Lung 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 548 genes and 14 clinical features across 300 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 7 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • HSA-LET-7G ,  HSA-MIR-499 ,  HSA-MIR-26B ,  HSA-MIR-491 ,  HSA-MIR-3653 ,  ...

  • 3 genes correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-29A ,  HSA-MIR-181A-2 ,  HSA-MIR-490

  • 13 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • HSA-MIR-26A-1 ,  HSA-MIR-326 ,  HSA-MIR-628 ,  HSA-MIR-106A ,  HSA-MIR-16-1 ,  ...

  • 2 genes correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.

    • HSA-MIR-3926-1 ,  HSA-MIR-651

  • 1 gene correlated to 'YEAROFTOBACCOSMOKINGONSET'.

    • HSA-MIR-125A

  • 2 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • HSA-MIR-3607 ,  HSA-MIR-3647

  • No genes correlated to 'Time to Death', 'AGE', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NUMBERPACKYEARSSMOKED'.

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=0        
AGE Spearman correlation test   N=0        
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=7 higher score N=0 lower score N=7
HISTOLOGICAL TYPE ANOVA test N=3        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=13        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
TOBACCOSMOKINGHISTORYINDICATOR ANOVA test N=2        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test N=1 higher yearoftobaccosmokingonset N=0 lower yearoftobaccosmokingonset N=1
COMPLETENESS OF RESECTION ANOVA test N=2        
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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

Time to Death Duration (Months) 0-173.8 (median=13.1)
  censored N = 177
  death N = 107
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 67.84 (8.6)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 78
  MALE 222
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

7 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S4.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 27.08 (39)
  Significant markers N = 7
  pos. correlated 0
  neg. correlated 7
List of 7 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S5.  Get Full Table List of 7 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-LET-7G -0.6441 7.83e-07 0.000429
HSA-MIR-499 -0.7751 1.278e-06 0.000699
HSA-MIR-26B -0.5845 1.287e-05 0.00702
HSA-MIR-491 -0.5612 3.334e-05 0.0182
HSA-MIR-3653 -0.5561 4.064e-05 0.0221
HSA-MIR-196B -0.5554 4.176e-05 0.0227
HSA-MIR-26A-1 -0.5443 6.364e-05 0.0345

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-LET-7G to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 7.83e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

3 genes related to 'HISTOLOGICAL.TYPE'.

Table S6.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 5
  LUNG PAPILLARY SQUAMOUS CELL CARCINOMA 1
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 1
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 292
     
  Significant markers N = 3
List of 3 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S7.  Get Full Table List of 3 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-29A 4.229e-08 2.3e-05
HSA-MIR-181A-2 3.292e-06 0.00179
HSA-MIR-490 2.818e-05 0.0153

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-29A to 'HISTOLOGICAL.TYPE'. P value = 4.23e-08 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.97 (0.75)
  N
  T1 72
  T2 181
  T3 31
  T4 16
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.5 (0.71)
  N
  N0 183
  N1 85
  N2 26
  N3 4
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

13 genes related to 'PATHOLOGICSPREAD(M)'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 261
  M1 3
  MX 30
     
  Significant markers N = 13
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
HSA-MIR-26A-1 4.388e-08 2.4e-05
HSA-MIR-326 6.942e-07 0.00038
HSA-MIR-628 1.647e-06 0.000899
HSA-MIR-106A 1.756e-05 0.00957
HSA-MIR-16-1 2.418e-05 0.0132
HSA-MIR-361 2.733e-05 0.0148
HSA-MIR-1277 3.007e-05 0.0163
HSA-MIR-320E 4.299e-05 0.0233
HSA-MIR-1180 6.044e-05 0.0326
HSA-MIR-374C 6.379e-05 0.0344

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-26A-1 to 'PATHOLOGICSPREAD(M)'. P value = 4.39e-08 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S12.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.7 (0.81)
  N
  Stage 1 153
  Stage 2 83
  Stage 3 58
  Stage 4 3
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 10
  YES 290
     
  Significant markers N = 0
Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S14.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 53.22 (33)
  Significant markers N = 0
Clinical variable #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

2 genes related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

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

TOBACCOSMOKINGHISTORYINDICATOR Labels N
  CURRENT REFORMED SMOKER FOR < OR = 15 YEARS 146
  CURRENT REFORMED SMOKER FOR > 15 YEARS 67
  CURRENT SMOKER 70
  LIFELONG NON-SMOKER 11
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

Table S16.  Get Full Table List of 2 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

ANOVA_P Q
HSA-MIR-3926-1 4.108e-05 0.0225
HSA-MIR-651 5.48e-05 0.03

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3926-1 to 'TOBACCOSMOKINGHISTORYINDICATOR'. P value = 4.11e-05 with ANOVA analysis.

Clinical variable #13: 'YEAROFTOBACCOSMOKINGONSET'

One gene related to 'YEAROFTOBACCOSMOKINGONSET'.

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

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1958.7 (12)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

Table S18.  Get Full Table List of one gene significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-125A -0.2853 3.7e-05 0.0203

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-125A to 'YEAROFTOBACCOSMOKINGONSET'. P value = 3.7e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #14: 'COMPLETENESS.OF.RESECTION'

2 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S19.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 232
  R1 5
  R2 3
  RX 14
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S20.  Get Full Table List of 2 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
HSA-MIR-3607 8.241e-06 0.00452
HSA-MIR-3647 1.828e-05 0.01

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3607 to 'COMPLETENESS.OF.RESECTION'. P value = 8.24e-06 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 300

  • Number of genes = 548

  • Number of clinical features = 14

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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