Colon/Rectal Adenocarcinoma: Correlation between miRseq expression and clinical features
(primary solid tumor cohort)
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 420 genes and 12 clinical features across 550 samples, statistically thresholded by Q value < 0.05, 10 clinical features related to at least one genes.

  • 6 genes correlated to 'AGE'.

    • HSA-MIR-141 ,  HSA-MIR-432 ,  HSA-MIR-153-2 ,  HSA-MIR-26A-1 ,  HSA-MIR-33B ,  ...

  • 38 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • HSA-MIR-1201 ,  HSA-MIR-10B ,  HSA-MIR-30C-2 ,  HSA-MIR-1259 ,  HSA-MIR-425 ,  ...

  • 1 gene correlated to 'GENDER'.

    • HSA-MIR-651

  • 45 genes correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-10B ,  HSA-MIR-1201 ,  HSA-MIR-30C-2 ,  HSA-MIR-92A-1 ,  HSA-MIR-425 ,  ...

  • 6 genes correlated to 'PATHOLOGY.T'.

    • HSA-MIR-206 ,  HSA-MIR-501 ,  HSA-MIR-144 ,  HSA-MIR-34C ,  HSA-MIR-191 ,  ...

  • 2 genes correlated to 'PATHOLOGY.N'.

    • HSA-MIR-625 ,  HSA-MIR-146A

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

    • HSA-LET-7F-2 ,  HSA-MIR-628 ,  HSA-MIR-106A ,  HSA-MIR-140 ,  HSA-MIR-616 ,  ...

  • 2 genes correlated to 'TUMOR.STAGE'.

    • HSA-MIR-625 ,  HSA-MIR-146A

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

    • HSA-MIR-136 ,  HSA-MIR-126 ,  HSA-LET-7A-2 ,  HSA-LET-7A-1 ,  HSA-LET-7A-3 ,  ...

  • 2 genes correlated to 'NUMBER.OF.LYMPH.NODES'.

    • HSA-MIR-146A ,  HSA-MIR-511-1

  • No genes correlated to 'Time to Death', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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=6 older N=4 younger N=2
PRIMARY SITE OF DISEASE t test N=38 rectum N=32 colon N=6
GENDER t test N=1 male N=0 female N=1
HISTOLOGICAL TYPE ANOVA test N=45        
PATHOLOGY T Spearman correlation test N=6 higher pT N=1 lower pT N=5
PATHOLOGY N Spearman correlation test N=2 higher pN N=0 lower pN N=2
PATHOLOGICSPREAD(M) ANOVA test N=27        
TUMOR STAGE Spearman correlation test N=2 higher stage N=0 lower stage N=2
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=54        
NUMBER OF LYMPH NODES Spearman correlation test N=2 higher number.of.lymph.nodes N=0 lower number.of.lymph.nodes 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=7)
  censored N = 356
  death N = 59
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

6 genes related to 'AGE'.

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

AGE Mean (SD) 66.8 (13)
  Significant markers N = 6
  pos. correlated 4
  neg. correlated 2
List of 6 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 6 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-141 0.2082 8.567e-07 0.00036
HSA-MIR-432 -0.2063 1.247e-06 0.000522
HSA-MIR-153-2 0.1921 5.84e-06 0.00244
HSA-MIR-26A-1 0.1825 1.682e-05 0.00701
HSA-MIR-33B 0.1709 5.807e-05 0.0242
HSA-MIR-410 -0.1663 9.859e-05 0.0409

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-141 to 'AGE'. P value = 8.57e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

38 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S4.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  COLON 406
  RECTUM 140
     
  Significant markers N = 38
  Higher in RECTUM 32
  Higher in COLON 6
List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

T(pos if higher in 'RECTUM') ttestP Q AUC
HSA-MIR-1201 8.96 4.995e-17 2.1e-14 0.7288
HSA-MIR-10B -8.83 2.683e-16 1.12e-13 0.7467
HSA-MIR-30C-2 8.51 1.108e-15 4.63e-13 0.7106
HSA-MIR-1259 7.67 2.311e-13 9.64e-11 0.6827
HSA-MIR-425 6.98 2.663e-11 1.11e-08 0.686
HSA-MIR-1977 6.87 3.675e-11 1.53e-08 0.6675
HSA-MIR-191 6.75 7.837e-11 3.24e-08 0.6736
HSA-MIR-20A 6.31 1.092e-09 4.51e-07 0.6667
HSA-LET-7G 6.26 1.822e-09 7.51e-07 0.6662
HSA-MIR-454 6.02 5.11e-09 2.1e-06 0.6481

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1201 to 'PRIMARY.SITE.OF.DISEASE'. P value = 5e-17 with T-test analysis.

Clinical variable #4: 'GENDER'

One gene related to 'GENDER'.

Table S6.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 258
  MALE 292
     
  Significant markers N = 1
  Higher in MALE 0
  Higher in FEMALE 1
List of one gene differentially expressed by 'GENDER'

Table S7.  Get Full Table List of one gene differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-651 -4.12 4.452e-05 0.0187 0.6018

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-651 to 'GENDER'. P value = 4.45e-05 with T-test analysis.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

45 genes related to 'HISTOLOGICAL.TYPE'.

