Correlation between mRNAseq expression and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1RN366R
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RN366R
Overview
Introduction

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

Summary

Testing the association between 18030 genes and 10 clinical features across 118 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 12 genes correlated to 'Time to Death'.

    • FBXL5|26234 ,  RYR2|6262 ,  LRRC43|254050 ,  DBR1|51163 ,  CCDC71|64925 ,  ...

  • 154 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • CLDN11|5010 ,  PAICS|10606 ,  PLK1|5347 ,  NEIL3|55247 ,  FOXM1|2305 ,  ...

  • 115 genes correlated to 'PATHOLOGY.T.STAGE'.

    • EPR1|8475 ,  NUF2|83540 ,  TROAP|10024 ,  NEIL3|55247 ,  PTHLH|5744 ,  ...

  • 2 genes correlated to 'PATHOLOGY.M.STAGE'.

    • IGF2BP3|10643 ,  CRELD1|78987

  • 54 genes correlated to 'GENDER'.

    • XIST|7503 ,  RPS4Y1|6192 ,  TSIX|9383 ,  PRKY|5616 ,  KDM5C|8242 ,  ...

  • No genes correlated to 'AGE', 'PATHOLOGY.N.STAGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', and 'YEAROFTOBACCOSMOKINGONSET'.

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=12 shorter survival N=4 longer survival N=8
AGE Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE ANOVA test N=154        
PATHOLOGY T STAGE Spearman correlation test N=115 higher stage N=104 lower stage N=11
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=2        
GENDER t test N=54 male N=18 female N=36
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

12 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-194.8 (median=14.1)
  censored N = 94
  death N = 15
     
  Significant markers N = 12
  associated with shorter survival 4
  associated with longer survival 8
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
FBXL5|26234 0.12 5.429e-07 0.0098 0.142
RYR2|6262 1.84 5.724e-07 0.01 0.873
LRRC43|254050 0.63 6.649e-07 0.012 0.169
DBR1|51163 0.04 7.107e-07 0.013 0.123
CCDC71|64925 0.07 7.546e-07 0.014 0.152
MGAT5B|146664 1.66 9.707e-07 0.017 0.869
CCDC13|152206 0.47 1.032e-06 0.019 0.119
MEIS3P1|4213 0.33 1.059e-06 0.019 0.182
BEX2|84707 0.58 1.096e-06 0.02 0.166
SPATA18|132671 0.59 1.158e-06 0.021 0.202

Figure S1.  Get High-res Image As an example, this figure shows the association of FBXL5|26234 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 5.43e-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) 59.82 (13)
  Significant markers N = 0
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

154 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 62
  STAGE II 8
  STAGE III 30
  STAGE IV 10
     
  Significant markers N = 154
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

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

ANOVA_P Q
CLDN11|5010 3.705e-10 6.68e-06
PAICS|10606 3.819e-10 6.89e-06
PLK1|5347 5.719e-10 1.03e-05
NEIL3|55247 6.953e-10 1.25e-05
FOXM1|2305 8.378e-10 1.51e-05
EPR1|8475 1.033e-09 1.86e-05
BIRC5|332 1.304e-09 2.35e-05
CDCA5|113130 1.574e-09 2.84e-05
MAD2L1|4085 1.677e-09 3.02e-05
TROAP|10024 1.738e-09 3.13e-05

Figure S2.  Get High-res Image As an example, this figure shows the association of CLDN11|5010 to 'NEOPLASM.DISEASESTAGE'. P value = 3.7e-10 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

115 genes related to 'PATHOLOGY.T.STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.77 (0.93)
  N
  1 66
  2 14
  3 37
  4 1
     
  Significant markers N = 115
  pos. correlated 104
  neg. correlated 11
List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S7.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
EPR1|8475 0.5393 3.54e-10 6.38e-06
NUF2|83540 0.5304 6.509e-10 1.17e-05
TROAP|10024 0.5239 1.139e-09 2.05e-05
NEIL3|55247 0.5217 2.25e-09 4.06e-05
PTHLH|5744 0.519 2.808e-09 5.06e-05
UCK2|7371 0.5053 5.364e-09 9.67e-05
COL5A3|50509 0.5017 7.161e-09 0.000129
PLK1|5347 0.4998 8.318e-09 0.00015
KIF4A|24137 0.4994 8.556e-09 0.000154
CDK1|983 0.4986 9.126e-09 0.000164

Figure S3.  Get High-res Image As an example, this figure shows the association of EPR1|8475 to 'PATHOLOGY.T.STAGE'. P value = 3.54e-10 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N.STAGE'

No gene related to 'PATHOLOGY.N.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.5 (0.68)
  N
  0 24
  1 12
  2 4
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

2 genes related to 'PATHOLOGY.M.STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 54
  M1 6
  MX 50
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'PATHOLOGY.M.STAGE'

Table S10.  Get Full Table List of 2 genes differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
IGF2BP3|10643 2.109e-07 0.0038
CRELD1|78987 1.206e-06 0.0217

Figure S4.  Get High-res Image As an example, this figure shows the association of IGF2BP3|10643 to 'PATHOLOGY.M.STAGE'. P value = 2.11e-07 with ANOVA analysis.

Clinical variable #7: 'GENDER'

54 genes related to 'GENDER'.

Table S11.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 37
  MALE 81
     
  Significant markers N = 54
  Higher in MALE 18
  Higher in FEMALE 36
List of top 10 genes differentially expressed by 'GENDER'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
XIST|7503 -18.91 6.147e-27 1.11e-22 0.9905
RPS4Y1|6192 15.41 3.358e-16 6.05e-12 0.9655
TSIX|9383 -10.51 3.967e-14 7.15e-10 0.9506
PRKY|5616 10.08 4.467e-14 8.05e-10 0.9347
KDM5C|8242 -9.28 3.691e-13 6.65e-09 0.9229
KDM6A|7403 -7.65 9.405e-11 1.69e-06 0.8735
ZFX|7543 -6.95 1.674e-09 3.02e-05 0.8445
AOX1|316 6.63 1.049e-08 0.000189 0.8378
EIF1AX|1964 -6.53 2.282e-08 0.000411 0.8278
KDM5D|8284 9.58 2.363e-08 0.000426 0.9406

Figure S5.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 6.15e-27 with T-test analysis.

Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S13.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 86.11 (25)
  Score N
  0 1
  40 1
  90 9
  100 7
     
  Significant markers N = 0
Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S14.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 13.75 (8.5)
  Value N
  5 1
  10 1
  15 1
  25 1
     
  Significant markers N = 0
Clinical variable #10: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S15.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1972 (17)
  Value N
  1960 1
  1965 1
  1991 1
     
  Significant markers N = 0
Methods & Data
Input

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

  • Clinical data file = KIRP-TP.clin.merged.picked.txt

  • Number of patients = 118

  • Number of genes = 18030

  • Number of clinical features = 10

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

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

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

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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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