Colon Adenocarcinoma: Correlation between miRseq expression and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

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

Summary

Testing the association between 415 genes and 9 clinical features across 378 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 8 genes correlated to 'AGE'.

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

  • 10 genes correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-31 ,  HSA-MIR-92A-1 ,  HSA-MIR-592 ,  HSA-MIR-181D ,  HSA-MIR-92A-2 ,  ...

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

    • HSA-MIR-142 ,  HSA-MIR-1180 ,  HSA-MIR-628 ,  HSA-MIR-106A ,  HSA-MIR-1975 ,  ...

  • 2 genes correlated to 'TUMOR.STAGE'.

    • HSA-MIR-625 ,  HSA-MIR-146A

  • 23 genes correlated to 'NEOADJUVANT.THERAPY'.

    • HSA-MIR-103-2 ,  HSA-MIR-1826 ,  HSA-MIR-331 ,  HSA-MIR-106A ,  HSA-MIR-26A-1 ,  ...

  • No genes correlated to 'Time to Death', 'GENDER', 'PATHOLOGY.T', and 'PATHOLOGY.N'.

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=6 younger N=2
GENDER t test   N=0        
HISTOLOGICAL TYPE t test N=10 colon mucinous adenocarcinoma N=2 colon adenocarcinoma N=8
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=16        
TUMOR STAGE Spearman correlation test N=2 higher stage N=0 lower stage N=2
NEOADJUVANT THERAPY t test N=23 yes N=21 no N=2
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=8.1)
  censored N = 231
  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) 67.37 (13)
  Significant markers N = 8
  pos. correlated 6
  neg. correlated 2
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-153-2 0.2421 1.92e-06 0.000797
HSA-MIR-432 -0.2361 4.165e-06 0.00172
HSA-MIR-26A-1 0.2325 4.927e-06 0.00204
HSA-MIR-141 0.2284 7.256e-06 0.00299
HSA-MIR-34A 0.2165 2.173e-05 0.00893
HSA-MIR-616 0.214 3.168e-05 0.013
HSA-MIR-410 -0.2015 8.49e-05 0.0347
HSA-MIR-142 0.1982 0.0001049 0.0428

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-153-2 to 'AGE'. P value = 1.92e-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 183
  MALE 195
     
  Significant markers N = 0
Clinical variable #4: 'HISTOLOGICAL.TYPE'

10 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  COLON ADENOCARCINOMA 327
  COLON MUCINOUS ADENOCARCINOMA 48
     
  Significant markers N = 10
  Higher in COLON MUCINOUS ADENOCARCINOMA 2
  Higher in COLON ADENOCARCINOMA 8
List of 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

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

T(pos if higher in 'COLON MUCINOUS ADENOCARCINOMA') ttestP Q AUC
HSA-MIR-31 5.71 2.887e-07 0.00012 0.7258
HSA-MIR-92A-1 -5.5 6.953e-07 0.000288 0.7229
HSA-MIR-592 -5.41 1.082e-06 0.000447 0.7302
HSA-MIR-181D -4.8 1.056e-05 0.00435 0.7024
HSA-MIR-92A-2 -4.45 3.781e-05 0.0155 0.6933
HSA-MIR-574 4.41 4.081e-05 0.0167 0.6903
HSA-MIR-196B -4.34 5.329e-05 0.0218 0.6803
HSA-MIR-181C -4.3 6.576e-05 0.0268 0.6933
HSA-MIR-1247 -4.28 7.04e-05 0.0287 0.6908
HSA-MIR-552 -4.13 0.0001193 0.0484 0.715

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-31 to 'HISTOLOGICAL.TYPE'. P value = 2.89e-07 with T-test analysis.

Clinical variable #5: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.87 (0.61)
  N
  T0 1
  T1 9
  T2 64
  T3 264
  T4 37
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.59 (0.77)
  N
  N0 222
  N1 89
  N2 66
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGICSPREAD(M)'

16 genes related to 'PATHOLOGICSPREAD(M)'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 286
  M1 49
  M1A 7
  M1B 1
  MX 28
     
  Significant markers N = 16
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
HSA-MIR-142 2.172e-06 0.000901
HSA-MIR-1180 2.749e-06 0.00114
HSA-MIR-628 1.011e-05 0.00418
HSA-MIR-106A 1.355e-05 0.00558
HSA-MIR-1975 1.976e-05 0.00812
HSA-MIR-140 2.054e-05 0.00842
HSA-MIR-539 2.587e-05 0.0106
HSA-MIR-126 2.837e-05 0.0116
HSA-LET-7F-2 4.438e-05 0.0181
HSA-MIR-136 4.499e-05 0.0183

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-142 to 'PATHOLOGICSPREAD(M)'. P value = 2.17e-06 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

2 genes related to 'TUMOR.STAGE'.

Table S11.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.42 (0.94)
  N
  Stage 1 62
  Stage 2 144
  Stage 3 106
  Stage 4 55
     
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S12.  Get Full Table List of 2 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-625 -0.208 5.925e-05 0.0246
HSA-MIR-146A -0.2067 6.618e-05 0.0274

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-625 to 'TUMOR.STAGE'. P value = 5.93e-05 with Spearman correlation analysis.

Clinical variable #9: 'NEOADJUVANT.THERAPY'

23 genes related to 'NEOADJUVANT.THERAPY'.

Table S13.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 55
  YES 323
     
  Significant markers N = 23
  Higher in YES 21
  Higher in NO 2
List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-103-2 6.62 1.966e-09 8.16e-07 0.7186
HSA-MIR-1826 6.19 2.183e-08 9.04e-06 0.7322
HSA-MIR-331 5.45 4.319e-07 0.000178 0.6946
HSA-MIR-106A 5.1 1.878e-06 0.000774 0.6786
HSA-MIR-26A-1 4.95 3.914e-06 0.00161 0.6887
HSA-MIR-1259 4.93 5.092e-06 0.00209 0.7025
HSA-MIR-455 4.89 5.256e-06 0.00215 0.696
HSA-LET-7F-2 -4.75 7.897e-06 0.00322 0.6514
HSA-MIR-7-1 4.69 9.867e-06 0.00402 0.6734
HSA-MIR-141 4.63 1.393e-05 0.00566 0.7077

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-103-2 to 'NEOADJUVANT.THERAPY'. P value = 1.97e-09 with T-test analysis.

Methods & Data
Input

  • Expresson data file = COAD.miRseq_RPKM_log2.txt

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

  • Number of patients = 378

  • Number of genes = 415

  • Number of clinical features = 9

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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