Correlation between miRseq expression and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1K0731P
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1K0731P
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 546 miRs and 11 clinical features across 171 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one miRs.

  • 1 miR correlated to 'Time to Death'.

    • HSA-MIR-570

  • 7 miRs correlated to 'AGE'.

    • HSA-MIR-200C ,  HSA-MIR-1269 ,  HSA-MIR-181D ,  HSA-MIR-296 ,  HSA-MIR-323 ,  ...

  • 3 miRs correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-3934 ,  HSA-MIR-550A-2 ,  HSA-MIR-122

  • 2 miRs correlated to 'GENDER'.

    • HSA-MIR-1266 ,  HSA-MIR-651

  • 7 miRs correlated to 'RACE'.

    • HSA-MIR-99B ,  HSA-MIR-1304 ,  HSA-MIR-3200 ,  HSA-MIR-330 ,  HSA-MIR-598 ,  ...

  • No miRs correlated to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'COMPLETENESS.OF.RESECTION', and 'ETHNICITY'.

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 miRs that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant miRs Associated with                 Associated with
Time to Death Cox regression test N=1 shorter survival N=1 longer survival N=0
AGE Spearman correlation test N=7 older N=1 younger N=6
NEOPLASM DISEASESTAGE Kruskal-Wallis test   N=0        
PATHOLOGY T STAGE Spearman correlation test N=3 higher stage N=2 lower stage N=1
PATHOLOGY N STAGE Wilcoxon test   N=0        
PATHOLOGY M STAGE Kruskal-Wallis test   N=0        
GENDER Wilcoxon test N=2 male N=2 female N=0
HISTOLOGICAL TYPE Kruskal-Wallis test   N=0        
COMPLETENESS OF RESECTION Kruskal-Wallis test   N=0        
RACE Kruskal-Wallis test N=7        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time to Death'

One miR related to 'Time to Death'.

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

Time to Death Duration (Months) 0-113 (median=13.6)
  censored N = 101
  death N = 66
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
List of one miR differentially expressed by 'Time to Death'

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

HazardRatio Wald_P Q C_index
HSA-MIR-570 1.62 6.867e-05 0.037 0.603
Clinical variable #2: 'AGE'

7 miRs related to 'AGE'.

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

AGE Mean (SD) 61.46 (14)
  Significant markers N = 7
  pos. correlated 1
  neg. correlated 6
List of 7 miRs differentially expressed by 'AGE'

Table S4.  Get Full Table List of 7 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-200C -0.2989 7.914e-05 0.0432
HSA-MIR-1269 0.2892 0.0001502 0.0819
HSA-MIR-181D -0.2849 0.000174 0.0946
HSA-MIR-296 -0.2812 0.0003457 0.188
HSA-MIR-323 -0.2776 0.000348 0.189
HSA-MIR-409 -0.2666 0.0004588 0.248
HSA-MIR-125A -0.2665 0.0004606 0.249
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S5.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 64
  STAGE II 39
  STAGE III 2
  STAGE IIIA 38
  STAGE IIIB 5
  STAGE IIIC 7
  STAGE IV 1
  STAGE IVA 1
  STAGE IVB 2
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.T.STAGE'

3 miRs related to 'PATHOLOGY.T.STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.01 (0.96)
  N
  1 68
  2 43
  3 49
  4 10
     
  Significant markers N = 3
  pos. correlated 2
  neg. correlated 1
List of 3 miRs differentially expressed by 'PATHOLOGY.T.STAGE'

Table S7.  Get Full Table List of 3 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-3934 0.3394 0.0001493 0.0815
HSA-MIR-550A-2 0.278 0.0002426 0.132
HSA-MIR-122 -0.2686 0.0003984 0.217
Clinical variable #5: 'PATHOLOGY.N.STAGE'

No miR related to 'PATHOLOGY.N.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 108
  class1 3
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

No miR related to 'PATHOLOGY.M.STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 128
  M1 3
  MX 40
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

2 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 64
  MALE 107
     
  Significant markers N = 2
  Higher in MALE 2
  Higher in FEMALE 0
List of 2 miRs differentially expressed by 'GENDER'

Table S11.  Get Full Table List of 2 miRs differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
HSA-MIR-1266 2246 0.0002294 0.125 0.6689
HSA-MIR-651 2211 0.0004126 0.225 0.6636
Clinical variable #8: 'HISTOLOGICAL.TYPE'

No miR related to 'HISTOLOGICAL.TYPE'.

Table S12.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  FIBROLAMELLAR CARCINOMA 2
  HEPATOCELLULAR CARCINOMA 166
  HEPATOCHOLANGIOCARCINOMA (MIXED) 3
     
  Significant markers N = 0
Clinical variable #9: 'COMPLETENESS.OF.RESECTION'

No miR related to 'COMPLETENESS.OF.RESECTION'.

Table S13.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 141
  R1 11
  R2 1
  RX 13
     
  Significant markers N = 0
Clinical variable #10: 'RACE'

7 miRs related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 41
  BLACK OR AFRICAN AMERICAN 8
  WHITE 115
     
  Significant markers N = 7
List of 7 miRs differentially expressed by 'RACE'

Table S15.  Get Full Table List of 7 miRs differentially expressed by 'RACE'

ANOVA_P Q
HSA-MIR-99B 5.117e-05 0.0279
HSA-MIR-1304 7.948e-05 0.0433
HSA-MIR-3200 9.01e-05 0.049
HSA-MIR-330 9.028e-05 0.049
HSA-MIR-598 0.0001444 0.0783
HSA-MIR-151 0.0002316 0.125
HSA-MIR-3677 0.0003292 0.178
Clinical variable #11: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 6
  NOT HISPANIC OR LATINO 151
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 171

  • Number of miRs = 546

  • 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

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] 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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