Cervical Squamous Cell Carcinoma: Correlation between gene mutation status and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.


Testing the association between 'NFE2L2 MUTATION ANALYSIS' and 2 clinical features across 12 patients, no significant finding detected with Q value < 0.25.

  • No gene mutations related to clinical features.

Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 1 genes and 2 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

nMutated (%) nWild-Type logrank test t-test
NFE2L2 3 (25%) 9 0.317
Methods & Data
  • Mutation data file = CESC.mutsig.cluster.txt

  • Clinical data file = CESC.clin.merged.picked.txt

  • Number of patients = 12

  • Number of significantly mutated genes = 1: 'NFE2L2 MUTATION ANALYSIS'

  • Number of selected clinical features = 2

  • Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' 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.

[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)