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 20231 genes and 11 clinical features across 216 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

- 5 genes correlated to 'Time to Death'.

- TM4SF19 , C2CD2 , ATP5SL , GPR135 , ZFP14

- 50 genes correlated to 'GENDER'.

- KIF4B , UTP14C , SNORA48 , DPPA5 , MYO5A , ...

- 45 genes correlated to 'HISTOLOGICAL.TYPE'.

- GOPC , PDCD11 , USMG5 , UBXN8 , TM7SF2 , ...

- 1 gene correlated to 'PATHOLOGY.N'.

- ANXA6

- 3 genes correlated to 'PATHOLOGICSPREAD(M)'.

- C3ORF58 , CASP6 , C2ORF73

- 30 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- BBX , ARRDC2 , SFMBT1 , MNAT1 , EHD3 , ...

- 3 genes correlated to 'NEOADJUVANT.THERAPY'.

- MYADM , TAL2 , MIR320C1

- No genes correlated to 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', '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=5 | shorter survival | N=3 | longer survival | N=2
AGE | Spearman correlation test | N=0 |  |  |  |
GENDER | t test | N=50 | male | N=1 | female | N=49
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
HISTOLOGICAL TYPE | ANOVA test | N=45 |  |  |  |
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=3 |  |  |  |
TUMOR STAGE | Spearman correlation test | N=0 |  |  |  |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=30 | yes | N=25 | no | N=5
NEOADJUVANT THERAPY | t test | N=3 | yes | N=2 | no | N=1

Clinical variable #1: 'Time to Death'

5 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 = 128
| death | N = 60
| Significant markers | N = 5
| associated with shorter survival | 3
| associated with longer survival | 2

List of 5 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of 5 genes significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
TM4SF19 | 0.01 | 2.446e-07 | 0.0049 | 0.328
C2CD2 | 901 | 7.714e-07 | 0.016 | 0.681
ATP5SL | 6.9e+23 | 1.37e-06 | 0.028 | 0.645
GPR135 | 59 | 1.404e-06 | 0.028 | 0.616
ZFP14 | 0 | 1.739e-06 | 0.035 | 0.377

Figure S1. Get High-res Image As an example, this figure shows the association of TM4SF19 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.45e-07 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) | 65.3 (9.8)
| Significant markers | N = 0

Clinical variable #3: 'GENDER'

50 genes related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 115
| MALE | 101
| Significant markers | N = 50
| Higher in MALE | 1
| Higher in FEMALE | 49

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
--- | --- | --- | --- | ---
KIF4B | -8.97 | 2.633e-16 | 5.33e-12 | 0.8214
UTP14C | 8.08 | 6.039e-14 | 1.22e-09 | 0.7868
SNORA48 | -6.86 | 8.928e-11 | 1.81e-06 | 0.7675
DPPA5 | -6.73 | 2.078e-10 | 4.2e-06 | 0.7485
MYO5A | -6.62 | 2.883e-10 | 5.83e-06 | 0.8133
SPESP1 | -6.53 | 6.783e-10 | 1.37e-05 | 0.7518
FRG1B | -6.37 | 1.402e-09 | 2.84e-05 | 0.7252
EIF4A1 | -5.98 | 1.029e-08 | 0.000208 | 0.7312
ADAMTSL2 | -5.63 | 6.872e-08 | 0.00139 | 0.7122
C11ORF36 | -5.58 | 7.685e-08 | 0.00155 | 0.7046

Figure S2. Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 2.63e-16 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) | 71.11 (34)
| Significant markers | N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

45 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE | Labels | N
| LUNG ACINAR ADENOCARCINOMA | 5
| LUNG ADENOCARCINOMA MIXED SUBTYPE | 49
| LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | 132
| LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | 3
| LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | 9
| 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 | 3
| Significant markers | N = 45

