Correlation between mRNAseq expression and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C11Z42F6
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/C11Z42F6
Overview
Introduction

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

Summary

Testing the association between 18295 genes and 7 clinical features across 480 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 2315 genes correlated to 'Time to Death'.

    • ANKRD56|345079 ,  B3GNTL1|146712 ,  COL7A1|1294 ,  DONSON|29980 ,  ADAMTS14|140766 ,  ...

  • 19 genes correlated to 'AGE'.

    • RANBP17|64901 ,  RFPL1S|10740 ,  WFDC1|58189 ,  UTY|7404 ,  PALLD|23022 ,  ...

  • 227 genes correlated to 'GENDER'.

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

  • 317 genes correlated to 'DISTANT.METASTASIS'.

    • GARNL3|84253 ,  IL20RB|53833 ,  PLEKHA9|51054 ,  C22ORF9|23313 ,  BIRC5|332 ,  ...

  • 31 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • PI3|5266 ,  CEP55|55165 ,  FAM64A|54478 ,  RPSAP52|204010 ,  UBE2T|29089 ,  ...

  • 2142 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • NR3C2|4306 ,  PLEKHA9|51054 ,  ALDH6A1|4329 ,  FKBP11|51303 ,  ACADSB|36 ,  ...

  • No genes correlated to '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=2315 shorter survival N=1496 longer survival N=819
AGE Spearman correlation test N=19 older N=3 younger N=16
GENDER t test N=227 male N=146 female N=81
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
DISTANT METASTASIS t test N=317 m1 N=264 m0 N=53
LYMPH NODE METASTASIS ANOVA test N=31        
NEOPLASM DISEASESTAGE ANOVA test N=2142        
Clinical variable #1: 'Time to Death'

2315 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-111 (median=34.3)
  censored N = 323
  death N = 154
     
  Significant markers N = 2315
  associated with shorter survival 1496
  associated with longer survival 819
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
ANKRD56|345079 0.71 0 0 0.319
B3GNTL1|146712 2.4 0 0 0.684
COL7A1|1294 1.32 0 0 0.676
DONSON|29980 2.7 0 0 0.686
ADAMTS14|140766 1.44 1.11e-16 2e-12 0.684
SLC16A12|387700 0.78 1.11e-16 2e-12 0.311
NUMBL|9253 1.85 2.22e-16 4.1e-12 0.687
ANAPC7|51434 5.8 3.331e-16 6.1e-12 0.677
STX1A|6804 1.73 3.331e-16 6.1e-12 0.678
RGS17|26575 1.43 4.441e-16 8.1e-12 0.666

Figure S1.  Get High-res Image As an example, this figure shows the association of ANKRD56|345079 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'

19 genes related to 'AGE'.

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

AGE Mean (SD) 60.58 (12)
  Significant markers N = 19
  pos. correlated 3
  neg. correlated 16
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
RANBP17|64901 -0.2556 1.392e-08 0.000255
RFPL1S|10740 -0.2559 1.702e-08 0.000311
WFDC1|58189 -0.2476 3.998e-08 0.000731
UTY|7404 -0.2758 1.044e-07 0.00191
PALLD|23022 -0.2349 1.983e-07 0.00363
NEFH|4744 -0.2324 2.701e-07 0.00494
DIO2|1734 -0.2285 4.416e-07 0.00808
FNDC1|84624 -0.2249 6.595e-07 0.0121
ZNF610|162963 -0.2249 6.608e-07 0.0121
KDM5D|8284 -0.248 8.594e-07 0.0157

Figure S2.  Get High-res Image As an example, this figure shows the association of RANBP17|64901 to 'AGE'. P value = 1.39e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

