This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 455 genes and 7 clinical features across 481 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.
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56 genes correlated to 'Time to Death'.
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HSA-MIR-223 , HSA-MIR-130B , HSA-MIR-34C , HSA-MIR-21 , HSA-MIR-10B , ...
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1 gene correlated to 'AGE'.
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HSA-MIR-590
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12 genes correlated to 'GENDER'.
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HSA-MIR-100 , HSA-MIR-455 , HSA-MIR-708 , HSA-MIR-599 , HSA-MIR-30A , ...
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21 genes correlated to 'DISTANT.METASTASIS'.
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HSA-MIR-106B , HSA-MIR-193A , HSA-MIR-155 , HSA-MIR-625 , HSA-MIR-28 , ...
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2 genes correlated to 'LYMPH.NODE.METASTASIS'.
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HSA-MIR-10B , HSA-MIR-223
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39 genes correlated to 'NEOPLASM.DISEASESTAGE'.
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HSA-MIR-139 , HSA-MIR-625 , HSA-LET-7I , HSA-MIR-28 , HSA-MIR-155 , ...
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No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'
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=56 | shorter survival | N=49 | longer survival | N=7 |
AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0 |
GENDER | t test | N=12 | male | N=3 | female | N=9 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
DISTANT METASTASIS | t test | N=21 | m1 | N=16 | m0 | N=5 |
LYMPH NODE METASTASIS | ANOVA test | N=2 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=39 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.1-111 (median=35.2) |
censored | N = 323 | |
death | N = 155 | |
Significant markers | N = 56 | |
associated with shorter survival | 49 | |
associated with longer survival | 7 |
Table S2. Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-223 | 1.64 | 2.721e-13 | 1.2e-10 | 0.652 |
HSA-MIR-130B | 2 | 9.949e-12 | 4.5e-09 | 0.653 |
HSA-MIR-34C | 1.28 | 5.06e-10 | 2.3e-07 | 0.644 |
HSA-MIR-21 | 2.1 | 1.033e-09 | 4.7e-07 | 0.659 |
HSA-MIR-10B | 0.56 | 6.213e-09 | 2.8e-06 | 0.366 |
HSA-MIR-101-1 | 0.56 | 1.314e-08 | 5.9e-06 | 0.393 |
HSA-MIR-1248 | 1.41 | 4.348e-08 | 2e-05 | 0.619 |
HSA-MIR-18A | 1.53 | 1.027e-07 | 4.6e-05 | 0.618 |
HSA-MIR-3614 | 1.61 | 1.914e-07 | 8.6e-05 | 0.616 |
HSA-MIR-138-2 | 1.42 | 2.924e-07 | 0.00013 | 0.651 |
Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-223 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.72e-13 with univariate Cox regression analysis using continuous log-2 expression values.

Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 60.58 (12) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
Table S4. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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HSA-MIR-590 | 0.1846 | 4.661e-05 | 0.0212 |
Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-590 to 'AGE'. P value = 4.66e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Table S5. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 163 | |
MALE | 318 | |
Significant markers | N = 12 | |
Higher in MALE | 3 | |
Higher in FEMALE | 9 |
Table S6. Get Full Table List of top 10 genes differentially expressed by 'GENDER'
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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HSA-MIR-100 | 8.16 | 7.964e-15 | 3.62e-12 | 0.7301 |
HSA-MIR-455 | -5.38 | 1.374e-07 | 6.24e-05 | 0.6593 |
HSA-MIR-708 | 5.39 | 1.444e-07 | 6.54e-05 | 0.6566 |
HSA-MIR-599 | -5.05 | 8.276e-07 | 0.000374 | 0.6747 |
HSA-MIR-30A | -4.36 | 1.707e-05 | 0.0077 | 0.6143 |
HSA-MIR-30C-2 | -4.36 | 1.805e-05 | 0.00812 | 0.6148 |
HSA-MIR-31 | 4.34 | 1.967e-05 | 0.00883 | 0.6288 |
HSA-MIR-500B | -4.18 | 3.773e-05 | 0.0169 | 0.6111 |
HSA-MIR-204 | -4.14 | 4.445e-05 | 0.0199 | 0.641 |
HSA-MIR-328 | -4.01 | 7.342e-05 | 0.0327 | 0.608 |
Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-100 to 'GENDER'. P value = 7.96e-15 with T-test analysis.

