Background:Follicular lymphoma (FL) is characterized by an indolent nature and generally favorable prognosis, yet poses a particular clinical challenge, since disease progression is observed in a notable subset of patients. Currently, it is not possible to anticipate which patients will be at risk of progression, highlighting the need for reliable predictive biomarkers that can be detected early in the disease.Methods:We applied tandem-mass-tag labelled nano-liquid chromatography tandem mass spectrometry (nLC-MS/MS) on 48 diagnostic formalin-fixed, paraffin-embedded tumor samples from patients with advanced-stage FL. Of these, 17 experienced subsequent progression (subsequently-progressing, sp-FL) while 31 did not (non-progressing, np-FL).Results:We identified 99 proteins that were significantly differentially expressed between sp-FL samples and np-FL samples (p< 0.05; log2-fold changes between 0.2 and −1.3). Based on this subset of proteins, we classified patients into high-risk and low-risk subgroups using unsupervised machine learning techniques. Pathway analyses of the identified proteins revealed aberrancies within the immune system and cellular energy metabolism. In addition, two proteins were selected for immunohistochemical evaluation, namely stimulator of interferon genes 1 (STING1) and isocitrate dehydrogenase 2 (IDH2). Notably, IDH2 retained significantly lower expression levels in sp-FL samples compared with np-FL samples (p= 0.034). Low IDH2 expression correlated with shorter progression-free survival (PFS,p= 0.020).Conclusions:This study provides evidence for some of the biological mechanisms likely to be involved in FL progression and, importantly, identifies potential predictive biomarkers for improvement of risk stratification up-front at time of FL diagnosis.
背景：滤泡性淋巴瘤（FL）具有惰性特征且总体预后良好，但由于相当一部分患者会出现疾病进展，这构成了特殊的临床挑战。目前尚无法预测哪些患者存在进展风险，凸显了对可在疾病早期检测的可靠预测性生物标志物的需求。 方法：我们对48例晚期FL患者的诊断性福尔马林固定石蜡包埋肿瘤样本进行了串联质量标签标记的纳升液相色谱-串联质谱（nLC-MS/MS）分析。其中17例后续出现疾病进展（进展组，sp-FL），31例未进展（非进展组，np-FL）。 结果：我们鉴定出99种在sp-FL样本与np-FL样本间存在显著差异表达的蛋白质（p<0.05；log2倍数变化介于0.2至-1.3之间）。基于该蛋白质亚群，我们采用无监督机器学习技术将患者分为高风险和低风险亚组。对已鉴定蛋白质的通路分析揭示了免疫系统和细胞能量代谢的异常。此外，我们选取了干扰素基因刺激蛋白1（STING1）和异柠檬酸脱氢酶2（IDH2）两种蛋白质进行免疫组化验证。值得注意的是，与np-FL样本相比，IDH2在sp-FL样本中的表达水平持续显著降低（p=0.034）。低IDH2表达与较短的无进展生存期相关（PFS，p=0.020）。 结论：本研究为参与FL进展的部分生物学机制提供了证据，更重要的是，识别出可在FL诊断时用于改善风险分层的潜在预测性生物标志物。
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