Overlap of dichotomized (absolute) strength of RP-nucleolar/cancer correlations by ribosomal location (mitochondria vs cytosolic) in five tissues
2020-June-30
Author: Charleen D. Adams
Abbreviations and definitions
- Gene-Tissue Expression (GTex) Project
- Ribosomal protein (RP)
- Transcripts per million (TPM)
- Expression quantitative trait loci (eQTL)
Background
I previously perfomred a cross-tissue expression quantitative trait loci (eQTL) analysis with Gene-Tissue Expression (GTEx) eGene summary data (top-hit eQTLs), which revealed some differences in eQTLs based on whether ribosomal proteins (RPs) were located in the mitochondria or cytosol. Specifically, eQTLs were more likely than chance to decrease expression if cytosolic and increase expression if mitochondrial: https://rpubs.com/Charleen_D_Adams/609097
The shorterm goal of the present analysis is to determine whether use of the GTEx transcripts per million (TPM) data can be used to glean any more information about potential differences between mitochondrial vs cytosolic RPs.
The longterm goal is to use these two analyses as pilot data to request the individual-level genetic data from GTex (which we may already have) to generate a cross-RP eQTL polygenic score for eQTLs decreasing RP expression. The motivation behind this is the RP haploinsufficiency observed in Diamond-Blackfan Anemia (DBA) having an impact on TP53 and cancer predisposition.
I have run various preliminary Mendelian randomization (MR) analyses using eQTL summmary data for the RPs (e.g., see this for cytosolic RPs and breast cancer: https://rpubs.com/Charleen_D_Adams/618226). An issue with cis-based eQTL summary data instrumenting exposures in MR is that all SNPs in linkage disequilibrium (LD) are pruned (dropped). By definition, SNPs in cis with a gene are going to be mostly in LD, which means that pleiotropy cannot be tested for by using multiple SNPs. While there are various exisiting methods for MR with eQTL GTEx data (including those that use colocalization and some using multivariable MR), none have instrumented an organelle, which makes this analysis unique and pioneering.
Methods
I downloaded the public TPM expression data from GTEx. After importing it into R and converting ensembl IDs to entrez ID symbols, I imported GTEx’s file that has the sample annotations. I used this to subset for samples denotating breast and Prostate, Lung, Colorectal, and Ovary (PLCO)-relevant tissues: prostate, lung, colon (sigmoid and transverse), and ovary. Since GTEx’s TPM data are already normalized, I only log2-transformed the data (expression data need to be normalized by gene length, but this step was done by them).
I ran Pearson correlations between a set of 150 RPs with select cancer-relevant genes (TP53, MYC, MKI67, ESR1, ESR2, ESRRA, ESRRB, ESRRG) and genes associated with the nucleolus.
I selected the correlations that were Bonferroni-significant (dividing 0.05 by the number of tests run, which was slightly different between tissues). Then I ran Fisher’s exact tests of RP location (mitochondria vs cytosol) and absolute strength of the correlations (dichotomized at >=0.50) between the RPs and the nucleolar/cancer geneset correlations.
(Side notes: The Fisher’s exact tests were only run for the nucleolar/cancer geneset for which the dichotomized variables created for the 2x2 tables contained two levels; i.e., nucleolar/cancer genes for which the dichotomized variables were invariant (only one level) were dropped. I also ran stratified tests in breast, the tissue I started with, by whether the correlations were positive or inverse, but since this didn’t reveal noticeably different sets of genes than by running the primary analysis on the absolute strength of the correlations, I didn’t take the stratified analyses forward for the other tissues.)
I then looked up each of the false-discovery rate (FDR)-significant sets of Fisher’s exact results for the five cancers in the Geneset Enrichment Analysis (GSEA) browser to determine whether the sets identified were enriched for pathways other than those related to nucleolar function. For breast tissue, the FDR set identified enrichment for BRCA1-PUNJANA genes. Fascinatingly, this set was not identified as enriched in lung, ovary, prostate, or colorectal tissue.
