Correlation between mRNAseq expression and clinical features
Adrenocortical Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1GT5KW8
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/C1GT5KW8
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 17733 genes and 7 clinical features across 77 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

  • 283 genes correlated to 'Time to Death'.

    • DNA2|1763 ,  DAZAP1|26528 ,  ENPP4|22875 ,  LMNB2|84823 ,  TMEM194A|23306 ,  ...

  • 6 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • YJEFN3|374887 ,  SFRS16|11129 ,  DIAPH3|81624 ,  CENPJ|55835 ,  C8ORFK29|340393 ,  ...

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

    • SETD8|387893 ,  LMNB2|84823 ,  SFRS16|11129 ,  MCM10|55388 ,  YJEFN3|374887 ,  ...

  • 4 genes correlated to 'GENDER'.

    • RND2|8153 ,  CLN8|2055 ,  MAFG|4097 ,  FAM117A|81558

  • No genes correlated to 'AGE', 'PATHOLOGY.N.STAGE', and 'ETHNICITY'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test N=283 shorter survival N=211 longer survival N=72
AGE Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE Kruskal-Wallis test N=6        
PATHOLOGY T STAGE Spearman correlation test N=83 higher stage N=81 lower stage N=2
PATHOLOGY N STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=4 male N=4 female N=0
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time to Death'

283 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 4.1-153.6 (median=32.7)
  censored N = 52
  death N = 25
     
  Significant markers N = 283
  associated with shorter survival 211
  associated with longer survival 72
List of top 10 genes differentially expressed by 'Time to Death'

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
DNA2|1763 3.1 2.21e-09 3.9e-05 0.85
DAZAP1|26528 26 1.033e-08 0.00018 0.79
ENPP4|22875 0.62 1.127e-08 2e-04 0.205
LMNB2|84823 5.6 1.364e-08 0.00024 0.851
TMEM194A|23306 4.5 1.761e-08 0.00031 0.794
NCAPD2|9918 7.2 2.103e-08 0.00037 0.855
LIN9|286826 5.7 2.9e-08 0.00051 0.83
NCAPH|23397 2.3 3.644e-08 0.00065 0.844
KIF11|3832 2.9 3.908e-08 0.00069 0.846
NCAPD3|23310 7.7 4.271e-08 0.00076 0.819
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 46.74 (16)
  Significant markers N = 0
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

6 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 8
  STAGE II 33
  STAGE III 16
  STAGE IV 15
     
  Significant markers N = 6
List of 6 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S5.  Get Full Table List of 6 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
YJEFN3|374887 1.087e-05 0.193
SFRS16|11129 1.114e-05 0.198
DIAPH3|81624 1.193e-05 0.211
CENPJ|55835 1.278e-05 0.227
C8ORFK29|340393 1.431e-05 0.254
ZWILCH|55055 1.52e-05 0.269
Clinical variable #4: 'PATHOLOGY.T.STAGE'

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

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

PATHOLOGY.T.STAGE Mean (SD) 2.5 (0.99)
  N
  1 8
  2 38
  3 8
  4 18
     
  Significant markers N = 83
  pos. correlated 81
  neg. correlated 2
List of top 10 genes differentially expressed by 'PATHOLOGY.T.STAGE'

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

SpearmanCorr corrP Q
SETD8|387893 0.6252 4.328e-09 7.68e-05
LMNB2|84823 0.5613 2.908e-07 0.00516
SFRS16|11129 0.5613 2.914e-07 0.00517
MCM10|55388 0.5598 3.172e-07 0.00562
YJEFN3|374887 0.5558 4.855e-07 0.00861
FANCI|55215 0.5508 5.365e-07 0.00951
DIAPH3|81624 0.5482 7.464e-07 0.0132
CDK1|983 0.5422 8.661e-07 0.0154
C8ORFK29|340393 0.5555 8.789e-07 0.0156
UHRF1|29128 0.5396 1.002e-06 0.0178
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 Labels N
  class0 64
  class1 9
     
  Significant markers N = 0
Clinical variable #6: 'GENDER'

4 genes related to 'GENDER'.

Table S9.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 48
  MALE 29
     
  Significant markers N = 4
  Higher in MALE 4
  Higher in FEMALE 0
List of 4 genes differentially expressed by 'GENDER'

Table S10.  Get Full Table List of 4 genes differentially expressed by 'GENDER'. 10 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
RND2|8153 269 7.334e-06 0.13 0.8068
CLN8|2055 270 7.704e-06 0.137 0.806
MAFG|4097 272 8.498e-06 0.151 0.8046
FAM117A|81558 281 1.315e-05 0.233 0.7981
Clinical variable #7: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 7
  NOT HISPANIC OR LATINO 28
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 77

  • Number of genes = 17733

  • 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

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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