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
--- | --- | ---
GOPC | 1.689e-129 | 3.42e-125
PDCD11 | 9.628e-110 | 1.95e-105
USMG5 | 5.034e-104 | 1.02e-99
UBXN8 | 5.922e-29 | 1.2e-24
TM7SF2 | 3.783e-28 | 7.65e-24
LRRC37B | 5.88e-20 | 1.19e-15
MURC | 1.318e-16 | 2.67e-12
LOC148189 | 3.222e-16 | 6.52e-12
ABHD15 | 4.277e-15 | 8.65e-11
MIR1975 | 1.556e-13 | 3.15e-09

Figure S3. Get High-res Image As an example, this figure shows the association of GOPC to 'HISTOLOGICAL.TYPE'. P value = 1.69e-129 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) | 1.91 (0.76)
|  | N
| T1 | 60
| T2 | 125
| T3 | 17
| T4 | 12
| Significant markers | N = 0

Clinical variable #7: 'PATHOLOGY.N'

One gene related to 'PATHOLOGY.N'.

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

PATHOLOGY.N | Mean (SD) | 0.61 (0.8)
|  | N
| N0 | 125
| N1 | 44
| N2 | 42
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S11. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
ANXA6 | 0.3333 | 7.212e-07 | 0.0146

Figure S4. Get High-res Image As an example, this figure shows the association of ANXA6 to 'PATHOLOGY.N'. P value = 7.21e-07 with Spearman correlation analysis.

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

3 genes related to 'PATHOLOGICSPREAD(M)'.

Table S12. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) | Labels | N
| M0 | 139
| M1 | 10
| MX | 61
| Significant markers | N = 3

List of 3 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S13. Get Full Table List of 3 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

| ANOVA_P | Q
--- | --- | ---
C3ORF58 | 6.426e-09 | 0.00013
CASP6 | 7.888e-08 | 0.0016
C2ORF73 | 3.453e-07 | 0.00699

Figure S5. Get High-res Image As an example, this figure shows the association of C3ORF58 to 'PATHOLOGICSPREAD(M)'. P value = 6.43e-09 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S14. Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE | Mean (SD) | 1.82 (0.95)
|  | N
| Stage 1 | 107
| Stage 2 | 47
| Stage 3 | 46
| Stage 4 | 11
| Significant markers | N = 0

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

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

Table S15. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 9
| YES | 207
| Significant markers | N = 30
| Higher in YES | 25
| Higher in NO | 5

List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S16. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
BBX | 7.62 | 5.396e-12 | 1.09e-07 | 0.7547
ARRDC2 | 7.67 | 1.052e-11 | 2.13e-07 | 0.6345
SFMBT1 | -8.28 | 1.589e-11 | 3.22e-07 | 0.7311
MNAT1 | 8.27 | 3.33e-10 | 6.74e-06 | 0.8148
EHD3 | 7.22 | 4.186e-10 | 8.47e-06 | 0.7091
LOC100287216 | 6.74 | 5.572e-10 | 1.13e-05 | 0.6414
CCL28 | 8.3 | 1.636e-09 | 3.31e-05 | 0.7654
C5ORF39 | 6.3 | 4.013e-09 | 8.12e-05 | 0.7456
PGBD1 | 6.07 | 7.419e-09 | 0.00015 | 0.5105
MIR30B | -6.49 | 1.414e-08 | 0.000286 | 0.7638

Figure S6. Get High-res Image As an example, this figure shows the association of BBX to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 5.4e-12 with T-test analysis.

Clinical variable #11: 'NEOADJUVANT.THERAPY'

3 genes related to 'NEOADJUVANT.THERAPY'.

Table S17. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY | Labels | N
| NO | 31
| YES | 185
| Significant markers | N = 3
| Higher in YES | 2
| Higher in NO | 1

List of 3 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S18. Get Full Table List of 3 genes differentially expressed by 'NEOADJUVANT.THERAPY'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
MYADM | 4.91 | 1.827e-06 | 0.037 | 0.6115
TAL2 | -5.02 | 1.845e-06 | 0.0373 | 0.6731
MIR320C1 | 4.89 | 2.466e-06 | 0.0499 | 0.6013

Figure S7. Get High-res Image As an example, this figure shows the association of MYADM to 'NEOADJUVANT.THERAPY'. P value = 1.83e-06 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 = 216

- Number of genes = 20231

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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