Colon Adenocarcinoma: 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 9 clinical features across 155 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • ANAPC1

  • 21 genes correlated to 'GENDER'.

    • DDX3Y ,  JARID1D ,  EIF1AY ,  RPS4Y1 ,  CYORF15A ,  ...

  • 153 genes correlated to 'HISTOLOGICAL.TYPE'.

    • C20ORF24 ,  DYNLRB1 ,  AGR2 ,  C20ORF4 ,  C10ORF65 ,  ...

  • 1 gene correlated to 'PATHOLOGICSPREAD(M)'.

    • KCNC2

  • 2 genes correlated to 'NEOADJUVANT.THERAPY'.

    • FAM100A ,  MYO10

  • No genes correlated to 'Time to Death', 'PATHOLOGY.T', 'PATHOLOGY.N', and 'TUMOR.STAGE'.

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=1 older N=0 younger N=1
GENDER t test N=21 male N=13 female N=8
HISTOLOGICAL TYPE t test N=153 colon mucinous adenocarcinoma N=48 colon adenocarcinoma N=105
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=1        
TUMOR STAGE Spearman correlation test   N=0        
NEOADJUVANT THERAPY t test N=2 yes N=2 no N=0
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) 0.9-52 (median=5)
  censored N = 64
  death N = 11
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 70.55 (12)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'AGE' by Spearman correlation test

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

SpearmanCorr corrP Q
ANAPC1 -0.385 7.58e-07 0.0135

Figure S1.  Get High-res Image As an example, this figure shows the association of ANAPC1 to 'AGE'. P value = 7.58e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

21 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 76
  MALE 79
     
  Significant markers N = 21
  Higher in MALE 13
  Higher in FEMALE 8
List of top 10 genes differentially expressed by 'GENDER'

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

T(pos if higher in 'MALE') ttestP Q AUC
DDX3Y 22.53 2.909e-50 5.18e-46 0.9742
JARID1D 20.65 4.391e-46 7.82e-42 0.9787
EIF1AY 20.41 2.042e-45 3.64e-41 0.9654
RPS4Y1 19.25 1.481e-42 2.64e-38 0.9509
CYORF15A 18.57 1.055e-40 1.88e-36 0.9557
RPS4Y2 18.38 3.613e-40 6.43e-36 0.9625
UTY 17.81 3.766e-38 6.71e-34 0.957
ZFY 15.52 3.264e-33 5.81e-29 0.947
CYORF15B 15.52 9.991e-33 1.78e-28 0.9452
USP9Y 10.82 1.525e-20 2.72e-16 0.8946

Figure S2.  Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 2.91e-50 with T-test analysis.

Clinical variable #4: 'HISTOLOGICAL.TYPE'

153 genes related to 'HISTOLOGICAL.TYPE'.

Table S6.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  COLON ADENOCARCINOMA 128
  COLON MUCINOUS ADENOCARCINOMA 24
     
  Significant markers N = 153
  Higher in COLON MUCINOUS ADENOCARCINOMA 48
  Higher in COLON ADENOCARCINOMA 105
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

T(pos if higher in 'COLON MUCINOUS ADENOCARCINOMA') ttestP Q AUC
C20ORF24 -8.52 1.126e-11 2.01e-07 0.8545
DYNLRB1 -8.19 3.266e-11 5.82e-07 0.8376
AGR2 8.12 1.298e-10 2.31e-06 0.8721
C20ORF4 -7.68 4.344e-10 7.74e-06 0.8398
C10ORF65 -7.31 9.01e-10 1.6e-05 0.8115
PLA2G12B -7.55 1.006e-09 1.79e-05 0.8301
RDHE2 7.41 1.669e-09 2.97e-05 0.8174
ASXL1 -7.45 1.882e-09 3.35e-05 0.847
SLC5A6 -7.37 2.279e-09 4.06e-05 0.8356
EIF6 -7.39 2.672e-09 4.76e-05 0.8363

Figure S3.  Get High-res Image As an example, this figure shows the association of C20ORF24 to 'HISTOLOGICAL.TYPE'. P value = 1.13e-11 with T-test analysis.

Clinical variable #5: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.83 (0.6)
  N
  T1 4
  T2 31
  T3 105
  T4 13
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.59 (0.81)
  N
  N0 95
  N1 28
  N2 32
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGICSPREAD(M)'

One gene related to 'PATHOLOGICSPREAD(M)'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 129
  M1 22
  M1A 1
     
  Significant markers N = 1
List of one gene differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11.  Get Full Table List of one gene differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
KCNC2 2.165e-14 3.86e-10

Figure S4.  Get High-res Image As an example, this figure shows the association of KCNC2 to 'PATHOLOGICSPREAD(M)'. P value = 2.16e-14 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S12.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.36 (0.95)
  N
  Stage 1 28
  Stage 2 63
  Stage 3 39
  Stage 4 22
     
  Significant markers N = 0
Clinical variable #9: 'NEOADJUVANT.THERAPY'

2 genes related to 'NEOADJUVANT.THERAPY'.

Table S13.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 7
  YES 148
     
  Significant markers N = 2
  Higher in YES 2
  Higher in NO 0
List of 2 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S14.  Get Full Table List of 2 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
FAM100A 11.08 9.418e-14 1.68e-09 0.8919
MYO10 7.88 1.691e-06 0.0301 0.8359

Figure S5.  Get High-res Image As an example, this figure shows the association of FAM100A to 'NEOADJUVANT.THERAPY'. P value = 9.42e-14 with T-test analysis.

Methods & Data
Input

  • Expresson data file = COAD.medianexp.txt

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

  • Number of patients = 155

  • Number of genes = 17814

  • Number of clinical features = 9

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

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

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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