Glioblastoma Multiforme: Correlation between mRNA expression and clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 12042 genes and 7 clinical features across 519 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

- 21 genes correlated to 'Time to Death'.

- CLEC5A , EFEMP2 , NCOA4 , ATP5C1 , DIRAS3 , ...

- 76 genes correlated to 'AGE'.

- RANBP17 , FBXO17 , TUSC3 , KIAA0495 , NOL3 , ...

- 22 genes correlated to 'GENDER'.

- DDX3Y , RPS4Y1 , EIF1AY , JARID1D , NLGN4Y , ...

- 15 genes correlated to 'HISTOLOGICAL.TYPE'.

- TMED2 , DNAJA2 , SUPT4H1 , PTHR1 , PDIA6 , ...

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

- HOXD10 , HOXD11

- 1 gene correlated to 'NEOADJUVANT.THERAPY'.

- HOXD10

- No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'

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=21 | shorter survival | N=11 | longer survival | N=10
AGE | Spearman correlation test | N=76 | older | N=42 | younger | N=34
GENDER | t test | N=22 | male | N=11 | female | N=11
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
HISTOLOGICAL TYPE | t test | N=15 | untreated primary (de novo) gbm | N=6 | treated primary gbm | N=9
RADIATIONS RADIATION REGIMENINDICATION | t test | N=2 | yes | N=2 | no | N=0
NEOADJUVANT THERAPY | t test | N=1 | yes | N=1 | no | N=0

Clinical variable #1: 'Time to Death'

21 genes related to 'Time to Death'.

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

Time to Death | Duration (Months) | 0.1-127.6 (median=9.9)
| censored | N = 116
| death | N = 403
| Significant markers | N = 21
| associated with shorter survival | 11
| 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
--- | --- | --- | --- | ---
CLEC5A | 1.23 | 7.107e-08 | 0.00086 | 0.584
EFEMP2 | 1.3 | 7.708e-08 | 0.00093 | 0.542
NCOA4 | 0.56 | 8.196e-08 | 0.00099 | 0.442
ATP5C1 | 0.59 | 8.228e-08 | 0.00099 | 0.451
DIRAS3 | 1.22 | 1.111e-07 | 0.0013 | 0.558
RANBP17 | 0.46 | 1.833e-07 | 0.0022 | 0.427
ANKRD26 | 0.39 | 2.458e-07 | 0.003 | 0.446
HIST3H2A | 0.82 | 3.552e-07 | 0.0043 | 0.427
ZIC3 | 0.48 | 6.625e-07 | 0.008 | 0.444
FZD7 | 1.23 | 1.054e-06 | 0.013 | 0.556

Figure S1. Get High-res Image As an example, this figure shows the association of CLEC5A to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 7.11e-08 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

76 genes related to 'AGE'.

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

AGE | Mean (SD) | 57.68 (14)
| Significant markers | N = 76
| pos. correlated | 42
| neg. correlated | 34

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
RANBP17 | -0.316 | 1.677e-13 | 2.02e-09
FBXO17 | 0.3024 | 1.966e-12 | 2.37e-08
TUSC3 | -0.2972 | 4.787e-12 | 5.76e-08
KIAA0495 | 0.279 | 9.796e-11 | 1.18e-06
NOL3 | 0.2745 | 2.002e-10 | 2.41e-06
PPA1 | -0.2725 | 2.734e-10 | 3.29e-06
H2AFY2 | -0.2638 | 1.037e-09 | 1.25e-05
DRG2 | 0.2628 | 1.203e-09 | 1.45e-05
NCOA4 | -0.2621 | 1.343e-09 | 1.62e-05
ENOSF1 | -0.2585 | 2.273e-09 | 2.73e-05

Figure S2. Get High-res Image As an example, this figure shows the association of RANBP17 to 'AGE'. P value = 1.68e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

22 genes related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 204
| MALE | 315
| Significant markers | N = 22
| Higher in MALE | 11
| Higher in FEMALE | 11

