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 17375 genes and 11 clinical features across 218 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

  • 15 genes correlated to 'Time to Death'.

    • MARVELD1 ,  FUT1 ,  LARP1 ,  LOC650368 ,  RRAGD ,  ...

  • 21 genes correlated to 'GENDER'.

    • CCNYL1 ,  KIF4B ,  RNASEH2C ,  EIF4A1 ,  FRG1B ,  ...

  • 3 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • KRT72 ,  TM7SF4 ,  KCNB2

  • 78 genes correlated to 'HISTOLOGICAL.TYPE'.

    • USMG5 ,  GOPC ,  PGAP1 ,  MURC ,  GNG10 ,  ...

  • 1 gene correlated to 'PATHOLOGY.N'.

    • KLHDC9

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

    • C2ORF73 ,  CARD6 ,  POT1 ,  ZSCAN20 ,  FAM190A ,  ...

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

    • TRNP1 ,  KIAA0495 ,  SLCO4C1 ,  LYPLA2P1 ,  ZNF642 ,  ...

  • 1 gene correlated to 'NEOADJUVANT.THERAPY'.

    • LOC100192426

  • No genes correlated to 'AGE', 'PATHOLOGY.T', and 'TUMOR.STAGE'.

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=15 shorter survival N=3 longer survival N=12
AGE Spearman correlation test   N=0        
GENDER t test N=21 male N=5 female N=16
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=3 higher score N=3 lower score N=0
HISTOLOGICAL TYPE ANOVA test N=78        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test N=1 higher pN N=1 lower pN N=0
PATHOLOGICSPREAD(M) ANOVA test N=6        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=12 yes N=8 no N=4
NEOADJUVANT THERAPY t test N=1 yes N=0 no N=1
Clinical variable #1: 'Time to Death'

15 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-224 (median=11.6)
  censored N = 132
  death N = 58
     
  Significant markers N = 15
  associated with shorter survival 3
  associated with longer survival 12
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
MARVELD1 0 1.391e-08 0.00024 0.324
FUT1 10001 3.035e-08 0.00053 0.66
LARP1 0 4.005e-08 7e-04 0.296
LOC650368 0 1.755e-07 0.003 0.339
RRAGD 231 3.394e-07 0.0059 0.63
APOBEC4 0 4.31e-07 0.0075 0.382
IBSP 0 6.325e-07 0.011 0.372
SLC10A6 0 6.677e-07 0.012 0.395
TM4SF19 0.03 8.72e-07 0.015 0.356
CD109 0.01 8.957e-07 0.016 0.344

Figure S1.  Get High-res Image As an example, this figure shows the association of MARVELD1 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.39e-08 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) 65.61 (9.7)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

21 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 118
  MALE 100
     
  Significant markers N = 21
  Higher in MALE 5
  Higher in FEMALE 16
List of top 10 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
CCNYL1 -20.29 3.988e-49 6.93e-45 0.9547
KIF4B -11.35 1.686e-23 2.93e-19 0.8687
RNASEH2C 7.76 3.413e-13 5.93e-09 0.7653
EIF4A1 -7.61 1.264e-12 2.2e-08 0.7886
FRG1B -7.15 1.917e-11 3.33e-07 0.7745
GPN1 -6.77 1.689e-10 2.93e-06 0.7357
ZNF839 -6.29 1.742e-09 3.03e-05 0.7372
SPESP1 -6.21 3.656e-09 6.35e-05 0.7214
COX7C -5.83 2.075e-08 0.00036 0.7136
MACC1 5.73 3.426e-08 0.000595 0.7207

Figure S2.  Get High-res Image As an example, this figure shows the association of CCNYL1 to 'GENDER'. P value = 3.99e-49 with T-test analysis.

