DataSheet2_A new molecular subclassification and in silico predictions for diagnosis and prognosis of papillary thyroid cancer by alternative splicing profile.xlsx

Introduction: Papillary thyroid cancer (PTC) is the most common endocrine malignancy. However, different PTC variants reveal high heterogeneity at histological, cytological, molecular and clinicopathological levels, which complicates the precise diagnosis and management of PTC. Alternative splicing (AS) has been reported to be potential cancer biomarkers and therapeutic targets.
Method: Here, we aim to find a more sophisticated molecular subclassification and characterization for PTC by integrating AS profiling. Based on six differentially expressed alternative splicing (DEAS) events, a new molecular subclassification was proposed to reclassify PTC into three new groups named as Cluster0, Cluster1 and Cluster2 respectively.
Results: An in silico prediction was performed for accurate recognition of new groups with the average accuracy of 91.2%. Moreover, series of analyses were implemented to explore the differences of clinicopathology, molecular and immune characteristics across them. It suggests that there are remarkable differences among them, but Cluster2 was characterized by poor prognosis, higher immune heterogeneity and more sensitive to anti-PD1 therapy. The splicing correlation networks proved the complicated regulation relationships between AS events and splicing factors (SFs). An independent prognostic indicator for PTC overall survival (OS) was established. Finally, three compounds (orantinib, tyrphostin-AG-1295 and AG-370) were discovered to be the potential therapeutic agents.
Discussion: Overall, the six DEAS events are not only potential biomarkers for precise diagnosis of PTC, but also the probable prognostic predictors. This research would be expected to highlight the effect of AS events on PTC characterization and also provide new insights into refining precise subclassification and improving medical therapy for PTC patients.

引言：甲状腺乳头状癌（Papillary Thyroid Cancer, PTC）是最常见的内分泌恶性肿瘤。然而，不同PTC亚型在组织学、细胞学、分子及临床病理层面均呈现高度异质性，这为PTC的精准诊断与临床管理带来了极大挑战。可变剪接（Alternative Splicing, AS）已被报道可作为潜在的癌症生物标志物与治疗靶点。
方法：本研究旨在通过整合可变剪接谱，为PTC构建更为精细的分子分型与特征解析体系。基于6个差异表达可变剪接（differentially expressed alternative splicing, DEAS）事件，本研究提出了一种全新的分子分型方法，将PTC重新划分为Cluster0、Cluster1与Cluster2三个新亚型。
结果：本研究开展了虚拟预测以精准识别各新亚型，平均预测准确率达91.2%。此外，本研究通过一系列分析探究了各亚型在临床病理、分子及免疫特征上的差异。结果显示，各亚型间存在显著差异，其中Cluster2以不良预后、更高的免疫异质性以及对抗PD-1治疗更敏感为特征。剪接调控网络分析证实了可变剪接事件与剪接因子（splicing factors, SFs）之间复杂的调控关系。本研究还构建了可用于PTC总生存期（overall survival, OS）预测的独立预后指标。最终，本研究筛选出orantinib、tyrphostin-AG-1295与AG-370三种化合物作为潜在治疗药物。
讨论：综上，这6个DEAS事件不仅可作为PTC精准诊断的潜在生物标志物，同时也是潜在的预后预测因子。本研究有望凸显可变剪接事件在PTC特征解析中的作用，并为优化PTC患者的精准分子分型与临床治疗方案提供全新的研究思路。