Correlation between mRNAseq expression and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1833PZJ
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Kidney Renal Papillary Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1833PZJ
Overview
Introduction

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

Summary

Testing the association between 18174 genes and 8 clinical features across 75 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • MCCC2|64087

  • 16 genes correlated to 'GENDER'.

    • XIST|7503 ,  RPS4Y1|6192 ,  KDM5C|8242 ,  ZFX|7543 ,  PRKY|5616 ,  ...

  • 64 genes correlated to 'PATHOLOGY.T'.

    • UCK2|7371 ,  PRCC|5546 ,  MAD2L1|4085 ,  ACBD6|84320 ,  EPR1|8475 ,  ...

  • 2 genes correlated to 'PATHOLOGY.N'.

    • KIAA0664|23277 ,  KCMF1|56888

  • 2 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • CA8|767 ,  GLB1L2|89944

  • 127 genes correlated to 'TUMOR.STAGE'.

    • MAD2L1|4085 ,  UCK2|7371 ,  PRCC|5546 ,  EPR1|8475 ,  GPR19|2842 ,  ...

  • No genes correlated to 'Time to Death', and '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=0        
AGE Spearman correlation test N=1 older N=1 younger N=0
GENDER t test N=16 male N=3 female N=13
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
PATHOLOGY T Spearman correlation test N=64 higher pT N=61 lower pT N=3
PATHOLOGY N Spearman correlation test N=2 higher pN N=1 lower pN N=1
PATHOLOGICSPREAD(M) ANOVA test N=2        
TUMOR STAGE Spearman correlation test N=127 higher stage N=110 lower stage N=17
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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

Time to Death Duration (Months) 0.5-182.7 (median=15.1)
  censored N = 59
  death N = 13
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 59.68 (13)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
MCCC2|64087 0.5368 1.166e-06 0.0212

Figure S1.  Get High-res Image As an example, this figure shows the association of MCCC2|64087 to 'AGE'. P value = 1.17e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

16 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 23
  MALE 52
     
  Significant markers N = 16
  Higher in MALE 3
  Higher in FEMALE 13
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
XIST|7503 -13.36 2.403e-14 4.36e-10 0.982
RPS4Y1|6192 11.88 2.538e-11 4.61e-07 0.9562
KDM5C|8242 -8.17 4.536e-10 8.24e-06 0.9356
ZFX|7543 -6.99 9.264e-09 0.000168 0.8921
PRKY|5616 7.5 9.69e-09 0.000176 0.9164
KDM6A|7403 -6.91 1.084e-08 0.000197 0.9105
TSIX|9383 -7.64 1.31e-08 0.000238 0.9309
TXLNG|55787 -6.28 5.748e-08 0.00104 0.8654
UBA1|7317 -6.13 4.028e-07 0.00731 0.8796
ZNF275|10838 -6.03 4.325e-07 0.00785 0.8671

Figure S2.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 2.4e-14 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) 86.67 (28)
  Score N
  0 1
  90 6
  100 5
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.T'

64 genes related to 'PATHOLOGY.T'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.88 (0.96)
  N
  T1 38
  T2 9
  T3 27
  T4 1
     
  Significant markers N = 64
  pos. correlated 61
  neg. correlated 3
List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
UCK2|7371 0.6687 5.507e-11 1e-06
PRCC|5546 0.6038 9.811e-09 0.000178
MAD2L1|4085 0.6012 1.171e-08 0.000213
ACBD6|84320 0.5906 2.436e-08 0.000443
EPR1|8475 0.5885 3.507e-08 0.000637
NUF2|83540 0.5837 3.874e-08 0.000704
CCT3|7203 0.5828 4.11e-08 0.000747
GPR19|2842 0.5841 5.797e-08 0.00105
UBE2T|29089 0.5753 6.702e-08 0.00122
KIF4A|24137 0.5679 1.072e-07 0.00195

Figure S3.  Get High-res Image As an example, this figure shows the association of UCK2|7371 to 'PATHOLOGY.T'. P value = 5.51e-11 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

2 genes related to 'PATHOLOGY.N'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.6 (0.67)
  N
  N0 15
  N1 12
  N2 3
     
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S10.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
KIAA0664|23277 -0.7671 7.624e-07 0.0139
KCMF1|56888 0.7449 2.35e-06 0.0427

Figure S4.  Get High-res Image As an example, this figure shows the association of KIAA0664|23277 to 'PATHOLOGY.N'. P value = 7.62e-07 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

2 genes related to 'PATHOLOGICSPREAD(M)'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 46
  M1 5
  MX 17
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S12.  Get Full Table List of 2 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
CA8|767 6.006e-08 0.00109
GLB1L2|89944 1.979e-06 0.036

Figure S5.  Get High-res Image As an example, this figure shows the association of CA8|767 to 'PATHOLOGICSPREAD(M)'. P value = 6.01e-08 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

127 genes related to 'TUMOR.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.03 (1.2)
  N
  Stage 1 35
  Stage 2 3
  Stage 3 21
  Stage 4 8
     
  Significant markers N = 127
  pos. correlated 110
  neg. correlated 17
List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S14.  Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MAD2L1|4085 0.7093 1.836e-11 3.34e-07
UCK2|7371 0.676 3.455e-10 6.28e-06
PRCC|5546 0.6482 3.017e-09 5.48e-05
EPR1|8475 0.6451 5.019e-09 9.12e-05
GPR19|2842 0.6463 6.082e-09 0.000111
TCTA|6988 -0.6347 8.052e-09 0.000146
CDCA5|113130 0.6219 1.939e-08 0.000352
ORC6L|23594 0.6218 1.956e-08 0.000355
TROAP|10024 0.6193 2.307e-08 0.000419
KIF4A|24137 0.6183 2.475e-08 0.00045

Figure S6.  Get High-res Image As an example, this figure shows the association of MAD2L1|4085 to 'TUMOR.STAGE'. P value = 1.84e-11 with Spearman correlation analysis.

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 = 75

  • Number of genes = 18174

  • Number of clinical features = 8

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.

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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