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 20045 genes and 12 clinical features across 323 samples, statistically thresholded by Q value < 0.05, 10 clinical features related to at least one genes.

  • 20 genes correlated to 'Time to Death'.

    • LARP1 ,  LOC650368 ,  ZNF195__1 ,  RRAGD ,  RAD52 ,  ...

  • 6 genes correlated to 'AGE'.

    • KIAA1143 ,  KIF15 ,  HCG11 ,  ACN9 ,  ZYG11A ,  ...

  • 131 genes correlated to 'GENDER'.

    • KIF4B ,  EIF4A1 ,  SNORA48 ,  RNASEH2C ,  FRG1B ,  ...

  • 97 genes correlated to 'HISTOLOGICAL.TYPE'.

    • MURC ,  DNAJC25-GNG10__1 ,  GNG10 ,  KRT39 ,  KAT5 ,  ...

  • 69 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • IFI16 ,  GHR ,  C13ORF16 ,  ZNF642 ,  NT5E ,  ...

  • 2 genes correlated to 'NUMBERPACKYEARSSMOKED'.

    • FAM128A__1 ,  LOC150776__1

  • 190 genes correlated to 'DISTANT.METASTASIS'.

    • PPP2R5B ,  C8ORF55 ,  RB1CC1 ,  HOMER3 ,  CCDC102A ,  ...

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

    • PIGG ,  ATG3 ,  SLC35A5 ,  APOBEC3B ,  CLVS1 ,  ...

  • 114 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • ANKIB1 ,  KRIT1 ,  FAM177A1 ,  CWC22 ,  SLC6A16 ,  ...

  • 10 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • MRPS14 ,  ASNA1 ,  PCSK4__1 ,  REEP6__1 ,  ZNF546 ,  ...

  • No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', and 'YEAROFTOBACCOSMOKINGONSET'.

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=20 shorter survival N=10 longer survival N=10
AGE Spearman correlation test N=6 older N=6 younger N=0
GENDER t test N=131 male N=10 female N=121
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=97        
RADIATIONS RADIATION REGIMENINDICATION t test N=69 yes N=45 no N=24
NUMBERPACKYEARSSMOKED Spearman correlation test N=2 higher numberpackyearssmoked N=0 lower numberpackyearssmoked N=2
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
DISTANT METASTASIS ANOVA test N=190        
LYMPH NODE METASTASIS ANOVA test N=9        
COMPLETENESS OF RESECTION ANOVA test N=114        
NEOPLASM DISEASESTAGE ANOVA test N=10        
Clinical variable #1: 'Time to Death'

20 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-224 (median=6.6)
  censored N = 224
  death N = 66
     
  Significant markers N = 20
  associated with shorter survival 10
  associated with longer survival 10
List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
LARP1 0.01 4.558e-08 0.00091 0.302
LOC650368 0 1.003e-07 0.002 0.355
ZNF195__1 0 1.003e-07 0.002 0.355
RRAGD 261 1.068e-07 0.0021 0.636
RAD52 0 2.649e-07 0.0053 0.334
ZNF117 0 1.027e-06 0.021 0.409
CLSTN3 491 1.188e-06 0.024 0.552
RBP5 491 1.188e-06 0.024 0.552
RFTN1 16 1.39e-06 0.028 0.677
DPH2 67001 1.455e-06 0.029 0.579

Figure S1.  Get High-res Image As an example, this figure shows the association of LARP1 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 4.56e-08 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

6 genes related to 'AGE'.

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

AGE Mean (SD) 65.14 (9.9)
  Significant markers N = 6
  pos. correlated 6
  neg. correlated 0
List of 6 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 6 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
KIAA1143 0.3127 4.089e-08 0.00082
KIF15 0.3127 4.089e-08 0.00082
HCG11 0.2996 1.563e-07 0.00313
ACN9 0.2971 2.101e-07 0.00421
ZYG11A 0.2849 6.471e-07 0.013
NHLRC1 0.2808 9.519e-07 0.0191

Figure S2.  Get High-res Image As an example, this figure shows the association of KIAA1143 to 'AGE'. P value = 4.09e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

131 genes related to 'GENDER'.

