SUPPORTING INFORMATION

SI MATERIALS AND METHODS

Ubiquitination assay. Ubiquitination assay was performed as described previously (1).

Briefly, cells were transfected with expression constructs including His-tagged ubiquitin and

treated with 5 μg/ml of MG132 for 4 h, lysed in buffer A (6 M guanidinium-HCl, 0.1 M

Sodium Phosphate buffer, 0.01 M Tris-HCl (pH 8.0), 5 mM imidazole, and 10 mM β-

mercaptoethanol), and incubated with Ni2+-NTA beads (QIAGEN) for 4 h at room

temperature. The beads were then washed sequentially with buffer A, buffer B (8 M urea, 0.1

M Sodium Phosphate buffer, 0.01 M Tris-HCl (pH 8.0), and 10 mM β-mercaptoethanol), and

buffer C (8 M urea, 0.1 M Sodium Phosphate buffer, 0.01 M Tris-HCl (pH 6.3), and 10 mM

β-mercaptoethanol). Bound proteins were eluted with buffer D (200 mM imidazole, 0.15 M

Tris-HCl (pH 6.7), 30% glycerol, 0.72 M β-mercaptoethanol, and 5% SDS), and subject to

immunoblot analysis.

Subcellular fractionation. For cytosolic and nuclear fractions, cells were lysed with Buffer

A (10 mM HEPES (pH 7.9), 10 mM KCl, 0.1 mM EDTA, 0.1 mM EGTA and 10% NP40

supplemented with DTT and protease inhibitors, incubated on ice for 5 min and centrifuged at

4000 rpm for 10 min at 4 °C. The supernatant was transferred to a separate tube as a cytosolic

fraction. The nuclear fraction was obtained by lysing the nuclear pellet by adding 100 μl of

buffer C (20 mM HEPES (pH 7.9), 0.4 M NaCl, 1 mM EDTA, and 1 mM EGTA

supplemented with DTT and protease inhibitors and centrifuging at 12000 rpm for 5 min at

4 °C.

Western immunoblotting. Protein assays were performed according to the Bradford method

using a Bio-Rad protein assay kit (Bio-Rad). Denatured proteins were separated using sodium

dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to

PVDF membranes. The membranes were blocked in 5% skimmed milk in Tris-buffered

saline with Tween 20 (TBST; 10 mM Tris–HCl, 150 mM NaCl, 0.1% Tween 20).

Immunoblotting was performed with a number of different primary antibodies at optimal

dilutions (see antibodies). The membranes were incubated overnight at 4°C, rinsed with

TBST, and incubated with horseradish peroxidase-conjugated anti-rabbit secondary antibody

(Cell Signaling Technology). After applying ECL detection reagents (GE Healthcare), protein

bands were visualized using X-ray film (Fujifilm).

Sample processing for LC-MRM/MS analysis

Protein concentration of flag pull down samples was determined using the Pierce BCA

protein assay kit (Thermo Scientific) according to the manufacturer’s ‘Microplate procedure’

protocol. Total protein amount of hydroxyl antibody enriched samples was estimated to be

roughly 3 µg based on a Coomassie blue stained protein gel band.

Flag pull down sample aliquots (25 µg) and all of hydroxyl antibody enriched samples (25

µL) were reduced and alkylated as follows: Samples were diluted to a final volume of 100 µL

containing 2% (w/v) sodium dodecyl sulfate (SDS) and 100 mM triethylammonium

bicarbonate buffer (TEAB, pH 8.5) followed by heat treatment for 5 min at 95ºC. Proteins

were reduced with 2 μL of 0.5 M tris (2-carboxyethyl) phosphine (TCEP neutralised to pH 7

with ammonium hydroxide, 10 mM final conc.) for 30 min at 60°C and then alkylated with 9

μL of 0.5 M iodoacetamide (40 mM final conc.) in the dark at 37°C for 30 min.

Sample proteins were co-precipitated with sequencing-grade modified trypsin (Promega

Madison, WI; trypsin to protein ratio of 1:100) by adding 10 volumes of methanol as follows:

One microliter of trypsin was added to the side of the Eppendorf tube, quickly flushed into

the sample solution with 1.1 mL of 100% methanol at -20°C and vigorously vortexed for 30 s.