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S7. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 88.33 (23) |
Score | N | |
0 | 2 | |
70 | 1 | |
80 | 3 | |
90 | 13 | |
100 | 17 | |
Significant markers | N = 0 |
Table S8. Basic characteristics of clinical feature: 'DISTANT.METASTASIS'
DISTANT.METASTASIS | Labels | N |
M0 | 405 | |
M1 | 76 | |
Significant markers | N = 21 | |
Higher in M1 | 16 | |
Higher in M0 | 5 |
Table S9. Get Full Table List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'
T(pos if higher in 'M1') | ttestP | Q | AUC | |
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HSA-MIR-106B | 5.7 | 6.381e-08 | 2.9e-05 | 0.6738 |
HSA-MIR-193A | 5.57 | 9.469e-08 | 4.3e-05 | 0.6407 |
HSA-MIR-155 | 5.4 | 3.791e-07 | 0.000172 | 0.6873 |
HSA-MIR-625 | 5.4 | 4.328e-07 | 0.000196 | 0.6887 |
HSA-MIR-28 | 5.22 | 7.987e-07 | 0.00036 | 0.659 |
HSA-LET-7I | 5.15 | 1.024e-06 | 0.000461 | 0.6667 |
HSA-MIR-144 | -5.19 | 1.063e-06 | 0.000477 | 0.6849 |
HSA-MIR-130B | 5.06 | 1.575e-06 | 0.000706 | 0.6683 |
HSA-MIR-27A | 4.74 | 5.384e-06 | 0.00241 | 0.6273 |
HSA-MIR-21 | 4.69 | 7.295e-06 | 0.00325 | 0.6458 |
Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-106B to 'DISTANT.METASTASIS'. P value = 6.38e-08 with T-test analysis.

Table S10. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 222 | |
N1 | 18 | |
NX | 241 | |
Significant markers | N = 2 |
Table S11. Get Full Table List of 2 genes differentially expressed by 'LYMPH.NODE.METASTASIS'
ANOVA_P | Q | |
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HSA-MIR-10B | 8.35e-06 | 0.0038 |
HSA-MIR-223 | 3.598e-05 | 0.0163 |
Figure S5. Get High-res Image As an example, this figure shows the association of HSA-MIR-10B to 'LYMPH.NODE.METASTASIS'. P value = 8.35e-06 with ANOVA analysis.

Table S12. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 228 | |
STAGE II | 51 | |
STAGE III | 125 | |
STAGE IV | 77 | |
Significant markers | N = 39 |
Table S13. Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'
ANOVA_P | Q | |
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HSA-MIR-139 | 6.337e-13 | 2.88e-10 |
HSA-MIR-625 | 4.138e-11 | 1.88e-08 |
HSA-LET-7I | 2.711e-09 | 1.23e-06 |
HSA-MIR-28 | 3.663e-09 | 1.66e-06 |
HSA-MIR-155 | 4.302e-09 | 1.94e-06 |
HSA-MIR-21 | 6.453e-09 | 2.9e-06 |
HSA-MIR-144 | 7.579e-09 | 3.4e-06 |
HSA-MIR-130B | 1.04e-08 | 4.66e-06 |
HSA-MIR-486 | 1.299e-08 | 5.81e-06 |
HSA-MIR-130A | 4.558e-08 | 2.03e-05 |
Figure S6. Get High-res Image As an example, this figure shows the association of HSA-MIR-139 to 'NEOPLASM.DISEASESTAGE'. P value = 6.34e-13 with ANOVA analysis.

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Expresson data file = KIRC-TP.miRseq_RPKM_log2.txt
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Clinical data file = KIRC-TP.clin.merged.picked.txt
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Number of patients = 481
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Number of genes = 455
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Number of clinical features = 7
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.