识别宫颈癌免疫微环境中的预后相关基因。
Identification of prognosis-related genes in the cervical cancer immune microenvironment.
机构信息
Central Laboratory of Yan'an Hospital Affiliated to Kunming Medical University, Kunming, Yunnan Province 650000, China; Key Laboratory of Tumor Immunological Prevention and Treatment of Yunnan Province, Kunming, Yunnan Province 650000, China; Kunming Medical University, Kunming, Yunnan Province 650500, China.
Central Laboratory of Yan'an Hospital Affiliated to Kunming Medical University, Kunming, Yunnan Province 650000, China; Key Laboratory of Tumor Immunological Prevention and Treatment of Yunnan Province, Kunming, Yunnan Province 650000, China; Yunnan Cell Biology and Clinical Translation Research Center, Kunming, Yunnan Province 650000, China.
出版信息
Gene. 2021 Jan 15;766:145119. doi: 10.1016/j.gene.2020.145119. Epub 2020 Sep 15.
BACKGROUND
Cervical cancer is the fourth most commonly diagnosed cancer in women worldwide. The metastasis and invasion of this type of cancer are closely related to the tumor microenvironment. Immune cells and stromal cells dominate the tumor microenvironment in cervical cancer. Therefore, we should further investigate the complex interplay between the tumor progression with immune cells or stromal cells.
METHODS
We downloaded the gene expression profiles and clinical data of 307 patients with cervical cancers based on the TCGA database. Subsequently, the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm was used to calculate the scores of stromal cells and immune cells in order to uncover differential expressed genes, and we analyzed the correlation between their scores and patient survival. Then the Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) deconvolution algorithm was applied to quantify the fraction and infiltration of 22 types of immune cells in cervical cancer. Moreover, we also used R language packs and network tools to analyze GO term, gene enrichment pathway, and protein-protein relationship to trace down genes related to inflammation and immune regulation.
RESULTS
The gene expression profiles and corresponding clinical data of 307 patients were obtained from TCGA database. The results showed that the scores were statistically significant between the high immunescore group and the low immunescore group. And the low immunescore group had shorter survival period than the high scores group (P = 0.035). Among the 22 types of immune cells, only T cells and mast cells were significantly related to the survival rate of cervical cancer patients. Moreover, PPI network analysis revealed that CCR5 and CXCL9, -10, -11/CXCR3 axis might be a new target for cervical cancer treatment. Finally, Kaplan-Meier survival curves found outnine representative genes significantly related to survival rate including BTNL8, CCR7, CD1E, CD6, CD27, CD79A, GRAP2, SP1B, LY9.
CONCLUSIONS
These genes can be used as markers for the prognosis and diagnosis of cervical cancer and also might be used as treatment targets.
背景
宫颈癌是全球女性中第四大常见的癌症。这种癌症的转移和侵袭与肿瘤微环境密切相关。免疫细胞和基质细胞在宫颈癌的肿瘤微环境中占主导地位。因此，我们应该进一步研究肿瘤进展与免疫细胞或基质细胞之间的复杂相互作用。
方法
我们根据 TCGA 数据库下载了 307 名宫颈癌患者的基因表达谱和临床数据。随后，使用基于表达数据估计肿瘤中基质和免疫细胞（ESTIMATE）算法计算基质细胞和免疫细胞的评分，以揭示差异表达基因，并分析其评分与患者生存的相关性。然后应用细胞类型鉴定通过估计已知 RNA 转录本的相对亚群（CIBERSORT）去卷积算法来量化宫颈癌中 22 种免疫细胞的分数和浸润。此外，我们还使用 R 语言包和网络工具分析 GO 术语、基因富集通路和蛋白质-蛋白质关系，以追踪与炎症和免疫调节相关的基因。
结果
从 TCGA 数据库获得了 307 名患者的基因表达谱和相应的临床数据。结果表明，高免疫评分组和低免疫评分组之间的评分差异具有统计学意义。低免疫评分组的生存期短于高评分组（P=0.035）。在 22 种免疫细胞中，只有 T 细胞和肥大细胞与宫颈癌患者的生存率显著相关。此外，PPI 网络分析显示，CCR5 和 CXCL9、-10、-11/CXCR3 轴可能是宫颈癌治疗的新靶点。最后，Kaplan-Meier 生存曲线发现 9 个与生存率显著相关的代表性基因，包括 BTNL8、CCR7、CD1E、CD6、CD27、CD79A、GRAP2、SP1B 和 LY9。
结论
这些基因可以作为宫颈癌预后和诊断的标志物，也可能作为治疗靶点。
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