Correlation between miRseq expression and clinical features
Stomach Adenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1GQ6W7T
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GQ6W7T
Overview
Introduction

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

Summary

Testing the association between 507 miRs and 11 clinical features across 308 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one miRs.

  • 10 miRs correlated to 'AGE'.

    • HSA-MIR-183 ,  HSA-MIR-100 ,  HSA-MIR-125B-1 ,  HSA-MIR-182 ,  HSA-LET-7C ,  ...

  • 22 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-217 ,  HSA-MIR-199A-1 ,  HSA-MIR-708 ,  HSA-MIR-199A-2 ,  HSA-MIR-199B ,  ...

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

    • HSA-MIR-217 ,  HSA-MIR-191 ,  HSA-MIR-490 ,  HSA-MIR-320B-2 ,  HSA-LET-7C ,  ...

  • 31 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-708 ,  HSA-MIR-100 ,  HSA-MIR-188 ,  HSA-MIR-33A ,  HSA-MIR-577 ,  ...

  • 3 miRs correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • HSA-MIR-320A ,  HSA-MIR-7-3 ,  HSA-MIR-548F-1

  • 32 miRs correlated to 'COMPLETENESS.OF.RESECTION'.

    • HSA-LET-7F-2 ,  HSA-MIR-628 ,  HSA-LET-7A-2 ,  HSA-LET-7A-1 ,  HSA-LET-7A-3 ,  ...

  • No miRs correlated to 'Time to Death', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 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 miRs that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant miRs Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=10 older N=4 younger N=6
NEOPLASM DISEASESTAGE ANOVA test N=22        
PATHOLOGY T STAGE Spearman correlation test N=31 higher stage N=10 lower stage N=21
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test   N=0        
GENDER t test   N=0        
HISTOLOGICAL TYPE ANOVA test N=31        
RADIATIONS RADIATION REGIMENINDICATION t test N=3 yes N=1 no N=2
COMPLETENESS OF RESECTION ANOVA test N=32        
NUMBER OF LYMPH NODES Spearman correlation 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) 0.1-105.1 (median=8.8)
  censored N = 216
  death N = 60
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

10 miRs related to 'AGE'.

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

AGE Mean (SD) 66 (11)
  Significant markers N = 10
  pos. correlated 4
  neg. correlated 6
List of 10 miRs significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 10 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-183 0.2714 1.763e-06 0.000894
HSA-MIR-100 -0.2626 3.873e-06 0.00196
HSA-MIR-125B-1 -0.2592 5.224e-06 0.00264
HSA-MIR-182 0.2586 5.463e-06 0.00275
HSA-LET-7C -0.2532 8.717e-06 0.00438
HSA-MIR-96 0.2469 1.467e-05 0.00737
HSA-MIR-616 0.2469 1.788e-05 0.00896
HSA-MIR-125B-2 -0.2463 1.946e-05 0.00973
HSA-MIR-195 -0.2347 3.925e-05 0.0196
HSA-MIR-99A -0.2327 4.57e-05 0.0228

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-183 to 'AGE'. P value = 1.76e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

22 miRs related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 2
  STAGE IA 11
  STAGE IB 27
  STAGE II 29
  STAGE IIA 32
  STAGE IIB 45
  STAGE III 3
  STAGE IIIA 47
  STAGE IIIB 38
  STAGE IIIC 29
  STAGE IV 29
     
  Significant markers N = 22
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'

ANOVA_P Q
HSA-MIR-217 1.355e-10 6.87e-08
HSA-MIR-199A-1 7.242e-09 3.66e-06
HSA-MIR-708 2.59e-08 1.31e-05
HSA-MIR-199A-2 3.239e-08 1.63e-05
HSA-MIR-199B 8.464e-08 4.26e-05
HSA-MIR-125B-1 9.763e-08 4.9e-05
HSA-MIR-214 2.768e-07 0.000139
HSA-MIR-100 4.645e-07 0.000232
HSA-MIR-152 5.21e-07 0.00026
HSA-MIR-654 8.233e-07 0.00041

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-217 to 'NEOPLASM.DISEASESTAGE'. P value = 1.36e-10 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

