Correlation between miRseq expression and clinical features
Rectum Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1MG7NM2
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1MG7NM2
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, 6 clinical features related to at least one miRs.

  • 1 miR correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-31

  • 30 miRs correlated to 'NEOPLASM_DISEASESTAGE'.

    • HSA-MIR-1296 ,  HSA-MIR-545 ,  HSA-LET-7A-2 ,  HSA-LET-7A-1 ,  HSA-MIR-484 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

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

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-218-1 ,  HSA-MIR-545 ,  HSA-MIR-511-2 ,  HSA-MIR-329-2 ,  HSA-MIR-514-3 ,  ...

  • 22 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-375 ,  HSA-MIR-31 ,  HSA-MIR-23B ,  HSA-MIR-1308 ,  HSA-MIR-625 ,  ...

  • 29 miRs correlated to 'NUMBER_OF_LYMPH_NODES'.

    • HSA-MIR-218-1 ,  HSA-MIR-545 ,  HSA-MIR-511-2 ,  HSA-MIR-1301 ,  HSA-MIR-514-3 ,  ...

  • No miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_M_STAGE', 'GENDER', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'COMPLETENESS_OF_RESECTION', 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
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
YEARS_TO_BIRTH Spearman correlation test N=1 older N=0 younger N=1
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=1 lower stage N=29
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=0 lower stage N=30
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Wilcoxon test N=22 rectal mucinous adenocarcinoma N=22 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=29 higher number_of_lymph_nodes N=0 lower number_of_lymph_nodes N=29
RACE Kruskal-Wallis test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No miR related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1.  Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0.4-129.3 (median=16.6)
  censored N = 121
  death N = 21
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

One miR related to 'YEARS_TO_BIRTH'.

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

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

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

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

30 miRs 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 = 30
List of top 10 miRs differentially expressed by 'NEOPLASM_DISEASESTAGE'

Table S5.  Get Full Table List of top 10 miRs differentially expressed by 'NEOPLASM_DISEASESTAGE'

kruskal_wallis_P Q
HSA-MIR-1296 0.002155 0.233
HSA-MIR-545 0.003731 0.233
HSA-LET-7A-2 0.00413 0.233
HSA-LET-7A-1 0.004141 0.233
HSA-MIR-484 0.004452 0.233
HSA-LET-7A-3 0.004563 0.233
HSA-MIR-548B 0.005097 0.233
HSA-MIR-625 0.005399 0.233
HSA-MIR-210 0.008141 0.233
HSA-MIR-196A-2 0.008145 0.233
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 miRs related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.76 (0.67)
  N
  T1 9
  T2 26
  T3 97
  T4 10
     
  Significant markers N = 30
  pos. correlated 1
  neg. correlated 29
List of top 10 miRs differentially expressed by 'PATHOLOGY_T_STAGE'

Table S7.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-7-1 -0.3087 0.0001853 0.0287
HSA-MIR-548J -0.4267 0.0001854 0.0287
HSA-MIR-25 -0.3005 0.0002801 0.0287
HSA-MIR-329-2 -0.35 0.0002894 0.0287
HSA-MIR-1274B -0.2985 0.0003577 0.0287
HSA-MIR-451 -0.293 0.0004027 0.0287
HSA-MIR-144 -0.2882 0.0005059 0.0309
HSA-MIR-486 -0.2808 0.0007136 0.0352
HSA-MIR-376C -0.2809 0.0007416 0.0352
HSA-MIR-1275 -0.2752 0.001238 0.0501
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 miRs related to 'PATHOLOGY_N_STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Mean (SD) 0.67 (0.79)
  N
  N0 74
  N1 38
  N2 28
     
  Significant markers N = 30
  pos. correlated 0
  neg. correlated 30
List of top 10 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

Table S9.  Get Full Table List of top 10 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 0.097
HSA-MIR-511-2 -0.2842 0.0006977 0.0993
HSA-MIR-329-2 -0.3216 0.001042 0.111
HSA-MIR-514-3 -0.3092 0.001655 0.141
HSA-MIR-30E -0.2484 0.00308 0.178
HSA-MIR-491 -0.2467 0.003537 0.178
HSA-MIR-509-1 -0.2414 0.004803 0.178
HSA-MIR-656 -0.2666 0.004865 0.178
HSA-MIR-548B -0.326 0.00489 0.178
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No miR related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 107
  class1 20
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

Table S11.  Basic characteristics of clinical feature: 'GENDER'

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

22 miRs related to 'HISTOLOGICAL_TYPE'.

Table S12.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  RECTAL ADENOCARCINOMA 127
  RECTAL MUCINOUS ADENOCARCINOMA 10
     
  Significant markers N = 22
  Higher in RECTAL MUCINOUS ADENOCARCINOMA 22
  Higher in RECTAL ADENOCARCINOMA 0
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S13.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

W(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA') wilcoxontestP Q AUC
HSA-MIR-375 1102 0.0001133 0.0484 0.8677
HSA-MIR-31 1027 0.0009468 0.164 0.8151
HSA-MIR-23B 1022 0.001383 0.164 0.8047
HSA-MIR-1308 818 0.001533 0.164 0.8188
HSA-MIR-625 1005 0.002232 0.191 0.7913
HSA-LET-7E 282 0.003536 0.252 0.778
HSA-MIR-592 294 0.00484 0.254 0.7685
HSA-MIR-632 358 0.006032 0.254 0.8404
HSA-MIR-935 936 0.006243 0.254 0.761
HSA-MIR-744 956 0.008001 0.254 0.7528
Clinical variable #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

No miR related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

Table S14.  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 S15.  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'

29 miRs related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.58 (5.3)
  Significant markers N = 29
  pos. correlated 0
  neg. correlated 29
List of top 10 miRs differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S17.  Get Full Table List of top 10 miRs significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-218-1 -0.4106 0.001118 0.131
HSA-MIR-545 -0.2925 0.001188 0.131
HSA-MIR-511-2 -0.2734 0.00158 0.131
HSA-MIR-1301 -0.2693 0.001795 0.131
HSA-MIR-514-3 -0.3175 0.001823 0.131
HSA-MIR-329-2 -0.3172 0.001841 0.131
HSA-MIR-511-1 -0.2531 0.003531 0.213
HSA-MIR-491 -0.2488 0.004318 0.213
HSA-MIR-624 -0.2607 0.004704 0.213
HSA-MIR-1284 -0.3395 0.005297 0.213
Clinical variable #12: 'RACE'

No miR related to 'RACE'.

Table S18.  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

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[4] 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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