Correlation between miRseq expression and clinical features
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 427 miRs and 12 clinical features across 143 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one miRs.

  • 1 miR correlated to 'AGE'.

    • HSA-MIR-31

  • 8 miRs correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-7-1 ,  HSA-MIR-548J ,  HSA-MIR-25 ,  HSA-MIR-329-2 ,  HSA-MIR-1274B ,  ...

  • 3 miRs correlated to 'PATHOLOGY.N.STAGE'.

    • HSA-MIR-218-1 ,  HSA-MIR-545 ,  HSA-MIR-511-2

  • 1 miR correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-375

  • No miRs correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'COMPLETENESS.OF.RESECTION', 'NUMBER.OF.LYMPH.NODES', and 'RACE'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant miRs Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=1 older N=0 younger N=1
NEOPLASM DISEASESTAGE Kruskal-Wallis test   N=0        
PATHOLOGY T STAGE Spearman correlation test N=8 higher stage N=0 lower stage N=8
PATHOLOGY N STAGE Spearman correlation test N=3 higher stage N=0 lower stage N=3
PATHOLOGY M STAGE Kruskal-Wallis test   N=0        
GENDER Wilcoxon test   N=0        
HISTOLOGICAL TYPE Wilcoxon test N=1 rectal mucinous adenocarcinoma N=1 rectal adenocarcinoma N=0
RADIATIONS RADIATION REGIMENINDICATION Wilcoxon test   N=0        
COMPLETENESS OF RESECTION Kruskal-Wallis test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
RACE Kruskal-Wallis test   N=0        
Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

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

Time to Death Duration (Months) 0.2-129.3 (median=15)
  censored N = 110
  death N = 21
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One miR related to 'AGE'.

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

AGE Mean (SD) 65.41 (11)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one miR differentially expressed by 'AGE'

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

SpearmanCorr corrP Q
HSA-MIR-31 -0.307 0.0002019 0.0862
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 28
  STAGE II 8
  STAGE IIA 33
  STAGE IIB 2
  STAGE IIC 1
  STAGE III 6
  STAGE IIIA 5
  STAGE IIIB 20
  STAGE IIIC 13
  STAGE IV 15
  STAGE IVA 7
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.T.STAGE'

8 miRs related to 'PATHOLOGY.T.STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.76 (0.67)
  N
  1 9
  2 26
  3 97
  4 10
     
  Significant markers N = 8
  pos. correlated 0
  neg. correlated 8
List of 8 miRs differentially expressed by 'PATHOLOGY.T.STAGE'

Table S6.  Get Full Table List of 8 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-7-1 -0.3087 0.0001853 0.0791
HSA-MIR-548J -0.4267 0.0001854 0.0791
HSA-MIR-25 -0.3005 0.0002801 0.119
HSA-MIR-329-2 -0.35 0.0002894 0.123
HSA-MIR-1274B -0.2985 0.0003577 0.151
HSA-MIR-451 -0.293 0.0004027 0.17
HSA-MIR-144 -0.2882 0.0005059 0.213
HSA-MIR-486 -0.2808 0.0007136 0.3
Clinical variable #5: 'PATHOLOGY.N.STAGE'

3 miRs related to 'PATHOLOGY.N.STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.67 (0.79)
  N
  0 74
  1 38
  2 28
     
  Significant markers N = 3
  pos. correlated 0
  neg. correlated 3
List of 3 miRs differentially expressed by 'PATHOLOGY.N.STAGE'

Table S8.  Get Full Table List of 3 miRs significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-218-1 -0.4497 0.0001931 0.0825
HSA-MIR-545 -0.3055 0.0004544 0.194
HSA-MIR-511-2 -0.2842 0.0006977 0.297
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 107
  M1 18
  M1A 2
  MX 14
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

Table S10.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 66
  MALE 77
     
  Significant markers N = 0
Clinical variable #8: 'HISTOLOGICAL.TYPE'

One miR related to 'HISTOLOGICAL.TYPE'.

Table S11.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  RECTAL ADENOCARCINOMA 127
  RECTAL MUCINOUS ADENOCARCINOMA 10
     
  Significant markers N = 1
  Higher in RECTAL MUCINOUS ADENOCARCINOMA 1
  Higher in RECTAL ADENOCARCINOMA 0
List of one miR differentially expressed by 'HISTOLOGICAL.TYPE'

Table S12.  Get Full Table List of one miR differentially expressed by 'HISTOLOGICAL.TYPE'

W(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA') wilcoxontestP Q AUC
HSA-MIR-375 1102 0.0001133 0.0462 0.8677
Clinical variable #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S13.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 6
  YES 137
     
  Significant markers N = 0
Clinical variable #10: 'COMPLETENESS.OF.RESECTION'

No miR related to 'COMPLETENESS.OF.RESECTION'.

Table S14.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 102
  R1 2
  R2 11
  RX 4
     
  Significant markers N = 0
Clinical variable #11: 'NUMBER.OF.LYMPH.NODES'

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

Table S15.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.58 (5.3)
  Significant markers N = 0
Clinical variable #12: 'RACE'

No miR related to 'RACE'.

Table S16.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 1
  BLACK OR AFRICAN AMERICAN 3
  WHITE 67
     
  Significant markers N = 0
Methods & Data
Input

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

  • Clinical data file = READ-TP.merged_data.txt

  • Number of patients = 143

  • Number of miRs = 427

  • 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.

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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