Correlation between miRseq expression and clinical features
Skin Cutaneous Melanoma (Metastatic)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1GQ6WFH
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/C1GQ6WFH
Overview
Introduction

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

Summary

Testing the association between 597 miRs and 12 clinical features across 265 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one miRs.

  • 6 miRs correlated to 'Time from Specimen Diagnosis to Death'.

    • HSA-MIR-1976 ,  HSA-MIR-155 ,  HSA-MIR-342 ,  HSA-MIR-625 ,  HSA-MIR-150 ,  ...

  • 2 miRs correlated to 'AGE'.

    • HSA-MIR-3200 ,  HSA-MIR-204

  • 15 miRs correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • HSA-MIR-342 ,  HSA-MIR-150 ,  HSA-MIR-410 ,  HSA-MIR-217 ,  HSA-MIR-205 ,  ...

  • 1 miR correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-1537

  • 1 miR correlated to 'BRESLOW.THICKNESS'.

    • HSA-MIR-1537

  • 1 miR correlated to 'GENDER'.

    • HSA-MIR-361

  • No miRs correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'MELANOMA.ULCERATION', and 'MELANOMA.PRIMARY.KNOWN'.

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 from Specimen Diagnosis to Death Cox regression test N=6 shorter survival N=0 longer survival N=6
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=2 older N=0 younger N=2
PRIMARY SITE OF DISEASE ANOVA test N=15        
NEOPLASM DISEASESTAGE ANOVA test   N=0        
PATHOLOGY T STAGE Spearman correlation test N=1 higher stage N=1 lower stage N=0
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test   N=0        
MELANOMA ULCERATION t test   N=0        
MELANOMA PRIMARY KNOWN t test   N=0        
BRESLOW THICKNESS Spearman correlation test N=1 higher breslow.thickness N=1 lower breslow.thickness N=0
GENDER t test N=1 male N=0 female N=1
Clinical variable #1: 'Time from Specimen Diagnosis to Death'

6 miRs related to 'Time from Specimen Diagnosis to Death'.

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

Time from Specimen Diagnosis to Death Duration (Months) 0-124.3 (median=13.9)
  censored N = 126
  death N = 126
     
  Significant markers N = 6
  associated with shorter survival 0
  associated with longer survival 6
List of 6 miRs significantly associated with 'Time from Specimen Diagnosis to Death' by Cox regression test

Table S2.  Get Full Table List of 6 miRs significantly associated with 'Time from Specimen Diagnosis to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-1976 0.63 1.37e-07 8.2e-05 0.356
HSA-MIR-155 0.79 1.977e-07 0.00012 0.355
HSA-MIR-342 0.72 5.226e-07 0.00031 0.348
HSA-MIR-625 0.69 3.774e-06 0.0022 0.356
HSA-MIR-150 0.86 4.035e-05 0.024 0.376
HSA-MIR-146B 0.73 4.744e-05 0.028 0.399

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1976 to 'Time from Specimen Diagnosis to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.37e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'Time to Death'

No miR related to 'Time to Death'.

Table S3.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0.2-357.4 (median=47.5)
  censored N = 131
  death N = 127
     
  Significant markers N = 0
Clinical variable #3: 'AGE'

2 miRs related to 'AGE'.

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

AGE Mean (SD) 55.64 (15)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 miRs significantly correlated to 'AGE' by Spearman correlation test

Table S5.  Get Full Table List of 2 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-3200 -0.2557 3.591e-05 0.0214
HSA-MIR-204 -0.2539 3.693e-05 0.022

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3200 to 'AGE'. P value = 3.59e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #4: 'PRIMARY.SITE.OF.DISEASE'

15 miRs related to 'PRIMARY.SITE.OF.DISEASE'.

