Correlation between miRseq expression and clinical features
Overview
Introduction

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

Summary

Testing the association between 493 genes and 7 clinical features across 104 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 2 genes correlated to 'Time to Death'.

    • HSA-MIR-141 ,  HSA-MIR-200C

  • 1 gene correlated to 'GENDER'.

    • HSA-MIR-3173

  • 7 genes correlated to 'DISTANT.METASTASIS'.

    • HSA-MIR-3607 ,  HSA-MIR-3647 ,  HSA-MIR-1245 ,  HSA-MIR-3653 ,  HSA-MIR-26A-1 ,  ...

  • 6 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • HSA-MIR-200B ,  HSA-MIR-224 ,  HSA-MIR-452 ,  HSA-MIR-200A ,  HSA-MIR-217 ,  ...

  • 5 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-224 ,  HSA-MIR-452 ,  HSA-MIR-200B ,  HSA-MIR-92A-1 ,  HSA-MIR-200A

  • No genes correlated to 'AGE', and 'KARNOFSKY.PERFORMANCE.SCORE'.

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=2 shorter survival N=2 longer survival N=0
AGE Spearman correlation test   N=0        
GENDER t test N=1 male N=1 female N=0
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
DISTANT METASTASIS ANOVA test N=7        
LYMPH NODE METASTASIS ANOVA test N=6        
NEOPLASM DISEASESTAGE ANOVA test N=5        
Clinical variable #1: 'Time to Death'

2 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-182.7 (median=13.7)
  censored N = 83
  death N = 14
     
  Significant markers N = 2
  associated with shorter survival 2
  associated with longer survival 0
List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-141 1.66 1.398e-05 0.0069 0.776
HSA-MIR-200C 1.56 3.357e-05 0.017 0.745

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-141 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.4e-05 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) 59.64 (12)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

One gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 34
  MALE 70
     
  Significant markers N = 1
  Higher in MALE 1
  Higher in FEMALE 0
List of one gene differentially expressed by 'GENDER'

Table S5.  Get Full Table List of one gene differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
HSA-MIR-3173 4.24 8.781e-05 0.0433 0.7756

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3173 to 'GENDER'. P value = 8.78e-05 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 87.73 (23)
  Score N
  0 1
  40 1
  90 11
  100 9
     
  Significant markers N = 0
Clinical variable #5: 'DISTANT.METASTASIS'

7 genes related to 'DISTANT.METASTASIS'.

Table S7.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 55
  M1 5
  MX 36
     
  Significant markers N = 7
List of 7 genes differentially expressed by 'DISTANT.METASTASIS'

Table S8.  Get Full Table List of 7 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
HSA-MIR-3607 7.251e-08 3.57e-05
HSA-MIR-3647 9.616e-08 4.73e-05
HSA-MIR-1245 2.738e-05 0.0134
HSA-MIR-3653 3.219e-05 0.0158
HSA-MIR-26A-1 3.479e-05 0.017
HSA-MIR-126 7.638e-05 0.0373
HSA-MIR-1248 9.26e-05 0.0451

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3607 to 'DISTANT.METASTASIS'. P value = 7.25e-08 with ANOVA analysis.

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

6 genes related to 'LYMPH.NODE.METASTASIS'.

Table S9.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 20
  N1 12
  N2 4
  NX 68
     
  Significant markers N = 6
List of 6 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S10.  Get Full Table List of 6 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
HSA-MIR-200B 6.946e-07 0.000342
HSA-MIR-224 1.473e-06 0.000725
HSA-MIR-452 1.781e-06 0.000874
HSA-MIR-200A 6.894e-06 0.00338
HSA-MIR-217 3.341e-05 0.0163
HSA-MIR-421 9.449e-05 0.0461

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-200B to 'LYMPH.NODE.METASTASIS'. P value = 6.95e-07 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

5 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S11.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 53
  STAGE II 7
  STAGE III 24
  STAGE IV 9
     
  Significant markers N = 5
List of 5 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S12.  Get Full Table List of 5 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
HSA-MIR-224 2.664e-06 0.00131
HSA-MIR-452 1.903e-05 0.00936
HSA-MIR-200B 2.832e-05 0.0139
HSA-MIR-92A-1 4.656e-05 0.0228
HSA-MIR-200A 9.274e-05 0.0453

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-224 to 'NEOPLASM.DISEASESTAGE'. P value = 2.66e-06 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 104

  • Number of genes = 493

  • Number of clinical features = 7

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.

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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