Tubes were incubated overnight at -20°C. Protein precipitates were harvested by

centrifugation at 15,000 x g for 15 min (4°C). Pellets were washed twice, once with 1 mL of

90% (v/v) methanol at -20°C and finally with 1 mL of 100% methanol at -20°C and

centrifuged each time as before. Protein pellets were allowed to air dry by placing tubes

upside down in a hood for 10 min. The final pellet for flag pull down and hydroxyl antibody

enriched samples were re-suspended in 25 µL (= 1 µg/µL protein concentration) and 10 µL,

respectively, of 100 mM TEAB buffer (pH 8.5) containing 5% acetonitrile by repeated

vortexing and incubating the tubes for 1 min in a sonication bath. Samples were incubated at

37°C for 2 hrs followed by the addition of a further trypsin aliquot (ratio 1:100) and

overnight digestion at 37°C. Digests were stored at -80°C until analysis.

Nano LC-MRM/MS analysis

For LC/MS analysis, trypsin-digested samples were acidified with formic acid and

centrifuged for 5 min at 15,000 g (4°C) prior to injection. During MRM method development,

0.5-5 pmol of sample was loaded onto the column. For final measurements, flag pull down

samples were diluted to 2 pmol/µL and 5 µL (= 10 pmol) were injected, whereas half of the

total starting material of hydroxyl antibody enriched samples was loaded.

Targeted mass spectrometric analysis was performed on a 1260 Infinity nanoflow LC coupled

to a 6490 iFunnel triple quadrupole mass spectrometer via chip-cube nanospray interface

(Agilent Technologies, Santa Clara, CA). Peptide separation was performed on microfluidic

HPLC chip Polaris-HR-Chip3C18 (II) (pn G4240-62030, Agilent) with a 360 nL enrichment

column and a 150 mm x 75 µm analytical column, both packed with Polaris C18-A (3 µm,

180 Å) stationary phase. The autosampler temperature was set to 4°C. Chromatographic

separation comprised the following conditions: Eluent A was milliQ water and eluent B was

acetonitrile with both eluents containing 0.1% formic acid. A flow rate of 2 µL/min was used

for sample loading with 3% eluent B via capillary pump and 300 nL/min was applied for

peptide separation via nanoflow pump. A total method runtime of 20 min with a 12-min

gradient was employed for peptide separation: 0 min (4% B) - 1 min (25% B) - 12 min (60%

B) - 13 min (95% B) - 14.5 min (95% B) - 15.5 min (4% B) - 20 min (4% B).

The LC/MS platform was controlled using MassHunter data acquisition software version

B.06.00 (Agilent) and the mass spectrometer was operated in positive ionization mode using

the following settings: Gas temperature and flow at 150°C and 11 L/min, respectively,

iFunnel high and low pressure RF voltages at 110 and 60 V, respectively, fragmentor voltage

380V, cell accelerator voltage 4 V and capillary voltage 2040 V. Quadrupoles 1 and 2 were

set to unit resolution and the collision energy (CE) for each peptide precursor was calculated

using equation CE = 0.036 x m/z - 4.8. Skyline-daily software

(https://skyline.ms/project/home/software/Skyline/begin.view) was used for method

development and refinement as well as for subsequent data processing and visualisation (2).

Initially, transitions (precursor/fragment pairs) were generated in-silico for unmodified and

hydroxylated peptides followed by gradual refinement of the transition list. The final LC/MS

assay contained 81 transitions from 5 peptides and 6 precursors with a dwell time of 10 ms

resulting in a cycle time of 1.09 s. All transitions are compiled in Table S1.

Microarray Statistical Analysis and Identification of Differentially Expressed Genes

(DEGs). Briefly, MCF7 cells transfected with non-silencing control shRNA or shRNA

targeting G9a were exposed to normoxia or hypoxia for 9 h, and total RNA was isolated with

the RNAeasy Mini Kit (Qiagen); 500 ng of total RNA was used for microarray analysis. The

Microarray analysis was performed using the Affymetrix Human Gene 1.0 ST Array. The full

dataset was submitted to Gene Expression Omnibus (GEO) under submission number

GSE59449.