Table S6.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  DISTANT METASTASIS 37
  PRIMARY TUMOR 1
  REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE 58
  REGIONAL LYMPH NODE 168
     
  Significant markers N = 15
List of top 10 miRs differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S7.  Get Full Table List of top 10 miRs differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

ANOVA_P Q
HSA-MIR-342 4.919e-07 0.000294
HSA-MIR-150 5.618e-07 0.000335
HSA-MIR-410 2.581e-06 0.00154
HSA-MIR-217 5.188e-06 0.00308
HSA-MIR-205 7.337e-06 0.00435
HSA-MIR-379 1.246e-05 0.00738
HSA-MIR-369 1.53e-05 0.00904
HSA-MIR-194-1 2.199e-05 0.013
HSA-MIR-136 2.329e-05 0.0137
HSA-MIR-194-2 3.503e-05 0.0206

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-342 to 'PRIMARY.SITE.OF.DISEASE'. P value = 4.92e-07 with ANOVA analysis.

Clinical variable #5: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S8.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  I OR II NOS 10
  STAGE 0 6
  STAGE I 22
  STAGE IA 10
  STAGE IB 25
  STAGE II 15
  STAGE IIA 9
  STAGE IIB 13
  STAGE IIC 9
  STAGE III 28
  STAGE IIIA 11
  STAGE IIIB 23
  STAGE IIIC 45
  STAGE IV 12
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.T.STAGE'

One miR related to 'PATHOLOGY.T.STAGE'.

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

PATHOLOGY.T.STAGE Mean (SD) 2.38 (1.3)
  N
  0 22
  1 29
  2 59
  3 50
  4 52
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S10.  Get Full Table List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-1537 0.2995 6.9e-05 0.0412

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1537 to 'PATHOLOGY.T.STAGE'. P value = 6.9e-05 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N.STAGE'

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

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.85 (1.1)
  N
  0 135
  1 46
  2 33
  3 33
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGY.M.STAGE'

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

Table S12.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 236
  M1 4
  M1A 2
  M1B 2
  M1C 5
     
  Significant markers N = 0
Clinical variable #9: 'MELANOMA.ULCERATION'

No miR related to 'MELANOMA.ULCERATION'.

Table S13.  Basic characteristics of clinical feature: 'MELANOMA.ULCERATION'

MELANOMA.ULCERATION Labels N
  NO 98
  YES 67
     
  Significant markers N = 0
Clinical variable #10: 'MELANOMA.PRIMARY.KNOWN'

No miR related to 'MELANOMA.PRIMARY.KNOWN'.

Table S14.  Basic characteristics of clinical feature: 'MELANOMA.PRIMARY.KNOWN'

MELANOMA.PRIMARY.KNOWN Labels N
  NO 33
  YES 232
     
  Significant markers N = 0
Clinical variable #11: 'BRESLOW.THICKNESS'

One miR related to 'BRESLOW.THICKNESS'.

Table S15.  Basic characteristics of clinical feature: 'BRESLOW.THICKNESS'

BRESLOW.THICKNESS Mean (SD) 3.55 (5.1)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one miR significantly correlated to 'BRESLOW.THICKNESS' by Spearman correlation test

Table S16.  Get Full Table List of one miR significantly correlated to 'BRESLOW.THICKNESS' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-1537 0.3754 1.087e-06 0.000649

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1537 to 'BRESLOW.THICKNESS'. P value = 1.09e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #12: 'GENDER'

One miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 99
  MALE 166
     
  Significant markers N = 1
  Higher in MALE 0
  Higher in FEMALE 1
List of one miR differentially expressed by 'GENDER'

Table S18.  Get Full Table List of one miR differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-361 -4.22 3.79e-05 0.0226 0.6543

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-361 to 'GENDER'. P value = 3.79e-05 with T-test analysis.

Methods & Data
Input

  • Expresson data file = SKCM-TM.miRseq_RPKM_log2.txt

  • Clinical data file = SKCM-TM.merged_data.txt

  • Number of patients = 265

  • Number of miRs = 597

  • 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)
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