Correlation between mRNAseq expression and clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1X92901
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/C1X92901
Overview
Introduction

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

Summary

Testing the association between 18555 genes and 5 clinical features across 477 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 378 genes correlated to 'AGE'.

    • DIO2|1734 ,  FAM107A|11170 ,  PTCH1|5727 ,  S100A1|6271 ,  HIF3A|64344 ,  ...

  • 3894 genes correlated to 'HISTOLOGICAL.TYPE'.

    • L1CAM|3897 ,  KIAA1324|57535 ,  CLDN6|9074 ,  FOXA2|3170 ,  HIF3A|64344 ,  ...

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

    • ANXA2P3|305 ,  RPL23AP82|284942 ,  UBE2MP1|606551 ,  LOC407835|407835 ,  PGAM4|441531 ,  ...

  • 8 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • ST3GAL4|6484 ,  SLC7A10|56301 ,  FRMD1|79981 ,  MAML3|55534 ,  CIRBP|1153 ,  ...

  • No genes correlated to 'Time to Death'

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=378 older N=229 younger N=149
HISTOLOGICAL TYPE ANOVA test N=3894        
RADIATIONS RADIATION REGIMENINDICATION t test N=116 yes N=9 no N=107
COMPLETENESS OF RESECTION ANOVA test N=8        
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) 0-191.8 (median=20.9)
  censored N = 416
  death N = 59
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

378 genes related to 'AGE'.

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

AGE Mean (SD) 63.71 (11)
  Significant markers N = 378
  pos. correlated 229
  neg. correlated 149
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
DIO2|1734 -0.3771 1.56e-17 2.89e-13
FAM107A|11170 0.3561 1.117e-15 2.07e-11
PTCH1|5727 -0.335 6.056e-14 1.12e-09
S100A1|6271 0.3254 3.369e-13 6.25e-09
HIF3A|64344 0.3264 4.185e-13 7.76e-09
MGAT4A|11320 0.3237 4.471e-13 8.29e-09
NR2F6|2063 0.3199 8.659e-13 1.61e-08
DLC1|10395 -0.3158 1.754e-12 3.25e-08
PTGS1|5742 0.3127 2.95e-12 5.47e-08
DUSP9|1852 0.3252 3.007e-12 5.58e-08

Figure S1.  Get High-res Image As an example, this figure shows the association of DIO2|1734 to 'AGE'. P value = 1.56e-17 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

3894 genes related to 'HISTOLOGICAL.TYPE'.

Table S4.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA 365
  MIXED SEROUS AND ENDOMETRIOID 18
  SEROUS ENDOMETRIAL ADENOCARCINOMA 94
     
  Significant markers N = 3894
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
L1CAM|3897 1.098e-62 2.04e-58
KIAA1324|57535 9.24e-56 1.71e-51
CLDN6|9074 9.346e-50 1.73e-45
FOXA2|3170 2.117e-48 3.93e-44
HIF3A|64344 9.469e-48 1.76e-43
SLC6A12|6539 3.981e-44 7.39e-40
CDKN1A|1026 2.09e-42 3.88e-38
TFF3|7033 5.905e-42 1.1e-37
SPDEF|25803 8.112e-42 1.5e-37
IL20RA|53832 2.57e-40 4.77e-36

Figure S2.  Get High-res Image As an example, this figure shows the association of L1CAM|3897 to 'HISTOLOGICAL.TYPE'. P value = 1.1e-62 with ANOVA analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S6.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 134
  YES 343
     
  Significant markers N = 116
  Higher in YES 9
  Higher in NO 107
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
ANXA2P3|305 -7.41 7.017e-13 1.3e-08 0.6531
RPL23AP82|284942 -7.4 9.041e-13 1.68e-08 0.6949
UBE2MP1|606551 -7.15 4.583e-12 8.5e-08 0.6585
LOC407835|407835 -7.12 5.052e-12 9.37e-08 0.6529
PGAM4|441531 -7.1 5.7e-12 1.06e-07 0.659
ANXA2P1|303 -7.06 8.673e-12 1.61e-07 0.6572
POTEE|445582 -6.86 2.478e-11 4.6e-07 0.6587
TPI1P3|728402 -6.78 4.127e-11 7.66e-07 0.6584
UBE2NL|389898 -6.74 5.501e-11 1.02e-06 0.655
PPIAL4G|644591 -6.49 2.559e-10 4.75e-06 0.6444

Figure S3.  Get High-res Image As an example, this figure shows the association of ANXA2P3|305 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.02e-13 with T-test analysis.

Clinical variable #5: 'COMPLETENESS.OF.RESECTION'

8 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S8.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 328
  R1 24
  R2 16
  RX 27
     
  Significant markers N = 8
List of 8 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S9.  Get Full Table List of 8 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
ST3GAL4|6484 2.331e-07 0.00433
SLC7A10|56301 4.066e-07 0.00754
FRMD1|79981 4.09e-07 0.00759
MAML3|55534 6.453e-07 0.012
CIRBP|1153 1.134e-06 0.021
SVOP|55530 1.145e-06 0.0212
TMEM171|134285 1.417e-06 0.0263
NYX|60506 1.531e-06 0.0284

Figure S4.  Get High-res Image As an example, this figure shows the association of ST3GAL4|6484 to 'COMPLETENESS.OF.RESECTION'. P value = 2.33e-07 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 477

  • Number of genes = 18555

  • Number of clinical features = 5

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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