Meta-analysis of breast cancer global gene expression. The genes identified from the

microarray analysis (212 genes) were investigated in three large gene expression datasets

from breast cancer; TCGA, ROCK and KM Plotter datasets. The breast cancer cases in each

of the three datasets (TCGA, ROCK and KM Plotter) were allocated to one of four quartiles

based on the hypoxia-G9a signature and the survival of these patients were compared. We

also used the KM Plotter dataset (version 2014) to compare the relapse-free survival of breast

cancer patients between tumors with the lowest expression (bottom 25%, quartile 1) to the

rest of the tumors. Survival curves were constructed using GraphPad Prism v6.0 (GraphPad

Software, San Diego, CA, USA), and the log-rank (Mantel-Cox) Test was used for statistical

comparisons of survival curves.

Quantitative Real-Time RT-PCR and ChIP Assays. Quantitative RT-PCR and ChIP

assays were conducted as previously described {Lee, 2010 #5}. Briefly, total RNA was

isolated using Trizol (Invitrogen) and reverse transcription was performed from 2.5µg of total

RNA using the Superscript III cDNA synthesis kit (Invitrogen). The abundance of mRNA

was detected by an ABI VIIA7 system with SYBR Green Master Mix (Life Technologies).

Primer pairs were designed to amplify 90-150 bp mRNA specific fragments and were

confirmed as unique products by melting curve analysis. The quantity of mRNA was

calculated using ΔΔCt method and normalized by using primers to detect HPRT. All

reactions were performed in triplicates. See Dataset S2 for primer sequences used.

IncuCyte real-time imaging, sulforhodamine B (SRB) and MTT assays. For proliferation

studies, cells (5 x 103) were seeded in 96-place wells and allowed to attach overnight, and

then incubated in fresh growth medium in the presence of either the G9a inhibitor UNC0642

(Sigma Aldrich) or the vehicle control DMSO (Sigma Aldrich). Proliferation was evaluated

via real-time imaging using IncuCyte Zoom (Essen BioScience) or by performing SRB

assays at the end of the treatment. For SRB assays, medium was aspirated and cells were

fixed in methylated spirits before being washed with water and stained with SRB (Sigma

Aldrich) solution (0.4% in 1% acetic acid). The intensity of the staining was obtained using

an optical plate reader at 564nm. Cell viability was determined using a 3-(4,5-

dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cells (3×103/well)

were seeded directly into 96-place well plates and allowed to adhere overnight. 96 h after

drug treatment, 20 μL of MTT (5 mg/mL; Sigma-Aldrich) was added. The plates were

incubated at 37°C for 3 h before the supernatants were removed, and 100 μL of isopropanol

was added to each well. The absorbance value (optical density) of each well was measured at

570 nm.

Immunohistochemical analysis. Tumor sections were fixed in 4% paraformaldehyde. The

antibodies used were rabbit polyclonal antibodies for HHEX (Abcam, ab34222) 1:500

dilution and ARNTL/BMAL1 (Abcam, ab3350) 1:1000 dilution. The universal secondary

protocol and the DAB (Biocare Medical) were used to detect and amplify the signal. Aperio

ImageScope software was used for imaging and quantitation of 5 non-overlapping tumor

regions and evaluating the number of positive pixels per unit area in each region. Empty areas

were manually excluded from quantification.

Statistical Analysis. Statistical differences in test and control samples were determined by

Student’s t test, ANOVA using the Statview package (Abacus Concepts, Inc.) Mann-Whitney

two-tailed test was used for group comparisons. Statistical analyses are performed using

GraphPad Prism software. Significant differences in tumour growth were determined by an

unpaired t or Mann-Whitney U test. Univariate and multivariate Cox proportional hazards

regression analyses were performed using MedCalc for Windows, version 12.7 (MedCalc

software).

Supplemental Figure Legends

Fig. S1. (A) GLP and SUV39h1 protein expression does not increase in hypoxic conditions.

