Breast Invasive Carcinoma: Correlation between mRNAseq expression and clinical features
Maintained by Juok Cho (Broad Institute)

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


Testing the association between 18254 genes and 4 clinical features across 834 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

  • 4 genes correlated to 'Time to Death'.

    • DIP2B|57609 ,  PGK1|5230 ,  CAND1|55832 ,  IRF2|3660

  • 689 genes correlated to 'AGE'.

    • ESR1|2099 ,  TFPI2|7980 ,  LRFN5|145581 ,  TMEFF1|8577 ,  SOBP|55084 ,  ...

  • 18 genes correlated to 'GENDER'.

    • NLGN4Y|22829 ,  ZFY|7544 ,  PRKY|5616 ,  C7ORF10|79783 ,  SYT9|143425 ,  ...


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=4 shorter survival N=3 longer survival N=1
AGE Spearman correlation test N=689 older N=163 younger N=526
GENDER t test N=18 male N=8 female N=10
Clinical variable #1: 'Time to Death'

4 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-223.4 (median=18.9)
  censored N = 683
  death N = 94
  Significant markers N = 4
  associated with shorter survival 3
  associated with longer survival 1
List of 4 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 4 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
DIP2B|57609 2.9 1.611e-07 0.0029 0.635
PGK1|5230 2 1.673e-06 0.031 0.685
CAND1|55832 2.1 1.686e-06 0.031 0.629
IRF2|3660 0.28 1.983e-06 0.036 0.329

Figure S1.  Get High-res Image As an example, this figure shows the association of DIP2B|57609 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.61e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

689 genes related to 'AGE'.

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

AGE Mean (SD) 58.18 (13)
  Significant markers N = 689
  pos. correlated 163
  neg. correlated 526
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
ESR1|2099 0.3524 9.285e-26 1.69e-21
TFPI2|7980 -0.268 4.569e-15 8.34e-11
LRFN5|145581 -0.2692 5.372e-15 9.81e-11
TMEFF1|8577 -0.2528 1.31e-13 2.39e-09
SOBP|55084 -0.2517 1.69e-13 3.08e-09
DBX2|440097 -0.2673 4.243e-13 7.74e-09
DZIP1|22873 -0.2461 5.847e-13 1.07e-08
PCDH18|54510 -0.2459 6.176e-13 1.13e-08
RELN|5649 -0.2468 9.321e-13 1.7e-08
FXYD6|53826 -0.2409 1.831e-12 3.34e-08

Figure S2.  Get High-res Image As an example, this figure shows the association of ESR1|2099 to 'AGE'. P value = 9.28e-26 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

18 genes related to 'GENDER'.

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

  FEMALE 825
  MALE 9
  Significant markers N = 18
  Higher in MALE 8
  Higher in FEMALE 10
List of top 10 genes differentially expressed by 'GENDER'

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

T(pos if higher in 'MALE') ttestP Q AUC
NLGN4Y|22829 41.17 2.294e-12 4.12e-08 1
ZFY|7544 41.94 1.786e-11 3.21e-07 1
PRKY|5616 27.63 6.061e-10 1.09e-05 1
C7ORF10|79783 8.61 3.928e-09 7.06e-05 0.6626
SYT9|143425 12.51 5.949e-09 0.000107 0.7954
GSTA1|2938 -16.04 8.563e-09 0.000154 0.8829
MMP11|4320 11.05 1.626e-08 0.000292 0.7461
RND2|8153 12.35 2.543e-08 0.000457 0.8361
SNORA74B|677841 -12 8.287e-08 0.00149 0.8348
HTR4|3360 -12.07 1.118e-07 0.00201 0.7909

Figure S3.  Get High-res Image As an example, this figure shows the association of NLGN4Y|22829 to 'GENDER'. P value = 2.29e-12 with T-test analysis.



Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

  NO 197
  YES 637
  Significant markers N = 0
Methods & Data
  • Expresson data file = BRCA-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

  • Clinical data file = BRCA-TP.clin.merged.picked.txt

  • Number of patients = 834

  • Number of genes = 18254

  • Number of clinical features = 4

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

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.

[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] 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)