This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 416 genes and 11 clinical features across 406 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.
-
1 gene correlated to 'Time to Death'.
-
HSA-MIR-135A-1
-
10 genes correlated to 'AGE'.
-
HSA-MIR-432 , HSA-MIR-26A-1 , HSA-MIR-153-2 , HSA-MIR-141 , HSA-MIR-410 , ...
-
1 gene correlated to 'NEOPLASM.DISEASESTAGE'.
-
HSA-MIR-625
-
1 gene correlated to 'PATHOLOGY.T.STAGE'.
-
HSA-MIR-501
-
16 genes correlated to 'PATHOLOGY.M.STAGE'.
-
HSA-MIR-1180 , HSA-MIR-140 , HSA-MIR-628 , HSA-MIR-106A , HSA-LET-7F-2 , ...
-
10 genes correlated to 'HISTOLOGICAL.TYPE'.
-
HSA-MIR-31 , HSA-MIR-592 , HSA-MIR-92A-1 , HSA-MIR-574 , HSA-MIR-196B , ...
-
31 genes correlated to 'COMPLETENESS.OF.RESECTION'.
-
HSA-LET-7A-2 , HSA-LET-7A-1 , HSA-LET-7A-3 , HSA-MIR-497 , HSA-LET-7F-2 , ...
-
No genes correlated to 'PATHOLOGY.N.STAGE', 'GENDER', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NUMBER.OF.LYMPH.NODES'.
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=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=10 | older | N=8 | younger | N=2 |
NEOPLASM DISEASESTAGE | ANOVA test | N=1 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=1 | higher stage | N=0 | lower stage | N=1 |
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY M STAGE | ANOVA test | N=16 | ||||
GENDER | t test | N=0 | ||||
HISTOLOGICAL TYPE | t test | N=10 | colon mucinous adenocarcinoma | N=2 | colon adenocarcinoma | N=8 |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 | ||||
COMPLETENESS OF RESECTION | ANOVA test | N=31 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.1-140.4 (median=12.7) |
censored | N = 258 | |
death | N = 60 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
Table S2. Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-135A-1 | 1.37 | 4.064e-05 | 0.017 | 0.634 |
Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-135A-1 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 4.06e-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) | 67.35 (13) |
Significant markers | N = 10 | |
pos. correlated | 8 | |
neg. correlated | 2 |
Table S4. Get Full Table List of 10 genes significantly correlated to 'AGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-432 | -0.234 | 2.301e-06 | 0.000957 |
HSA-MIR-26A-1 | 0.2323 | 2.31e-06 | 0.000959 |
HSA-MIR-153-2 | 0.2263 | 4.247e-06 | 0.00176 |
HSA-MIR-141 | 0.2262 | 4.267e-06 | 0.00176 |
HSA-MIR-410 | -0.2048 | 3.507e-05 | 0.0144 |
HSA-MIR-34A | 0.1982 | 5.898e-05 | 0.0242 |
HSA-MIR-653 | 0.2003 | 6.076e-05 | 0.0249 |
HSA-MIR-616 | 0.1964 | 8.019e-05 | 0.0328 |
HSA-MIR-577 | 0.1945 | 8.134e-05 | 0.0332 |
HSA-MIR-142 | 0.1937 | 8.738e-05 | 0.0356 |
Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-432 to 'AGE'. P value = 2.3e-06 with Spearman correlation analysis. The straight line presents the best linear regression.
![](V2ex.png)
Table S5. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 66 | |
STAGE IA | 1 | |
STAGE II | 33 | |
STAGE IIA | 115 | |
STAGE IIB | 8 | |
STAGE IIC | 1 | |
STAGE III | 22 | |
STAGE IIIA | 12 | |
STAGE IIIB | 48 | |
STAGE IIIC | 32 | |
STAGE IV | 42 | |
STAGE IVA | 16 | |
STAGE IVB | 1 | |
Significant markers | N = 1 |
Table S6. Get Full Table List of one gene differentially expressed by 'NEOPLASM.DISEASESTAGE'
ANOVA_P | Q | |
---|---|---|
HSA-MIR-625 | 2.712e-05 | 0.0113 |
Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-625 to 'NEOPLASM.DISEASESTAGE'. P value = 2.71e-05 with ANOVA analysis.
![](V3ex.png)
Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'
PATHOLOGY.T.STAGE | Mean (SD) | 2.89 (0.63) |
N | ||
0 | 1 | |
1 | 10 | |
2 | 69 | |
3 | 279 | |
4 | 46 | |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
Table S8. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-501 | -0.1975 | 6.285e-05 | 0.0261 |
Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-501 to 'PATHOLOGY.T.STAGE'. P value = 6.29e-05 with Spearman correlation analysis.
