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 17543 genes and 14 clinical features across 130 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • GRIK4

  • 2 genes correlated to 'GENDER'.

    • KIF4B ,  DDX43

  • 136 genes correlated to 'HISTOLOGICAL.TYPE'.

    • CAB39L ,  SETDB2 ,  SLC27A1 ,  CHMP4A ,  BSDC1 ,  ...

  • 17 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • CR2 ,  AGPS ,  PECR ,  LRP2 ,  L3MBTL2 ,  ...

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

    • PHACTR3 ,  NCR1 ,  SYT6 ,  NTRK3 ,  B3GAT1 ,  ...

  • 13 genes correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.

    • TP53I3 ,  AP1S3 ,  PCNP ,  MOAP1 ,  BNIP2 ,  ...

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

    • DDX54 ,  KLRA1 ,  MYO1C ,  C20ORF117 ,  WDR81 ,  ...

  • No genes correlated to 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'NUMBERPACKYEARSSMOKED', 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=1 shorter survival N=1 longer survival N=0
AGE Spearman correlation test   N=0        
GENDER t test N=2 male N=0 female N=2
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=136        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=17        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=43 yes N=42 no N=1
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
TOBACCOSMOKINGHISTORYINDICATOR ANOVA test N=13        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=752        
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) 0-173.8 (median=19.8)
  censored N = 64
  death N = 53
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
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
GRIK4 4201 1.969e-06 0.035 0.598

Figure S1.  Get High-res Image As an example, this figure shows the association of GRIK4 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.97e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 69.16 (8.7)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

2 genes related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 34
  MALE 96
     
  Significant markers N = 2
  Higher in MALE 0
  Higher in FEMALE 2
List of 2 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 2 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -7.44 5.141e-10 9.02e-06 0.8747
DDX43 -5.74 1.158e-07 0.00203 0.7981

Figure S2.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 5.14e-10 with T-test analysis.

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

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

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 25.86 (39)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

136 genes related to 'HISTOLOGICAL.TYPE'.

Table S7.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 3
  LUNG PAPILLARY SQUAMOUS CELL CARCINOMA 1
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 125
     
  Significant markers N = 136
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
CAB39L 1.469e-74 2.58e-70
SETDB2 1.469e-74 2.58e-70
SLC27A1 1.206e-68 2.12e-64
CHMP4A 3.983e-49 6.99e-45
BSDC1 4.719e-49 8.28e-45
EP300 1.916e-33 3.36e-29
FAM168B 7.314e-33 1.28e-28
UQCRB 2.036e-26 3.57e-22
MUTED 3.652e-25 6.4e-21
NACC2 7.726e-25 1.35e-20

Figure S3.  Get High-res Image As an example, this figure shows the association of CAB39L to 'HISTOLOGICAL.TYPE'. P value = 1.47e-74 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.92 (0.69)
  N
  T1 33
  T2 79
  T3 14
  T4 4
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.43 (0.64)
  N
  N0 83
  N1 36
  N2 10
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

17 genes related to 'PATHOLOGICSPREAD(M)'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 109
  M1 1
  MX 18
     
  Significant markers N = 17
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
CR2 1.944e-45 3.41e-41
AGPS 8.938e-17 1.57e-12
PECR 1.195e-12 2.1e-08
LRP2 1.366e-11 2.4e-07
L3MBTL2 2.615e-11 4.59e-07
TACR1 2.185e-09 3.83e-05
ZNF30 9.047e-09 0.000159
TRMT1 3.422e-08 6e-04
POLR2E 7.75e-08 0.00136
IVD 1.141e-07 0.002

Figure S4.  Get High-res Image As an example, this figure shows the association of CR2 to 'PATHOLOGICSPREAD(M)'. P value = 1.94e-45 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.65 (0.77)
  N
  Stage 1 67
  Stage 2 39
  Stage 3 20
  Stage 4 1
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S14.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 7
  YES 123
     
  Significant markers N = 43
  Higher in YES 42
  Higher in NO 1
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
PHACTR3 9.65 1.331e-12 2.33e-08 0.8873
NCR1 8.41 2.032e-12 3.56e-08 0.863
SYT6 7.17 7.395e-11 1.3e-06 0.7468
NTRK3 7.23 4.265e-10 7.48e-06 0.7991
B3GAT1 7.19 1.393e-09 2.44e-05 0.8397
STK38 7.18 6.021e-09 0.000106 0.8406
PAPPA 6.3 6.271e-09 0.00011 0.7666
C16ORF48 6.2 7.778e-09 0.000136 0.5889
ADRA2C 6.27 9.35e-09 0.000164 0.7364
GPR123 6.09 1.223e-08 0.000215 0.7712

Figure S5.  Get High-res Image As an example, this figure shows the association of PHACTR3 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.33e-12 with T-test analysis.

Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S16.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 52.5 (33)
  Significant markers N = 0
Clinical variable #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

13 genes related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Table S17.  Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'

TOBACCOSMOKINGHISTORYINDICATOR Labels N
  CURRENT REFORMED SMOKER FOR < OR = 15 YEARS 64
  CURRENT REFORMED SMOKER FOR > 15 YEARS 29
  CURRENT SMOKER 27
  LIFELONG NON-SMOKER 6
     
  Significant markers N = 13
List of top 10 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

Table S18.  Get Full Table List of top 10 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

ANOVA_P Q
TP53I3 5.18e-10 9.09e-06
AP1S3 1.163e-07 0.00204
PCNP 1.188e-07 0.00208
MOAP1 5.538e-07 0.00971
BNIP2 5.848e-07 0.0103
BBX 6.291e-07 0.011
SLC35A1 8.139e-07 0.0143
PRND 8.619e-07 0.0151
C1ORF174 9.242e-07 0.0162
ATAD1 1.044e-06 0.0183

Figure S6.  Get High-res Image As an example, this figure shows the association of TP53I3 to 'TOBACCOSMOKINGHISTORYINDICATOR'. P value = 5.18e-10 with ANOVA analysis.

Clinical variable #13: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S19.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1956.38 (11)
  Significant markers N = 0
Clinical variable #14: 'COMPLETENESS.OF.RESECTION'

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

Table S20.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 93
  R1 2
  R2 2
  RX 7
     
  Significant markers N = 752
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
DDX54 5.332e-17 9.35e-13
KLRA1 2.081e-15 3.65e-11
MYO1C 4.112e-15 7.21e-11
C20ORF117 4.441e-15 7.79e-11
WDR81 5.162e-15 9.05e-11
SGSM2 6.237e-15 1.09e-10
CABLES2 6.617e-15 1.16e-10
PITPNM1 6.631e-15 1.16e-10
LPPR3 6.888e-15 1.21e-10
ASB16 7.194e-15 1.26e-10

Figure S7.  Get High-res Image As an example, this figure shows the association of DDX54 to 'COMPLETENESS.OF.RESECTION'. P value = 5.33e-17 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = LUSC-TP.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 130

  • Number of genes = 17543

  • Number of clinical features = 14

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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