Correlation between mRNAseq expression and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C14J0CQS
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/C14J0CQS
Overview
Introduction

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

Summary

Testing the association between 18263 genes and 11 clinical features across 198 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 35 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • KLF9|687 ,  SFRP2|6423 ,  SFRP4|6424 ,  RGS1|5996 ,  CTHRC1|115908 ,  ...

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

    • KLF9|687 ,  IGF1|3479 ,  TPST1|8460 ,  CTHRC1|115908 ,  PCSK5|5125 ,  ...

  • 5 genes correlated to 'PATHOLOGY.N.STAGE'.

    • OCLN|4950 ,  GFPT1|2673 ,  GOLGA4|2803 ,  ZNRD1|30834 ,  CREB3L2|64764

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

    • LHX3|8022 ,  COX5A|9377

  • 17 genes correlated to 'GENDER'.

    • USP9Y|8287 ,  PRKY|5616 ,  XIST|7503 ,  TMSB4Y|9087 ,  CYORF15A|246126 ,  ...

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

    • LOC283070|283070

  • 7 genes correlated to 'NUMBER.OF.LYMPH.NODES'.

    • C6ORF141|135398 ,  PRR15L|79170 ,  OCLN|4950 ,  SLC44A2|57153 ,  ANXA9|8416 ,  ...

  • No genes correlated to 'Time to Death', 'AGE', 'NUMBERPACKYEARSSMOKED', and 'GLEASON_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=0        
NEOPLASM DISEASESTAGE ANOVA test N=35        
PATHOLOGY T STAGE Spearman correlation test N=30 higher stage N=30 lower stage N=0
PATHOLOGY N STAGE Spearman correlation test N=5 higher stage N=4 lower stage N=1
PATHOLOGY M STAGE ANOVA test N=2        
GENDER t test N=17 male N=11 female N=6
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=0 lower score N=1
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test N=7 higher number.of.lymph.nodes N=6 lower number.of.lymph.nodes N=1
GLEASON_SCORE Spearman correlation test   N=0        
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) 0.1-140.8 (median=8.2)
  censored N = 133
  death N = 58
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 67.51 (11)
  Significant markers N = 0
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

35 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S3.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 2
  STAGE II 66
  STAGE III 67
  STAGE IV 59
     
  Significant markers N = 35
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S4.  Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
KLF9|687 1.045e-08 0.000191
SFRP2|6423 2.098e-08 0.000383
SFRP4|6424 3.155e-08 0.000576
RGS1|5996 4.937e-08 0.000901
CTHRC1|115908 5.193e-08 0.000948
LIMD1|8994 1.017e-07 0.00186
PXT1|222659 1.189e-07 0.00217
GAS1|2619 2.164e-07 0.00395
TPST1|8460 2.485e-07 0.00454
SLC41A2|84102 2.783e-07 0.00508

Figure S1.  Get High-res Image As an example, this figure shows the association of KLF9|687 to 'NEOPLASM.DISEASESTAGE'. P value = 1.04e-08 with ANOVA analysis.

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

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

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.82 (0.72)
  N
  0 1
  1 1
  2 57
  3 91
  4 29
     
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S6.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
KLF9|687 0.3797 1.592e-07 0.00291
IGF1|3479 0.3771 1.958e-07 0.00358
TPST1|8460 0.3758 2.168e-07 0.00396
CTHRC1|115908 0.3701 3.411e-07 0.00623
PCSK5|5125 0.3672 4.617e-07 0.00843
RRAGD|58528 0.3653 4.958e-07 0.00905
TIMP2|7077 0.364 5.477e-07 0.01
CRISPLD2|83716 0.3631 5.871e-07 0.0107
SGIP1|84251 0.3631 5.882e-07 0.0107
CCDC80|151887 0.357 9.297e-07 0.017

Figure S2.  Get High-res Image As an example, this figure shows the association of KLF9|687 to 'PATHOLOGY.T.STAGE'. P value = 1.59e-07 with Spearman correlation analysis.

