Correlation between miRseq expression and clinical features
Sarcoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C14749BF
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C14749BF
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features. The input file " SARC-TP.miRseq_RPKM_log2.txt " is generated in the pipeline miRseq_Preprocess in the stddata run.

Summary

Testing the association between 509 miRs and 9 clinical features across 259 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one miRs.

  • 30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-3605 ,  HSA-MIR-1284 ,  HSA-MIR-92A-2 ,  HSA-MIR-1226 ,  HSA-MIR-3680 ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-511-1 ,  HSA-MIR-155 ,  HSA-MIR-511-2 ,  HSA-MIR-21 ,  HSA-MIR-1245 ,  ...

  • 30 miRs correlated to 'TUMOR_TISSUE_SITE'.

    • HSA-MIR-21 ,  HSA-LET-7F-1 ,  HSA-MIR-185 ,  HSA-MIR-145 ,  HSA-MIR-27A ,  ...

  • 30 miRs correlated to 'GENDER'.

    • HSA-MIR-22 ,  HSA-MIR-887 ,  HSA-MIR-488 ,  HSA-MIR-187 ,  HSA-MIR-301A ,  ...

  • 30 miRs correlated to 'RADIATION_THERAPY'.

    • HSA-MIR-185 ,  HSA-MIR-590 ,  HSA-MIR-200C ,  HSA-MIR-98 ,  HSA-MIR-103-1 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-145 ,  HSA-MIR-26A-2 ,  HSA-MIR-133A-1 ,  HSA-MIR-143 ,  HSA-MIR-1-2 ,  ...

  • 30 miRs correlated to 'RESIDUAL_TUMOR'.

    • HSA-MIR-574 ,  HSA-MIR-301B ,  HSA-MIR-1226 ,  HSA-MIR-3605 ,  HSA-MIR-3926-2 ,  ...

  • No miRs correlated to 'RACE', and 'ETHNICITY'.

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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=27 younger N=3
TUMOR_TISSUE_SITE Kruskal-Wallis test N=30        
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 miRs 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) 0.5-188.2 (median=31)
  censored N = 160
  death N = 98
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
HSA-MIR-3605 1.85e-05 0.006 0.646
HSA-MIR-1284 2.34e-05 0.006 0.615
HSA-MIR-92A-2 6.92e-05 0.012 0.648
HSA-MIR-1226 9.83e-05 0.013 0.629
HSA-MIR-3680 0.000134 0.014 0.679
HSA-MIR-3677 0.000195 0.014 0.652
HSA-MIR-29C 0.000202 0.014 0.381
HSA-MIR-301B 0.000214 0.014 0.636
HSA-MIR-33A 0.000282 0.016 0.629
HSA-MIR-301A 0.000446 0.023 0.649
Clinical variable #2: 'YEARS_TO_BIRTH'

30 miRs related to 'YEARS_TO_BIRTH'.

Table S3.  Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH Mean (SD) 60.85 (15)
  Significant markers N = 30
  pos. correlated 27
  neg. correlated 3
List of top 10 miRs differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of top 10 miRs significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-511-1 0.297 1.657e-06 0.000844
HSA-MIR-155 0.273 8.648e-06 0.00208
HSA-MIR-511-2 0.2721 1.226e-05 0.00208
HSA-MIR-21 0.2621 2.011e-05 0.00209
HSA-MIR-1245 0.2674 2.05e-05 0.00209
HSA-MIR-22 0.2555 3.275e-05 0.00278
HSA-MIR-589 0.2409 9.273e-05 0.00674
HSA-MIR-142 0.2321 0.0001689 0.00959
HSA-MIR-223 0.2321 0.0001695 0.00959
HSA-MIR-221 0.2246 0.0002769 0.0141
Clinical variable #3: 'TUMOR_TISSUE_SITE'

30 miRs related to 'TUMOR_TISSUE_SITE'.

Table S5.  Basic characteristics of clinical feature: 'TUMOR_TISSUE_SITE'

TUMOR_TISSUE_SITE Labels N
  CHEST - BREAST 1
  CHEST - CHEST WALL 7
  CHEST - LUNG/PLEURA 2
  CHEST - MEDIASTINUM 1
  CHEST - OTHER (PLEASE SPECIFY 2
  GYNECOLOGICAL - OVARY 1
  GYNECOLOGICAL - UTERUS 28
  HEAD AND NECK - HEAD 2
  HEAD AND NECK - NECK 1
  HEAD AND NECK - OTHER (PLEASE SPECIFY 2
  LOWER ABDOMINAL/PELVIC - BLADDER 1
  LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY 2
  LOWER ABDOMINAL/PELVIC - PELVIC 11
  LOWER ABDOMINAL/PELVIC - SPERMATIC CORD 2
  LOWER EXTREMITY - FOOT/ANKLE 4
  LOWER EXTREMITY - GROIN 2
  LOWER EXTREMITY - LOWER LEG/CALF 16
  LOWER EXTREMITY - OTHER (PLEASE SPECIFY 5
  LOWER EXTREMITY - THIGH/KNEE 45
  RETROPERITONEUM/UPPER ABDOMINAL - COLON 4
  RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC 2
  RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL 5
  RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY 8
  RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY 2
  RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS 1
  RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM 74
  RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES 3
  SUPERFICIAL TRUNK - ABDOMINAL WALL 2
  SUPERFICIAL TRUNK - BACK 5
  SUPERFICIAL TRUNK - BUTTOCK 4
  SUPERFICIAL TRUNK - FLANK 1
  UPPER EXTREMITY - SHOULDER/AXILLA 7
  UPPER EXTREMITY - UPPER ARM/ELBOW 5
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'TUMOR_TISSUE_SITE'

