Correlation between miRseq expression and clinical features
Pancreatic Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C13777CW
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/C13777CW
Overview
Introduction

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

Summary

Testing the association between 505 miRs and 12 clinical features across 70 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.

  • 1 miR correlated to 'AGE'.

    • HSA-MIR-548B

  • 5 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-143 ,  HSA-MIR-133A-1 ,  HSA-MIR-1247 ,  HSA-MIR-133B ,  HSA-MIR-1-2

  • 6 miRs correlated to 'COMPLETENESS.OF.RESECTION'.

    • HSA-MIR-411 ,  HSA-MIR-758 ,  HSA-MIR-377 ,  HSA-MIR-329-1 ,  HSA-MIR-409 ,  ...

  • No miRs correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'NUMBERPACKYEARSSMOKED', 'YEAROFTOBACCOSMOKINGONSET', 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 miRs that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant miRs Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=1 older N=1 younger N=0
NEOPLASM DISEASESTAGE ANOVA test   N=0        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE t test   N=0        
PATHOLOGY M STAGE ANOVA test   N=0        
GENDER t test   N=0        
HISTOLOGICAL TYPE ANOVA test N=5        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=6        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

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

Time to Death Duration (Months) 0-49.4 (median=7.2)
  censored N = 41
  death N = 26
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One miR related to 'AGE'.

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

AGE Mean (SD) 65.64 (11)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one miR significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of one miR significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-548B 0.5351 4.366e-05 0.022

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-548B to 'AGE'. P value = 4.37e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE IA 2
  STAGE IB 4
  STAGE IIA 10
  STAGE IIB 50
  STAGE III 2
  STAGE IV 2
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.T.STAGE'

No miR related to 'PATHOLOGY.T.STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.9 (0.46)
  N
  1 2
  2 5
  3 61
  4 2
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 17
  class1 52
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 37
  M1 2
  MX 31
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

Table S8.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 36
  MALE 34
     
  Significant markers N = 0
Clinical variable #8: 'HISTOLOGICAL.TYPE'

5 miRs related to 'HISTOLOGICAL.TYPE'.

Table S9.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  PANCREAS-ADENOCARCINOMA DUCTAL TYPE 62
  PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE 5
  PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA 2
     
  Significant markers N = 5
List of 5 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S10.  Get Full Table List of 5 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-143 1.765e-06 0.000891
HSA-MIR-133A-1 9.155e-06 0.00461
HSA-MIR-1247 1.699e-05 0.00855
HSA-MIR-133B 3.058e-05 0.0154
HSA-MIR-1-2 6.069e-05 0.0304

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-143 to 'HISTOLOGICAL.TYPE'. P value = 1.76e-06 with ANOVA analysis.

Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No miR related to 'NUMBERPACKYEARSSMOKED'.

Table S11.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 24.51 (14)
  Significant markers N = 0
Clinical variable #10: 'YEAROFTOBACCOSMOKINGONSET'

No miR related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S12.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1971.74 (14)
  Significant markers N = 0
Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

6 miRs related to 'COMPLETENESS.OF.RESECTION'.

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

COMPLETENESS.OF.RESECTION Labels N
  R0 40
  R1 24
  RX 2
     
  Significant markers N = 6
List of 6 miRs differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S14.  Get Full Table List of 6 miRs differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
HSA-MIR-411 1.867e-06 0.000943
HSA-MIR-758 3.059e-05 0.0154
HSA-MIR-377 4.086e-05 0.0206
HSA-MIR-329-1 4.982e-05 0.025
HSA-MIR-409 6.744e-05 0.0338
HSA-MIR-493 8.958e-05 0.0448

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-411 to 'COMPLETENESS.OF.RESECTION'. P value = 1.87e-06 with ANOVA analysis.

Clinical variable #12: 'NUMBER.OF.LYMPH.NODES'

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

Table S15.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.91 (3.2)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 70

  • Number of miRs = 505

  • Number of clinical features = 12

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)
Copyright © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle