Breast Invasive Carcinoma: Correlation between miRseq expression and clinical features
(primary solid tumor cohort)
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

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

Summary

Testing the association between 510 genes and 8 clinical features across 848 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 2 genes correlated to 'Time to Death'.

    • HSA-MIR-874 ,  HSA-MIR-148B

  • 28 genes correlated to 'AGE'.

    • HSA-MIR-424 ,  HSA-MIR-381 ,  HSA-MIR-31 ,  HSA-MIR-598 ,  HSA-MIR-542 ,  ...

  • 7 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • HSA-MIR-874 ,  HSA-MIR-374C ,  HSA-MIR-328 ,  HSA-MIR-326 ,  HSA-MIR-197 ,  ...

  • 11 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-143 ,  HSA-MIR-210 ,  HSA-LET-7F-2 ,  HSA-MIR-200A ,  HSA-MIR-338 ,  ...

  • No genes correlated to 'GENDER', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'DISTANT.METASTASIS', and 'NUMBER.OF.LYMPH.NODES'.

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=2 shorter survival N=2 longer survival N=0
AGE Spearman correlation test N=28 older N=2 younger N=26
GENDER t test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
DISTANT METASTASIS ANOVA test   N=0        
LYMPH NODE METASTASIS ANOVA test N=7        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE ANOVA test N=11        
Clinical variable #1: 'Time to Death'

2 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=18.5)
  censored N = 694
  death N = 95
     
  Significant markers N = 2
  associated with shorter survival 2
  associated with longer survival 0
List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-874 1.58 7.248e-06 0.0037 0.606
HSA-MIR-148B 1.8 4.365e-05 0.022 0.629

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-874 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 7.25e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

28 genes related to 'AGE'.

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

AGE Mean (SD) 58.42 (13)
  Significant markers N = 28
  pos. correlated 2
  neg. correlated 26
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
HSA-MIR-424 -0.2256 3.078e-11 1.57e-08
HSA-MIR-381 -0.2078 1.017e-09 5.18e-07
HSA-MIR-31 -0.2039 3.324e-09 1.69e-06
HSA-MIR-598 -0.1981 6.079e-09 3.08e-06
HSA-MIR-542 -0.1967 7.796e-09 3.94e-06
HSA-MIR-99A -0.1892 2.891e-08 1.46e-05
HSA-MIR-652 -0.1768 2.235e-07 0.000113
HSA-LET-7C -0.1722 4.622e-07 0.000232
HSA-MIR-450B -0.1633 1.803e-06 0.000905
HSA-MIR-125B-1 -0.1531 7.592e-06 0.0038

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-424 to 'AGE'. P value = 3.08e-11 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 839
  MALE 9
     
  Significant markers N = 0
Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S6.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 210
  YES 638
     
  Significant markers N = 0
Clinical variable #5: 'DISTANT.METASTASIS'

No gene related to 'DISTANT.METASTASIS'.

Table S7.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  CM0 (I+) 2
  M0 756
  M1 14
  MX 76
     
  Significant markers N = 0
Clinical variable #6: 'LYMPH.NODE.METASTASIS'

7 genes related to 'LYMPH.NODE.METASTASIS'.

Table S8.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 248
  N0 (I+) 22
  N0 (I-) 128
  N0 (MOL+) 1
  N1 101
  N1A 126
  N1B 32
  N1C 2
  N1MI 23
  N2 50
  N2A 50
  N3 18
  N3A 29
  N3B 2
  N3C 1
  NX 15
     
  Significant markers N = 7
List of 7 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S9.  Get Full Table List of 7 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
HSA-MIR-874 1.492e-08 7.61e-06
HSA-MIR-374C 2.336e-07 0.000119
HSA-MIR-328 7.44e-06 0.00378
HSA-MIR-326 1.194e-05 0.00605
HSA-MIR-197 1.4e-05 0.00708
HSA-MIR-331 2.72e-05 0.0137
HSA-MIR-574 2.995e-05 0.0151

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-874 to 'LYMPH.NODE.METASTASIS'. P value = 1.49e-08 with ANOVA analysis.

Clinical variable #7: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S10.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.22 (4.4)
  Significant markers N = 0
Clinical variable #8: 'NEOPLASM.DISEASESTAGE'

11 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S11.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 72
  STAGE IA 64
  STAGE IB 7
  STAGE II 8
  STAGE IIA 289
  STAGE IIB 189
  STAGE III 2
  STAGE IIIA 120
  STAGE IIIB 24
  STAGE IIIC 40
  STAGE IV 14
  STAGE TIS 1
  STAGE X 17
     
  Significant markers N = 11
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

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

ANOVA_P Q
HSA-MIR-143 3.311e-09 1.69e-06
HSA-MIR-210 1.467e-08 7.47e-06
HSA-LET-7F-2 3e-07 0.000152
HSA-MIR-200A 1.097e-06 0.000556
HSA-MIR-338 1.269e-05 0.00642
HSA-MIR-374C 2.413e-05 0.0122
HSA-MIR-3607 2.578e-05 0.013
HSA-MIR-3653 6.027e-05 0.0303
HSA-MIR-642A 9.235e-05 0.0464
HSA-MIR-200B 9.28e-05 0.0465

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-143 to 'NEOPLASM.DISEASESTAGE'. P value = 3.31e-09 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = BRCA-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 848

  • Number of genes = 510

  • Number of clinical features = 8

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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