Halle Mari K, Munk Ane Cecilie, Engesæter Birgit, Akbari Saleha, Frafjord Astri, Hoivik Erling A, Forsse David, Fasmer Kristine E, Woie Kathrine, Haldorsen Ingfrid S, Bertelsen Bjørn I, Janssen Emiel A M, Gudslaugsson Einar, Krakstad Camilla, Øvestad Irene T
Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, 5053 Bergen, Norway.
Department of Obstetrics and Gynaecology, Haukeland University Hospital, 5053 Bergen, Norway.
Cancers (Basel). 2021 Nov 16;13(22):5737. doi: 10.3390/cancers13225737.
The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation ( = 239). Transcriptomic analyses identified ( = 0.004), ( < 0.001), ( = 0.04), ( = 0.02), and ( = 0.005) as upregulated, and downregulated ( = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation ( < 0.001). Low signature score was associated with poor survival ( = 0.007) and large tumors ( = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.
本研究的目的是建立一种基因特征，该特征可预测CIN3消退，并有助于选择可安全避免锥切术的患者。比较了来自21个确诊消退病变和28个持续性CIN3病变的Oncomine mRNA数据，其中包括398个免疫相关基因。来自宫颈癌队列的L1000 mRNA数据可用于验证（n = 239）。转录组分析确定，与持续性CIN3病变相比，CIN3消退中[具体基因1]（P = 0.004）、[具体基因2]（P < 0.001）、[具体基因3]（P = 0.04）、[具体基因4]（P = 0.02）和[具体基因5]（P = 0.005）上调，而[具体基因6]下调（P = 0.01）。据此，建立了一种预测CIN3消退的基因特征，其灵敏度为91%（AUC = 0.85）。转录组分析显示增殖与持续性CIN3显著相关。在癌症队列中，通过基因集富集分析（GSEA），高消退特征评分与免疫激活相关，通过组织病理学评估，与免疫细胞浸润相关（P < 0.001）。低特征评分与较差的生存率（P = 0.007）和大肿瘤（P = 0.01）相关。总之，所提出的六基因特征可预测CIN消退和良好的宫颈癌预后，并指出了癌前病变和宫颈癌病变的共同驱动因素。
