Indraprastha Institute of Information Technology-Delhi, Department of Computational Biology, New Delhi, India.
PLoS One. 2021 Nov 12;16(11):e0259534. doi: 10.1371/journal.pone.0259534. eCollection 2021.
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.
过去曾有研究表明，凋亡基因的异常表达与甲状腺乳头状癌（PTC）有关，但它们的预后作用和作为生物标志物的效用仍知之甚少。在这项研究中，我们通过 Cox-PH 回归技术、预后指数模型和机器学习方法分析了 505 例 PTC 患者，以阐明这些基因的表达与 PTC 患者总生存（OS）之间的关系。结果观察到，有 9 个基因（ANXA1、TGFBR3、CLU、PSEN1、TNFRSF12A、GPX4、TIMP3、LEF1、BNIP3L）与 PTC 患者的 OS 显著相关。这 9 个基因中有 5 个与患者 OS 呈正相关，而其余 4 个与患者 OS 呈负相关。这些基因可用于开发风险预测模型，该模型可用于将死亡风险较高的患者与预后良好的患者区分开来。我们的投票模型取得了最高的性能（HR = 41.59，p = 3.36x10-4，C = 0.84，logrank-p = 3.8x10-8）。当我们使用患者年龄和预后生物标志物基因来改进投票模型时，其性能得到了显著提高，HR = 57.04，p = 10-4（C = 0.88，logrank-p = 1.44x10-9）。我们还开发了分类模型，可以对高危患者（生存≤6 年）和低危患者（生存>6 年）进行分类。我们的最佳模型达到了 AUROC 为 0.92。此外，在 mRNA 水平上验证了这些预后基因的表达模式，显示了它们在正常和 PTC 样本之间的差异表达。HPA 的免疫组化结果也验证了这些发现。由于这些基因也可作为 PTC 的潜在治疗靶点，我们还确定了可能调节其表达谱的潜在药物分子。该研究简要揭示了凋亡途径中的关键预后生物标志物基因，其异常表达与 PTC 的进展和侵袭性有关。除此之外，这里提出的风险评估模型可以帮助有效地管理 PTC 患者。
