Correlation between mRNA expression and clinical features
Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C1BV7G07
Overview
Introduction

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

Summary

Testing the association between 17814 genes and 12 clinical features across 88 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one genes.

  • 1 gene correlated to 'YEARS_TO_BIRTH'.

    • OPN4

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • MTFMT ,  NOP5/NOP58 ,  BMP5 ,  TRIM11 ,  GNL3L ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • BMP5 ,  TRIM11 ,  GNL3L ,  TARS2 ,  HIST1H3A ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • UBIAD1 ,  WIF1 ,  SOAT2 ,  SNAP29 ,  ISLR ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • AGXT2L2 ,  CASP8 ,  GPRIN1 ,  STARD3 ,  B4GALT2 ,  ...

  • 16 genes correlated to 'GENDER'.

    • CYORF15A ,  JARID1D ,  CYORF15B ,  UTX ,  CXORF15 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • ZFYVE20 ,  CSNK1A1L ,  WHDC1L1 ,  SPATA13 ,  SOX17 ,  ...

  • 30 genes correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • RTN1 ,  ZBTB12 ,  GFI1 ,  C17ORF77 ,  ACOT2 ,  ...

  • 30 genes correlated to 'RACE'.

    • PCDH20 ,  TNNI3 ,  DUSP1 ,  FUT8 ,  CASQ2 ,  ...

  • 30 genes correlated to 'ETHNICITY'.

    • C14ORF73 ,  ADPRH ,  GBAS ,  VSNL1 ,  CAMK2B ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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=0        
YEARS_TO_BIRTH Spearman correlation test N=1 older N=1 younger N=0
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=18 lower stage N=12
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=28 n0 N=0
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=16 male N=16 female N=0
HISTOLOGICAL_TYPE Wilcoxon test N=30 kidney papillary renal cell carcinoma N=29 kidney clear cell renal carcinoma N=0
NUMBER_PACK_YEARS_SMOKED Spearman correlation test N=30 higher number_pack_years_smoked N=17 lower number_pack_years_smoked N=13
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test N=30 not hispanic or latino N=30 hispanic or latino N=0
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene 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=37.6)
  censored N = 68
  death N = 19
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

One gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 60.06 (12)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene differentially expressed by 'YEARS_TO_BIRTH'

Table S3.  Get Full Table List of one gene significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
OPN4 0.4458 1.513e-05 0.269
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGIC_STAGE'

PATHOLOGIC_STAGE Labels N
  STAGE I 45
  STAGE II 16
  STAGE III 15
  STAGE IV 6
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

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

kruskal_wallis_P Q
MTFMT 1.604e-05 0.11
NOP5/NOP58 1.71e-05 0.11
BMP5 2.82e-05 0.11
TRIM11 3.192e-05 0.11
GNL3L 3.647e-05 0.11
JARID1C 4.018e-05 0.11
ZNF646 4.718e-05 0.11
FKSG83 5.397e-05 0.11
SYPL1 6.081e-05 0.11
OSBPL1A 6.851e-05 0.11
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 1.67 (0.81)
  N
  T1 48
  T2 21
  T3 19
     
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
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
BMP5 -0.4491 1.141e-05 0.104
TRIM11 0.4486 1.17e-05 0.104
GNL3L 0.4289 3.05e-05 0.136
TARS2 0.4222 4.181e-05 0.136
HIST1H3A 0.4218 4.255e-05 0.136
OAT -0.4153 5.744e-05 0.136
PCBP2 -0.4145 5.944e-05 0.136
FAM89A 0.4139 6.122e-05 0.136
RNF125 0.4033 9.789e-05 0.176
ZNF233 -0.403 9.905e-05 0.176
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Labels N
  N0 37
  N1 4
     
  Significant markers N = 30
  Higher in N1 28
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S9.  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 0.001239 0.233 1
WIF1 148 0.00124 0.233 1
SOAT2 148 0.00124 0.233 1
SNAP29 0 0.00124 0.233 1
ISLR 148 0.00124 0.233 1
FLJ40869 148 0.00124 0.233 1
LEPRE1 147 0.001445 0.233 0.9932
RPS27L 1 0.001445 0.233 0.9932
SPEG 147 0.001445 0.233 0.9932
OR1J1 147 0.001445 0.233 0.9932
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 77
  class1 6
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
AGXT2L2 452 0.0001056 0.131 0.9784
CASP8 449 0.000131 0.131 0.9719
GPRIN1 447 0.000151 0.131 0.9675
STARD3 445 0.0001738 0.131 0.9632
B4GALT2 445 0.0001738 0.131 0.9632
MCM4 444 0.0001864 0.131 0.961
TRIM41 441 0.0002296 0.131 0.9545
BTF3 21 0.0002296 0.131 0.9545
KIAA1737 21 0.0002296 0.131 0.9545
C17ORF56 440 0.000246 0.131 0.9524
Clinical variable #7: 'GENDER'

16 genes related to 'GENDER'.