I still need more time to think through the interpretation. There is a difference between the strength of the correlation of RPs with nucleolar genes in breast depending on whether the RPs are located in the mitochondria or cytosol. This might be relevant for breast cancer eitiology.
Background
The list for the nucleolar genes came from the Human Protein Atlas: https://www.proteinatlas.org/humanproteome/tissue
I verified that the list of RPs from Human Protein Atlas were not pseudogenes by looking them up in Ensembl. (I’ve read there are over 2000 known RP pseudogenes…)
Number of samples for each tissue
read.url <- function(url, ...){
tmpFile <- tempfile()
download.file(url, destfile = tmpFile, method = "curl")
url.data <- fread(tmpFile, ...)
return(url.data)
}
samples_v8=read.url("https://storage.googleapis.com/gtex_analysis_v8/annotations/GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt")
PLCO_samples=samples_v8[SMTSD == "Breast - Mammary Tissue" | SMTSD == "Lung" |
SMTSD == "Ovary" | SMTSD == "Prostate" | SMTSD == "Colon - Sigmoid" |
SMTSD == "Colon - Transverse"]
table(PLCO_samples$SMTSD)##
## Breast - Mammary Tissue Colon - Sigmoid Colon - Transverse
## 480 389 432
## Lung Ovary Prostate
## 867 195 262Overlap of FDR-significant Fisher’s exact nucleolar/cancer subsets results for breast, lung, ovary, prostate, and colorectal cancer
- Code included to show the number of FDR genes per tissue.
- Overlap plots are shown for all five tissues, then with colon removed to more easily see that there are some sets that are unique to the tissues.
- I haven’t looked to see what the unique sets are. The total sets are displayed below the graphs.
library(RBGL)
library(graph)
library(Vennerable)
breast_fdr=read.csv('/n/home04/cdadams/TPM/fishers_6-28.csv')
breast_fdr_sig=breast_fdr[which(breast_fdr$fdr<0.05),]
dim(breast_fdr_sig)## [1] 108 4lung_fdr=read.csv('/n/home04/cdadams/TPM/lung/fishers3_lung2_6-29.csv')
lung_fdr_sig=lung_fdr[which(lung_fdr$fdr<0.05),]
dim(lung_fdr_sig)## [1] 476 4ovary_fdr=read.csv('/n/home04/cdadams/TPM/ovary/fishers3_ovary2_6-29.csv')
ovary_fdr_sig=ovary_fdr[which(ovary_fdr$fdr<0.05),]
dim(ovary_fdr_sig)## [1] 393 4prostate_fdr=read.csv('/n/home04/cdadams/TPM/prostate/fishers3_prostate2_6-29.csv')
