Correlation between gene methylation status and clinical features
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 17159 genes and 7 clinical features across 142 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • NR4A3

  • 13 genes correlated to 'AGE'.

    • NUP155 ,  NIPAL2 ,  ITGA8 ,  HAPLN1 ,  STBD1 ,  ...

  • 95 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • NT5M ,  IGSF6 ,  CLECL1 ,  LTA ,  ANKRD13A ,  ...

  • 1 gene correlated to 'GENDER'.

    • UTP14C

  • 309 genes correlated to 'DISTANT.METASTASIS'.

    • CCNG1 ,  IFT57 ,  FAM65C ,  LMF1 ,  POLE4 ,  ...

  • 43 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • CCDC25 ,  KIF20A ,  NGLY1 ,  MBIP ,  TMEM184B ,  ...

  • 10 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • UBE2I ,  PIM3 ,  FUT11 ,  RAB11FIP4 ,  ELP2 ,  ...

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1.  Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test N=1 shorter survival N=0 longer survival N=1
AGE Spearman correlation test N=13 older N=13 younger N=0
PRIMARY SITE OF DISEASE t test N=95 regional lymph node N=33 regional cutaneous or subcutaneous tissue (includes satellite and in-transit metastasis) N=62
GENDER t test N=1 male N=1 female N=0
DISTANT METASTASIS ANOVA test N=309        
LYMPH NODE METASTASIS ANOVA test N=43        
NEOPLASM DISEASESTAGE ANOVA test N=10        
Clinical variable #1: 'Time to Death'

One gene related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 2.6-346 (median=47.5)
  censored N = 74
  death N = 66
     
  Significant markers N = 1
  associated with shorter survival 0
  associated with longer survival 1
List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
NR4A3 0.05 1.231e-06 0.021 0.319

Figure S1.  Get High-res Image As an example, this figure shows the association of NR4A3 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.23e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

13 genes related to 'AGE'.

Table S3.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 55.8 (16)
  Significant markers N = 13
  pos. correlated 13
  neg. correlated 0
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
NUP155 0.4438 3.169e-08 0.000544
NIPAL2 0.4291 9.914e-08 0.0017
ITGA8 0.4084 4.512e-07 0.00774
HAPLN1 0.3992 8.589e-07 0.0147
STBD1 0.3945 1.182e-06 0.0203
FAS 0.3935 1.272e-06 0.0218
TRPV4 0.392 1.404e-06 0.0241
POSTN 0.3895 1.663e-06 0.0285
BBX 0.3894 1.673e-06 0.0287
PTX3 0.3864 2.034e-06 0.0349

Figure S2.  Get High-res Image As an example, this figure shows the association of NUP155 to 'AGE'. P value = 3.17e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

95 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S5.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) 32
  REGIONAL LYMPH NODE 110
     
  Significant markers N = 95
  Higher in REGIONAL LYMPH NODE 33
  Higher in REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) 62
List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

T(pos if higher in 'REGIONAL LYMPH NODE') ttestP Q AUC
NT5M 7.59 4.503e-12 7.73e-08 0.7778
IGSF6 -7.13 4.971e-11 8.53e-07 0.7557
CLECL1 -6.58 8.669e-10 1.49e-05 0.719
LTA -6.56 9.814e-10 1.68e-05 0.7426
ANKRD13A -6.54 1.224e-09 2.1e-05 0.733
ISCU -6.63 1.345e-09 2.31e-05 0.7534
DCLRE1C -6.43 1.971e-09 3.38e-05 0.7384
IL4I1 -6.31 3.794e-09 6.51e-05 0.7347
ABTB1 -6.35 3.847e-09 6.6e-05 0.7739
TAP1 -6.29 9.945e-09 0.000171 0.7611

Figure S3.  Get High-res Image As an example, this figure shows the association of NT5M to 'PRIMARY.SITE.OF.DISEASE'. P value = 4.5e-12 with T-test analysis.

Clinical variable #4: 'GENDER'

One gene related to 'GENDER'.

Table S7.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 50
  MALE 92
     
  Significant markers N = 1
  Higher in MALE 1
  Higher in FEMALE 0
List of one gene differentially expressed by 'GENDER'

Table S8.  Get Full Table List of one gene differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
UTP14C 10.06 3.89e-14 6.67e-10 0.9387

Figure S4.  Get High-res Image As an example, this figure shows the association of UTP14C to 'GENDER'. P value = 3.89e-14 with T-test analysis.

Clinical variable #5: 'DISTANT.METASTASIS'

309 genes related to 'DISTANT.METASTASIS'.

Table S9.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 123
  M1 1
  M1A 2
  M1B 2
  M1C 1
     
  Significant markers N = 309
List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
CCNG1 6.583e-116 1.13e-111
IFT57 9.002e-21 1.54e-16
FAM65C 2.861e-20 4.91e-16
LMF1 1.076e-17 1.85e-13
POLE4 1.463e-17 2.51e-13
CASC5 3.479e-17 5.97e-13
COL5A1 3.599e-17 6.17e-13
C10ORF88 4.154e-16 7.12e-12
PXK 4.773e-16 8.19e-12
MAN2A1 5.262e-16 9.02e-12

Figure S5.  Get High-res Image As an example, this figure shows the association of CCNG1 to 'DISTANT.METASTASIS'. P value = 6.58e-116 with ANOVA analysis.

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

43 genes related to 'LYMPH.NODE.METASTASIS'.

Table S11.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 76
  N1 2
  N1A 5
  N1B 11
  N2 1
  N2A 4
  N2B 10
  N2C 4
  N3 15
  NX 2
     
  Significant markers N = 43
List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
CCDC25 3.889e-140 6.67e-136
KIF20A 3.319e-67 5.69e-63
NGLY1 4.605e-59 7.9e-55
MBIP 2.013e-56 3.45e-52
TMEM184B 2.829e-35 4.85e-31
C6ORF162 1.001e-33 1.72e-29
C17ORF63 2.596e-24 4.45e-20
ASAP3 6.556e-20 1.12e-15
NOS1 1.416e-18 2.43e-14
CSRP2BP 2.935e-18 5.03e-14

Figure S6.  Get High-res Image As an example, this figure shows the association of CCDC25 to 'LYMPH.NODE.METASTASIS'. P value = 3.89e-140 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

10 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S13.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 16
  STAGE IA 8
  STAGE IB 13
  STAGE II 17
  STAGE IIA 7
  STAGE IIB 8
  STAGE IIC 4
  STAGE III 7
  STAGE IIIA 4
  STAGE IIIB 15
  STAGE IIIC 20
  STAGE IV 4
     
  Significant markers N = 10
List of 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S14.  Get Full Table List of 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
UBE2I 3.514e-20 6.03e-16
PIM3 2.913e-10 5e-06
FUT11 1.347e-08 0.000231
RAB11FIP4 1.223e-07 0.0021
ELP2 4.445e-07 0.00763
SLC39A6 4.445e-07 0.00763
JAM2 1.186e-06 0.0203
MME 1.36e-06 0.0233
RPL38 1.496e-06 0.0257
KDELC1 2.225e-06 0.0382

Figure S7.  Get High-res Image As an example, this figure shows the association of UBE2I to 'NEOPLASM.DISEASESTAGE'. P value = 3.51e-20 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = SKCM-Regional_Metastatic.meth.for_correlation.filtered_data.txt

  • Clinical data file = SKCM-Regional_Metastatic.clin.merged.picked.txt

  • Number of patients = 142

  • Number of genes = 17159

  • Number of clinical features = 7

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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