PANCANCER: Correlation between mRNA expression and clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between 17814 genes and 11 clinical features across 1643 samples, statistically thresholded by Q value < 0.05, 11 clinical features related to at least one genes.

  • 2941 genes correlated to 'Time to Death'.

    • KLHL14 ,  ASS1 ,  UPK1B ,  C4ORF32 ,  ZRSR1 ,  ...

  • 4207 genes correlated to 'AGE'.

    • GALNTL1 ,  DUOX2 ,  GREB1 ,  BACE1 ,  OBSL1 ,  ...

  • 17490 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • KLHL14 ,  ZG16 ,  SLC22A2 ,  FOXE1 ,  PDZD3 ,  ...

  • 8363 genes correlated to 'GENDER'.

    • ESR1 ,  DDX3Y ,  RPS4Y1 ,  RSPO1 ,  EIF1AY ,  ...

  • 1133 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • PPP1R9A ,  MGRN1 ,  SMARCC2 ,  IL8 ,  LGALS8 ,  ...

  • 17227 genes correlated to 'HISTOLOGICAL.TYPE'.

    • KLHL14 ,  PDZD3 ,  EMX2 ,  NPR1 ,  FUT4 ,  ...

  • 6063 genes correlated to 'PATHOLOGY.T'.

    • C13ORF3 ,  CDCA7 ,  LIFR ,  NR3C1 ,  PDSS1 ,  ...

  • 11 genes correlated to 'PATHOLOGY.N'.

    • TNFRSF10C ,  FAM73A ,  ZNF273 ,  GABARAP ,  MGC50559 ,  ...

  • 32 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • KCNC2 ,  TCF7 ,  HCN1 ,  C8ORF30A ,  SPACA3 ,  ...

  • 5989 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • CYP3A5 ,  TCF21 ,  AADAC ,  CYP3A4 ,  CYP3A7 ,  ...

  • 7770 genes correlated to 'NEOADJUVANT.THERAPY'.

    • UGT1A6 ,  ATP6V1B1 ,  NPAS3 ,  SCARA3 ,  GPX2 ,  ...

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=2941 shorter survival N=1335 longer survival N=1606
AGE Spearman correlation test N=4207 older N=2057 younger N=2150
PRIMARY SITE OF DISEASE ANOVA test N=17490        
GENDER t test N=8363 male N=3793 female N=4570
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1133 higher score N=599 lower score N=534
HISTOLOGICAL TYPE ANOVA test N=17227        
PATHOLOGY T Spearman correlation test N=6063 higher pT N=3246 lower pT N=2817
PATHOLOGY N Spearman correlation test N=11 higher pN N=3 lower pN N=8
PATHOLOGICSPREAD(M) ANOVA test N=32        
RADIATIONS RADIATION REGIMENINDICATION t test N=5989 yes N=2990 no N=2999
NEOADJUVANT THERAPY t test N=7770 yes N=3933 no N=3837
Clinical variable #1: 'Time to Death'

2941 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-223.4 (median=23)
  censored N = 1035
  death N = 450
     
  Significant markers N = 2941
  associated with shorter survival 1335
  associated with longer survival 1606
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
KLHL14 1.26 0 0 0.603
ASS1 1.3 0 0 0.625
UPK1B 1.21 0 0 0.637
C4ORF32 0.78 0 0 0.388
ZRSR1 0.57 0 0 0.347
MDS1 1.36 0 0 0.623
GALNT12 1.3 0 0 0.619
MUC4 1.22 0 0 0.636
NUBP1 0.46 0 0 0.351
BTG3 1.44 0 0 0.641

Figure S1.  Get High-res Image As an example, this figure shows the association of KLHL14 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'

4207 genes related to 'AGE'.

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

AGE Mean (SD) 60.96 (13)
  Significant markers N = 4207
  pos. correlated 2057
  neg. correlated 2150
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
GALNTL1 -0.2892 1.048e-32 1.87e-28
DUOX2 0.2882 1.728e-32 3.08e-28
GREB1 -0.2813 5.687e-31 1.01e-26
BACE1 -0.2806 8.134e-31 1.45e-26
OBSL1 -0.2799 1.173e-30 2.09e-26
BBOX1 -0.2779 3.106e-30 5.53e-26
ADORA1 -0.2749 1.363e-29 2.43e-25
LDOC1 -0.2705 1.135e-28 2.02e-24
KRTAP4-10 0.2695 1.843e-28 3.28e-24
DUOXA2 0.2643 2.119e-27 3.77e-23

Figure S2.  Get High-res Image As an example, this figure shows the association of GALNTL1 to 'AGE'. P value = 1.05e-32 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

17490 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S5.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  BREAST 529
  CENTRAL NERVOUS SYSTEM 27
  COLON 154
  ENDOMETRIAL 54
  KIDNEY 88
  LUNG 186
  OMENTUM 2
  OVARY 531
  PERITONEUM (OVARY) 2
  RECTUM 68
     
  Significant markers N = 17490
List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

ANOVA_P Q
KLHL14 0 0
ZG16 0 0
SLC22A2 0 0
FOXE1 0 0
PDZD3 0 0
EMX2 0 0
NPR1 0 0
ASS1 0 0
FUT4 0 0
ACF 0 0

Figure S3.  Get High-res Image As an example, this figure shows the association of KLHL14 to 'PRIMARY.SITE.OF.DISEASE'. P value = 0 with ANOVA analysis.