Immunoblotting analysis of GLP and SUV9h1 in nuclear extracts from MCF7 cells exposed

to normoxic and hypoxic conditions. (B) Ubiquitination of HIF-1α from MCF7 cells

transfected with His-ubiquitin, exposed to normoxia or hypoxia. (C) Immunoblotting of G9a

from MCF7 and MDA231 cells in the presence or absence of 100 µM hypoxia-mimicking

agent is shown. Deferoxamine (DFO).

Fig. S2. G9a interacts with PHDs for its hydroxylation at P676 and P1207. (A) Co-

immunoprecipitation of endogenous PHD1, PHD2 and PHD3 with G9a. (B) G9a proline

hydroxylation was determined in MCF7 cells overexpressing GFP-tagged PHD1, PHD2 or

PHD3 in the presence of MG132 (20 µM). Immunoprecipitation of hydroxylated G9a was

performed using anti-hydroxylproline antibody followed by immunoblotting with anti-Flag

antibody. (C) Interaction between G9a and pVHL was determined from MCF7 cells

transfected with the indicated expression plasmids in the presence or absence of DMOG (1

mM), immunoprecipitated with anti-HA and immunoblotted using antibodies indicated. (D)

Immunoblot showing the effect of hypoxia on G9a protein levels in a VHL deficient cell line

(RCC4). (E) Proline hydroxylation sites on G9a are shown in red as compared to consensus

sequence LXXLAP. (F) Co-immunoprecipitation of VHL and G9a in normal and VHL-

reconstituted RCC4 cells treated with MG132 proteasome inhibitor, investigating VHL

binding together with the wild type or the mutant forms (P676A, P1194A and P1207A) of

G9a. (G and H) Multiple reaction monitoring mass spectrometry analysis of G9a. Shown are

peptides spanning proline 676 (G) and proline 1207 (H). (I) Immunoblotting analysis of G9a,

in nuclear extracts from G9a-/- MEFs reconstituted with G9a WT and P2A mutant exposed to

normoxic and hypoxic conditions as indicated. (J) Immunoprecipitation of hydroxylated G9a

with anti-hydroxylproline antibody from MCF7 cells overexpressing Flag-tagged G9a WT or

P2A mutant either exposed to normoxia or hypoxia in the presence of MG132 (20 µM).

Fig. S3. Heatmap of genes repressed in hypoxic condition. (A) G9a-dependent and

independent gene sets. (B and C) Heatmaps of G9a-depndent and –independent genes. NS

(non-silencing shRNA) and shG9a (G9a shRNA).

Fig. S4. Flow diagram of G9a-suppressed gene signature. (A) G9a-dependent gene set from

the microarray analysis was filtered for its inverse relationship with G9a and HIF target gene

expression. 44 genes in each of the ER-positive and ER-negative groups identified were

analysed for commonality between the three datasets. 10 genes in each of the ER-positive and

ER-negative groups were identified. Out of the 20 genes combined, 14 distinct genes were

present and 10 genes in bold associated with relapse-free survival. (C) Relapse-free survival

analysis of breast cancer patients between tumors with the lowest expression (bottom 25%,

quartile 1) to the rest of the tumors is shown using the G9a-suppressed gene signature in the

different breast cancer subtypes from the ROCK and TCGA databases (C). (D and E)

Relapse-free survival analysis of breast cancer patients from KM Plotter database for ER-

positive and ER-negative (D) and Luminal A, Luminal B, HER2-enriched and Basal-like (E).

Fig. S5. Expression of G9a-suppressed gene signature. (A) Quantitative RT-PCR analysis of

the 10 G9a-suppressed genes identified from Fig. 3 in normoxia (21% O2) and hypoxia (1%

O2) in MCF7, MDA231, MDA468 and MDA157 breast cancer cells. Empty graphs

represents lack of detectable gene expression. (B) Quantitative RT-PCR analysis of the 10

G9a-suppressed genes identified from Fig. 3 in normoxia (21% O2) and hypoxia (1% O2) in

MCF7, in the presence or absence of G9a knock down (shG9a). Results are expressed as

relative mRNA levels compared to normoxic condition. (C) Western immunoblotting of G9a

following G9a knock down is shown.

Fig. S6. The effect of UNC0642 on H3K9me2. (A) ChIP analysis of H3K9me2 in the 8

promoters of the 10 G9a suppressed genes in MCF7 cells treated with either vehicle (DMSO)

or 3 μM UNC0642 in normoxic or hypoxic conditions (9 h). Statistical differences were

determined by unpaired t-test (*P < 0.05, **P < 0.01), n=3. (B) Coimmunoprecipitation

assay showing the lack of physical interaction between G9a and DEC1 in normoxic or

hypoxic conditions.

Fig. S7. Functional annotation of G9a-dependent genes. (A) Top molecular and cellular

functions altered in shG9a MCF7 cells in hypoxia include cellular development, growth and

proliferation. (B) Functional annotation network analysis as part of Ingenuity Pathway

analysis revealed that downregulation of seven genes out of 10 genes in the G9a-suppressed

gene signature are predicted to inhibit organismal death (P = 2.16 x10-3) (i.e. upregulation of

G9a-suppressed gene signature promotes organismal death). (C) 10 genes belong to the Cell

death and survival, Drug metabolism, Endocrine System development and function network.

Fig. S8. G9a inhibition reduces proliferation. (A) Immunoblotting analysis of G9a protein

levels in various breast epithelial cells in hypoxia. (B) Cell survival was analysed through

SRB assay on various breast epithelial cells under hypoxic conditions, following vehicle or

G9a inhibitor treatment for 96 hours. Data is presented as the mean ± SEM, significant

comparisons were determined by unpaired t-test and indicated as follows: *P < 0.05, **P <

0.005, n=3. (C) IncuCyte ZOOM time-lapse imaging analysis of MDA231 treated with

various concentrations of G9a inhibitor for 48 hours. Inserted graph represents cell

confluency reached at the end of the treatment, normalised to the vehicle. (D) Cell survival

was analysed by performing SRB assay on MDA231 cells following vehicle or G9a inhibitor

(UNC0642) treatment in normoxia and hypoxia. Data is presented as the mean ± SEM,

significant comparisons were determined by unpaired t-test and indicated as follows: **P <

0.01, ***P < 0.0005, ****P < 0.0001, n=3. (E) Immunoblotting analysis of H3K9me1,

H3K9me2 and G9a from nuclear extracts of MCF7 grown in both normoxic (21% O2) and

hypoxic (1% O2) conditions, in the presence or in the absence of G9a inhibitor (3 µM).

Fig. S9. G9a inhibition suppresses proliferation of AT3 mouse breast cancer cell line. (A)

SRB assay of AT3 mouse mammary tumor treated with various concentrations of G9a

inhibitor for 48 hours. Data is presented as the mean ± SEM, significant comparisons are

indicated as follows: **P < 0.005, n=3 (Mann-Whitney two-tailed). (B) Phase contrast

microscopy picture of AT3 cells treated with G9a inhibitor or the vehicle. Scale bar

corresponds to 400 µm.

Fig. S10. G9a inhibition affects tumour growth of AT3 mouse mammary cell in vivo. (A)

Groups of B6 WT mice (n=6-9) were S.C. injected with AT3 tumour (1 x 106 cells) on day 0.

Tumour bearing mice were treated with 5 mg/kg UNC0642 intraperitoneally every two days.

Tumour growth was measured using a digital caliper and data are shown independently for

each experiment as the mean ± SEM. (B) Comparison of tumour volumes at the end of the

treatment between vehicle and G9a inhibitor treated mice. Data are shown independently for

each experiment as the mean ± SEM, significant comparisons are indicated as follows: *P <

0.01, ** P < 0.005, (unpaired t-test).