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
--- | --- | --- | --- | ---
DDX3Y | 36.26 | 1.84e-136 | 2.22e-132 | 0.9553
RPS4Y1 | 38.53 | 3.922e-134 | 4.72e-130 | 0.9462
EIF1AY | 33.94 | 9.44e-130 | 1.14e-125 | 0.95
JARID1D | 33.27 | 3.066e-129 | 3.69e-125 | 0.9532
NLGN4Y | 29.89 | 3.328e-113 | 4.01e-109 | 0.9427
USP9Y | 20.76 | 6.502e-70 | 7.83e-66 | 0.9124
CYORF15B | 18.9 | 6.583e-61 | 7.92e-57 | 0.8974
UTY | 19.11 | 1.285e-57 | 1.55e-53 | 0.8926
HDHD1A | -12.33 | 2.408e-29 | 2.9e-25 | 0.8028
ZFX | -12.16 | 1.049e-28 | 1.26e-24 | 0.8149

Figure S3. Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 1.84e-136 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) | 77.12 (14)
| Significant markers | N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

15 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE | Labels | N
| TREATED PRIMARY GBM | 20
| UNTREATED PRIMARY (DE NOVO) GBM | 499
| Significant markers | N = 15
| Higher in UNTREATED PRIMARY (DE NOVO) GBM | 6
| Higher in TREATED PRIMARY GBM | 9

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'

| T(pos if higher in 'UNTREATED PRIMARY (DE NOVO) GBM') | ttestP | Q | AUC
--- | --- | --- | --- | ---
TMED2 | -7.93 | 4.225e-08 | 0.000509 | 0.8323
DNAJA2 | -7.11 | 1.706e-07 | 0.00205 | 0.7856
SUPT4H1 | -6.94 | 2.052e-07 | 0.00247 | 0.776
PTHR1 | 6.5 | 4.273e-07 | 0.00514 | 0.7586
PDIA6 | -6.74 | 9.665e-07 | 0.0116 | 0.832
PPARD | 6.73 | 1.023e-06 | 0.0123 | 0.8429
RBM3 | -6.22 | 1.266e-06 | 0.0152 | 0.726
NUDCD3 | 6.39 | 1.842e-06 | 0.0222 | 0.8206
NSUN3 | -6.32 | 2.198e-06 | 0.0264 | 0.8186
RPL23AP13 | 6.25 | 2.398e-06 | 0.0289 | 0.7973

Figure S4. Get High-res Image As an example, this figure shows the association of TMED2 to 'HISTOLOGICAL.TYPE'. P value = 4.22e-08 with T-test analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 359
| YES | 160
| Significant markers | N = 2
| Higher in YES | 2
| Higher in NO | 0

List of 2 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S11. Get Full Table List of 2 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
HOXD10 | 5.31 | 2.351e-07 | 0.00283 | 0.6441
HOXD11 | 4.79 | 2.65e-06 | 0.0319 | 0.6314

Figure S5. Get High-res Image As an example, this figure shows the association of HOXD10 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.35e-07 with T-test analysis.

Clinical variable #7: 'NEOADJUVANT.THERAPY'

One gene related to 'NEOADJUVANT.THERAPY'.

Table S12. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY | Labels | N
| NO | 330
| YES | 189
| Significant markers | N = 1
| Higher in YES | 1
| Higher in NO | 0

List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

Table S13. Get Full Table List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
HOXD10 | 4.68 | 4.004e-06 | 0.0482 | 0.6262

Figure S6. Get High-res Image As an example, this figure shows the association of HOXD10 to 'NEOADJUVANT.THERAPY'. P value = 4e-06 with T-test analysis.

Methods & Data
Input

- Expresson data file = GBM.medianexp.txt

- Clinical data file = GBM.clin.merged.picked.txt

- Number of patients = 519

- Number of genes = 12042

- Number of clinical features = 7

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

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. Location of data archives could not be determined.

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] 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)
Meta

- Maintainer = TCGA GDAC Team

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