This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 504 genes and 8 clinical features across 104 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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3 genes correlated to 'Time to Death'.
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HSA-MIR-141 , HSA-MIR-200C , HSA-MIR-937
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8 genes correlated to 'PATHOLOGY.T'.
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HSA-MIR-1293 , HSA-MIR-224 , HSA-MIR-217 , HSA-MIR-452 , HSA-MIR-200A , ...
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7 genes correlated to 'PATHOLOGICSPREAD(M)'.
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HSA-MIR-3607 , HSA-MIR-3647 , HSA-MIR-1245 , HSA-MIR-1248 , HSA-MIR-126 , ...
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10 genes correlated to 'TUMOR.STAGE'.
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HSA-MIR-224 , HSA-MIR-452 , HSA-MIR-200A , HSA-MIR-200B , HSA-MIR-217 , ...
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No genes correlated to 'AGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'PATHOLOGY.N'.
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 | ||
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Time to Death | Cox regression test | N=3 | shorter survival | N=3 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
PATHOLOGY T | Spearman correlation test | N=8 | higher pT | N=6 | lower pT | N=2 |
PATHOLOGY N | Spearman correlation test | N=0 | ||||
PATHOLOGICSPREAD(M) | ANOVA test | N=7 | ||||
TUMOR STAGE | Spearman correlation test | N=10 | higher stage | N=7 | lower stage | N=3 |
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 = 3 | |
associated with shorter survival | 3 | |
associated with longer survival | 0 |
Table S2. Get Full Table List of 3 genes significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
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HSA-MIR-141 | 1.66 | 1.771e-05 | 0.0089 | 0.761 |
HSA-MIR-200C | 1.56 | 3.258e-05 | 0.016 | 0.749 |
HSA-MIR-937 | 4.1 | 9.584e-05 | 0.048 | 0.847 |
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 = 1.77e-05 with univariate Cox regression analysis using continuous log-2 expression values.
![](V1ex.png)
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 59.64 (12) |
Significant markers | N = 0 |
Table S4. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 34 | |
MALE | 70 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S5. 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 |
Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.T'
PATHOLOGY.T | Mean (SD) | 1.77 (0.93) |
N | ||
T1 | 58 | |
T2 | 13 | |
T3 | 32 | |
T4 | 1 | |
Significant markers | N = 8 | |
pos. correlated | 6 | |
neg. correlated | 2 |
Table S7. Get Full Table List of 8 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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HSA-MIR-1293 | 0.4951 | 1.957e-06 | 0.000986 |
HSA-MIR-224 | 0.4446 | 2.536e-06 | 0.00128 |
HSA-MIR-217 | 0.4564 | 2.616e-06 | 0.00131 |
HSA-MIR-452 | 0.4379 | 3.337e-06 | 0.00167 |
HSA-MIR-200A | -0.4308 | 4.983e-06 | 0.00249 |
HSA-MIR-216A | 0.5253 | 1.381e-05 | 0.00689 |
HSA-MIR-200B | -0.4047 | 2.031e-05 | 0.0101 |
HSA-MIR-184 | 0.4326 | 8.52e-05 | 0.0423 |
Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-1293 to 'PATHOLOGY.T'. P value = 1.96e-06 with Spearman correlation analysis.
![](V5ex.png)
Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.N'
PATHOLOGY.N | Mean (SD) | 0.56 (0.69) |
N | ||
N0 | 20 | |
N1 | 12 | |
N2 | 4 | |
Significant markers | N = 0 |
Table S9. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'
PATHOLOGICSPREAD(M) | Labels | N |
M0 | 54 | |
M1 | 5 | |
MX | 36 | |
Significant markers | N = 7 |
Table S10. Get Full Table List of 7 genes differentially expressed by 'PATHOLOGICSPREAD(M)'
ANOVA_P | Q | |
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HSA-MIR-3607 | 1.225e-07 | 6.17e-05 |
HSA-MIR-3647 | 2.697e-07 | 0.000136 |
HSA-MIR-1245 | 5.241e-06 | 0.00263 |
HSA-MIR-1248 | 4.578e-05 | 0.0229 |
HSA-MIR-126 | 7.863e-05 | 0.0393 |
HSA-MIR-26A-1 | 8.577e-05 | 0.0428 |
HSA-MIR-424 | 9.465e-05 | 0.0471 |
Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-3607 to 'PATHOLOGICSPREAD(M)'. P value = 1.22e-07 with ANOVA analysis.
![](V7ex.png)
Table S11. Basic characteristics of clinical feature: 'TUMOR.STAGE'
TUMOR.STAGE | Mean (SD) | 1.88 (1.1) |
N | ||
Stage 1 | 53 | |
Stage 2 | 7 | |
Stage 3 | 24 | |
Stage 4 | 9 | |
Significant markers | N = 10 | |
pos. correlated | 7 | |
neg. correlated | 3 |
Table S12. Get Full Table List of 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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HSA-MIR-224 | 0.5359 | 3.695e-08 | 1.86e-05 |
HSA-MIR-452 | 0.5142 | 1.353e-07 | 6.81e-05 |
HSA-MIR-200A | -0.5047 | 2.484e-07 | 0.000125 |
HSA-MIR-200B | -0.4717 | 1.818e-06 | 0.000911 |
HSA-MIR-217 | 0.4662 | 3.646e-06 | 0.00182 |
HSA-MIR-216A | 0.5578 | 4.431e-06 | 0.00221 |
HSA-MIR-429 | -0.4331 | 1.449e-05 | 0.00721 |
HSA-MIR-1293 | 0.471 | 1.756e-05 | 0.00873 |
HSA-MIR-92A-2 | 0.4045 | 5.788e-05 | 0.0287 |
HSA-MIR-1269 | 0.4236 | 7.318e-05 | 0.0362 |
Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-224 to 'TUMOR.STAGE'. P value = 3.7e-08 with Spearman correlation analysis.
![](V8ex.png)
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Expresson data file = KIRP-TP.miRseq_RPKM_log2.txt
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Clinical data file = KIRP-TP.clin.merged.picked.txt
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Number of patients = 104
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Number of genes = 504
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Number of clinical features = 8
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
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
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
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
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.