Correlation between mRNAseq expression and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1ZK5FBC
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1ZK5FBC
Overview
Introduction

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

Summary

Testing the association between 18339 genes and 6 clinical features across 359 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 1429 genes correlated to 'Time to Death'.

    • CRTAC1|55118 ,  SLITRK5|26050 ,  FNDC3B|64778 ,  LOC254559|254559 ,  ARL3|403 ,  ...

  • 755 genes correlated to 'AGE'.

    • SYT6|148281 ,  PRSS35|167681 ,  ABI1|10006 ,  EN1|2019 ,  IL17RC|84818 ,  ...

  • 34 genes correlated to 'GENDER'.

    • XIST|7503 ,  ZFY|7544 ,  PRKY|5616 ,  RPS4Y1|6192 ,  NLGN4Y|22829 ,  ...

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

    • RPL3|6122 ,  RPL15|6138 ,  C12ORF5|57103 ,  VAV3|10451 ,  EEF1A1|1915 ,  ...

  • 3904 genes correlated to 'HISTOLOGICAL.TYPE'.

    • AK2|204 ,  TXNDC12|51060 ,  NADK|65220 ,  HDAC1|3065 ,  ASAP3|55616 ,  ...

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

    • EXD3|54932 ,  NSUN5P2|260294 ,  RHOT2|89941 ,  MAN2C1|4123 ,  CENPT|80152 ,  ...

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=1429 shorter survival N=779 longer survival N=650
AGE Spearman correlation test N=755 older N=361 younger N=394
GENDER t test N=34 male N=19 female N=15
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=30 higher score N=18 lower score N=12
HISTOLOGICAL TYPE ANOVA test N=3904        
RADIATIONS RADIATION REGIMENINDICATION t test N=334 yes N=299 no N=35
Clinical variable #1: 'Time to Death'

1429 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-211.2 (median=14.9)
  censored N = 288
  death N = 68
     
  Significant markers N = 1429
  associated with shorter survival 779
  associated with longer survival 650
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
CRTAC1|55118 0.61 0 0 0.214
SLITRK5|26050 0.47 1.11e-16 2e-12 0.25
FNDC3B|64778 3.7 3.331e-16 6.1e-12 0.789
LOC254559|254559 0.55 1.221e-15 2.2e-11 0.198
ARL3|403 0.17 1.665e-15 3.1e-11 0.2
CUEDC2|79004 0.1 3.109e-15 5.7e-11 0.221
IGFBP2|3485 1.56 8.216e-15 1.5e-10 0.806
RANBP17|64901 0.63 9.77e-15 1.8e-10 0.293
CNRIP1|25927 0.3 9.992e-15 1.8e-10 0.24
IGF2BP3|10643 1.5 1.721e-14 3.2e-10 0.768

Figure S1.  Get High-res Image As an example, this figure shows the association of CRTAC1|55118 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 0 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

755 genes related to 'AGE'.

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

AGE Mean (SD) 43.57 (14)
  Significant markers N = 755
  pos. correlated 361
  neg. correlated 394
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
SYT6|148281 -0.4568 6.54e-20 1.2e-15
PRSS35|167681 -0.4265 2.677e-17 4.91e-13
ABI1|10006 -0.4215 6.815e-17 1.25e-12
EN1|2019 0.4627 1.589e-16 2.91e-12
IL17RC|84818 0.4026 2.006e-15 3.68e-11
RIN1|9610 0.4008 2.773e-15 5.08e-11
CTBP2|1488 -0.4003 3.021e-15 5.54e-11
SFRP2|6423 -0.3992 3.625e-15 6.65e-11
CNTN3|5067 -0.3986 4.064e-15 7.45e-11
TCTA|6988 0.3975 4.846e-15 8.88e-11

Figure S2.  Get High-res Image As an example, this figure shows the association of SYT6|148281 to 'AGE'. P value = 6.54e-20 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

