Head and Neck Squamous Cell Carcinoma: 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 551 genes and 12 clinical features across 325 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 8 genes correlated to 'Time to Death'.

    • HSA-MIR-377 ,  HSA-MIR-337 ,  HSA-MIR-493 ,  HSA-MIR-154 ,  HSA-MIR-654 ,  ...

  • 1 gene correlated to 'PATHOLOGY.N'.

    • HSA-MIR-195

  • 1 gene correlated to 'PATHOLOGICSPREAD(M)'.

    • HSA-MIR-184

  • 4 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • HSA-MIR-1274B ,  HSA-MIR-532 ,  HSA-MIR-374A ,  HSA-MIR-660

  • 3 genes correlated to 'NUMBERPACKYEARSSMOKED'.

    • HSA-MIR-1180 ,  HSA-MIR-183 ,  HSA-MIR-1224

  • 3 genes correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.

    • HSA-MIR-301B ,  HSA-MIR-296 ,  HSA-MIR-570

  • No genes correlated to 'AGE', 'GENDER', 'PATHOLOGY.T', 'TUMOR.STAGE', 'YEAROFTOBACCOSMOKINGONSET', 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 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=8 shorter survival N=8 longer survival N=0
AGE Spearman correlation test   N=0        
GENDER t test   N=0        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test N=1 higher pN N=1 lower pN N=0
PATHOLOGICSPREAD(M) t test N=1 mx N=0 m0 N=1
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=4 yes N=3 no N=1
NUMBERPACKYEARSSMOKED Spearman correlation test N=3 higher numberpackyearssmoked N=3 lower numberpackyearssmoked N=0
TOBACCOSMOKINGHISTORYINDICATOR ANOVA test N=3        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

8 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-210.9 (median=14.3)
  censored N = 198
  death N = 123
     
  Significant markers N = 8
  associated with shorter survival 8
  associated with longer survival 0
List of 8 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 8 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-377 1.46 1.49e-06 0.00082 0.635
HSA-MIR-337 1.4 1.037e-05 0.0057 0.634
HSA-MIR-493 1.45 1.472e-05 0.0081 0.631
HSA-MIR-154 1.43 1.821e-05 0.01 0.63
HSA-MIR-654 1.38 1.918e-05 0.01 0.626
HSA-MIR-382 1.47 4.81e-05 0.026 0.623
HSA-MIR-758 1.39 5.004e-05 0.027 0.613
HSA-MIR-127 1.47 6.738e-05 0.037 0.621

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-377 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.49e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 61.17 (12)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 92
  MALE 233
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

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

PATHOLOGY.T Mean (SD) 2.88 (1)
  N
  T1 28
  T2 83
  T3 65
  T4 106
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N'

One gene related to 'PATHOLOGY.N'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 1.03 (0.96)
  N
  N0 106
  N1 36
  N2 103
  N3 5
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S7.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-195 0.2819 6e-06 0.00331

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-195 to 'PATHOLOGY.N'. P value = 6e-06 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGICSPREAD(M)'

One gene related to 'PATHOLOGICSPREAD(M)'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 59
  MX 6
     
  Significant markers N = 1
  Higher in MX 0
  Higher in M0 1
List of one gene differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S9.  Get Full Table List of one gene differentially expressed by 'PATHOLOGICSPREAD(M)'

T(pos if higher in 'MX') ttestP Q AUC
HSA-MIR-184 -7.89 4.415e-08 2.24e-05 0.94

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-184 to 'PATHOLOGICSPREAD(M)'. P value = 4.42e-08 with T-test analysis.

Clinical variable #7: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S10.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 3.26 (1)
  N
  Stage 1 20
  Stage 2 51
  Stage 3 44
  Stage 4 162
     
  Significant markers N = 0
Clinical variable #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

4 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S11.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 77
  YES 248
     
  Significant markers N = 4
  Higher in YES 3
  Higher in NO 1
List of 4 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S12.  Get Full Table List of 4 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-1274B 5.51 2.017e-07 0.000111 0.6977
HSA-MIR-532 4.81 3.781e-06 0.00208 0.6638
HSA-MIR-374A -4.82 3.926e-06 0.00216 0.6634
HSA-MIR-660 4.76 4.467e-06 0.00245 0.6602

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1274B to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.02e-07 with T-test analysis.

Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

3 genes related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 50.56 (41)
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S14.  Get Full Table List of 3 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-1180 0.3095 2.491e-05 0.0137
HSA-MIR-183 0.3075 2.828e-05 0.0156
HSA-MIR-1224 0.3922 5.958e-05 0.0327

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1180 to 'NUMBERPACKYEARSSMOKED'. P value = 2.49e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

3 genes related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

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

TOBACCOSMOKINGHISTORYINDICATOR Labels N
  CURRENT REFORMED SMOKER FOR < OR = 15 YEARS 85
  CURRENT REFORMED SMOKER FOR > 15 YEARS 58
  CURRENT SMOKER 105
  LIFELONG NON-SMOKER 66
     
  Significant markers N = 3
List of 3 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

Table S16.  Get Full Table List of 3 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

ANOVA_P Q
HSA-MIR-301B 1.858e-05 0.0102
HSA-MIR-296 3.974e-05 0.0219
HSA-MIR-570 4.591e-05 0.0252

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-301B to 'TOBACCOSMOKINGHISTORYINDICATOR'. P value = 1.86e-05 with ANOVA analysis.

Clinical variable #11: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S17.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1964.98 (12)
  Significant markers N = 0
Clinical variable #12: 'NUMBER.OF.LYMPH.NODES'

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

Table S18.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.6 (5.2)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 325

  • Number of genes = 551

  • 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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