PDF(Pubmed)
Abstract：
Background: Cervical cancer (CC) is the fourth most common cancer among women worldwide. As part of the brisk cross-talk between the host and the tumor, prognosis can be affected through inflammatory responses or the tumor microenvironment. However, further exploration of the inflammatory response-related genes that have prognostic value, microenvironment infiltration, and chemotherapeutic therapies in CC is needed. Methods: The clinical data and mRNA expression profiles of CC patients were downloaded from a public database for this study. In the TCGA cohort, a multigene prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) and Cox analyses. CC patients from the GEO cohort were used for validation. K‒M analysis was used to compare overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors of OS. The immune cell infiltration and immune-related functional score were calculated by single-sample gene set enrichment analysis (GSEA). Immunohistochemistry was utilized to validate the protein expression of prognostic genes in CC tissues. Results: A genetic signature model associated with the inflammatory response was built by LASSO Cox regression analysis. Patients in the high-risk group had a significantly lower OS rate. The predictive ability of the prognostic genes was evaluated by means of receiver operating characteristic (ROC) curve analysis. The risk score was confirmed to be an independent predictor of OS by univariate and multivariate Cox analyses. The immune status differed between the high-risk and low-risk groups, and the cancer-related pathways were enriched in the high-risk group according to functional analysis. The risk score was significantly related to tumor stage and immune infiltration type. The expression levels of five prognostic genes (LCK, GCH1, TNFRSF9, ITGA5, and SLC7A1) were positively related to sensitivity to antitumor drugs. Additionally, the expression of prognostic genes was significantly different between CC tissues and myoma patient cervix (non-tumorous) tissues in the separate sample cohort. Conclusion: A model consisting of 5 inflammation-related genes can be used to predict prognosis and influence immune status in CC patients. Furthermore, the inhibition or enhancement of these genes may become a novel alternative therapy.
摘要：
背景：宫颈癌（CC）是全球女性中第四大最常见的癌症。作为宿主和肿瘤之间快速交叉对话的一部分，预后可通过炎症反应或肿瘤微环境来影响。然而,进一步探索具有预后价值的炎症反应相关基因，微环境渗透，和化疗治疗CC是必要的。方法：本研究从公共数据库下载CC患者的临床数据和mRNA表达谱。在TCGA队列中，通过最小绝对收缩和选择算子(LASSO)和Cox分析构建多基因预后特征.来自GEO队列的CC患者用于验证。K-M分析用于比较高危组和低危组之间的总体生存率（OS）。应用单变量和多变量Cox分析来确定OS的独立预测因子。通过单样本基因集富集分析(GSEA)计算免疫细胞浸润和免疫相关功能评分。免疫组织化学用于验证CC组织中预后基因的蛋白质表达。结果：通过LASSOCox回归分析建立了与炎症反应相关的遗传特征模型。高风险组患者的OS率明显较低。通过受试者工作特征（ROC）曲线分析评估预后基因的预测能力。通过单变量和多变量Cox分析证实风险评分是OS的独立预测因子。高风险和低风险组之间的免疫状态不同，根据功能分析，在高危人群中富集了癌症相关通路。风险评分与肿瘤分期、免疫浸润类型显著相干。五个预后基因(LCK，GCH1、TNFRSF9、ITGA5、SLC7A1）与抗肿瘤药物敏理性呈正相干。此外,在单独的样本队列中,CC组织和肌瘤患者宫颈(非肿瘤)组织之间的预后基因表达存在显著差异.结论：由5个炎症相关基因组成的模型可用于预测CC患者的预后和影响免疫状态。此外，抑制或增强这些基因可能成为一种新的替代疗法。