34 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 165
  MALE 194
     
  Significant markers N = 34
  Higher in MALE 19
  Higher in FEMALE 15
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
XIST|7503 -67.1 4.35e-197 7.98e-193 0.9998
ZFY|7544 79.71 1.071e-143 1.96e-139 0.9999
PRKY|5616 45.7 5.208e-134 9.55e-130 0.9996
RPS4Y1|6192 67.79 2.96e-118 5.43e-114 0.9997
NLGN4Y|22829 43.23 3.162e-115 5.8e-111 0.9976
DDX3Y|8653 75.5 8.097e-103 1.48e-98 0.9998
KDM5D|8284 69.07 4.7e-93 8.61e-89 0.9998
TSIX|9383 -30.81 3.631e-89 6.65e-85 0.997
USP9Y|8287 72.32 6.872e-82 1.26e-77 0.9998
NCRNA00183|554203 -17.33 5.516e-49 1.01e-44 0.9112

Figure S3.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 4.35e-197 with T-test analysis.

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

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

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 87.97 (12)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
List of top 10 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
RPL3|6122 0.3719 7.441e-08 0.00136
RPL15|6138 0.3704 8.444e-08 0.00155
C12ORF5|57103 -0.3664 1.189e-07 0.00218
VAV3|10451 -0.3651 1.323e-07 0.00243
EEF1A1|1915 0.3641 1.441e-07 0.00264
RPL23|9349 0.3603 1.987e-07 0.00364
HNRNPA1|3178 0.3545 3.193e-07 0.00585
BCAT1|586 -0.3544 3.244e-07 0.00595
CD101|9398 -0.3474 5.705e-07 0.0105
UGP2|7360 -0.3465 6.085e-07 0.0112

Figure S4.  Get High-res Image As an example, this figure shows the association of RPL3|6122 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 7.44e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

3904 genes related to 'HISTOLOGICAL.TYPE'.

Table S9.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 122
  OLIGOASTROCYTOMA 100
  OLIGODENDROGLIOMA 137
     
  Significant markers N = 3904
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
AK2|204 9.22e-35 1.69e-30
TXNDC12|51060 3.278e-34 6.01e-30
NADK|65220 1.627e-33 2.98e-29
HDAC1|3065 8.19e-32 1.5e-27
ASAP3|55616 2.468e-31 4.53e-27
CAPZB|832 4.913e-31 9.01e-27
RPF1|80135 9.039e-31 1.66e-26
STK40|83931 1.078e-30 1.98e-26
SF3A3|10946 1.571e-30 2.88e-26
WDR77|79084 3.306e-30 6.06e-26

Figure S5.  Get High-res Image As an example, this figure shows the association of AK2|204 to 'HISTOLOGICAL.TYPE'. P value = 9.22e-35 with ANOVA analysis.

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

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

Table S11.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 89
  YES 270
     
  Significant markers N = 334
  Higher in YES 299
  Higher in NO 35
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
EXD3|54932 7.97 9.323e-14 1.71e-09 0.7333
NSUN5P2|260294 7.99 1.44e-13 2.64e-09 0.7436
RHOT2|89941 7.98 2.221e-13 4.07e-09 0.7532
MAN2C1|4123 7.88 3.414e-13 6.26e-09 0.746
CENPT|80152 7.83 3.679e-13 6.75e-09 0.7402
CSAD|51380 7.84 7.317e-13 1.34e-08 0.7559
HOOK2|29911 7.68 7.944e-13 1.46e-08 0.739
CCDC154|645811 7.66 2.296e-12 4.21e-08 0.7457
MAMDC4|158056 7.66 3.173e-12 5.82e-08 0.7541
NCRNA00105|80161 7.44 5.31e-12 9.73e-08 0.7377

Figure S6.  Get High-res Image As an example, this figure shows the association of EXD3|54932 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 9.32e-14 with T-test analysis.

Methods & Data
Input

  • Expresson data file = LGG-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

  • Clinical data file = LGG-TP.merged_data.txt

  • Number of patients = 359

  • Number of genes = 18339

  • Number of clinical features = 6

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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