![](V4ex.png)
Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'
PATHOLOGY.N.STAGE | Mean (SD) | 0.59 (0.77) |
N | ||
0 | 239 | |
1 | 95 | |
2 | 71 | |
Significant markers | N = 0 |
Table S10. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'
PATHOLOGY.M.STAGE | Labels | N |
M0 | 305 | |
M1 | 50 | |
M1A | 7 | |
M1B | 1 | |
MX | 36 | |
Significant markers | N = 16 |
Table S11. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1180 | 1.292e-06 | 0.000538 |
HSA-MIR-140 | 1.317e-06 | 0.000547 |
HSA-MIR-628 | 1.473e-06 | 0.00061 |
HSA-MIR-106A | 4.488e-06 | 0.00185 |
HSA-LET-7F-2 | 7.055e-06 | 0.00291 |
HSA-MIR-142 | 7.81e-06 | 0.00321 |
HSA-MIR-301A | 2.744e-05 | 0.0113 |
HSA-MIR-1277 | 2.969e-05 | 0.0121 |
HSA-MIR-539 | 3.182e-05 | 0.013 |
HSA-LET-7A-1 | 3.487e-05 | 0.0142 |
Figure S5. Get High-res Image As an example, this figure shows the association of HSA-MIR-1180 to 'PATHOLOGY.M.STAGE'. P value = 1.29e-06 with ANOVA analysis.
![](V6ex.png)
Table S12. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 191 | |
MALE | 215 | |
Significant markers | N = 0 |
Table S13. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'
HISTOLOGICAL.TYPE | Labels | N |
COLON ADENOCARCINOMA | 351 | |
COLON MUCINOUS ADENOCARCINOMA | 53 | |
Significant markers | N = 10 | |
Higher in COLON MUCINOUS ADENOCARCINOMA | 2 | |
Higher in COLON ADENOCARCINOMA | 8 |
Table S14. Get Full Table List of 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'
T(pos if higher in 'COLON MUCINOUS ADENOCARCINOMA') | ttestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-31 | 6.2 | 2.71e-08 | 1.13e-05 | 0.7312 |
HSA-MIR-592 | -6.03 | 7.13e-08 | 2.96e-05 | 0.7412 |
HSA-MIR-92A-1 | -5.09 | 2.603e-06 | 0.00108 | 0.6971 |
HSA-MIR-574 | 4.99 | 4.353e-06 | 0.0018 | 0.7041 |
HSA-MIR-196B | -4.72 | 1.215e-05 | 0.00501 | 0.6884 |
HSA-MIR-1247 | -4.43 | 3.631e-05 | 0.0149 | 0.687 |
HSA-MIR-92A-2 | -4.35 | 4.64e-05 | 0.019 | 0.6801 |
HSA-MIR-374B | -4.21 | 6.535e-05 | 0.0267 | 0.6605 |
HSA-MIR-29A | -4.23 | 7.401e-05 | 0.0302 | 0.6831 |
HSA-MIR-98 | -4.16 | 8.323e-05 | 0.0339 | 0.6678 |
Figure S6. Get High-res Image As an example, this figure shows the association of HSA-MIR-31 to 'HISTOLOGICAL.TYPE'. P value = 2.71e-08 with T-test analysis.
![](V8ex.png)
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table S15. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 3 | |
YES | 403 | |
Significant markers | N = 0 |
Table S16. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 280 | |
R1 | 3 | |
R2 | 25 | |
RX | 22 | |
Significant markers | N = 31 |
Table S17. Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'
ANOVA_P | Q | |
---|---|---|
HSA-LET-7A-2 | 7.206e-09 | 3e-06 |
HSA-LET-7A-1 | 7.45e-09 | 3.09e-06 |
HSA-LET-7A-3 | 7.583e-09 | 3.14e-06 |
HSA-MIR-497 | 4.045e-08 | 1.67e-05 |
HSA-LET-7F-2 | 5.402e-08 | 2.23e-05 |
HSA-MIR-1180 | 6.339e-08 | 2.61e-05 |
HSA-MIR-16-1 | 2.536e-07 | 0.000104 |
HSA-MIR-136 | 4.626e-07 | 0.000189 |
HSA-MIR-126 | 4.709e-07 | 0.000192 |
HSA-MIR-590 | 6.116e-07 | 0.000249 |
Figure S7. Get High-res Image As an example, this figure shows the association of HSA-LET-7A-2 to 'COMPLETENESS.OF.RESECTION'. P value = 7.21e-09 with ANOVA analysis.
![](V10ex.png)
-
Expresson data file = COAD-TP.miRseq_RPKM_log2.txt
-
Clinical data file = COAD-TP.clin.merged.picked.txt
-
Number of patients = 406
-
Number of genes = 416
-
Number of clinical features = 11
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 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 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 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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.