Correlation between miRseq expression and clinical features
Colon Adenocarcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C19C6VDN
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/C19C6VDN
Overview
Introduction

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

Summary

Testing the association between 417 genes and 10 clinical features across 407 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 8 genes correlated to 'AGE'.

    • HSA-MIR-141 ,  HSA-MIR-26A-1 ,  HSA-MIR-153-2 ,  HSA-MIR-34A ,  HSA-MIR-432 ,  ...

  • 12 genes correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-592 ,  HSA-MIR-31 ,  HSA-MIR-92A-1 ,  HSA-MIR-374B ,  HSA-MIR-196B ,  ...

  • 16 genes correlated to 'DISTANT.METASTASIS'.

    • HSA-MIR-628 ,  HSA-MIR-1180 ,  HSA-MIR-140 ,  HSA-MIR-106A ,  HSA-LET-7F-2 ,  ...

  • 13 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • HSA-LET-7C ,  HSA-MIR-203 ,  HSA-MIR-616 ,  HSA-MIR-29B-1 ,  HSA-MIR-19A ,  ...

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

    • HSA-LET-7A-2 ,  HSA-LET-7A-1 ,  HSA-LET-7A-3 ,  HSA-LET-7F-2 ,  HSA-MIR-497 ,  ...

  • 1 gene correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-625

  • No genes correlated to 'Time to Death', 'GENDER', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NUMBER.OF.LYMPH.NODES'.

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=8 older N=7 younger N=1
GENDER t test   N=0        
HISTOLOGICAL TYPE t test N=12 colon mucinous adenocarcinoma N=2 colon adenocarcinoma N=10
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
DISTANT METASTASIS ANOVA test N=16        
LYMPH NODE METASTASIS ANOVA test N=13        
COMPLETENESS OF RESECTION ANOVA test N=31        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE ANOVA test N=1        
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) 0.1-135.5 (median=7.5)
  censored N = 262
  death N = 49
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

8 genes related to 'AGE'.

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

AGE Mean (SD) 67.29 (13)
  Significant markers N = 8
  pos. correlated 7
  neg. correlated 1
List of 8 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 8 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-141 0.2362 1.484e-06 0.000619
HSA-MIR-26A-1 0.2238 5.295e-06 0.0022
HSA-MIR-153-2 0.2191 8.38e-06 0.00348
HSA-MIR-34A 0.2147 1.283e-05 0.00531
HSA-MIR-432 -0.2118 1.94e-05 0.00801
HSA-MIR-33B 0.1961 7.079e-05 0.0292
HSA-MIR-616 0.1945 9.184e-05 0.0377
HSA-MIR-653 0.1932 0.0001073 0.044

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-141 to 'AGE'. P value = 1.48e-06 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 191
  MALE 216
     
  Significant markers N = 0
Clinical variable #4: 'HISTOLOGICAL.TYPE'

12 genes related to 'HISTOLOGICAL.TYPE'.

Table S5.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  COLON ADENOCARCINOMA 350
  COLON MUCINOUS ADENOCARCINOMA 55
     
  Significant markers N = 12
  Higher in COLON MUCINOUS ADENOCARCINOMA 2
  Higher in COLON ADENOCARCINOMA 10
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

T(pos if higher in 'COLON MUCINOUS ADENOCARCINOMA') ttestP Q AUC
HSA-MIR-592 -6.3 1.962e-08 8.18e-06 0.746
HSA-MIR-31 5.74 1.627e-07 6.77e-05 0.7138
HSA-MIR-92A-1 -5.54 4.121e-07 0.000171 0.7121
HSA-MIR-374B -4.79 7.522e-06 0.00311 0.6796
HSA-MIR-196B -4.78 9.05e-06 0.00374 0.6864
HSA-MIR-92A-2 -4.78 9.099e-06 0.00375 0.6943
HSA-MIR-29A -4.65 1.538e-05 0.00632 0.6978
HSA-MIR-574 4.62 1.55e-05 0.00636 0.6863
HSA-MIR-1247 -4.59 1.913e-05 0.00782 0.69
HSA-MIR-98 -4.53 2.096e-05 0.00855 0.6803

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-592 to 'HISTOLOGICAL.TYPE'. P value = 1.96e-08 with T-test analysis.

Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 3
  YES 404
     
  Significant markers N = 0
Clinical variable #6: 'DISTANT.METASTASIS'

16 genes related to 'DISTANT.METASTASIS'.

Table S8.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 306
  M1 50
  M1A 7
  M1B 1
  MX 36
     
  Significant markers N = 16
List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
HSA-MIR-628 1.369e-06 0.000571
HSA-MIR-1180 1.443e-06 6e-04
HSA-MIR-140 1.86e-06 0.000772
HSA-MIR-106A 4.284e-06 0.00177
HSA-LET-7F-2 5.228e-06 0.00216
HSA-MIR-142 7.027e-06 0.0029
HSA-MIR-301A 1.322e-05 0.00543
HSA-LET-7A-1 2.477e-05 0.0102
HSA-MIR-126 2.605e-05 0.0107
HSA-LET-7A-2 2.826e-05 0.0115

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-628 to 'DISTANT.METASTASIS'. P value = 1.37e-06 with ANOVA analysis.

Clinical variable #7: 'LYMPH.NODE.METASTASIS'

13 genes related to 'LYMPH.NODE.METASTASIS'.

Table S10.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 240
  N1 68
  N1A 13
  N1B 12
  N1C 2
  N2 57
  N2A 4
  N2B 9
  NX 1
     
  Significant markers N = 13
List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
HSA-LET-7C 1.107e-05 0.00462
HSA-MIR-203 1.636e-05 0.00681
HSA-MIR-616 2.556e-05 0.0106
HSA-MIR-29B-1 2.693e-05 0.0112
HSA-MIR-19A 2.995e-05 0.0124
HSA-MIR-153-1 3.53e-05 0.0145
HSA-MIR-26A-1 3.553e-05 0.0146
HSA-MIR-29B-2 5.028e-05 0.0206
HSA-MIR-455 5.267e-05 0.0215
HSA-MIR-1201 5.402e-05 0.022

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-LET-7C to 'LYMPH.NODE.METASTASIS'. P value = 1.11e-05 with ANOVA analysis.

Clinical variable #8: 'COMPLETENESS.OF.RESECTION'

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

Table S12.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 281
  R1 3
  R2 25
  RX 22
     
  Significant markers N = 31
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
HSA-LET-7A-2 4.465e-09 1.86e-06
HSA-LET-7A-1 4.651e-09 1.93e-06
HSA-LET-7A-3 4.727e-09 1.96e-06
HSA-LET-7F-2 3.946e-08 1.63e-05
HSA-MIR-497 3.941e-08 1.63e-05
HSA-MIR-1180 8.194e-08 3.38e-05
HSA-MIR-16-1 2.278e-07 9.36e-05
HSA-MIR-126 3.924e-07 0.000161
HSA-MIR-136 4.221e-07 0.000173
HSA-MIR-199B 5.443e-07 0.000222

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-LET-7A-2 to 'COMPLETENESS.OF.RESECTION'. P value = 4.46e-09 with ANOVA analysis.

Clinical variable #9: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S14.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.04 (4.5)
  Significant markers N = 0
Clinical variable #10: 'NEOPLASM.DISEASESTAGE'

One gene related to 'NEOPLASM.DISEASESTAGE'.

Table S15.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 66
  STAGE IA 1
  STAGE II 33
  STAGE IIA 114
  STAGE IIB 8
  STAGE IIC 1
  STAGE III 22
  STAGE IIIA 12
  STAGE IIIB 47
  STAGE IIIC 32
  STAGE IV 41
  STAGE IVA 16
  STAGE IVB 1
     
  Significant markers N = 1
List of one gene differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S16.  Get Full Table List of one gene differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
HSA-MIR-625 3.701e-05 0.0154

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-625 to 'NEOPLASM.DISEASESTAGE'. P value = 3.7e-05 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 407

  • Number of genes = 417

  • Number of clinical features = 10

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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