)
data(geneList)
y <- gseDO(geneList,
nPerm = 100,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
head(y, 3)## ID Description setSize
## DOID:114 DOID:114 heart disease 462
## DOID:1492 DOID:1492 eye and adnexa disease 459
## DOID:5614 DOID:5614 eye disease 450
## enrichmentScore NES pvalue p.adjust
## DOID:114 -0.2978223 -1.347617 0.01234568 0.1121429
## DOID:1492 -0.3105160 -1.403120 0.01234568 0.1121429
## DOID:5614 -0.3125247 -1.401403 0.01265823 0.1121429
## qvalues rank leading_edge
## DOID:114 0.06992481 1904 tags=22%, list=15%, signal=19%
## DOID:1492 0.06992481 1793 tags=22%, list=14%, signal=19%
## DOID:5614 0.06992481 1768 tags=22%, list=14%, signal=19%
## core_enrichment
## DOID:114 4057/6649/10268/3567/4882/3910/3371/6548/3082/4153/29119/3791/182/3554/5813/1129/5624/3240/8743/7450/947/78987/1843/4179/7168/948/4314/10272/4881/2628/5021/4018/4256/187/6403/4322/2308/3752/1907/1511/283/3953/7078/2247/2281/10398/5468/10411/10203/1281/4023/83700/11167/7056/3952/126/6310/4313/5502/2944/6444/3075/2273/2099/3480/1471/7079/775/1909/2690/1363/4306/23414/5167/213/5350/5744/11188/2152/2697/185/2952/367/4982/7349/2200/4056/3572/2053/7122/1489/3479/2006/10266/9370/10699/4629/2167/652/1524/7021
## DOID:1492 8106/3371/3082/5914/2878/4153/3791/23247/1543/80184/6750/1958/2098/7450/596/9187/2034/482/948/1490/1280/3931/5737/4314/4881/2261/3426/187/629/6403/7042/6785/7507/2934/5176/4060/1277/7078/5950/2057/727/10516/4311/2247/1295/358/10203/2192/582/10218/57125/3485/585/1675/6310/2202/4313/2944/4254/3075/1501/2099/3480/4653/1195/6387/3305/1471/857/4016/1909/4053/6678/1296/7033/4915/55812/1191/5654/10631/2152/2697/7043/2952/6935/2200/3572/7177/7031/3479/2006/10451/9370/771/3117/125/652/4693/5346/1524
## DOID:5614 3371/3082/5914/2878/4153/3791/23247/1543/80184/6750/1958/2098/7450/596/9187/2034/482/948/1490/1280/3931/5737/4314/4881/2261/3426/187/629/6403/7042/6785/7507/2934/5176/4060/1277/7078/5950/2057/727/10516/4311/2247/1295/358/10203/2192/582/10218/57125/3485/585/1675/6310/2202/4313/2944/4254/3075/1501/2099/3480/4653/6387/3305/1471/857/4016/1909/4053/6678/1296/7033/4915/55812/1191/5654/10631/2152/2697/7043/2952/6935/2200/3572/7177/7031/3479/2006/10451/9370/771/3117/125/652/4693/5346/15244.5 gseNCG fuction
ncg <- gseNCG(geneList,
nPerm = 100,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
ncg <- setReadable(ncg, 'org.Hs.eg.db')
head(ncg, 3)## ID Description setSize enrichmentScore
## breast breast breast 133 -0.4869070
## lung lung lung 173 -0.3880662
## lymphoma lymphoma lymphoma 188 0.2999589
## NES pvalue p.adjust qvalues rank
## breast -1.904542 0.01492537 0.06666667 0.03508772 2930
## lung -1.592997 0.02816901 0.06666667 0.03508772 2775
## lymphoma 1.346949 0.03333333 0.06666667 0.03508772 2087
## leading_edge
## breast tags=33%, list=23%, signal=26%
## lung tags=31%, list=22%, signal=25%
## lymphoma tags=21%, list=17%, signal=18%
## core_enrichment
## breast