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

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.94 (0.84)
  N
  1 13
  2 75
  3 126
  4 84
     
  Significant markers N = 31
  pos. correlated 10
  neg. correlated 21
List of top 10 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

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-217 0.3503 5.343e-10 2.71e-07
HSA-MIR-191 -0.2849 5.694e-07 0.000288
HSA-MIR-490 0.3101 9.098e-07 0.000459
HSA-MIR-320B-2 -0.2732 1.679e-06 0.000846
HSA-LET-7C 0.2652 3.436e-06 0.00173
HSA-MIR-16-1 -0.2595 5.674e-06 0.00285
HSA-MIR-143 0.2549 8.348e-06 0.00418
HSA-MIR-100 0.2524 1.031e-05 0.00515
HSA-MIR-429 -0.2523 1.04e-05 0.00519
HSA-MIR-7-1 -0.2514 1.123e-05 0.00559

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-217 to 'PATHOLOGY.T.STAGE'. P value = 5.34e-10 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

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

PATHOLOGY.N.STAGE Mean (SD) 1.22 (1.1)
  N
  0 99
  1 87
  2 55
  3 55
     
  Significant markers N = 0
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 273
  M1 20
  MX 15
     
  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 120
  MALE 188
     
  Significant markers N = 0
Clinical variable #8: 'HISTOLOGICAL.TYPE'

31 miRs related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  STOMACH ADENOCARCINOMA DIFFUSE TYPE 52
  STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) 144
  STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) 45
  STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE 40
  STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE 16
  STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE 6
  STOMACH ADENOCARCINOMA SIGNET RING TYPE 3
     
  Significant markers N = 31
List of top 10 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S12.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-708 4.688e-11 2.38e-08
HSA-MIR-100 5.414e-09 2.74e-06
HSA-MIR-188 1.375e-08 6.94e-06
HSA-MIR-33A 1.063e-07 5.36e-05
HSA-MIR-577 1.482e-07 7.45e-05
HSA-MIR-96 4.246e-07 0.000213
HSA-MIR-99A 1.361e-06 0.000682
HSA-MIR-199A-1 1.833e-06 0.000916
HSA-MIR-105-1 3.969e-06 0.00198
HSA-LET-7C 4.197e-06 0.00209

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-708 to 'HISTOLOGICAL.TYPE'. P value = 4.69e-11 with ANOVA analysis.

Clinical variable #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

3 miRs related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 7
  YES 301
     
  Significant markers N = 3
  Higher in YES 1
  Higher in NO 2
List of 3 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S14.  Get Full Table List of 3 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-320A -7.84 1.095e-05 0.00538 0.7978
HSA-MIR-7-3 -6.54 5.67e-05 0.0278 0.77
HSA-MIR-548F-1 8.15 6.029e-05 0.0295 0.8742

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-320A to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.1e-05 with T-test analysis.

Clinical variable #10: 'COMPLETENESS.OF.RESECTION'

32 miRs related to 'COMPLETENESS.OF.RESECTION'.

Table S15.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 242
  R1 10
  R2 12
  RX 25
     
  Significant markers N = 32
List of top 10 miRs differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S16.  Get Full Table List of top 10 miRs differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
HSA-LET-7F-2 2.221e-25 1.13e-22
HSA-MIR-628 5.876e-22 2.97e-19
HSA-LET-7A-2 1.254e-18 6.33e-16
HSA-LET-7A-1 1.69e-18 8.52e-16
HSA-LET-7A-3 3.765e-18 1.89e-15
HSA-MIR-26A-1 5.219e-18 2.62e-15
HSA-MIR-361 2.901e-17 1.45e-14
HSA-MIR-106A 1.383e-11 6.92e-09
HSA-MIR-3607 3.437e-10 1.71e-07
HSA-MIR-3605 6.447e-10 3.21e-07

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-LET-7F-2 to 'COMPLETENESS.OF.RESECTION'. P value = 2.22e-25 with ANOVA analysis.

Clinical variable #11: 'NUMBER.OF.LYMPH.NODES'

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

Table S17.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 4.99 (7.5)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 308

  • Number of miRs = 507

  • Number of clinical features = 11

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.

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] 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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