Correlation between miRseq expression and clinical features
Lung Adenocarcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1J1014K
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Lung Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1J1014K
Overview
Introduction

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

Summary

Testing the association between 528 genes and 14 clinical features across 386 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 2 genes correlated to 'Time to Death'.

    • HSA-MIR-181C ,  HSA-MIR-582

  • 2 genes correlated to 'GENDER'.

    • HSA-MIR-105-2 ,  HSA-MIR-3940

  • 9 genes correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-194-1 ,  HSA-MIR-194-2 ,  HSA-MIR-21 ,  HSA-MIR-192 ,  HSA-MIR-200B ,  ...

  • 6 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • HSA-MIR-628 ,  HSA-MIR-424 ,  HSA-MIR-140 ,  HSA-MIR-3607 ,  HSA-MIR-616 ,  ...

  • 2 genes correlated to 'NUMBERPACKYEARSSMOKED'.

    • HSA-MIR-210 ,  HSA-MIR-339

  • 2 genes correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.

    • HSA-MIR-1-2 ,  HSA-MIR-421

  • No genes correlated to 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'RADIATIONS.RADIATION.REGIMENINDICATION', '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 genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test N=2 shorter survival N=1 longer survival N=1
AGE Spearman correlation test   N=0        
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        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=6        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test N=2 higher numberpackyearssmoked N=2 lower numberpackyearssmoked N=0
TOBACCOSMOKINGHISTORYINDICATOR ANOVA test N=2        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
COMPLETENESS OF RESECTION ANOVA test   N=0        
Clinical variable #1: 'Time to Death'

2 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-224 (median=8)
  censored N = 263
  death N = 83
     
  Significant markers N = 2
  associated with shorter survival 1
  associated with longer survival 1
List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-181C 0.56 1.171e-05 0.0062 0.342
HSA-MIR-582 1.28 8.349e-05 0.044 0.628

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-181C to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.17e-05 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 65.21 (9.9)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

2 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 209
  MALE 177
     
  Significant markers N = 2
  Higher in MALE 2
  Higher in FEMALE 0
List of 2 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 2 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-105-2 4.53 9.583e-06 0.00506 0.6635
HSA-MIR-3940 3.95 9.453e-05 0.0498 0.6129

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-105-2 to 'GENDER'. P value = 9.58e-06 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 74.07 (33)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

9 genes related to 'HISTOLOGICAL.TYPE'.

Table S7.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG ACINAR ADENOCARCINOMA 11
  LUNG ADENOCARCINOMA MIXED SUBTYPE 78
  LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 242
  LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS 4
  LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS 17
  LUNG CLEAR CELL ADENOCARCINOMA 2
  LUNG MICROPAPILLARY ADENOCARCINOMA 2
  LUNG MUCINOUS ADENOCARCINOMA 2
  LUNG PAPILLARY ADENOCARCINOMA 18
  LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA 3
  MUCINOUS (COLLOID) ADENOCARCINOMA 4
  MUCINOUS (COLLOID) CARCINOMA 3
     
  Significant markers N = 9
List of 9 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of 9 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-194-1 3.016e-09 1.59e-06
HSA-MIR-194-2 4.078e-09 2.15e-06
HSA-MIR-21 6.809e-07 0.000358
HSA-MIR-192 3.468e-06 0.00182
HSA-MIR-200B 1.069e-05 0.0056
HSA-MIR-656 2.023e-05 0.0106
HSA-MIR-215 3.032e-05 0.0158
HSA-MIR-200A 5.787e-05 0.0302
HSA-MIR-369 6.711e-05 0.0349

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-194-1 to 'HISTOLOGICAL.TYPE'. P value = 3.02e-09 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.92 (0.76)
  N
  T1 79
  T2 165
  T3 23
  T4 16
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.58 (0.8)
  N
  N0 169
  N1 57
  N2 51
  N3 1
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

6 genes related to 'PATHOLOGICSPREAD(M)'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 199
  M1 14
  MX 65
     
  Significant markers N = 6
List of 6 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S12.  Get Full Table List of 6 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
HSA-MIR-628 4.943e-06 0.00261
HSA-MIR-424 1.802e-05 0.0095
HSA-MIR-140 3.276e-05 0.0172
HSA-MIR-3607 3.432e-05 0.018
HSA-MIR-616 4.721e-05 0.0247
HSA-MIR-126 9.39e-05 0.0491

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-628 to 'PATHOLOGICSPREAD(M)'. P value = 4.94e-06 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.79 (0.95)
  N
  Stage 1 148
  Stage 2 59
  Stage 3 58
  Stage 4 15
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 19
  YES 367
     
  Significant markers N = 0
Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

2 genes related to 'NUMBERPACKYEARSSMOKED'.

Table S15.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 41.38 (27)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S16.  Get Full Table List of 2 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-210 0.2412 6.041e-05 0.0319
HSA-MIR-339 0.2362 8.624e-05 0.0454

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-210 to 'NUMBERPACKYEARSSMOKED'. P value = 6.04e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

2 genes related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Table S17.  Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'

TOBACCOSMOKINGHISTORYINDICATOR Labels N
  CURRENT REFORMED SMOKER FOR < OR = 15 YEARS 96
  CURRENT REFORMED SMOKER FOR > 15 YEARS 77
  CURRENT SMOKER 62
  LIFELONG NON-SMOKER 37
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

Table S18.  Get Full Table List of 2 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

ANOVA_P Q
HSA-MIR-1-2 5.565e-05 0.0294
HSA-MIR-421 7.807e-05 0.0411

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1-2 to 'TOBACCOSMOKINGHISTORYINDICATOR'. P value = 5.56e-05 with ANOVA analysis.

Clinical variable #13: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S19.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1965.15 (13)
  Significant markers N = 0
Clinical variable #14: 'COMPLETENESS.OF.RESECTION'

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

Table S20.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 229
  R1 9
  R2 4
  RX 14
     
  Significant markers N = 0
Methods & Data
Input

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

  • Clinical data file = LUAD-TP.clin.merged.picked.txt

  • Number of patients = 386

  • Number of genes = 528

  • Number of clinical features = 14

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

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

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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