Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway

Abstract

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10-4, C = 0.84, logrank-p = 3.8x10-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10−4 (C = 0.88, logrank-p = 1.44x10-9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.

Identification and Validation of a Prognostic Signature for Thyroid Cancer Based on Ferroptosis-Related Genes

“…Zeng Ye et al discovered that FBW7 enhanced the lethal effect of gemcitabine in pancreatic cancer by inducing ferroptosis and apoptosis [ 20 ]. In previous studies, BID, FBXW7, and GPX4 had already been found to be significant in thyroid cancer [ 21 , 22 , 23 ], whereas the role of AKR1C3 and MAP3K5 in the development and progression of thyroid cancer has not yet been reported.…”

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

“…The embedded technique merges both filter-wrapper methods, integrating the training procedure to the feature selection procedure to determine the optimal feature subset. One popular technique of feature selection in embedded methods is the least absolute shrinkage and selector operation (LASSO) method, which has been widely used to identify predictive and prognostic biomarkers as seen in [18,23,26,32,37,38]. Additionally, LASSSO was used to identify important features in most of the subgroup analyses.…”

MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma

“…3,29 The role of BRAF V600E mutation in benign-malignant discrimination, aggressiveness, prognosis and targeted therapy has been confirmed by various studies. 8,[30][31][32] Li et al 33 suggested that BRAF V600E mutation test was highly recommended in mutation endemic areas. However, the BRAF V600E mutation status needs to be obtained by puncture biopsy or postoperative pathology, and genetic sequencing is time-consuming and expensive.…”