Correlation between mRNAseq expression and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1445JX9
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/C1445JX9
Overview
Introduction

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

Summary

Testing the association between 18310 genes and 6 clinical features across 277 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 683 genes correlated to 'Time to Death'.

    • SLITRK5|26050 ,  FNDC3B|64778 ,  CRTAC1|55118 ,  ARL3|403 ,  RANBP17|64901 ,  ...

  • 290 genes correlated to 'AGE'.

    • SYT6|148281 ,  PRSS35|167681 ,  SFRP2|6423 ,  ABI1|10006 ,  CNTN3|5067 ,  ...

  • 30 genes correlated to 'GENDER'.

    • XIST|7503 ,  ZFY|7544 ,  RPS4Y1|6192 ,  PRKY|5616 ,  DDX3Y|8653 ,  ...

  • 1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • RCVRN|5957

  • 2876 genes correlated to 'HISTOLOGICAL.TYPE'.

    • TXNDC12|51060 ,  AK2|204 ,  WLS|79971 ,  RPF1|80135 ,  NADK|65220 ,  ...

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

    • MAMDC4|158056 ,  GOLGA6L9|440295 ,  NUDT3|11165 ,  RHOT2|89941 ,  MAN2C1|4123 ,  ...

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=683 shorter survival N=345 longer survival N=338
AGE Spearman correlation test N=290 older N=123 younger N=167
GENDER t test N=30 male N=17 female N=13
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=0 lower score N=1
HISTOLOGICAL TYPE ANOVA test N=2876        
RADIATIONS RADIATION REGIMENINDICATION t test N=22 yes N=19 no N=3
Clinical variable #1: 'Time to Death'

683 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=15.4)
  censored N = 217
  death N = 59
     
  Significant markers N = 683
  associated with shorter survival 345
  associated with longer survival 338
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
SLITRK5|26050 0.47 6.029e-14 1.1e-09 0.269
FNDC3B|64778 3.6 7.246e-13 1.3e-08 0.782
CRTAC1|55118 0.63 7.39e-13 1.4e-08 0.231
ARL3|403 0.17 8.55e-13 1.6e-08 0.204
RANBP17|64901 0.63 9.789e-13 1.8e-08 0.297
CUEDC2|79004 0.1 1.69e-12 3.1e-08 0.226
ZNF217|7764 3.5 3.944e-12 7.2e-08 0.778
IGFBP2|3485 1.54 5.369e-12 9.8e-08 0.79
CBARA1|10367 0.16 6.389e-12 1.2e-07 0.244
LOC254559|254559 0.56 7.081e-12 1.3e-07 0.212

Figure S1.  Get High-res Image As an example, this figure shows the association of SLITRK5|26050 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 6.03e-14 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

290 genes related to 'AGE'.

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

AGE Mean (SD) 43.06 (13)
  Significant markers N = 290
  pos. correlated 123
  neg. correlated 167
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 -0.4316 5.34e-14 9.78e-10
PRSS35|167681 -0.4232 1.841e-13 3.37e-09
SFRP2|6423 -0.4231 1.854e-13 3.39e-09
ABI1|10006 -0.3995 4.905e-12 8.98e-08
CNTN3|5067 -0.3927 1.196e-11 2.19e-07
EN1|2019 0.439 1.371e-11 2.51e-07
SIM2|6493 0.3883 2.115e-11 3.87e-07
MKX|283078 -0.3816 4.951e-11 9.06e-07
CTBP2|1488 -0.3805 5.638e-11 1.03e-06
TCTA|6988 0.3802 5.9e-11 1.08e-06

Figure S2.  Get High-res Image As an example, this figure shows the association of SYT6|148281 to 'AGE'. P value = 5.34e-14 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 127
  MALE 150
     
  Significant markers N = 30
  Higher in MALE 17
  Higher in FEMALE 13
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 -60.6 1.202e-157 2.2e-153 0.9999
ZFY|7544 77.07 3.883e-125 7.11e-121 1
RPS4Y1|6192 70.23 8.973e-110 1.64e-105 1
PRKY|5616 38.7 5.18e-99 9.48e-95 0.9995
DDX3Y|8653 80.71 2.394e-92 4.38e-88 1
KDM5D|8284 79.19 1.261e-90 2.31e-86 1
NLGN4Y|22829 39.05 9.021e-89 1.65e-84 0.9978
USP9Y|8287 82.49 6.386e-85 1.17e-80 1
TSIX|9383 -27.6 1.265e-68 2.31e-64 0.9997
EIF1AY|9086 87.34 3.022e-66 5.53e-62 1

Figure S3.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 1.2e-157 with T-test analysis.

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

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 87.82 (11)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S8.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
RCVRN|5957 -0.4258 8.189e-07 0.015

Figure S4.  Get High-res Image As an example, this figure shows the association of RCVRN|5957 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 8.19e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

2876 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 88
  OLIGOASTROCYTOMA 77
  OLIGODENDROGLIOMA 112
     
  Significant markers N = 2876
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
TXNDC12|51060 3.158e-27 5.78e-23
AK2|204 3.095e-25 5.67e-21
WLS|79971 4.57e-24 8.37e-20
RPF1|80135 7.576e-24 1.39e-19
NADK|65220 3.155e-23 5.78e-19
WDR77|79084 5.982e-23 1.1e-18
TRAPPC3|27095 1.177e-22 2.15e-18
SEP15|9403 1.216e-22 2.23e-18
STK40|83931 1.643e-22 3.01e-18
LRRC42|115353 1.686e-22 3.09e-18

Figure S5.  Get High-res Image As an example, this figure shows the association of TXNDC12|51060 to 'HISTOLOGICAL.TYPE'. P value = 3.16e-27 with ANOVA analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 89
  YES 188
     
  Significant markers N = 22
  Higher in YES 19
  Higher in NO 3
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
MAMDC4|158056 5.72 5.216e-08 0.000955 0.7018
GOLGA6L9|440295 5.69 5.887e-08 0.00108 0.7001
NUDT3|11165 -5.57 6.525e-08 0.00119 0.6831
RHOT2|89941 5.51 1.091e-07 0.002 0.6892
MAN2C1|4123 5.37 2.146e-07 0.00393 0.6811
NSUN5P2|260294 5.26 3.438e-07 0.00629 0.6746
CSAD|51380 5.29 3.571e-07 0.00654 0.6902
CCDC154|645811 5.25 4.392e-07 0.00804 0.6758
EIF5B|9669 -5.23 4.564e-07 0.00835 0.6814
CENPT|80152 5.03 1.037e-06 0.019 0.6713

Figure S6.  Get High-res Image As an example, this figure shows the association of MAMDC4|158056 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 5.22e-08 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 = 277

  • Number of genes = 18310

  • 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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