Correlation between miRseq expression and clinical features
Lung Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1K072WG
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/C1K072WG
Overview
Introduction

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

Summary

Testing the association between 530 miRs and 13 clinical features across 459 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one miRs.

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

    • HSA-MIR-30C-2 ,  HSA-MIR-451 ,  HSA-MIR-101-2 ,  HSA-MIR-150 ,  HSA-MIR-146A ,  ...

  • 9 miRs correlated to 'PATHOLOGY.M.STAGE'.

    • HSA-MIR-628 ,  HSA-MIR-21 ,  HSA-MIR-340 ,  HSA-MIR-660 ,  HSA-MIR-374A ,  ...

  • 2 miRs correlated to 'GENDER'.

    • HSA-MIR-105-2 ,  HSA-MIR-105-1

  • 9 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-21 ,  HSA-MIR-194-1 ,  HSA-MIR-194-2 ,  HSA-MIR-200B ,  HSA-MIR-219-2 ,  ...

  • 1 miR correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • HSA-MIR-143

  • 4 miRs correlated to 'NUMBERPACKYEARSSMOKED'.

    • HSA-MIR-339 ,  HSA-MIR-345 ,  HSA-MIR-210 ,  HSA-MIR-296

  • No miRs correlated to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'YEAROFTOBACCOSMOKINGONSET', and 'COMPLETENESS.OF.RESECTION'.

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=0        
NEOPLASM DISEASESTAGE ANOVA test   N=0        
PATHOLOGY T STAGE Spearman correlation test N=9 higher stage N=0 lower stage N=9
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=9        
GENDER t test N=2 male N=2 female N=0
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=9        
RADIATIONS RADIATION REGIMENINDICATION t test N=1 yes N=0 no N=1
NUMBERPACKYEARSSMOKED Spearman correlation test N=4 higher numberpackyearssmoked N=4 lower numberpackyearssmoked N=0
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
COMPLETENESS OF RESECTION ANOVA 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) 0-224 (median=13.2)
  censored N = 308
  death N = 119
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No miR related to 'AGE'.

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

AGE Mean (SD) 65.55 (9.7)
  Significant markers N = 0
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S3.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 4
  STAGE IA 111
  STAGE IB 133
  STAGE IIA 44
  STAGE IIB 65
  STAGE IIIA 68
  STAGE IIIB 11
  STAGE IV 22
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.T.STAGE'

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

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.86 (0.73)
  N
  1 141
  2 256
  3 41
  4 18
     
  Significant markers N = 9
  pos. correlated 0
  neg. correlated 9
List of 9 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S5.  Get Full Table List of 9 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-30C-2 -0.2201 2.074e-06 0.0011
HSA-MIR-451 -0.2067 8.567e-06 0.00453
HSA-MIR-101-2 -0.2062 9.082e-06 0.0048
HSA-MIR-150 -0.1998 1.719e-05 0.00906
HSA-MIR-146A -0.1991 1.851e-05 0.00974
HSA-MIR-374B -0.1946 2.872e-05 0.0151
HSA-MIR-342 -0.1914 3.885e-05 0.0204
HSA-MIR-218-2 -0.1888 4.955e-05 0.0259
HSA-MIR-486 -0.1842 7.589e-05 0.0396

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-30C-2 to 'PATHOLOGY.T.STAGE'. P value = 2.07e-06 with Spearman correlation analysis.

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 Mean (SD) 0.51 (0.77)
  N
  0 292
  1 84
  2 70
  3 2
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

9 miRs related to 'PATHOLOGY.M.STAGE'.

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

PATHOLOGY.M.STAGE Labels N
  M0 314
  M1 16
  M1A 1
  M1B 3
  MX 121
     
  Significant markers N = 9
List of 9 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S8.  Get Full Table List of 9 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
HSA-MIR-628 5.953e-07 0.000316
HSA-MIR-21 3.943e-06 0.00209
HSA-MIR-340 1.565e-05 0.00826
HSA-MIR-660 1.738e-05 0.00916
HSA-MIR-374A 1.746e-05 0.00918
HSA-MIR-532 2.258e-05 0.0119
HSA-MIR-1468 7.145e-05 0.0374
HSA-MIR-16-1 7.258e-05 0.038
HSA-MIR-424 7.635e-05 0.0399

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-628 to 'PATHOLOGY.M.STAGE'. P value = 5.95e-07 with ANOVA analysis.

Clinical variable #7: 'GENDER'

2 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 247
  MALE 212
     
  Significant markers N = 2
  Higher in MALE 2
  Higher in FEMALE 0
List of 2 miRs differentially expressed by 'GENDER'

Table S10.  Get Full Table List of 2 miRs differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-105-2 5.12 6.08e-07 0.000322 0.6705
HSA-MIR-105-1 4.21 3.512e-05 0.0186 0.648

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-105-2 to 'GENDER'. P value = 6.08e-07 with T-test analysis.

Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S11.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 83.16 (23)
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL.TYPE'

9 miRs related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  LUNG ACINAR ADENOCARCINOMA 13
  LUNG ADENOCARCINOMA MIXED SUBTYPE 92
  LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 294
  LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS 4
  LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS 19
  LUNG CLEAR CELL ADENOCARCINOMA 2
  LUNG MICROPAPILLARY ADENOCARCINOMA 3
  LUNG MUCINOUS ADENOCARCINOMA 2
  LUNG PAPILLARY ADENOCARCINOMA 18
  LUNG SIGNET RING ADENOCARCINOMA 1
  LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA 4
  MUCINOUS (COLLOID) CARCINOMA 7
     
  Significant markers N = 9
List of 9 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S13.  Get Full Table List of 9 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-21 2.794e-08 1.48e-05
HSA-MIR-194-1 1.404e-07 7.43e-05
HSA-MIR-194-2 1.817e-07 9.59e-05
HSA-MIR-200B 1.92e-05 0.0101
HSA-MIR-219-2 4.935e-05 0.026
HSA-MIR-192 5.061e-05 0.0266
HSA-MIR-656 6.214e-05 0.0326
HSA-MIR-200A 8.222e-05 0.043
HSA-MIR-532 9.519e-05 0.0497

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-21 to 'HISTOLOGICAL.TYPE'. P value = 2.79e-08 with ANOVA analysis.

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

One miR related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S14.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 20
  YES 439
     
  Significant markers N = 1
  Higher in YES 0
  Higher in NO 1
List of one miR differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S15.  Get Full Table List of one miR differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-143 -4.97 5.264e-05 0.0278 0.7436

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-143 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 5.26e-05 with T-test analysis.

Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

4 miRs related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 42.25 (27)
  Significant markers N = 4
  pos. correlated 4
  neg. correlated 0
List of 4 miRs significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S17.  Get Full Table List of 4 miRs significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-339 0.2557 4.463e-06 0.00237
HSA-MIR-345 0.2548 4.802e-06 0.00254
HSA-MIR-210 0.2543 5.014e-06 0.00265
HSA-MIR-296 0.2491 8.231e-06 0.00434

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-339 to 'NUMBERPACKYEARSSMOKED'. P value = 4.46e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #12: 'YEAROFTOBACCOSMOKINGONSET'

No miR related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S18.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1964.63 (12)
  Significant markers N = 0
Clinical variable #13: 'COMPLETENESS.OF.RESECTION'

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

Table S19.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 314
  R1 10
  R2 4
  RX 19
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 459

  • Number of miRs = 530

  • Number of clinical features = 13

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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