Fig. S11. Proposed model for G9a’s role as a transcriptional repressor in hypoxia. A

proportion of G9a is hydroxylated in normoxic conditions and targeted for pVHL-mediated

proteasomal degradation and allow limited histone H3K9 methylation and hence, expression

of its target genes. This mechanism may be used to maintain histone methylation balance for

homeostasis. Hypoxia results in increased G9a protein stability via escaping proline

hydroxylation and subsequent degradation. Elevated G9a level leads to excessive methylation

of histone H3K9, thereby negatively regulating gene expression in hypoxic condition. This

gene repression can be relieved by G9a inhibitor and blocking the effects of G9a in hypoxic

conditions.

References

1. Lee JM, Lee JS, Kim H, Kim K, Park H, Kim JY, et al. EZH2 generates a methyl degron that is recognized by the DCAF1/DDB1/CUL4 E3 ubiquitin ligase complex. Mol Cell. (2012); 48(4): 572-86.

2. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. (2010); 26(7): 966-8.

GLP

SUV39h1

Pontin

0 6 1% O2 (h)

HIF-1α

1% O2 - + Ni+ NTA pull down

His Ub + +

Ub(n)

-HIF-1α

Input: HIF-1α

HIF-1α

G9a

Lamin A/C

DFO - + - + MCF7 MDA231

A B C

Fig. S1

PHD3

PHD2

PHD1 Input

G9a

G9a α

PH

D1

αP

HD

2

αP

HD

3

IP: α

IgG

1% O2 (h) 0 4 12 24 36 48

RCC4

Lamin A/C

G9a

A B

WT P676A P1194A P1207A

HA pVHL

Flag G9a

- + - + - + - +

IP αFlag

HA

Flag

HA Input

G

1 G9a

736 1263 965

GELQKVILMLLDNLDPNFQ

LXXLAP

661

1192 LDPHPELLPELGSLPPVNT

consensus

679

1210

ARD SET

F

Flag

HA pVHL + + DMOG - +

IP:αHA

Flag

HA Input

Flag G9a + +

Flag

IP: αOH Pro P2A WT

+ - + - 1% O2

Lamin A/C

Flag

Input

Flag G9a

GFP

+ + + +

Flag

IP:αOH-Pro

Flag

Tubulin

Input

GFP PHD 1 2 3 EV

Flag G9a

C D

I G9a -/- MEFs

G9a

G9a WT G9a P2A 1% O2 - - + +

Lamin A/C

E

H

J

Fig. S2

C

A

G9a-independent (385 genes)

G9a-dependent (212 genes)

36%

64%

B Fig. S3

212 genes downregulated in hypoxia by G9a

Inversely correlated to G9a

Inversely correlated to hypoxia

ER-positive ER-negative

44 genes 44 genes

ER-positive ER-negative

10 genes 10 genes

14 genes

10 genes

A

ER-positive

Luminal A Luminal B

ER-negative

HER2-enriched Basal-like

B

C D

E

Fig. S4

shN

S

shG

9a

G9a

Tubulin

A

B

C

Fig. S5

21% O2 1% O2

H3K9me2 in the promoters

αFlag G9a

αGFP DEC1

IP:

Hypoxia - - - + + + - +

Input

A

B

Fig. S6

Function Annotation Network analysis

Predicted inhibition

Upregulated

Downregulated

A B

C

Fig. S7

47%

18%

MDA231

UNC0642 - +

H3K9me1

H3K9me2

H3

G9a

- +

21% O2 1% O2

MCF7

HIF-1

C

G9a

H3

ER-positive ER-negative

Hypoxia (1% O₂)

A

B

D E

Fig. S8

A B

Veh

icle

U

NC

0642

5 µ

M

Fig. S9

A B Fig. S10

Normoxia

Me G9a

G9a proteasomal degradation

Gene repression

Increased cancer cell survival

Hypoxia

H3K9me2

G9a G9a

G9a

Me Me

G9a

G9a protein stabilisation

H3K9me2 G9a G9a G9a

G9a G9a inhibitor

Me Me

PHD1

Gene expression

G9a OH

OH

G9a OH

OH G9a

G9a

Gene re-expression

Me

Fig. S11

K.VILMLLDNLDP676NFQSDQQSK.R Mock PHD

overexpression

Precursor mass (m/z)

Precursor charge

(z)