prostate_fdr_sig=prostate_fdr[which(prostate_fdr$fdr<0.05),]
dim(prostate_fdr_sig)## [1] 26 4colon_fdr=read.csv('/n/home04/cdadams/TPM/colon/fishers3_colon2_6-29.csv')
colon_fdr_sig=prostate_fdr[which(colon_fdr$fdr<0.05),]
dim(colon_fdr_sig)## [1] 167 4geneList <- list(Breast=breast_fdr_sig$gene, Lung=lung_fdr_sig$gene, Ovary=ovary_fdr_sig$gene,
Prostate=prostate_fdr_sig$gene, Colon=colon_fdr_sig$gene)
Overlap_PLCO_fisher <- Venn(geneList)
plot(Overlap_PLCO_fisher, doWeights = FALSE )
# Plot removing colon
geneList_no_colon <- list(Breast=breast_fdr_sig$gene, Lung=lung_fdr_sig$gene, Ovary=ovary_fdr_sig$gene, Prostate=prostate_fdr_sig$gene)
Overlap_PLCO_fisher_no_colon <- Venn(geneList_no_colon)
plot(Overlap_PLCO_fisher_no_colon, doWeights = FALSE )
TL; DR (lists of the FDR-significant nucleolar genes by tissue)
library(RBGL)
library(graph)
library(Vennerable)
breast_fdr=read.csv('/n/home04/cdadams/TPM/fishers_6-28.csv')
breast_fdr_sig=breast_fdr[which(breast_fdr$fdr<0.05),]
breast_fdr_sig$gene## [1] IMP3 POP5 TXN2 RPL26L1 GOLGA3 XPO6
## [7] DOLK ASNA1 PPP1CA PGRMC1 HINT2 CPT2
## [13] DCXR RPS19BP1 ANAPC11 HDDC2 RBM28 TNPO2
## [19] RRN3 DDX28 LSM3 DDX42 DDX5 RPP40
## [25] IK SCD FAM32A NUDT8 PLEKHM1 POLR2I
## [31] SNAPIN PPP1R7 IMP4 C1QBP EWSR1 BMS1
## [37] TSR2 LARP4B STOX1 TOX4 PHTF1 SDHAF2
## [43] CENPBD1 DDX41 SMUG1 ZFP91 BCKDHB ZNF330
## [49] CDKN2AIPNL UBR3 POP7 ATXN1L AGTPBP1 CDK8
## [55] MESP1 DNAJC21 NUP188 KIF3B UBE2R2 IDH3G
## [61] EBNA1BP2 ANP32B ATP13A3 YPEL2 EXOSC4 ARL6IP4
## [67] POP4 MDM2 PIK3CB RETSAT TAF1B RPF1
## [73] FAM204A GIN1 DEDD ARID5A ALG14 PTBP1
## [79] MAD2L1BP SRP68 PDHA1 PPIL1 PAFAH1B2 FBXW11
## [85] TAOK2 SNX15 NAA10 KIF2A NUB1 KLLN
## [91] SPON1 C19orf53 IQSEC1 MAP1S GDE1 POLR1B
## [97] METTL1 HNRNPM NOP10 SRP54 NSUN5 MPND
## [103] SETD7 ACAT2 ZNF275 TEX264 DDX56 DDX19A
## 1082 Levels: AATF ABCB8 ABCC4 ABCC8 ABCF3 ABHD14B ABL1 ABT1 ACADVL ... ZXDClung_fdr=read.csv('/n/home04/cdadams/TPM/lung/fishers3_lung2_6-29.csv')
lung_fdr_sig=lung_fdr[which(lung_fdr$fdr<0.05),]
lung_fdr_sig$gene## [1] POP4 EXOSC1 TRMT5 ELP3 NSUN5 PPP1R7