Clinical variable #4: 'GENDER'

8363 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 1323
  MALE 320
     
  Significant markers N = 8363
  Higher in MALE 3793
  Higher in FEMALE 4570
List of top 10 genes differentially expressed by 'GENDER'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
ESR1 -37.51 1.165e-186 2.08e-182 0.8756
DDX3Y 56.03 5.842e-184 1.04e-179 0.9837
RPS4Y1 55.35 1.483e-176 2.64e-172 0.9729
RSPO1 -31.87 1.76e-171 3.13e-167 0.8484
EIF1AY 51.15 2.653e-168 4.72e-164 0.9839
RPS4Y2 51.22 8.915e-166 1.59e-161 0.9817
JARID1D 46.66 6.18e-165 1.1e-160 0.9749
SCGB2A2 -30.33 1.122e-156 2e-152 0.8545
CYORF15A 45.72 1.089e-155 1.94e-151 0.9805
SCGB1D2 -30.48 1.33e-127 2.37e-123 0.8621

Figure S4.  Get High-res Image As an example, this figure shows the association of ESR1 to 'GENDER'. P value = 1.16e-186 with T-test analysis.

Clinical variable #5: 'KARNOFSKY.PERFORMANCE.SCORE'

1133 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S9.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 64.05 (34)
  Significant markers N = 1133
  pos. correlated 599
  neg. correlated 534
List of top 10 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S10.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
PPP1R9A 0.6224 3.048e-13 5.43e-09
MGRN1 0.607 1.646e-12 2.93e-08
SMARCC2 0.6067 1.685e-12 3e-08
IL8 -0.604 2.25e-12 4.01e-08
LGALS8 -0.6039 2.266e-12 4.04e-08
RNF40 0.603 2.49e-12 4.43e-08
PEA15 0.601 3.091e-12 5.51e-08
SDC1 -0.6002 3.368e-12 6e-08
CORO2B 0.5995 3.598e-12 6.41e-08
TTYH1 0.5989 3.849e-12 6.85e-08

Figure S5.  Get High-res Image As an example, this figure shows the association of PPP1R9A to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 3.05e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #6: 'HISTOLOGICAL.TYPE'

17227 genes related to 'HISTOLOGICAL.TYPE'.

Table S11.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 10
  COLON ADENOCARCINOMA 128
  COLON MUCINOUS ADENOCARCINOMA 24
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA 41
  KIDNEY CLEAR CELL RENAL CARCINOMA 72
  KIDNEY PAPILLARY RENAL CELL CARCINOMA 16
  LUNG ADENOCARCINOMA MIXED SUBTYPE 1
  LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 30
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 5
  LUNG CLEAR CELL ADENOCARCINOMA 1
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 148
  MIXED SEROUS AND ENDOMETRIOID 1
  OLIGOASTROCYTOMA 9
  OLIGODENDROGLIOMA 8
  RECTAL ADENOCARCINOMA 58
  RECTAL MUCINOUS ADENOCARCINOMA 7
  SEROUS CYSTADENOCARCINOMA 535
  SEROUS ENDOMETRIAL ADENOCARCINOMA 12
     
  Significant markers N = 17227
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

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

ANOVA_P Q
KLHL14 0 0
PDZD3 0 0
EMX2 0 0
NPR1 0 0
FUT4 0 0
FXYD2 0 0
CLDN3 0 0
SPINK4 0 0
SFTPC 0 0
SLC12A2 0 0

Figure S6.  Get High-res Image As an example, this figure shows the association of KLHL14 to 'HISTOLOGICAL.TYPE'. P value = 0 with ANOVA analysis.

Clinical variable #7: 'PATHOLOGY.T'

6063 genes related to 'PATHOLOGY.T'.

Table S13.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.28 (0.85)
  N
  T1 99
  T2 186
  T3 182
  T4 29
     
  Significant markers N = 6063
  pos. correlated 3246
  neg. correlated 2817
List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S14.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
C13ORF3 0.5362 2.934e-38 5.23e-34
CDCA7 0.525 1.745e-36 3.11e-32
LIFR -0.5247 1.959e-36 3.49e-32
NR3C1 -0.5182 1.991e-35 3.55e-31
PDSS1 0.5137 9.432e-35 1.68e-30
NOXO1 0.5123 1.561e-34 2.78e-30
MAL -0.509 4.748e-34 8.45e-30
C20ORF151 0.5069 9.891e-34 1.76e-29
C20ORF42 0.5054 1.641e-33 2.92e-29
ADHFE1 -0.505 1.837e-33 3.27e-29

Figure S7.  Get High-res Image As an example, this figure shows the association of C13ORF3 to 'PATHOLOGY.T'. P value = 2.93e-38 with Spearman correlation analysis.