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

5 genes related to 'PATHOLOGY.N.STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.59 (0.92)
  N
  0 122
  1 18
  2 34
  3 7
     
  Significant markers N = 5
  pos. correlated 4
  neg. correlated 1
List of 5 genes significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

Table S8.  Get Full Table List of 5 genes significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
OCLN|4950 0.3493 1.439e-06 0.0263
GFPT1|2673 0.3476 1.634e-06 0.0298
GOLGA4|2803 0.3445 2.044e-06 0.0373
ZNRD1|30834 -0.344 2.121e-06 0.0387
CREB3L2|64764 0.3427 2.336e-06 0.0426

Figure S3.  Get High-res Image As an example, this figure shows the association of OCLN|4950 to 'PATHOLOGY.N.STAGE'. P value = 1.44e-06 with Spearman correlation analysis.

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 106
  M1 5
  MX 86
     
  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
LHX3|8022 4.237e-10 7.74e-06
COX5A|9377 1.664e-06 0.0304

Figure S4.  Get High-res Image As an example, this figure shows the association of LHX3|8022 to 'PATHOLOGY.M.STAGE'. P value = 4.24e-10 with ANOVA analysis.

Clinical variable #7: 'GENDER'

17 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 49
  MALE 149
     
  Significant markers N = 17
  Higher in MALE 11
  Higher in FEMALE 6
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
USP9Y|8287 44.26 2.129e-68 3.89e-64 1
PRKY|5616 29.36 1.931e-53 3.52e-49 0.9973
XIST|7503 -27.63 1.454e-52 2.65e-48 0.9947
TMSB4Y|9087 27.79 2.145e-51 3.91e-47 0.9859
CYORF15A|246126 36.74 3.827e-48 6.98e-44 1
RPS4Y1|6192 41.62 2.563e-39 4.68e-35 1
ZFY|7544 33.43 1.725e-38 3.15e-34 0.9994
DDX3Y|8653 38.47 6.387e-35 1.17e-30 1
TSIX|9383 -20.58 1.471e-33 2.68e-29 0.9926
NLGN4Y|22829 21.3 1.012e-28 1.85e-24 0.9863

Figure S5.  Get High-res Image As an example, this figure shows the association of USP9Y|8287 to 'GENDER'. P value = 2.13e-68 with T-test analysis.

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

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

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 78.91 (16)
  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 S14.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
LOC283070|283070 -0.6604 4.109e-08 0.00075

Figure S6.  Get High-res Image As an example, this figure shows the association of LOC283070|283070 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 4.11e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 37.72 (25)
  Significant markers N = 0
Clinical variable #10: 'NUMBER.OF.LYMPH.NODES'

7 genes related to 'NUMBER.OF.LYMPH.NODES'.

Table S16.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 1.51 (3.3)
  Significant markers N = 7
  pos. correlated 6
  neg. correlated 1
List of 7 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S17.  Get Full Table List of 7 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
C6ORF141|135398 -0.4374 1.009e-07 0.00184
PRR15L|79170 0.4005 5.977e-07 0.0109
OCLN|4950 0.3947 8.985e-07 0.0164
SLC44A2|57153 0.3917 1.108e-06 0.0202
ANXA9|8416 0.3885 1.374e-06 0.0251
CREB3L2|64764 0.3827 2.027e-06 0.037
MCEE|84693 0.3793 2.546e-06 0.0465

Figure S7.  Get High-res Image As an example, this figure shows the association of C6ORF141|135398 to 'NUMBER.OF.LYMPH.NODES'. P value = 1.01e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #11: 'GLEASON_SCORE'

No gene related to 'GLEASON_SCORE'.

Table S18.  Basic characteristics of clinical feature: 'GLEASON_SCORE'

GLEASON_SCORE Mean (SD) 6.43 (0.67)
  Score N
  6 27
  7 13
  8 1
  9 1
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 198

  • Number of genes = 18263

  • Number of clinical features = 11

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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