## [7] DIEXF SMUG1 NVL UTP14C POLR3K CCDC58
## [13] ALG14 NOL9 RBBP5 GLE1 ACTR10 COX10
## [19] TSEN15 TOE1 REXO4 TMA16 TRMT10A IMP3
## [25] MCRS1 NUDT19 RPP30 GTF3C3 SDHAF2 TTC27
## [31] COIL RPL26L1 C12orf43 TXN2 PIGO NOL11
## [37] POP5 FCF1 ABCF3 FTSJ3 LLPH WDR18
## [43] RPP21 ZNF202 MTX2 SURF2 MPHOSPH10 RBM34
## [49] SNAPC5 ZNF696 RAD17 UBXN8 CDKN2AIPNL SDAD1
## [55] PRMT6 ZCCHC9 RNF20 MAD2L1BP FAM32A ESF1
## [61] DDX49 TSG101 PWP1 LSM3 C7orf26 EXOSC5
## [67] DOLK RPF1 NOL7 TSEN2 RPP38 ARID5A
## [73] GTPBP10 APEX1 CENPBD1 ASNA1 GAR1 ABT1
## [79] AGPS CLN6 DDX28 TMEM179B MFSD9 DDX1
## [85] RPL7L1 NUFIP1 CPT2 RNFT1 UBLCP1 CIAPIN1
## [91] POLR1B TGDS TEX264 TP53 POP7 NOL8
## [97] ABCB8 DDX50 HDHD3 ANAPC11 DNTTIP1 NUDT8
## [103] RNPEPL1 UTP3 WDR12 THUMPD3 RPP40 EXOSC6
## [109] PBDC1 DDX55 C1D PPP1CA DHX57 HOMEZ
## [115] EEF1E1 SENP3 DGCR8 EXOSC3 FRG1 EXOSC2
## [121] DOHH METTL5 DDX59 HS1BP3 GEMIN2 UTP6
## [127] BRF2 ZXDC CCDC137 GSKIP TBC1D13 C18orf21
## [133] ELMOD2 UTP23 EXOSC9 APTX SURF6 YPEL2
## [139] LYAR ZNF668 CYB5R1 ANKS6 THTPA UTP14A
## [145] KNOP1 SNX15 CKAP5 UBTF SNAPIN GLOD4
## [151] DTD1 ZNF622 PRRG2 RRP36 C1orf109 COQ5
## [157] PES1 MPHOSPH6 SMARCB1 EXOSC7 RRP15 SLC29A2
## [163] UTP15 CTCF ATP6V1G1 HINT2 RPS19BP1 PSPC1
## [169] PPP1R3D SNRNP35 SLC30A5 TYW1 AMDHD2 PAK1IP1
## [175] BRIX1 RETSAT SMC2 DDX52 CETN3 CDK4
## [181] NIP7 KAT5 POLE3 TLDC1 DDX41 EXOSC4
## [187] KRCC1 GMPR2 WDR3 ZNF583 JRK PNRC1
## [193] DROSHA PPIL1 MPND OLA1 NLE1 UTP20
## [199] C6orf89 MFSD1 ORC4 DDX31 EBNA1BP2 PARN
## [205] ZNF324B STEAP3 TFIP11 FEN1 ZNF691 GIN1
## [211] PARP1 FAM131A NKIRAS2 RRP9 CHD2 NAT10
## [217] RPF2 ZFP41 DCUN1D5 ATF6B C1QBP CUL2
## [223] P3H4 RPAP2 NOC4L UBE2R2 PML ZBED9
## [229] C22orf46 USP46 VEPH1 C16orf58 YY1AP1 NIFK
## [235] ZNF16 TAF1D VRK1 ZNF483 PAPSS1 RRP1B
## [241] DCXR PIH1D1 COX7A2L RRS1 GDE1 TBL3
## [247] MED1 TTF1 DNAL4 CDKN1A FGFR1 ZNF77
## [253] MMP24 POLR1A DCAF13 NUDT16 RRN3 SMIM20
## [259] ZBTB14 TRIM41 ABL1 GTF2H5 KIF3B ESRRA
## [265] TGS1 DHX33 NUDT14 HES7 TMX1 LRRC40
## [271] NOM1 ZNF71 PDCD11 SETD4 NOP56 WDR74
## [277] YPEL4 SNX30 HLTF ARL14EP DDX19B ZNF507
## [283] PHLDA1 IRAK4 SRSF5 URB2 NCAPD2 NCOA4
## [289] FEZ2 IMP4 TOX4 MAK16 UBFD1 SCAF11
## [295] SDCBP2 DCTN3 DDX18 PELI1 TRAF6 PCDHAC1
