Correlation between miRseq expression and clinical features
Lung Adenocarcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1154F3B
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1154F3B
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 12 clinical features across 386 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 2 genes correlated to 'GENDER'.

    • HSA-MIR-105-2 ,  HSA-MIR-3940

  • 11 genes correlated to 'HISTOLOGICAL.TYPE'.

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

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

    • HSA-MIR-873

  • 2 genes correlated to 'NUMBERPACKYEARSSMOKED'.

    • HSA-MIR-210 ,  HSA-MIR-339

  • 6 genes correlated to 'DISTANT.METASTASIS'.

    • HSA-MIR-628 ,  HSA-MIR-660 ,  HSA-MIR-21 ,  HSA-MIR-532 ,  HSA-MIR-424 ,  ...

  • No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'YEAROFTOBACCOSMOKINGONSET', 'LYMPH.NODE.METASTASIS', 'COMPLETENESS.OF.RESECTION', and 'NEOPLASM.DISEASESTAGE'.

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=0        
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=11        
RADIATIONS RADIATION REGIMENINDICATION t test N=1 yes N=1 no N=0
NUMBERPACKYEARSSMOKED Spearman correlation test N=2 higher numberpackyearssmoked N=2 lower numberpackyearssmoked N=0
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
DISTANT METASTASIS ANOVA test N=6        
LYMPH NODE METASTASIS ANOVA test   N=0        
COMPLETENESS OF RESECTION ANOVA test   N=0        
NEOPLASM DISEASESTAGE ANOVA test   N=0        
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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

Time to Death Duration (Months) 0-224 (median=8.4)
  censored N = 265
  death N = 85
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

Table S2.  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 S3.  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 S4.  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 S1.  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 S5.  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'

11 genes related to 'HISTOLOGICAL.TYPE'.

Table S6.  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) CARCINOMA 7
     
  Significant markers N = 11
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-194-1 1.593e-09 8.41e-07
HSA-MIR-194-2 2.153e-09 1.13e-06
HSA-MIR-21 3.383e-07 0.000178
HSA-MIR-192 1.86e-06 0.000976
HSA-MIR-200B 5.779e-06 0.00303
HSA-MIR-656 1.132e-05 0.00592
HSA-MIR-215 1.553e-05 0.00811
HSA-MIR-200A 3.101e-05 0.0162
HSA-MIR-369 3.585e-05 0.0186
HSA-MIR-377 4.815e-05 0.025

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-194-1 to 'HISTOLOGICAL.TYPE'. P value = 1.59e-09 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S8.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 17
  YES 369
     
  Significant markers N = 1
  Higher in YES 1
  Higher in NO 0
List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S9.  Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-873 4.88 7.968e-05 0.042 0.6391

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-873 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.97e-05 with T-test analysis.

Clinical variable #7: 'NUMBERPACKYEARSSMOKED'

2 genes related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 41.73 (27)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S11.  Get Full Table List of 2 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-210 0.2598 1.586e-05 0.00838
HSA-MIR-339 0.2476 4.033e-05 0.0213

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-210 to 'NUMBERPACKYEARSSMOKED'. P value = 1.59e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #8: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

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

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1964.98 (13)
  Significant markers N = 0
Clinical variable #9: 'DISTANT.METASTASIS'

6 genes related to 'DISTANT.METASTASIS'.

Table S13.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 259
  M1 15
  M1A 1
  M1B 3
  MX 104
     
  Significant markers N = 6
List of 6 genes differentially expressed by 'DISTANT.METASTASIS'

Table S14.  Get Full Table List of 6 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
HSA-MIR-628 4.763e-06 0.00251
HSA-MIR-660 6.136e-06 0.00323
HSA-MIR-21 8.827e-06 0.00464
HSA-MIR-532 2.078e-05 0.0109
HSA-MIR-424 8.58e-05 0.045
HSA-MIR-3607 9.527e-05 0.0498

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-628 to 'DISTANT.METASTASIS'. P value = 4.76e-06 with ANOVA analysis.

Clinical variable #10: 'LYMPH.NODE.METASTASIS'

No gene related to 'LYMPH.NODE.METASTASIS'.

Table S15.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 245
  N1 71
  N2 58
  N3 2
  NX 8
     
  Significant markers N = 0
Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

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

Table S16.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 239
  R1 9
  R2 4
  RX 14
     
  Significant markers N = 0
Clinical variable #12: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S17.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 3
  STAGE IA 96
  STAGE IB 113
  STAGE IIA 32
  STAGE IIB 51
  STAGE IIIA 58
  STAGE IIIB 11
  STAGE IV 20
     
  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 = 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

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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