Fragment ions indicating presence or

absence of proline hydroxylation unmodified Yes Yes 773.4 3 y10, y11, y19, b11, b12, b14, b19

hydroxylated No Yes 778.7 3 y11, b11, b12, b14, b19

R.LDPHPELLPELGSLPP1207 VNT no

pull-down

anti OH pull-down

Precursor mass (m/z)

Precursor charge

(z)

Fragment ions indicating presence or

absence of proline hydroxylation unmodified Yes Yes 680 3 y4, y5, y8, y11, b16, b17

hydroxylated No Yes 685.4 3 y5, b16

Table S1. Multiple reaction monitoring mass spectrometry

Multiple reaction monitoring (MRM) mass spectrometry of the 3+ ions confirms the presence of hydroxyproline on P676 and P1207.

Table S2. Multivariate survival analysis of the G9a-suppressed gene signature in KMPlotter, ROCK and TCGA datasets. KM Plotter (2010 version) Covariate P HR 95% CI of HR G9a-supressed genes (1st quartile) <0.0001 2.5262 1.7039 to 3.7453 G9a-supressed genes (2nd quartile) 0.1755 1.3096 0.8882 to 1.9307 G9a-supressed genes (3rd quartile) 0.3526 1.2075 0.8130 to 1.7935 Size (1: < 2cm, 2: > 2 cm) 0.0002 0.5604 0.4129 to 0.7606 Grade (1, 2, 3) 0.0212 0.6196 0.4132 to 0.9292 Age (1: <50, 2: >50) 0.0312 1.3801 1.0311 to 1.8473 Lymph node status (1: +, 0: -) 0.1134 1.3102 0.9393 to 1.8274 ER status (0: ER -, 1: ER +) 0.1814 1.2697 0.8961 to 1.7991 ROCK dataset Covariate P HR 95% CI of HR G9a-supressed genes (1st quartile) 0.0334 3.6594 1.1140 to 12.0211 G9a-supressed genes (2nd quartile) 0.0444 2.9571 1.0329 to 8.4657 G9a-supressed genes (3rd quartile) 0.4367 1.6004 0.4923 to 5.2028 Size (1: < 2cm, 2: > 2 cm) 0.4438 1.8996 0.3707 to 9.7341 Grade (1, 2, 3) 0.41 1.4647 0.5936 to 3.6139 Age (1: <50, 2: >50) 0.1905 1.7437 0.7617 to 3.9917 Lymph node status (1: +, 0: -) 0.7009 0.8602 0.4006 to 1.8475 ER status (0: ER -, 1: ER +) 0.9151 0.9413 0.3114 to 2.8451 PR status (0: PR -, 1: PR +) 0.7668 1.1796 0.3981 to 3.4950 PAM50 subtypes 0.9715 0.9751 0.2470 to 3.8500 TCGA RNA-seq Covariate P HR 95% CI of HR G9a-supressed genes (1st quartile) 0.0177 1.9991 1.1958 to 6.2210 G9a-supressed genes (2nd quartile) 0.8947 1.0918 0.2995 to 3.9803 G9a-supressed genes (4th quartile) 0.4017 0.5287 0.1201 to 2.3271 Size (1: < 2cm, 2: > 2 cm) 0.1079 2.2867 0.8385 to 6.2358 Age (1: <50, 2: >50) 0.6967 0.8201 0.3040 to 2.2120 Lymph node status (1: +, 0: -) 0.1325 0.4818 0.1870 to 1.2415 ER status (0: ER -, 1: ER +) 0.1197 0.3566 0.0979 to 1.2984 PR status (0: PR -, 1: PR +) 0.2234 2.2775 0.6095 to 8.5106 HER2 status (0: HER2 -, 1: HER2 +) 0.9936 0.9918 0.1376 to 7.1491 PAM50 subtypes 0.6882 1.4536 0.2361 to 8.9488 The G9a-suppressed gene signature associates with the survival of breast cancer patients. Three gene expression data sets were used to validate the G9a-suppressed gene signature as a predictor of survival. Abbreviations: HR, hazard ration; CI, confidence interval; ER, estrogen receptor. All variables were included in the multivariate Cox proportional hazards model analysis.