## [301] HIRIP3 NMD3 POP1 H2AFY SCD OARD1
## [307] CEBPA CRIPT ATXN1 UTP18 KIF20B DHX9
## [313] NF2 DDX6 DDX20 FXYD1 TMEM187 PYCARD
## [319] PNKP NHP2 CCDC149 DDX19A RSL1D1 DYNC1I2
## [325] C1orf210 EIF6 SKP2 MACROD2 ZNF57 LSM6
## [331] HELQ PCTP PRR19 KRR1 USP36 TRIB3
## [337] ATP13A3 SIRT7 VSIG4 AATF HDDC2 IPMK
## [343] CPNE3 SF3B3 NSMAF UBE2T DDX5 NSUN2
## [349] DDX54 PSTK CLDND1 NOC2L DFFB KDM5A
## [355] FILIP1 NAA10 ACAT2 GPR137 IRX2 WDR82
## [361] ZCCHC10 KAT6A CCT5 NFX1 ZNF362 BAZ2A
## [367] KLHL7 DENND5B DCAF17 NOA1 HEATR1 DHX37
## [373] TICAM1 CHTOP MED16 NUCKS1 TERF1 SPRN
## [379] MAPK13 GPATCH4 ZNF860 PCGF5 VASN ASXL1
## [385] IPPK ZNF816 SSRP1 SRP68 CUTC RCN2
## [391] NPM3 VMP1 CSTB KATNBL1 TTLL1 ACOX1
## [397] PARP10 BMI1 PPID MESP1 MOB1B METTL1
## [403] ISG20 DDX3X CYTH1 MRTO4 GCFC2 WDR46
## [409] FAM105A PNO1 UPF3B CASP8AP2 SRP19 ZNF354A
## [415] EXOSC8 RRNAD1 WDSUB1 SYDE2 SRPK2 YWHAZ
## [421] SS18L2 SBDS RLF ZNF444 WASL NBAS
## [427] ZNF571 EIF3A CMAS SPIN1 CDV3 NPM1
## [433] FBXO11 YWHAB ZNF865 ATF3 TEX10 NUP153
## [439] SMPDL3A CDCA7L C7orf50 DNAJA2 ENC1 NOL12
## [445] MAFB BEND3 PHF2 TSR1 SPTBN1 STAU2
## [451] NOP10 ERCC6 S100A13 SP5 VRK3 CCND2
## [457] ZNF800 CENPH PTTG2 GRWD1 IFI16 HECTD1
## [463] ZNF627 MALT1 MIDN SNAPC3 PHF6 PCID2
## [469] ATXN7 KIAA0319L SLBP PIK3CB TAF1B ZNF554
## [475] ZPR1 NBN
## 972 Levels: AATF ABCB8 ABCC4 ABCF3 ABHD14B ABL1 ABT1 ACAT2 ACIN1 ACOX1 ... ZXDCovary_fdr=read.csv('/n/home04/cdadams/TPM/ovary/fishers3_ovary2_6-29.csv')
ovary_fdr_sig=ovary_fdr[which(ovary_fdr$fdr<0.05),]
ovary_fdr_sig$gene## [1] TRIM41 TRMT5 MFSD9 NVL NUDT16 POLR3K
## [7] PPID TDP2 SNAPIN COIL PYCARD SMUG1
## [13] ZNF583 DAXX CCDC137 COQ5 SKP2 CPT2
## [19] C18orf21 GLE1 TMEM187 POP7 PRKRIP1 EXOSC3
## [25] PDCD7 UTP23 MPHOSPH10 PPP1R3D TGDS TSEN2
## [31] NBAS DDX20 ZBTB14 GTF3C3 HLTF TLDC1
## [37] CCDC86 BRIX1 RAD17 EXOSC1 CHTOP PRMT6
## [43] ZNF507 DDX55 NGDN RBBP6 DOLK RBBP5
## [49] GEMIN2 RPP30 ARL14EP HINT2 CKAP5 POLR1B
## [55] HIRIP3 KRCC1 SIRT7 TAF1 SREK1IP1 DCTN3
## [61] ZNF397 ZNF696 LSM6 C1orf109 NOL9 DLG3
## [67] PJA1 TBC1D13 EXOSC5 NOP56 DKC1 TTF1
## [73] LRWD1 SPC24 TUT1 ESF1 C22orf46 PRKDC
## [79] EMG1 KANSL3 RPP40 CD3EAP POP5 FTSJ3