PTPRD/KMT2A/ERBB3/SETD2/ARID1A/GPS2/NCOR1/RB1/MAP2K4/NF1/TP53/PIK3R1/STK11/CDKN1B/PTGFR/APC/CCND1/TRAF5/MAP3K1/ESR1/TBX3/FOXA1/GATA3
## lung PIK3C2B/SETD2/ATXN3L/LRP1B/BRD3/ARID1A/INHBA/RB1/ADCY1/LYRM9/NF1/CTNNB1/TP53/SATB2/STK11/CTIF/CTNNA3/KDR/COL11A1/FLT3/APC/ADGRL3/FGFR3/NCAM2/DIP2C/APLNR/SLIT2/EPHA3/RUNX1T1/ZMYND10/ZFHX4/GLI3/TNN/PLSCR4/DACH1/ERBB4
## lymphoma DUSP2/EZH2/PRDM1/MYC/ZWILCH/IKZF3/PLCG2/IDH2/H1-2/MAGEC3/CD79B/ETV6/H1-4/H1-5/IRF8/CD28/SLC29A2/DUSP9/TNFAIP3/DNMT3A/SYK/TNF/BCR/H1-3/DSC3/UBE2A/PABPC14.6 gseDGN fuction
dgn <- gseDGN(geneList,
nPerm = 100,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
dgn <- setReadable(dgn, 'org.Hs.eg.db')
head(dgn, 3)## ID Description setSize
## umls:C0029456 umls:C0029456 Osteoporosis 375
## umls:C0011570 umls:C0011570 Mental Depression 483
## umls:C0042133 umls:C0042133 Uterine Fibroids 289
## enrichmentScore NES pvalue
## umls:C0029456 -0.3439046 -1.519917 0.01190476
## umls:C0011570 -0.2874181 -1.281686 0.01265823
## umls:C0042133 -0.3210059 -1.374001 0.01265823
## p.adjust qvalues rank
## umls:C0029456 0.1123876 0.06861559 1766
## umls:C0011570 0.1123876 0.06861559 2587
## umls:C0042133 0.1123876 0.06861559 2105
## leading_edge
## umls:C0029456 tags=23%, list=14%, signal=20%
## umls:C0011570 tags=25%, list=21%, signal=20%
## umls:C0042133 tags=25%, list=17%, signal=21%
## core_enrichment
## umls:C0029456 RXRG/HGF/PTH1R/CYP1A1/JAG1/ROR2/FLT3/CUL9/EEF1A2/THSD4/BCL2/ITGAV/WIF1/GREM2/COL15A1/HPGDS/VGLL3/SLIT3/NRIP1/TMEM135/MGP/PLCL1/OSBPL1A/PIBF1/SELP/SPRY1/MMP13/ID4/SPP2/COL1A2/AOX1/ARHGEF3/GSN/TSC22D3/ATP1B1/NR5A2/ANKH/COL1A1/LEPR/THSD7A/GC/FGF2/PPARG/NOX4/ZNF266/GHRH/BHLHE40/SLC19A2/THBD/FLNB/KL/LEP/HSD17B4/CTSK/FTO/MMP2/ESR1/IGF1R/PTN/IRAK3/HSPA1L/CST3/GHR/SPARC/KDM4B/LRP1/INPP4B/BMPR1B/PTHLH/DPT/FRZB/GSTT1/AR/TNFRSF11B/IRS1/WLS/GSTM3/TGFBR3/TPH1/IGF1/SFRP4/CORIN/BMP4/CHAD/FOXA1/PGR
## umls:C0011570 SMPD1/ETS2/RGN/GRIA1/PTGS1/NLGN1/PDE4A/ADAMTS2/EHD3/NR5A1/SORCS3/A2M/KCNQ1/CRY1/ADRB2/FZD1/MYOM2/ADCY1/POU6F1/MAPK3/BICC1/SLC6A4/AHI1/TP53/DBP/SLC12A2/BDNF/NR3C1/SRSF5/PCLO/GABRA6/WWC1/IL5/GLUL/ELK3/GAD1/RARA/GRM5/ASAH1/IMPACT/CHRM2/WFS1/TSPAN31/ARGLU1/HP/PVALB/HTR1A/GPM6A/CYP2A6/DUSP1/NLGN4Y/F2R/CD36/DBH/BECN1/CCND1/PER3/OXTR/SGCE/CFB/CLASP2/LPAR1/NRP1/AVPR1B/ARSD/GC/FAAH/BHLHE41/FGF2/CD1C/ABCB1/PPARG/SRPX/RAPGEF3/CRHBP/CDH13/HSPA2/BHLHE40/PDE1A/LEP/FTO/PER2/ALPK1/GSTM1/DIXDC1/XBP1/TCF4/ESR1/IGF1R/NTF3/CACNA1C/NR3C2/SLC18A2/NTRK2/RAPGEF4/F3/AGTR1/TAC1/GSTT1/AR/UCN/FBN1/MAOA/CARTPT/TAT/ADRA2A/MUC1/TGFBR3/TPH1/IGF1/MAOB/ADIPOQ/TBC1D9/ADH1B/EMX2/MAPT/CRY2/GATA3/TFAP2B
## umls:C0042133 PBX1/CTNNB1/TP53/FZD2/CYP2A13/SMAD3/ADAM12/COL4A6/HSD17B7/KAT6B/CYP1A1/BCL6/SST/EGR1/SALL1/NAALADL1/IGFBP7/BCL2/CD34/CCN2/HPGDS/MMP3/AHR/CCND1/HOXA5/OXTR/FERMT2/NR4A2/LAMB1/ADGRV1/FOXO1/FNDC3A/FOS/MME/FGF2/PPARG/TAGLN/CCNG1/ALDH1A1/IGFBP2/WNT5B/LEP/MMP2/GSTM1/GAS6/ESR1/IGF1R/CAV1/VCAN/EDNRA/GHR/LTBP2/SLC7A8/PTHLH/NTS/DPT/MST1/ZKSCAN7/F3/GJA1/ANO1/TGFB3/AR/FBN1/COL4A5/XIST/IGF1/MYH11/CCN5/CXCL14/PGRReferences
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