Table 1.
The table shows results of univariate cox regression with >median cut-off.
Fig 1.
KM plot showing risk stratification of PTC patients based on gene voting model.
Patients with greater than five ‘high risk’ labels in the 9-bit risk vector are assigned (blue) as High Risk (HR = 41.59, p = 3.36x10-4, C = 0.84, logrank-p = 3.8x10-8) while others were assigned as Low Risk (red).
Table 2.
The performance of different models developed using multiple gene expression profile-based method.
Table 3.
Univariate analysis using clinico-pathological features.
Age is seen to be the most significant factor. In laterality, unilateral: right lobe, left lobe and isthmus.
Fig 2.
Voting model sub-stratifies high risk groups.
(a) Patients with with age>60y (n = 113) were stratified into high and low risk groups with HR = 9.49, p = 3.08x10-2 and C = 0.72. (b) Stage III/IV patients (n = 167) were stratified into high and low risk groups with HR = 15, p = 0.01 and C = 0.81. p-values from logrank tests are shown in the KM plots.
Fig 3.
Hybrid models for risk stratification.
(a) Multivariate analysis reveals Age (HR = 13.3, p = 0.02) and Voting model (HR = 13.3, p = 0.015) as two independent covariates, while tumour stage was found to be insignificant. (b) Risk stratification by hybrid model with age cut-off >60y (HR = 54.82, p = 1.18x10-4, C = 0.87, %95CI: 7.14–420.90 and logrank-p = 2.3x10-9). (b) (b) Risk stratification by hybrid model with age cut-off >65y (HR = 57.04, p~10−4, C = 0.88, %95CI: 7.44–437.41 and logrank-p = 1.4x10-9).
Fig 4.
Predictive validation of voting based model and hybrid models.
(a) Grouped boxplots corresponding to estimated Hazard Ratio (y-axis) for 100 iterations of data sampling (x-axis). (b) Similarly, estimation of Concordance index (y-axis) for different models using random sampling (x-axis).
Fig 5.
Hybrid models for classification of PTC patients using OS.
(a) Terminology used for evaluation of confusion matrix. Initial risk labelling was done using an OS cut-off with patients having OS> cut-off labelled as positive or low risk and vice-versa for patients with OS≤cut-off. (b) ROC curve for hybrid model using age cut-off of 65y. AUROC of 0.92 was obtained.
Fig 6.
Differential gene-expression analysis.
Boxplots representing the differential gene expression between normal and tumour samples on a log scale. GEPIA webserver was used to plot these by using TCGA THCA dataset. T: Tumour in red, N: Normal (TCGA, GTEX) in grey.
Fig 7.
The protein expression patterns of the prognostic genes from the Human Protein Atlas (HPA) database (proteinatlas.org).
(a) ANXA1, (b) PSEN1, (c) CLU, (d) TNFRSF12A, (e) GPX4, (f) TGFBR3. The staining intensity was annotated as High, Medium, Low and Not detected. The bar plots represent the number of samples with different staining intensity in HPA.
Fig 8.
Functional enrichment analysis.
The figure represents the significant biological process terms for the gene signatures. Orange color represents the prognostic genes; green color denotes significant biological process.