## [85] CASP8AP2 NCAPD2 CDKN2AIP UTP14C FKBP15 PIN4
## [91] WDR36 BCKDHB EXOSC2 HNRNPM GTPBP10 RPL7L1
## [97] ZNF461 CCT5 DDX28 SENP3 IK WDR18
## [103] EXOSC7 MCRS1 EXOSC9 PARP1 SAP30L NIFK
## [109] EBNA1BP2 C9orf3 RNFT1 MCEE UBE2T IPPK
## [115] SURF6 RRP1B EXOSC4 ANKS6 GRWD1 GPALPP1
## [121] CLN6 RPAP2 RBM34 POP4 DIEXF C16orf58
## [127] NOP16 ELMOD2 SF3B3 FCF1 NOP2 KDM2B
## [133] HOMEZ ACSL5 BCAS3 GPATCH4 DDA1 LLPH
## [139] DHX57 PCTP ZCCHC9 EWSR1 IMP3 ZMYM4
## [145] MAK16 TGS1 ABCF3 ZNF668 STAT1 PTPN6
## [151] BYSL NOL11 EEF1E1 EXOSC6 CDKN2AIPNL DOHH
## [157] TP53 BLCAP NSUN2 NUFIP1 GPR137 ZC3H14
## [163] C11orf68 AKAP8 VRK3 WDR46 DROSHA DSN1
## [169] HDDC2 UBTF ZFX UBLCP1 KRI1 CENPBD1
## [175] NAT10 MAGEH1 RPP21 LDOC1 ABHD14B ZNF324B
## [181] IFI16 NOP10 TWISTNB ANKRD49 DDX23 PARN
## [187] CRBN ELAVL1 ZNF71 KCNC4 SNX15 RRP15
## [193] POLA1 DTWD1 ZNF597 VRK1 TSEN15 RBM4
## [199] ACIN1 PELP1 RNF20 SIN3A ZNF627 RTF1
## [205] RIOK1 DHX9 CRYL1 CBX5 SELENBP1 DNTTIP1
## [211] ABT1 RRP8 NSUN5 RRP36 SRP14 C9orf85
## [217] TOE1 C12orf43 NOC2L MBIP PPP1CA KAT5
## [223] TFIP11 LYAR SURF2 ILF2 PPP1R7 WDR3
## [229] KLHL7 TCOF1 POP1 MAD2L1BP NBN NUDT19
## [235] DDX10 KIF20B RRP9 CCDC58 IQSEC1 DCAF13
## [241] BEND3 RPF1 IRAK4 SCAF11 DYNC1I2 MED27
## [247] SNAPC3 DDX24 EXOSC8 URB2 DDX49 AMDHD2
## [253] ASNA1 ZXDC FBXO36 PSPC1 GOLGA3 DDX39B
## [259] LSM3 TBL3 XPO6 ZNF678 APEX1 GEMIN4
## [265] CCDC136 OSBP TXN2 WDR12 IDH3G IMP4
## [271] PAK1IP1 KDM6A CMAS BRF2 NAA10 NOC3L
## [277] MYBBP1A RFC1 BLM CDCA8 RNF215 ENDOV
## [283] TAOK2 OTUD7A SRP19 SDHAF2 ELP3 RPL26L1
## [289] POLR1A ZNF622 PWP1 HS1BP3 ZPR1 RRN3
## [295] ITPR1 ALG14 LZTS1 SSRP1 CYB5R1 RRP7A
## [301] ABCB8 MIF4GD MDM2 TRIM27 C1QBP SLC30A5
## [307] UTP18 WDR43 MRTO4 FTSJ1 SRFBP1 BAHCC1
## [313] TTLL1 H2AFY SIRT2 SDAD1 YWHAB KRR1
## [319] DGCR8 SNRNP35 DDX54 SPATA5L1 THUMPD3 ADAR
## [325] RAN PIGO ORC4 GTF2H5 HELZ ATXN1L
## [331] AATF RGS6 SNX30 TMX1 ZBTB43 RRS1
## [337] ATF6B CUTC UTP15 BMS1 NCL POLE3
## [343] GPR65 C7orf26 FRG1 ERI1 MED1 GLOD4
## [349] COX10 CTCF TEX10 XRCC1 TTC27 SPIN1
## [355] YWHAH TSEN54 UTP20 ACAT2 NOL8 SPG11
## [361] REXO2 PES1 ZCCHC10 RRNAD1 CCDC149 NOLC1
## [367] NHP2 ZCCHC17 SYDE2 PNO1 NLK WDFY3
