Lung Adenocarcinoma: Correlation between gene methylation status and clinical features
Maintained by Juok Cho (Broad Institute)
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 17374 genes and 11 clinical features across 194 samples, statistically thresholded by Q value < 0.05, 9 clinical features related to at least one genes.

  • 17 genes correlated to 'Time to Death'.

    • ANO9 ,  MARVELD1 ,  FUT1 ,  LARP1 ,  IBSP ,  ...

  • 2 genes correlated to 'AGE'.

    • MRPS33 ,  MYST3

  • 19 genes correlated to 'GENDER'.

    • CCNYL1 ,  TBCA ,  RNASEH2C ,  EIF4A1 ,  GPN1 ,  ...

  • 71 genes correlated to 'HISTOLOGICAL.TYPE'.

    • MGST2 ,  PGAP1 ,  GOPC ,  MLLT1 ,  UBXN8 ,  ...

  • 2 genes correlated to 'PATHOLOGY.T'.

    • PCDHGC4 ,  HDAC5

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

    • CARD6 ,  VEZT ,  PAPSS1 ,  C2ORF73 ,  C3ORF58

  • 2 genes correlated to 'TUMOR.STAGE'.

    • PCDHGC4 ,  NKX2-8

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

    • SLC25A28 ,  NDUFA6 ,  ZNF506 ,  KIAA0495 ,  TRNP1 ,  ...

  • 3 genes correlated to 'NEOADJUVANT.THERAPY'.

    • CPPED1 ,  ADRB2 ,  LOC100192426

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

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=17 shorter survival N=6 longer survival N=11
AGE Spearman correlation test N=2 older N=2 younger N=0
GENDER t test N=19 male N=5 female N=14
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=71        
PATHOLOGY T Spearman correlation test N=2 higher pT N=1 lower pT N=1
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=5        
TUMOR STAGE Spearman correlation test N=2 higher stage N=1 lower stage N=1
RADIATIONS RADIATION REGIMENINDICATION t test N=11 yes N=7 no N=4
NEOADJUVANT THERAPY t test N=3 yes N=2 no N=1
Clinical variable #1: 'Time to Death'

17 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-224 (median=13.9)
  censored N = 110
  death N = 57
     
  Significant markers N = 17
  associated with shorter survival 6
  associated with longer survival 11
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
ANO9 3301 9.859e-10 1.7e-05 0.676
MARVELD1 0 2.49e-08 0.00043 0.332
FUT1 13001 2.844e-08 0.00049 0.655
LARP1 0 2.85e-08 5e-04 0.28
IBSP 0 9.364e-08 0.0016 0.373
RRAGD 231 4.737e-07 0.0082 0.626
CD109 0.01 5.283e-07 0.0092 0.342
SLC10A6 0 6.358e-07 0.011 0.398
MTTP 5.8e+22 8.186e-07 0.014 0.595
TM4SF19 0.05 1.317e-06 0.023 0.329

Figure S1.  Get High-res Image As an example, this figure shows the association of ANO9 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 9.86e-10 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

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

AGE Mean (SD) 65.34 (9.9)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
MRPS33 0.4434 2.195e-09 3.81e-05
MYST3 0.3696 9.575e-07 0.0166

Figure S2.  Get High-res Image As an example, this figure shows the association of MRPS33 to 'AGE'. P value = 2.19e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

19 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 107
  MALE 87
     
  Significant markers N = 19
  Higher in MALE 5
  Higher in FEMALE 14
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
CCNYL1 -19.11 1.765e-43 3.07e-39 0.9516
TBCA -7.63 1.304e-12 2.27e-08 0.7845
RNASEH2C 7.54 1.841e-12 3.2e-08 0.7747
EIF4A1 -7.52 3.353e-12 5.82e-08 0.8008
GPN1 -6.94 9.319e-11 1.62e-06 0.7601
DKFZP434L187 6.53 5.857e-10 1.02e-05 0.8454
FRG1B -6.27 2.928e-09 5.09e-05 0.7454
GPX1 -6.04 1.15e-08 2e-04 0.7633
MC5R -6.07 1.261e-08 0.000219 0.7331
MACC1 5.83 2.298e-08 0.000399 0.73

Figure S3.  Get High-res Image As an example, this figure shows the association of CCNYL1 to 'GENDER'. P value = 1.76e-43 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) 65.38 (39)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