Table S5.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 175
  MALE 148
     
  Significant markers N = 131
  Higher in MALE 10
  Higher in FEMALE 121
List of top 10 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -12.98 9.502e-31 1.9e-26 0.8505
EIF4A1 -9.96 3.437e-20 6.89e-16 0.8029
SNORA48 -9.96 3.437e-20 6.89e-16 0.8029
RNASEH2C 8.85 6.282e-17 1.26e-12 0.7444
FRG1B -7.8 1.057e-13 2.12e-09 0.7548
CCDC121__1 -7.56 5.776e-13 1.16e-08 0.7188
GPN1__1 -7.56 5.776e-13 1.16e-08 0.7188
MACC1 7.26 2.971e-12 5.95e-08 0.7255
COX7C -7.05 1.108e-11 2.22e-07 0.7124
KRT6A -6.93 2.615e-11 5.24e-07 0.7135

Figure S3.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 9.5e-31 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 74.55 (32)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

97 genes related to 'HISTOLOGICAL.TYPE'.

Table S8.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG ACINAR ADENOCARCINOMA 11
  LUNG ADENOCARCINOMA MIXED SUBTYPE 64
  LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 195
  LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS 4
  LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS 17
  LUNG CLEAR CELL ADENOCARCINOMA 1
  LUNG MICROPAPILLARY ADENOCARCINOMA 2
  LUNG MUCINOUS ADENOCARCINOMA 2
  LUNG PAPILLARY ADENOCARCINOMA 17
  LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA 3
  MUCINOUS (COLLOID) CARCINOMA 7
     
  Significant markers N = 97
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
MURC 1.108e-34 2.22e-30
DNAJC25-GNG10__1 3.395e-33 6.8e-29
GNG10 3.395e-33 6.8e-29
KRT39 1.078e-27 2.16e-23
KAT5 1.296e-25 2.6e-21
MED17 1.287e-23 2.58e-19
PDCD11__1 3.856e-19 7.73e-15
USMG5__1 3.856e-19 7.73e-15
GOPC 6.496e-18 1.3e-13
C8ORF42 1.708e-16 3.42e-12

Figure S4.  Get High-res Image As an example, this figure shows the association of MURC to 'HISTOLOGICAL.TYPE'. P value = 1.11e-34 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

69 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S10.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 15
  YES 308
     
  Significant markers N = 69
  Higher in YES 45
  Higher in NO 24
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
IFI16 7.87 1.05e-12 2.11e-08 0.6755
GHR 7.08 1.744e-11 3.5e-07 0.7102
C13ORF16 -6.83 1.484e-10 2.97e-06 0.7379
ZNF642 6.32 9.739e-10 1.95e-05 0.6656
NT5E 6.22 2.103e-09 4.22e-05 0.5344
ARL6IP1 -7.61 2.199e-09 4.41e-05 0.7216
EPHA4 6.48 2.231e-09 4.47e-05 0.5366
HIST1H4J 6.16 3.318e-09 6.65e-05 0.6727
GALNT14 5.92 8.561e-09 0.000172 0.6352
SLC16A12 -7.49 1.397e-08 0.00028 0.7641

Figure S5.  Get High-res Image As an example, this figure shows the association of IFI16 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.05e-12 with T-test analysis.

Clinical variable #7: 'NUMBERPACKYEARSSMOKED'

2 genes related to 'NUMBERPACKYEARSSMOKED'.

Table S12.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 40.62 (27)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S13.  Get Full Table List of 2 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
FAM128A__1 -0.3103 2.188e-06 0.0439
LOC150776__1 -0.3103 2.188e-06 0.0439

Figure S6.  Get High-res Image As an example, this figure shows the association of FAM128A__1 to 'NUMBERPACKYEARSSMOKED'. P value = 2.19e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #8: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S14.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1965.18 (12)
  Significant markers N = 0
Clinical variable #9: 'DISTANT.METASTASIS'

190 genes related to 'DISTANT.METASTASIS'.