## [373] CMSS1 PBDC1 HECTD1 WDR75 TMEM179B GTF3C1
## [379] ARID5A NF2 TRMT10A SRSF9 DDX50 WDR82
## [385] KLLN XRCC5 KIAA1841 UTP14A ZNF106 C6orf89
## [391] MPHOSPH6 CYB561A3 DDX1
## 1109 Levels: AATF ABCB8 ABCC4 ABCC8 ABCF3 ABHD14B ABL1 ABT1 ABTB1 ... ZXDCprostate_fdr=read.csv('/n/home04/cdadams/TPM/prostate/fishers3_prostate2_6-29.csv')
prostate_fdr_sig=prostate_fdr[which(prostate_fdr$fdr<0.05),]
prostate_fdr_sig$gene## [1] NOP10 EEF1E1 GSKIP POP7 SRP14 NOA1 ASNA1 RPL26L1
## [9] DCAF13 PFDN1 GLE1 CTCF YWHAE POLR3K FAM32A UTP18
## [17] REV3L PPP1R7 DDX28 ZNF622 RNF20 PCID2 NHP2 SNX15
## [25] C1orf109 SNRNP35
## 1025 Levels: AATF ABCB8 ABCC4 ABCF3 ABHD14B ABL1 ABT1 ABTB1 ACADVL ... ZXDCcolon_fdr=read.csv('/n/home04/cdadams/TPM/colon/fishers3_colon2_6-29.csv')
colon_fdr_sig=prostate_fdr[which(colon_fdr$fdr<0.05),]
colon_fdr_sig$gene## [1] NOP10 EEF1E1 GSKIP POP7 SRP14 NOA1
## [7] ASNA1 RPL26L1 DCAF13 PFDN1 GLE1 CTCF
## [13] YWHAE POLR3K FAM32A UTP18 REV3L PPP1R7
## [19] DDX28 ZNF622 RNF20 PCID2 NHP2 SNX15
## [25] C1orf109 SNRNP35 POP4 NCAPD2 ZFY CHTOP
## [31] PPIL1 GPATCH4 GTPBP4 PCGF5 URB2 RNF215
## [37] XPO6 INO80E TTC27 DDX52 KRR1 COX10
## [43] NIP7 DDX51 ZCCHC17 ZNF444 WASL P3H4
## [49] ATXN1 UTP15 KAT5 TNPO2 NUP188 IMP4
## [55] GPATCH2 RNFT1 FEN1 SNAPC3 C2orf68 WDR12
## [61] MAGED2 TSEN15 GTF3C3 XRCC1 TRMT1L FCF1
## [67] ELMOD2 PBDC1 EBNA1BP2 DCXR RPP30 ALG14
## [73] RBM4B UTP20 PNO1 RAD17 PARP10 CENPBD1
## [79] FAM98C DOLK SURF6 DCAF17 EXOC4 SRP19
## [85] NIFK HSPA8 DIAPH2 PINX1 NUFIP1 MPND
## [91] ZFAND2B TAF13 NOL11 MTX2 DCTN3 RRS1
## [97] EIF2S2 C7orf26 KRCC1 GAR1 IMP3 SERF2
## [103] HSPA9 APEX1 ZPR1 RPS19BP1 NOLC1 NLE1
## [109] ARL14EP ATXN7L2 ANKRD49 DCUN1D5 UTP14C MPHOSPH10
## [115] LARP4B PLEKHM1 RETSAT ILF2 RBM3 PAPSS1
## [121] DGAT1 VRK1 TLDC1 POP5 FILIP1 CLDND1
## [127] NVL ENDOV POLR1B PFKFB4 MIS18BP1 NOL12
## [133] PLOD2 CDKN2AIPNL NF2 NOC3L LLPH PSPC1
## [139] NOC2L HLTF BMS1 MAD2L1BP TRMT5 POLE3
## [145] KIF20B TEX264 EXOSC3 TRDMT1 COIL ZC3H14
## [151] DDX17 PRMT2 EIF4A3 RAN FMN2 SYDE2
## [157] DDX19B COQ5 NUCKS1 TRMT10A FAM133B ATP6V1G1
## [163] CHD3 UTP23 DTX3 TWISTNB C22orf46
## 1025 Levels: AATF ABCB8 ABCC4 ABCF3 ABHD14B ABL1 ABT1 ABTB1 ACADVL ... ZXDC