71 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  LUNG ACINAR ADENOCARCINOMA 3
  LUNG ADENOCARCINOMA MIXED SUBTYPE 43
  LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 120
  LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS 3
  LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS 8
  LUNG CLEAR CELL ADENOCARCINOMA 1
  LUNG MICROPAPILLARY ADENOCARCINOMA 1
  LUNG MUCINOUS ADENOCARCINOMA 2
  LUNG PAPILLARY ADENOCARCINOMA 9
  LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA 1
  MUCINOUS (COLLOID) ADENOCARCINOMA 3
     
  Significant markers N = 71
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
MGST2 3.447e-69 5.99e-65
PGAP1 6.623e-31 1.15e-26
GOPC 2.383e-25 4.14e-21
MLLT1 3.179e-23 5.52e-19
UBXN8 1.476e-17 2.56e-13
KRT39 4.486e-17 7.79e-13
C8ORF42 1.947e-14 3.38e-10
MED17 1.085e-13 1.88e-09
LOC440354 4.809e-13 8.35e-09
ADAM17 1.472e-12 2.56e-08

Figure S4.  Get High-res Image As an example, this figure shows the association of MGST2 to 'HISTOLOGICAL.TYPE'. P value = 3.45e-69 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

2 genes related to 'PATHOLOGY.T'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.93 (0.75)
  N
  T1 50
  T2 117
  T3 15
  T4 11
     
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S11.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
PCDHGC4 0.3569 3.48e-07 0.00605
HDAC5 -0.3442 9.519e-07 0.0165

Figure S5.  Get High-res Image As an example, this figure shows the association of PCDHGC4 to 'PATHOLOGY.T'. P value = 3.48e-07 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S12.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.63 (0.8)
  N
  N0 110
  N1 41
  N2 39
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

5 genes related to 'PATHOLOGICSPREAD(M)'.

Table S13.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 131
  M1 9
  MX 48
     
  Significant markers N = 5
List of 5 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S14.  Get Full Table List of 5 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
CARD6 2.647e-07 0.0046
VEZT 9.764e-07 0.017
PAPSS1 1.618e-06 0.0281
C2ORF73 1.63e-06 0.0283
C3ORF58 1.995e-06 0.0347

Figure S6.  Get High-res Image As an example, this figure shows the association of CARD6 to 'PATHOLOGICSPREAD(M)'. P value = 2.65e-07 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

2 genes related to 'TUMOR.STAGE'.

Table S15.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.8 (0.93)
  N
  Stage 1 96
  Stage 2 42
  Stage 3 43
  Stage 4 8
     
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S16.  Get Full Table List of 2 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
PCDHGC4 0.3912 2.617e-08 0.000455
NKX2-8 -0.3414 1.527e-06 0.0265

Figure S7.  Get High-res Image As an example, this figure shows the association of PCDHGC4 to 'TUMOR.STAGE'. P value = 2.62e-08 with Spearman correlation analysis.

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S17.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 13
  YES 181
     
  Significant markers N = 11
  Higher in YES 7
  Higher in NO 4
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S18.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
SLC25A28 8.26 2.141e-09 3.72e-05 0.8445
NDUFA6 -6.05 6.218e-08 0.00108 0.6838
ZNF506 5.53 1.038e-07 0.0018 0.5695
KIAA0495 5.41 1.921e-07 0.00334 0.5674
TRNP1 -5.52 2.154e-07 0.00374 0.5665
LYPLA2P1 -5.54 3.147e-07 0.00547 0.6239
SLCO4C1 5.14 7.136e-07 0.0124 0.7637
C12ORF62 5.74 9.944e-07 0.0173 0.7667
TRAPPC5 4.91 1.938e-06 0.0337 0.6286
LENG8 -5.04 2.038e-06 0.0354 0.541

Figure S8.  Get High-res Image As an example, this figure shows the association of SLC25A28 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.14e-09 with T-test analysis.

Clinical variable #11: 'NEOADJUVANT.THERAPY'

3 genes related to 'NEOADJUVANT.THERAPY'.

Table S19.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 29
  YES 165
     
  Significant markers N = 3
  Higher in YES 2
  Higher in NO 1
List of 3 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S20.  Get Full Table List of 3 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
CPPED1 5.54 1.06e-07 0.00184 0.6318
ADRB2 5.09 9.486e-07 0.0165 0.6171
LOC100192426 -5.06 1.049e-06 0.0182 0.6063

Figure S9.  Get High-res Image As an example, this figure shows the association of CPPED1 to 'NEOADJUVANT.THERAPY'. P value = 1.06e-07 with T-test analysis.

Methods & Data
Input

  • Expresson data file = LUAD.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 194

  • Number of genes = 17374

  • Number of clinical features = 11

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.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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