Clinical variable #8: 'PATHOLOGY.N'

11 genes related to 'PATHOLOGY.N'.

Table S15.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.51 (0.77)
  N
  N0 291
  N1 92
  N2 61
  N3 5
     
  Significant markers N = 11
  pos. correlated 3
  neg. correlated 8
List of top 10 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S16.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
TNFRSF10C -0.2625 1.633e-08 0.000291
FAM73A -0.2567 3.455e-08 0.000616
ZNF273 0.2506 7.399e-08 0.00132
GABARAP -0.2445 1.558e-07 0.00277
MGC50559 -0.2441 1.635e-07 0.00291
LUZP2 0.2375 3.786e-07 0.00674
RASD1 -0.2271 1.165e-06 0.0207
IFIT3 -0.2242 1.598e-06 0.0285
CNDP2 -0.2221 2.001e-06 0.0356
DHCR7 0.2215 2.148e-06 0.0382

Figure S8.  Get High-res Image As an example, this figure shows the association of TNFRSF10C to 'PATHOLOGY.N'. P value = 1.63e-08 with Spearman correlation analysis.

Clinical variable #9: 'PATHOLOGICSPREAD(M)'

32 genes related to 'PATHOLOGICSPREAD(M)'.

Table S17.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 438
  M1 46
  M1A 1
     
  Significant markers N = 32
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S18.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
KCNC2 9.882e-10 1.76e-05
TCF7 1.647e-08 0.000293
HCN1 1.791e-08 0.000319
C8ORF30A 9.153e-08 0.00163
SPACA3 1.758e-07 0.00313
F10 2.716e-07 0.00484
SERPINA4 4.041e-07 0.0072
FGFR4 4.465e-07 0.00795
ACSL6 5.06e-07 0.00901
NPAS2 5.501e-07 0.00979

Figure S9.  Get High-res Image As an example, this figure shows the association of KCNC2 to 'PATHOLOGICSPREAD(M)'. P value = 9.88e-10 with ANOVA analysis.

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

5989 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S19.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 198
  YES 1445
     
  Significant markers N = 5989
  Higher in YES 2990
  Higher in NO 2999
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S20.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
CYP3A5 22.13 4.726e-79 8.42e-75 0.7776
TCF21 22.3 3.888e-75 6.93e-71 0.7946
AADAC 21.81 4.45e-75 7.93e-71 0.7813
CYP3A4 20.79 1.525e-73 2.72e-69 0.7649
CYP3A7 20.65 6.453e-72 1.15e-67 0.7555
REG1A 20.52 6.755e-69 1.2e-64 0.7779
CCDC71 22.26 1.905e-68 3.39e-64 0.8276
MAP2K7 21.01 4.837e-67 8.61e-63 0.7894
FBXO34 20.31 4.321e-64 7.69e-60 0.7825
PDZD3 18 4.552e-64 8.1e-60 0.6824

Figure S10.  Get High-res Image As an example, this figure shows the association of CYP3A5 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.73e-79 with T-test analysis.

Clinical variable #11: 'NEOADJUVANT.THERAPY'

7770 genes related to 'NEOADJUVANT.THERAPY'.

Table S21.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 715
  YES 928
     
  Significant markers N = 7770
  Higher in YES 3933
  Higher in NO 3837
List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S22.  Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
UGT1A6 21.77 1.433e-90 2.55e-86 0.7346
ATP6V1B1 -21.49 1.115e-89 1.99e-85 0.774
NPAS3 -21.51 1.933e-89 3.44e-85 0.7819
SCARA3 -21.35 3.558e-88 6.34e-84 0.776
GPX2 21.34 5.735e-87 1.02e-82 0.728
ZNF667 -20.87 8.483e-86 1.51e-81 0.7649
PPARG 20.84 9.932e-86 1.77e-81 0.7589
RNF150 -20.81 6.742e-85 1.2e-80 0.7658
SLC27A6 -20.65 1.755e-83 3.13e-79 0.7727
LRRN2 -20.33 4e-82 7.12e-78 0.7541

Figure S11.  Get High-res Image As an example, this figure shows the association of UGT1A6 to 'NEOADJUVANT.THERAPY'. P value = 1.43e-90 with T-test analysis.

Methods & Data
Input

  • Expresson data file = PANCANCER.medianexp.txt

  • Clinical data file = PANCANCER.clin.merged.picked.txt

  • Number of patients = 1643

  • Number of genes = 17814

  • Number of clinical features = 11

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

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] 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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