Table S6.  Get Full Table List of top 10 miRs differentially expressed by 'TUMOR_TISSUE_SITE'

kruskal_wallis_P Q
HSA-MIR-21 7.534e-07 0.000384
HSA-LET-7F-1 4.686e-06 0.000878
HSA-MIR-185 5.174e-06 0.000878
HSA-MIR-145 1.79e-05 0.00228
HSA-MIR-27A 2.554e-05 0.0026
HSA-MIR-133A-1 5.203e-05 0.00441
HSA-MIR-23A 7.352e-05 0.00535
HSA-MIR-10A 0.0001375 0.00875
HSA-LET-7A-2 0.0001944 0.00957
HSA-LET-7A-3 0.0002017 0.00957
Clinical variable #4: 'GENDER'

30 miRs related to 'GENDER'.

Table S7.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 140
  MALE 119
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 miRs differentially expressed by 'GENDER'

Table S8.  Get Full Table List of top 10 miRs differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
HSA-MIR-22 10873 2.318e-05 0.0118 0.6526
HSA-MIR-887 5941 7.023e-05 0.0179 0.6434
HSA-MIR-488 1088 0.0002123 0.0315 0.6978
HSA-MIR-187 2842 0.0002475 0.0315 0.6586
HSA-MIR-301A 6180 0.0003466 0.0353 0.6291
HSA-MIR-26A-2 10416 0.0005182 0.044 0.6252
HSA-MIR-454 6287 0.0006748 0.0491 0.6226
HSA-MIR-205 1838 0.001065 0.0668 0.6571
HSA-MIR-106A 6393 0.001268 0.0668 0.6163
HSA-MIR-1180 6399 0.001313 0.0668 0.6159
Clinical variable #5: 'RADIATION_THERAPY'

30 miRs related to 'RADIATION_THERAPY'.

Table S9.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 180
  YES 73
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

Table S10.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-185 9106 1.527e-06 0.000777 0.693
HSA-MIR-590 8756 3.412e-05 0.00623 0.6664
HSA-MIR-200C 8743 3.798e-05 0.00623 0.6654
HSA-MIR-98 8675 6.594e-05 0.00623 0.6602
HSA-MIR-103-1 8666 7.085e-05 0.00623 0.6595
HSA-MIR-206 5520 8.315e-05 0.00623 0.674
HSA-MIR-27A 8625 9.793e-05 0.00623 0.6564
HSA-MIR-576 8619 0.0001026 0.00623 0.6559
HSA-MIR-23A 8610 0.0001101 0.00623 0.6553
HSA-MIR-338 8577 0.000142 0.00723 0.6527
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  DEDIFFERENTIATED LIPOSARCOMA 59
  DESMOID TUMOR 2
  GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS 1
  LEIOMYOSARCOMA (LMS) 104
  MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) 9
  MYXOFIBROSARCOMA 25
  PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA 29
  SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED 2
  SYNOVIAL SARCOMA - BIPHASIC 2
  SYNOVIAL SARCOMA - MONOPHASIC 6
  UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS) 20
     
  Significant markers N = 30
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'

kruskal_wallis_P Q
HSA-MIR-145 5.55e-26 2.82e-23
HSA-MIR-26A-2 7.063e-24 1.8e-21
HSA-MIR-133A-1 1.552e-23 2.63e-21
HSA-MIR-143 8.268e-23 1.05e-20
HSA-MIR-1-2 5.349e-22 5.45e-20
HSA-MIR-22 7.248e-20 6.15e-18
HSA-MIR-511-2 1.201e-19 8.73e-18
HSA-MIR-511-1 1.686e-19 1.07e-17
HSA-MIR-199B 3.827e-19 2.16e-17
HSA-MIR-127 5.339e-19 2.72e-17
Clinical variable #7: 'RESIDUAL_TUMOR'

30 miRs related to 'RESIDUAL_TUMOR'.

Table S13.  Basic characteristics of clinical feature: 'RESIDUAL_TUMOR'

RESIDUAL_TUMOR Labels N
  R0 154
  R1 69
  R2 9
  RX 26
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RESIDUAL_TUMOR'

Table S14.  Get Full Table List of top 10 miRs differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
HSA-MIR-574 0.0002909 0.0614
HSA-MIR-301B 0.0003202 0.0614
HSA-MIR-1226 0.0004907 0.0614
HSA-MIR-3605 0.0005401 0.0614
HSA-MIR-3926-2 0.000864 0.0614
HSA-MIR-505 0.000872 0.0614
HSA-MIR-1-2 0.0009528 0.0614
HSA-MIR-324 0.000965 0.0614
HSA-MIR-152 0.001141 0.0645
HSA-MIR-301A 0.001875 0.0865
Clinical variable #8: 'RACE'

No miR related to 'RACE'.

Table S15.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 6
  BLACK OR AFRICAN AMERICAN 18
  WHITE 227
     
  Significant markers N = 0
Clinical variable #9: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 5
  NOT HISPANIC OR LATINO 222
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 259

  • Number of miRs = 509

  • Number of clinical features = 9

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • 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, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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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