Correlation between miRseq expression and clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1Z037JZ
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Z037JZ
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features. The input file " DLBC-TP.miRseq_RPKM_log2.txt " is generated in the pipeline miRseq_Preprocess in the stddata run.

Summary

Testing the association between 540 miRs and 8 clinical features across 47 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one miRs.

  • 30 miRs correlated to 'RADIATION_THERAPY'.

    • HSA-MIR-101-2 ,  HSA-MIR-1229 ,  HSA-MIR-3136 ,  HSA-MIR-3677 ,  HSA-MIR-33B ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-376C ,  HSA-MIR-577 ,  HSA-MIR-584 ,  HSA-MIR-134 ,  HSA-MIR-409 ,  ...

  • 30 miRs correlated to 'ETHNICITY'.

    • HSA-MIR-497 ,  HSA-MIR-3194 ,  HSA-MIR-3136 ,  HSA-MIR-23B ,  HSA-MIR-101-2 ,  ...

  • No miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'GENDER', and 'RACE'.

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
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
YEARS_TO_BIRTH Spearman correlation test   N=0        
TUMOR_TISSUE_SITE Kruskal-Wallis test   N=0        
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test N=30 not hispanic or latino N=30 hispanic or latino N=0
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No miR related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1.  Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0-211.2 (median=27.4)
  censored N = 37
  death N = 9
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

No miR related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 56.28 (14)
  Significant markers N = 0
Clinical variable #3: 'TUMOR_TISSUE_SITE'

No miR related to 'TUMOR_TISSUE_SITE'.

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

TUMOR_TISSUE_SITE Labels N
  ADRENAL 2
  ASCITES/PERITONEUM 2
  BONE 4
  BRAIN 3
  BREAST 1
  COLON 2
  LIVER 1
  OTHER EXTRANODAL SITE 1
  PAROTID GLAND 1
  SMALL INTESTINE 3
  SOFT TISSUE (MUSCLE LIGAMENTS SUBCUTANEOUS) 1
  STOMACH 2
  THYROID 1
     
  Significant markers N = 0
Clinical variable #4: 'GENDER'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 25
  MALE 22
     
  Significant markers N = 0
Clinical variable #5: 'RADIATION_THERAPY'

30 miRs related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 40
  YES 6
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

Table S6.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-101-2 226 0.0005795 0.229 0.9417
HSA-MIR-1229 8 0.001166 0.229 0.9556
HSA-MIR-3136 24 0.002012 0.229 0.8974
HSA-MIR-3677 27 0.002553 0.229 0.8875
HSA-MIR-33B 27 0.002805 0.229 0.8846
HSA-MIR-579 27 0.003094 0.229 0.8816
HSA-MIR-24-1 29 0.003159 0.229 0.8792
HSA-MIR-3187 15 0.003396 0.229 0.8958
HSA-MIR-1976 36 0.00646 0.299 0.85
HSA-MIR-1270-2 33 0.0066 0.299 0.8514
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL) 40
  PRIMARY DLBCL OF THE CNS 3
  PRIMARY MEDIASTINAL (THYMIC) DLBCL 4
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S8.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
HSA-MIR-376C 0.001704 0.185
HSA-MIR-577 0.001768 0.185
HSA-MIR-584 0.00234 0.185
HSA-MIR-134 0.002594 0.185
HSA-MIR-409 0.002789 0.185
HSA-MIR-125B-2 0.002866 0.185
HSA-MIR-127 0.003399 0.185
HSA-MIR-136 0.003425 0.185
HSA-MIR-758 0.004036 0.185
HSA-LET-7D 0.004665 0.185
Clinical variable #7: 'RACE'

No miR related to 'RACE'.

Table S9.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 18
  BLACK OR AFRICAN AMERICAN 1
  WHITE 28
     
  Significant markers N = 0
Clinical variable #8: 'ETHNICITY'

30 miRs related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 35
     
  Significant markers N = 30
  Higher in NOT HISPANIC OR LATINO 30
  Higher in HISPANIC OR LATINO 0
List of top 10 miRs differentially expressed by 'ETHNICITY'

Methods & Data
Input

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

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

  • Number of patients = 47

  • Number of miRs = 540

  • Number of clinical features = 8

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[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)
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