Table S8.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  COLON ADENOCARCINOMA 349
  COLON MUCINOUS ADENOCARCINOMA 55
  RECTAL ADENOCARCINOMA 127
  RECTAL MUCINOUS ADENOCARCINOMA 10
     
  Significant markers N = 45
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-10B 3.1e-17 1.3e-14
HSA-MIR-1201 9.522e-15 3.99e-12
HSA-MIR-30C-2 3.092e-13 1.29e-10
HSA-MIR-92A-1 2.209e-12 9.21e-10
HSA-MIR-425 1.205e-11 5.01e-09
HSA-MIR-20A 1.349e-11 5.6e-09
HSA-LET-7G 1.393e-11 5.77e-09
HSA-MIR-592 2.677e-11 1.11e-08
HSA-MIR-1259 5.326e-11 2.19e-08
HSA-MIR-1977 4.507e-10 1.85e-07

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-10B to 'HISTOLOGICAL.TYPE'. P value = 3.1e-17 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

6 genes related to 'PATHOLOGY.T'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.85 (0.64)
  N
  T0 1
  T1 19
  T2 95
  T3 376
  T4 55
     
  Significant markers N = 6
  pos. correlated 1
  neg. correlated 5
List of 6 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S11.  Get Full Table List of 6 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-206 -0.2294 2.813e-05 0.0118
HSA-MIR-501 -0.1781 2.848e-05 0.0119
HSA-MIR-144 -0.1747 4.038e-05 0.0169
HSA-MIR-34C 0.1743 6.165e-05 0.0257
HSA-MIR-191 -0.1677 8.232e-05 0.0342
HSA-MIR-500 -0.1647 0.000111 0.0461

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-206 to 'PATHOLOGY.T'. P value = 2.81e-05 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N'

2 genes related to 'PATHOLOGY.N'.

Table S12.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.6 (0.77)
  N
  N0 315
  N1 132
  N2 98
     
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

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

SpearmanCorr corrP Q
HSA-MIR-625 -0.1735 4.657e-05 0.0196
HSA-MIR-146A -0.1722 5.299e-05 0.0222

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-625 to 'PATHOLOGY.N'. P value = 4.66e-05 with Spearman correlation analysis.

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

27 genes related to 'PATHOLOGICSPREAD(M)'.

Table S14.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 412
  M1 68
  M1A 9
  M1B 1
  MX 50
     
  Significant markers N = 27
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
HSA-LET-7F-2 5.4e-09 2.27e-06
HSA-MIR-628 1.185e-08 4.97e-06
HSA-MIR-106A 1.566e-08 6.55e-06
HSA-MIR-140 2.081e-07 8.68e-05
HSA-MIR-616 5.03e-07 0.000209
HSA-LET-7A-1 7.029e-07 0.000292
HSA-LET-7A-2 8.193e-07 0.000339
HSA-MIR-142 8.899e-07 0.000368
HSA-LET-7A-3 9.156e-07 0.000377
HSA-MIR-26A-1 1.571e-06 0.000646

Figure S7.  Get High-res Image As an example, this figure shows the association of HSA-LET-7F-2 to 'PATHOLOGICSPREAD(M)'. P value = 5.4e-09 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

2 genes related to 'TUMOR.STAGE'.

Table S16.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.41 (0.94)
  N
  Stage 1 93
  Stage 2 199
  Stage 3 156
  Stage 4 77
     
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S17.  Get Full Table List of 2 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-625 -0.2024 2.926e-06 0.00123
HSA-MIR-146A -0.1948 6.898e-06 0.00289

Figure S8.  Get High-res Image As an example, this figure shows the association of HSA-MIR-625 to 'TUMOR.STAGE'. P value = 2.93e-06 with Spearman correlation analysis.

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S18.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 9
  YES 541
     
  Significant markers N = 0
Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

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

Table S19.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 383
  R1 3
  R2 36
  RX 25
     
  Significant markers N = 54
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S20.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
HSA-MIR-136 6.444e-09 2.71e-06
HSA-MIR-126 8.52e-09 3.57e-06
HSA-LET-7A-2 1.272e-08 5.32e-06
HSA-LET-7A-1 1.36e-08 5.67e-06
HSA-LET-7A-3 1.376e-08 5.73e-06
HSA-MIR-590 1.562e-08 6.48e-06
HSA-MIR-1180 2.268e-08 9.39e-06
HSA-MIR-16-1 2.801e-08 1.16e-05
HSA-MIR-330 3.066e-08 1.26e-05
HSA-MIR-497 3.068e-08 1.26e-05

Figure S9.  Get High-res Image As an example, this figure shows the association of HSA-MIR-136 to 'COMPLETENESS.OF.RESECTION'. P value = 6.44e-09 with ANOVA analysis.

Clinical variable #12: 'NUMBER.OF.LYMPH.NODES'

2 genes related to 'NUMBER.OF.LYMPH.NODES'.

Table S21.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.2 (4.7)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S22.  Get Full Table List of 2 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-146A -0.1768 5.781e-05 0.0243
HSA-MIR-511-1 -0.1731 8.951e-05 0.0375

Figure S10.  Get High-res Image As an example, this figure shows the association of HSA-MIR-146A to 'NUMBER.OF.LYMPH.NODES'. P value = 5.78e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

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

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

  • Number of patients = 550

  • Number of genes = 420

  • 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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