Correlation between miRseq expression and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
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/C1028PM4
Overview
Introduction

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

Summary

Testing the association between 542 genes and 12 clinical features across 314 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • HSA-MIR-99A

  • 6 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • HSA-MIR-499 ,  HSA-LET-7G ,  HSA-MIR-26B ,  HSA-MIR-196B ,  HSA-MIR-3653 ,  ...

  • 3 genes correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-29A ,  HSA-MIR-490 ,  HSA-MIR-22

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

    • HSA-MIR-383

  • 2 genes correlated to 'YEAROFTOBACCOSMOKINGONSET'.

    • HSA-MIR-337 ,  HSA-MIR-125A

  • 10 genes correlated to 'DISTANT.METASTASIS'.

    • HSA-MIR-26A-1 ,  HSA-MIR-628 ,  HSA-MIR-140 ,  HSA-MIR-16-1 ,  HSA-MIR-361 ,  ...

  • 2 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • HSA-MIR-3607 ,  HSA-MIR-3647

  • No genes correlated to 'Time to Death', 'GENDER', 'NUMBERPACKYEARSSMOKED', 'LYMPH.NODE.METASTASIS', 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=1 older N=1 younger N=0
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=6 higher score N=0 lower score N=6
HISTOLOGICAL TYPE ANOVA test N=3        
RADIATIONS RADIATION REGIMENINDICATION t test N=1 yes N=1 no N=0
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test N=2 higher yearoftobaccosmokingonset N=0 lower yearoftobaccosmokingonset N=2
DISTANT METASTASIS ANOVA test N=10        
LYMPH NODE METASTASIS ANOVA test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=2        
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) 0-173.8 (median=12)
  censored N = 187
  death N = 110
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 67.76 (8.6)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

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

SpearmanCorr corrP Q
HSA-MIR-99A 0.2446 1.463e-05 0.00793

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-99A to 'AGE'. P value = 1.46e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 77
  MALE 237
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

6 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 27.08 (39)
  Significant markers N = 6
  pos. correlated 0
  neg. correlated 6
List of 6 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S6.  Get Full Table List of 6 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-499 -0.7735 1.391e-06 0.000754
HSA-LET-7G -0.6246 2.089e-06 0.00113
HSA-MIR-26B -0.5885 1.084e-05 0.00586
HSA-MIR-196B -0.5611 3.342e-05 0.018
HSA-MIR-3653 -0.5587 3.683e-05 0.0198
HSA-MIR-26A-1 -0.5515 4.856e-05 0.0261

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-499 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 1.39e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

3 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 5
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 2
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 306
     
  Significant markers N = 3
List of 3 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of 3 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-29A 9.925e-07 0.000533
HSA-MIR-490 1.259e-05 0.00675
HSA-MIR-22 2.502e-05 0.0134

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-29A to 'HISTOLOGICAL.TYPE'. P value = 9.92e-07 with ANOVA analysis.

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

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

Table S9.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 11
  YES 303
     
  Significant markers N = 1
  Higher in YES 1
  Higher in NO 0
List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S10.  Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-383 8.39 2.811e-05 0.0149 0.8519

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-383 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.81e-05 with T-test analysis.

Clinical variable #7: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 53.53 (33)
  Significant markers N = 0
Clinical variable #8: 'YEAROFTOBACCOSMOKINGONSET'

2 genes related to 'YEAROFTOBACCOSMOKINGONSET'.

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

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1958.83 (11)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

Table S13.  Get Full Table List of 2 genes significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-337 -0.2789 3.802e-05 0.0206
HSA-MIR-125A -0.2663 8.631e-05 0.0467

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-337 to 'YEAROFTOBACCOSMOKINGONSET'. P value = 3.8e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #9: 'DISTANT.METASTASIS'

10 genes related to 'DISTANT.METASTASIS'.

Table S14.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 266
  M1 3
  MX 39
     
  Significant markers N = 10
List of 10 genes differentially expressed by 'DISTANT.METASTASIS'

Table S15.  Get Full Table List of 10 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
HSA-MIR-26A-1 2.116e-10 1.15e-07
HSA-MIR-628 1.883e-08 1.02e-05
HSA-MIR-140 2.973e-07 0.000161
HSA-MIR-16-1 3.384e-07 0.000182
HSA-MIR-361 6.91e-07 0.000372
HSA-MIR-3607 1.34e-05 0.00719
HSA-MIR-1180 2.181e-05 0.0117
HSA-MIR-106A 2.672e-05 0.0143
HSA-MIR-1277 4.659e-05 0.0249
HSA-MIR-181A-1 5.499e-05 0.0293

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-26A-1 to 'DISTANT.METASTASIS'. P value = 2.12e-10 with ANOVA analysis.

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

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

Table S16.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 194
  N1 86
  N2 28
  N3 4
  NX 2
     
  Significant markers N = 0
Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

2 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S17.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 246
  R1 5
  R2 3
  RX 15
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S18.  Get Full Table List of 2 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
HSA-MIR-3607 8.262e-06 0.00448
HSA-MIR-3647 2.291e-05 0.0124

Figure S7.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3607 to 'COMPLETENESS.OF.RESECTION'. P value = 8.26e-06 with ANOVA analysis.

Clinical variable #12: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S19.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 1
  STAGE IA 52
  STAGE IB 106
  STAGE II 1
  STAGE IIA 31
  STAGE IIB 57
  STAGE IIIA 43
  STAGE IIIB 17
  STAGE IV 3
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = LUSC-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 314

  • Number of genes = 542

  • 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)