Table S12.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 33
  MALE 55
     
  Significant markers N = 16
  Higher in MALE 16
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 14 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
CYORF15A 1746 5.095e-13 3.03e-09 0.962
JARID1D 1735 1.019e-12 3.22e-09 0.9559
CYORF15B 1714 3.733e-12 8.31e-09 0.9444
UTX 402 1.345e-05 0.0141 0.7785
CXORF15 426 3.387e-05 0.0335 0.7653
CBLN3 431 4.084e-05 0.0383 0.7625
PRPF39 1381 4.566e-05 0.0402 0.7609
JARID1C 435 4.738e-05 0.0402 0.7603
PCDH21 1374 5.908e-05 0.0478 0.757
UCHL5IP 1368 7.347e-05 0.0565 0.7537
Clinical variable #8: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

Table S14.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  KIDNEY CLEAR CELL RENAL CARCINOMA 72
  KIDNEY PAPILLARY RENAL CELL CARCINOMA 16
     
  Significant markers N = 30
  Higher in KIDNEY PAPILLARY RENAL CELL CARCINOMA 29
  Higher in KIDNEY CLEAR CELL RENAL CARCINOMA 0
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

W(pos if higher in 'KIDNEY PAPILLARY RENAL CELL CARCINOMA') wilcoxontestP Q AUC
ZFYVE20 c("1152", "4.78e-10") c("1152", "4.78e-10") 1.37e-06 1
CSNK1A1L c("0", "4.782e-10") c("0", "4.782e-10") 1.37e-06 1
WHDC1L1 c("3", "5.88e-10") c("3", "5.88e-10") 1.37e-06 0.9974
SPATA13 c("4", "6.297e-10") c("4", "6.297e-10") 1.37e-06 0.9965
SOX17 c("5", "6.744e-10") c("5", "6.744e-10") 1.37e-06 0.9957
SFRS2IP c("7", "7.731e-10") c("7", "7.731e-10") 1.37e-06 0.9939
RBM22 c("10", "9.482e-10") c("10", "9.482e-10") 1.37e-06 0.9913
PLDN c("14", "1.243e-09") c("14", "1.243e-09") 1.37e-06 0.9878
ODC1 c("1137", "1.329e-09") c("1137", "1.329e-09") 1.37e-06 0.987
SCLT1 c("16", "1.422e-09") c("16", "1.422e-09") 1.37e-06 0.9861
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

30 genes related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 15.6 (8.6)
  Value N
  8 1
  10 1
  15 2
  30 1
     
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
List of top 10 genes differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

Table S17.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_PACK_YEARS_SMOKED' by Spearman correlation test

SpearmanCorr corrP Q
RTN1 0.9747 0.004818 0.293
ZBTB12 -0.9747 0.004818 0.293
GFI1 0.9747 0.004818 0.293
C17ORF77 -0.9747 0.004818 0.293
ACOT2 0.9747 0.004818 0.293
SOCS3 -0.9747 0.004818 0.293
ADAM29 0.9747 0.004818 0.293
ALOX5AP 0.9747 0.004818 0.293
ITGAD 0.9747 0.004818 0.293
FLJ43826 -0.9747 0.004818 0.293
Clinical variable #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1977.67 (12)
  Value N
  1965 1
  1979 1
  1989 1
     
  Significant markers N = 0
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

Table S19.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 1
  BLACK OR AFRICAN AMERICAN 12
  WHITE 71
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S20.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
PCDH20 2.277e-06 0.0223
TNNI3 5.313e-06 0.0223
DUSP1 7.28e-06 0.0223
FUT8 9.127e-06 0.0223
CASQ2 9.143e-06 0.0223
LGI4 1.06e-05 0.0223
GMCL1L 1.381e-05 0.0223
SCEL 1.517e-05 0.0223
GLRB 2.29e-05 0.0223
RAMP3 2.462e-05 0.0223
Clinical variable #12: 'ETHNICITY'

30 genes related to 'ETHNICITY'.

Table S21.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 7
  NOT HISPANIC OR LATINO 58
     
  Significant markers N = 30
  Higher in NOT HISPANIC OR LATINO 30
  Higher in HISPANIC OR LATINO 0
List of top 10 genes differentially expressed by 'ETHNICITY'

Methods & Data
Input
  • Expresson data file = KIPAN-TP.medianexp.txt

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

  • Number of patients = 88

  • Number of genes = 17814

  • Number of clinical features = 12

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)