Ou Tingyu, Wei Yousheng, Long Ying, Pan Xinbin, Yao Desheng
Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People's Republic of China.
Department of Gynecology, The Third Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
Int J Gen Med. 2022 Feb 24;15:2057-2073. doi: 10.2147/IJGM.S353576. eCollection 2022.
Pyroptosis has vital roles in tumorigenesis and cancer development; however, its relationship with cervical squamous cell cancer (CSCC) remains unexplored. In this study, we aimed to develop a CSCC prediction signature related to pyroptosis.
Consensus clustering analysis was conducted to detect pyroptosis-related subclusters for CSCC. Next, differentially expressed genes (DEGs) between subclusters were identified. Univariate, least absolute shrinkage and selection operator, and stepwise multivariate Cox regression analyses were applied to establish a prognostic model and a nomogram drawn. Additionally, functional enrichment analysis, tumor mutation burden, and immune characteristics associated with this signature were investigated.
We constructed a seven-gene signature that functions as an independent predictor of prognosis in CSCC using data from The Cancer Genome Atlas. Patients with CSCC were divided into two groups based on median risk score, and patients in the low-risk group had significantly longer survival time than those in the high-risk group. Our findings were validated using Gene Expression Omnibus cohort data. We also established a nomogram, to expand the clinical applicability of our findings. The seven gene signature was associated with various molecular pathways, tumor mutation status, and immune microenvironment.
The pyroptosis-related risk signature consisting of seven genes developed here represents a potential robust biomarker for predicting prognosis and immunotherapy response in patients with CSCC.
细胞焦亡在肿瘤发生和癌症发展中起着至关重要的作用；然而，其与宫颈鳞状细胞癌（CSCC）的关系仍未得到探索。在本研究中，我们旨在开发一种与细胞焦亡相关的CSCC预测特征。
进行共识聚类分析以检测CSCC的细胞焦亡相关亚群。接下来，鉴定亚群之间的差异表达基因（DEG）。应用单因素、最小绝对收缩和选择算子以及逐步多变量Cox回归分析来建立预后模型并绘制列线图。此外，还研究了与该特征相关的功能富集分析、肿瘤突变负担和免疫特征。
我们使用来自癌症基因组图谱的数据构建了一个七基因特征，该特征可作为CSCC预后的独立预测因子。根据中位风险评分将CSCC患者分为两组，低风险组患者的生存时间明显长于高风险组患者。我们的发现使用基因表达综合数据库队列数据进行了验证。我们还建立了列线图，以扩大我们研究结果的临床适用性。这七个基因特征与各种分子途径、肿瘤突变状态和免疫微环境相关。
这里开发的由七个基因组成的细胞焦亡相关风险特征代表了一种潜在的强大生物标志物，可用于预测CSCC患者的预后和免疫治疗反应。
