Correlation between mRNA expression and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1DF6QKV
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DF6QKV
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features. The input file "KIRC-TP.medianexp.txt" is generated in the pipeline mRNA_Preprocess_Median in the stddata run.

Summary

Testing the association between 17814 genes and 9 clinical features across 72 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one genes.

  • 25 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • FLJ35848 ,  HIST1H3F ,  FNDC1 ,  TARBP1 ,  DNA2L ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • COL18A1 ,  C5ORF35 ,  OPN4 ,  MS4A6E ,  RGS12 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • NOP5/NOP58 ,  GNL3L ,  JARID1C ,  MOV10 ,  TRIM11 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • TRIM11 ,  LOC201725 ,  TARS2 ,  ZNF652 ,  GNL3L ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • UBIAD1 ,  CCBP2 ,  CORIN ,  LHX4 ,  ANAPC13 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • NLGN4Y ,  OPRS1 ,  NMT1 ,  KPNB1 ,  SAMD13 ,  ...

  • 17 genes correlated to 'GENDER'.

    • CYORF15A ,  JARID1D ,  CYORF15B ,  UCHL5IP ,  RERG ,  ...

  • No genes correlated to 'RACE', 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
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test N=25   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=13 younger N=17
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=17 lower stage N=13
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=20 n0 N=0
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=17 male N=17 female N=0
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

25 genes related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1.  Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0.5-117.8 (median=39)
  censored N = 56
  death N = 15
     
  Significant markers N = 25
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
FLJ35848 2e-06 0.036 0.349
HIST1H3F 5.94e-06 0.053 0.619
FNDC1 1.49e-05 0.077 0.56
TARBP1 1.73e-05 0.077 0.739
DNA2L 3.65e-05 0.13 0.766
SLC26A8 6.89e-05 0.2 0.746
KRT36 9.95e-05 0.2 0.327
C9ORF3 0.000121 0.2 0.221
RNF168 0.000124 0.2 0.224
PPP3CA 0.000125 0.2 0.196
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 60.54 (12)
  Significant markers N = 30
  pos. correlated 13
  neg. correlated 17
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
COL18A1 -0.5036 7.595e-06 0.119
C5ORF35 0.4881 1.575e-05 0.119
OPN4 0.4828 2.009e-05 0.119
MS4A6E 0.4492 8.528e-05 0.286
RGS12 -0.4491 8.551e-05 0.286
BANP -0.4382 0.0001326 0.286
PUM1 -0.4365 0.0001415 0.286
HIP1R -0.4335 0.0001592 0.286
SGSH -0.4323 0.0001667 0.286
C1ORF53 0.4278 0.0001982 0.286
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 40
  STAGE II 13
  STAGE III 14
  STAGE IV 5
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
NOP5/NOP58 9.01e-06 0.06
GNL3L 1.626e-05 0.06
JARID1C 2.521e-05 0.06
MOV10 2.663e-05 0.06
TRIM11 3.671e-05 0.06
MTFMT 4.48e-05 0.06
KHSRP 4.686e-05 0.06
HELB 4.771e-05 0.06
PARP14 4.925e-05 0.06
GSTO2 5.044e-05 0.06
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 1.67 (0.84)
  N
  T1 41
  T2 14
  T3 17
     
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
TRIM11 0.5186 3.057e-06 0.0306
LOC201725 -0.5164 3.432e-06 0.0306
TARS2 0.4975 8.747e-06 0.0414
ZNF652 0.4963 9.286e-06 0.0414
GNL3L 0.4808 1.917e-05 0.0596
C9ORF138 0.4797 2.008e-05 0.0596
RRP1 0.4714 2.915e-05 0.0742
LIG3 0.4676 3.458e-05 0.077
DKFZP451M2119 -0.4635 4.135e-05 0.0818
ATM 0.4608 4.632e-05 0.0825
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 35
  N1 3
     
  Significant markers N = 30
  Higher in N1 20
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
UBIAD1 0 0.004874 0.22 1
CCBP2 105 0.004874 0.22 1
CORIN 105 0.004877 0.22 1
LHX4 105 0.004877 0.22 1
ANAPC13 0 0.004877 0.22 1
JMJD2A 105 0.004877 0.22 1
BTBD14B 105 0.004879 0.22 1
DEFB106B 105 0.004879 0.22 1
FBXL3 0 0.004879 0.22 1
ZNF783 105 0.004879 0.22 1
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 67
  class1 5
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
NLGN4Y 1 0.0002358 0.0677 0.997
OPRS1 333 0.0002571 0.0677 0.994
NMT1 330 0.0003325 0.0677 0.9851
KPNB1 330 0.0003325 0.0677 0.9851
SAMD13 5 0.0003325 0.0677 0.9851
CASP8 330 0.0003325 0.0677 0.9851
JARID1C 330 0.0003325 0.0677 0.9851
TCF23 329 0.0003618 0.0677 0.9821
UTY 6 0.0003619 0.0677 0.9821
BAT2 329 0.0003619 0.0677 0.9821
Clinical variable #7: 'GENDER'

17 genes related to 'GENDER'.

Table S13.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 29
  MALE 43
     
  Significant markers N = 17
  Higher in MALE 17
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 13 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
CYORF15A 1204 2.753e-11 1.77e-07 0.9655
JARID1D 1195 5.531e-11 1.77e-07 0.9583
CYORF15B 1174 2.705e-10 5.35e-07 0.9415
UCHL5IP 1000 1.581e-05 0.0188 0.8019
RERG 970 7.11e-05 0.0704 0.7779
DAZL 965.5 8.82e-05 0.0805 0.7743
SLITRK6 282 9.034e-05 0.0805 0.7739
MGC14425 290 0.0001317 0.112 0.7674
JARID1C 293 0.0001513 0.123 0.765
PCDH21 950 0.0001819 0.136 0.7618
Clinical variable #8: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 1
  BLACK OR AFRICAN AMERICAN 5
  WHITE 62
     
  Significant markers N = 0
Clinical variable #9: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S16.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 7
  NOT HISPANIC OR LATINO 42
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = KIRC-TP.medianexp.txt

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

  • Number of patients = 72

  • Number of genes = 17814

  • Number of clinical features = 9

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[4] 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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