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

3 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 72.11 (34)
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S7.  Get Full Table List of 3 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
KRT72 0.8691 1.371e-06 0.0238
TM7SF4 0.8682 1.449e-06 0.0252
KCNB2 0.8601 2.341e-06 0.0407

Figure S3.  Get High-res Image As an example, this figure shows the association of KRT72 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 1.37e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

78 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  LUNG ACINAR ADENOCARCINOMA 6
  LUNG ADENOCARCINOMA MIXED SUBTYPE 49
  LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 131
  LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS 3
  LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS 10
  LUNG CLEAR CELL ADENOCARCINOMA 1
  LUNG MICROPAPILLARY ADENOCARCINOMA 2
  LUNG MUCINOUS ADENOCARCINOMA 2
  LUNG PAPILLARY ADENOCARCINOMA 9
  LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA 1
  MUCINOUS (COLLOID) ADENOCARCINOMA 4
     
  Significant markers N = 78
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
USMG5 9.134e-178 1.59e-173
GOPC 1.272e-97 2.21e-93
PGAP1 9.985e-34 1.73e-29
MURC 1.529e-22 2.66e-18
GNG10 1.015e-21 1.76e-17
UBXN8 3.747e-20 6.51e-16
KRT39 9.274e-19 1.61e-14
BCR 9.524e-18 1.65e-13
KAT5 4.217e-17 7.32e-13
INSIG1 5.761e-16 1e-11

Figure S4.  Get High-res Image As an example, this figure shows the association of USMG5 to 'HISTOLOGICAL.TYPE'. P value = 9.13e-178 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

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

PATHOLOGY.T Mean (SD) 1.91 (0.77)
  N
  T1 62
  T2 125
  T3 17
  T4 13
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

One gene related to 'PATHOLOGY.N'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.58 (0.79)
  N
  N0 130
  N1 43
  N2 41
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S12.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
KLHDC9 0.3147 2.644e-06 0.0459

Figure S5.  Get High-res Image As an example, this figure shows the association of KLHDC9 to 'PATHOLOGY.N'. P value = 2.64e-06 with Spearman correlation analysis.

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

6 genes related to 'PATHOLOGICSPREAD(M)'.

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

PATHOLOGICSPREAD(M) Labels N
  M0 138
  M1 10
  MX 64
     
  Significant markers N = 6
List of 6 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S14.  Get Full Table List of 6 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
C2ORF73 9.555e-08 0.00166
CARD6 6.811e-07 0.0118
POT1 8.332e-07 0.0145
ZSCAN20 1.494e-06 0.026
FAM190A 1.523e-06 0.0265
PAPSS1 2.265e-06 0.0393

Figure S6.  Get High-res Image As an example, this figure shows the association of C2ORF73 to 'PATHOLOGICSPREAD(M)'. P value = 9.56e-08 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

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

TUMOR.STAGE Mean (SD) 1.79 (0.94)
  N
  Stage 1 110
  Stage 2 47
  Stage 3 46
  Stage 4 10
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S16.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 13
  YES 205
     
  Significant markers N = 12
  Higher in YES 8
  Higher in NO 4
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
TRNP1 -5.96 3.286e-08 0.000571 0.57
KIAA0495 5.73 3.434e-08 0.000597 0.5771
SLCO4C1 5.43 1.533e-07 0.00266 0.7685
LYPLA2P1 -5.77 1.645e-07 0.00286 0.6319
ZNF642 5.25 3.754e-07 0.00652 0.6957
TRAPPC5 5.12 6.654e-07 0.0116 0.636
NRCAM 5.18 7.441e-07 0.0129 0.6214
RSAD2 5.72 7.652e-07 0.0133 0.7475
PRELP -5.04 9.815e-07 0.017 0.6552
PELI1 -5.37 1.416e-06 0.0246 0.6349

Figure S7.  Get High-res Image As an example, this figure shows the association of TRNP1 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 3.29e-08 with T-test analysis.

Clinical variable #11: 'NEOADJUVANT.THERAPY'

One gene related to 'NEOADJUVANT.THERAPY'.

Table S18.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 32
  YES 186
     
  Significant markers N = 1
  Higher in YES 0
  Higher in NO 1
List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

Table S19.  Get Full Table List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
LOC100192426 -5.11 7.768e-07 0.0135 0.6048

Figure S8.  Get High-res Image As an example, this figure shows the association of LOC100192426 to 'NEOADJUVANT.THERAPY'. P value = 7.77e-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 = 218

  • Number of genes = 17375

  • 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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