Table S15.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 200
  M1 11
  M1A 1
  M1B 3
  MX 104
     
  Significant markers N = 190
List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

Table S16.  Get Full Table List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
PPP2R5B 5.205e-76 1.04e-71
C8ORF55 1.454e-49 2.91e-45
RB1CC1 5.354e-33 1.07e-28
HOMER3 4.484e-32 8.99e-28
CCDC102A 1.107e-25 2.22e-21
THEM4 4.483e-25 8.98e-21
KILLIN 9.37e-25 1.88e-20
PTEN 9.37e-25 1.88e-20
POLR2J2 7.621e-24 1.53e-19
B3GNT2 1.363e-23 2.73e-19

Figure S7.  Get High-res Image As an example, this figure shows the association of PPP2R5B to 'DISTANT.METASTASIS'. P value = 5.2e-76 with ANOVA analysis.

Clinical variable #10: 'LYMPH.NODE.METASTASIS'

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

Table S17.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 207
  N1 58
  N2 49
  N3 1
  NX 6
     
  Significant markers N = 9
List of 9 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S18.  Get Full Table List of 9 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
PIGG 1.149e-42 2.3e-38
ATG3 1.523e-37 3.05e-33
SLC35A5 1.523e-37 3.05e-33
APOBEC3B 4.145e-15 8.31e-11
CLVS1 1.403e-14 2.81e-10
FLJ30679 1.015e-07 0.00203
MTHFSD 1.015e-07 0.00203
RWDD2B 1.732e-06 0.0347
ANP32B 2.063e-06 0.0413

Figure S8.  Get High-res Image As an example, this figure shows the association of PIGG to 'LYMPH.NODE.METASTASIS'. P value = 1.15e-42 with ANOVA analysis.

Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

114 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S19.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 186
  R1 7
  R2 1
  RX 12
     
  Significant markers N = 114
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S20.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
ANKIB1 1.472e-70 2.95e-66
KRIT1 1.472e-70 2.95e-66
FAM177A1 9.573e-54 1.92e-49
CWC22 2.143e-53 4.29e-49
SLC6A16 2.272e-53 4.55e-49
ZBTB1 2.804e-45 5.62e-41
ZBTB25 2.804e-45 5.62e-41
CDK13 1.938e-37 3.88e-33
THRAP3 3.129e-30 6.27e-26
ZNF619 2.399e-29 4.81e-25

Figure S9.  Get High-res Image As an example, this figure shows the association of ANKIB1 to 'COMPLETENESS.OF.RESECTION'. P value = 1.47e-70 with ANOVA analysis.

Clinical variable #12: 'NEOPLASM.DISEASESTAGE'

10 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S21.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 3
  STAGE IA 81
  STAGE IB 92
  STAGE IIA 29
  STAGE IIB 42
  STAGE IIIA 49
  STAGE IIIB 9
  STAGE IV 16
     
  Significant markers N = 10
List of 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S22.  Get Full Table List of 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
MRPS14 1.011e-11 2.03e-07
ASNA1 1.29e-09 2.58e-05
PCSK4__1 2.112e-09 4.23e-05
REEP6__1 2.112e-09 4.23e-05
ZNF546 9.782e-09 0.000196
PAFAH1B3__1 1.291e-08 0.000259
PRR19__1 1.291e-08 0.000259
TMED2 1.397e-08 0.00028
RNF187 5.127e-08 0.00103
GBAP1 2.434e-07 0.00488

Figure S10.  Get High-res Image As an example, this figure shows the association of MRPS14 to 'NEOPLASM.DISEASESTAGE'. P value = 1.01e-11 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = LUAD-TP.meth.by_min_expr_corr.data.txt

  • Clinical data file = LUAD-TP.clin.merged.picked.txt

  • Number of patients = 323

  • Number of genes = 20045

  • Number of clinical features = 12

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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