227 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 167
  MALE 313
     
  Significant markers N = 227
  Higher in MALE 146
  Higher in FEMALE 81
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 -44.56 1.4e-165 2.56e-161 0.9853
PRKY|5616 40.36 1.88e-110 3.44e-106 0.9858
NLGN4Y|22829 38.51 1.975e-84 3.61e-80 0.9856
RPS4Y1|6192 36.63 5.211e-74 9.53e-70 0.9894
ZFY|7544 37.95 7.956e-71 1.46e-66 0.9873
TSIX|9383 -23.93 6.198e-70 1.13e-65 0.9675
DDX3Y|8653 32.19 2.102e-59 3.84e-55 0.9819
KDM5C|8242 -17.04 1.013e-49 1.85e-45 0.8987
NCRNA00183|554203 -16.62 4.036e-46 7.38e-42 0.8681
KDM5D|8284 26.38 9.922e-41 1.81e-36 0.9806

Figure S3.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 1.4e-165 with T-test analysis.

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

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

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 90.88 (18)
  Score N
  0 1
  70 1
  80 3
  90 12
  100 17
     
  Significant markers N = 0
Clinical variable #5: 'DISTANT.METASTASIS'

317 genes related to 'DISTANT.METASTASIS'.

Table S8.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 403
  M1 77
     
  Significant markers N = 317
  Higher in M1 264
  Higher in M0 53
List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

T(pos if higher in 'M1') ttestP Q AUC
GARNL3|84253 -6.98 2.078e-10 3.8e-06 0.7458
IL20RB|53833 6.7 1.089e-09 1.99e-05 0.7308
PLEKHA9|51054 6.68 1.135e-09 2.08e-05 0.7383
C22ORF9|23313 6.61 1.462e-09 2.67e-05 0.735
BIRC5|332 6.63 1.615e-09 2.95e-05 0.7295
INHBE|83729 6.55 2.516e-09 4.6e-05 0.7275
NFE2L3|9603 6.48 2.544e-09 4.65e-05 0.7237
TYMP|1890 6.38 3.096e-09 5.66e-05 0.7102
OIP5|11339 6.38 4.433e-09 8.11e-05 0.7251
CENPA|1058 6.35 6.395e-09 0.000117 0.7237

Figure S4.  Get High-res Image As an example, this figure shows the association of GARNL3|84253 to 'DISTANT.METASTASIS'. P value = 2.08e-10 with T-test analysis.

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

31 genes related to 'LYMPH.NODE.METASTASIS'.

Table S10.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 228
  N1 17
  NX 235
     
  Significant markers N = 31
List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
PI3|5266 4.011e-09 7.34e-05
CEP55|55165 7.237e-08 0.00132
FAM64A|54478 7.259e-08 0.00133
RPSAP52|204010 8.717e-08 0.00159
UBE2T|29089 8.96e-08 0.00164
SKA1|220134 9.772e-08 0.00179
FOXM1|2305 1.654e-07 0.00302
IQGAP3|128239 1.943e-07 0.00355
CDCA8|55143 2.399e-07 0.00439
AURKB|9212 3.879e-07 0.00709

Figure S5.  Get High-res Image As an example, this figure shows the association of PI3|5266 to 'LYMPH.NODE.METASTASIS'. P value = 4.01e-09 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

2142 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S12.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 233
  STAGE II 49
  STAGE III 120
  STAGE IV 78
     
  Significant markers N = 2142
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
NR3C2|4306 3.868e-18 7.08e-14
PLEKHA9|51054 4.808e-18 8.8e-14
ALDH6A1|4329 6.075e-18 1.11e-13
FKBP11|51303 7.418e-18 1.36e-13
ACADSB|36 1.267e-17 2.32e-13
BIRC5|332 2.499e-17 4.57e-13
TRAF6|7189 6.463e-17 1.18e-12
UBE2C|11065 6.442e-17 1.18e-12
TMEM150C|441027 8.129e-17 1.49e-12
FAM160A1|729830 9.057e-17 1.66e-12

Figure S6.  Get High-res Image As an example, this figure shows the association of NR3C2|4306 to 'NEOPLASM.DISEASESTAGE'. P value = 3.87e-18 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 480

  • Number of genes = 18295

  • Number of clinical features = 7

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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