Correlation between miRseq expression and clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C1NP23WH
Overview
Introduction

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

Summary

Testing the association between 554 miRs and 4 clinical features across 538 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one miRs.

  • 30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-LET-7B ,  HSA-MIR-3616 ,  HSA-MIR-598 ,  HSA-MIR-1224 ,  HSA-MIR-146A ,  ...

  • 20 miRs correlated to 'RADIATION_THERAPY'.

    • HSA-MIR-509-2 ,  HSA-MIR-508 ,  HSA-MIR-509-1 ,  HSA-MIR-618 ,  HSA-MIR-509-3 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-9-3 ,  HSA-MIR-9-1 ,  HSA-MIR-9-2 ,  HSA-MIR-934 ,  HSA-MIR-34A ,  ...

  • 1 miR correlated to 'RESIDUAL_TUMOR'.

    • HSA-MIR-1229

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=30   N=NA   N=NA
RADIATION_THERAPY Wilcoxon test N=20 yes N=20 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RESIDUAL_TUMOR Kruskal-Wallis test N=1        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 miRs 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) 0.1-225.5 (median=29.9)
  censored N = 449
  death N = 88
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
HSA-LET-7B 0.000138 0.025 0.346
HSA-MIR-3616 0.000163 0.025 0.367
HSA-MIR-598 0.000203 0.025 0.607
HSA-MIR-1224 0.000207 0.025 0.415
HSA-MIR-146A 0.00023 0.025 0.387
HSA-MIR-3607 0.000375 0.032 0.446
HSA-MIR-663 0.00042 0.032 0.471
HSA-MIR-374C 0.000466 0.032 0.331
HSA-MIR-34A 0.000701 0.043 0.373
HSA-MIR-190B 0.000885 0.049 0.351
Clinical variable #2: 'RADIATION_THERAPY'

20 miRs related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 287
  YES 226
     
  Significant markers N = 20
  Higher in YES 20
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

Table S4.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-509-2 24218 0.0005071 0.199 0.5915
HSA-MIR-508 26993 0.001441 0.199 0.582
HSA-MIR-509-1 25473 0.002178 0.199 0.5801
HSA-MIR-618 9046 0.002195 0.199 0.6022
HSA-MIR-509-3 25591 0.002388 0.199 0.5794
HSA-MIR-143 37416 0.002786 0.199 0.5769
HSA-MIR-1270-2 19775 0.003097 0.199 0.5822
HSA-MIR-885 23987 0.003219 0.199 0.5782
HSA-MIR-3131 10730 0.003232 0.199 0.595
HSA-MIR-153-2 27754 0.005021 0.278 0.5721
Clinical variable #3: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA 407
  MIXED SEROUS AND ENDOMETRIOID 22
  SEROUS ENDOMETRIAL ADENOCARCINOMA 109
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
HSA-MIR-9-3 3.293e-31 1.82e-28
HSA-MIR-9-1 1.314e-28 2.5e-26
HSA-MIR-9-2 1.353e-28 2.5e-26
HSA-MIR-934 4.813e-25 6.67e-23
HSA-MIR-34A 2.017e-23 2.24e-21
HSA-MIR-375 4.142e-22 3.82e-20
HSA-MIR-221 2.536e-21 2.01e-19
HSA-MIR-452 1.101e-18 7.62e-17
HSA-MIR-497 2.735e-18 1.68e-16
HSA-MIR-195 4.357e-18 2.41e-16
Clinical variable #4: 'RESIDUAL_TUMOR'

One miR related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 369
  R1 22
  R2 15
  RX 40
     
  Significant markers N = 1
List of one miR differentially expressed by 'RESIDUAL_TUMOR'

Table S8.  Get Full Table List of one miR differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
HSA-MIR-1229 0.0003671 0.203
Methods & Data
Input
  • Expresson data file = UCEC-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 538

  • Number of miRs = 554

  • Number of clinical features = 4

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.

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] 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)
[3] 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)