目的: 探索宫颈高级别神经内分泌癌（HGNECC）基因表达特点和免疫浸润状态。方法: 通过整合GTEx、TCGA、GEO数据库关于HGNECC的测序数据,寻找正常样本和肿瘤样本间的差异基因（DEGs）,运用GO和KEGG分析探索差异基因相关通路的富集情况。在IMMPORT网站找出免疫相关基因（IRGs）和DEGs通过VENN图求得免疫相关差异基因,应用蛋白互作网络（PPI）及cytoscape软件中MCODE及cytohubba插件找出核心基因（Hubs）,运用 CIBERSORT算法进行正常宫颈组织与HGENCC之间的免疫浸润的差异分析,并用ssGSEA算法来评估HGNECC肿瘤内部微环境的免疫浸润情况,比较不同的免疫检查点对在不同免疫活性患者内部的表达情况,并在DGIdb网站预测相应药物-靶点相互作用网络期望探索HGNECC具有潜在疗效的小分子靶向药物。最后免疫组化验证CXCL8在宫颈癌组织中的表达。结果: 通过差异分析,获得了HGNECC中CXCL8、IL1B、CSF2、CCL4、IL10、CCL3、CCL3L3、PTPN11、CBL、SOS1等十个Hub基因;在通过CIBERSORT算法和ssGSEA算法进行免疫浸润分析中,我们发现宫颈高级别神经内分泌肿瘤患者的肥大细胞从静止转变成活化状态具有显著的改变,我们推测患者体内肥大细胞活化程度增大;最后根据预测的Hub基因,药物选择推荐使用ABX-IL8,RILONACEPT等药物。结论: 该研究通过对公共数据进行生物信息学分析,为HGNECC 的临床诊疗提供了新思路。
Abstract
Objective To explore gene expression characteristics and immune infiltration status of high-grade neuroendocrine cervical carcinoma (HGNECC). Methods We integrated the sequencing data of GTEx, TCGA, and GEO databases about patients with normal cervix and HGNECC. Then we searched for differential genes (DEGs) between normal and tumor samples, and GO and KEGG analysis were used to explore the enrichment of differential gene-related pathways. Then immune-related genes (IRGs) and DEGs were identified in IMMPORT website to find immune-related differential genes by VENN plots, and then core genes (Hubs) were identified by protein interaction network (PPI) and MCODE and cytohubba plugins in cytoscape software. Next, we performed the analysis of difference in immune infiltration between normal cervical tissue and HGENCC, and the ssGSEA algorithm was used to assess the immune infiltration of the microenvironment in HGNECC and to compare the expression of different immune checkpoint genes within patients with different immune activity. Finally, we predicted the drug-target interaction network in DGIdb website, expecting to explore potential small molecule targeted drugs with efficacy in HGNECC. Results Through analyzing expression of DEGs between normal and tumor samples, ten Hub genes including CXCL8, IL1B, CSF2, CCL4, IL10, CCL3, CCL3L3, PTPN11, CBL and SOS1 were identified in HGNECC. In the immune infiltration analysis by CIBERSORT algorithm and ssGSEA algorithm, we found a significant change in mast cells from quiescent to activated state in patients with high-grade neuroendocrine tumors of the cervix, and then we hypothesized that the mast cells of activation status were increased in HGNECC patients; Finally, based on the predicted Hub genes, the drug selection for HGNECC patients was recommended for ABX-IL8, RILONACEPT and other drugs. Conclusion This study provides new ideas for the clinical treatment of HGNECC by bioinformatics analysis based on public data.
关键词
宫颈神经内分泌癌 /
免疫景观 /
生物信息学 /
差异基因
Key words
high-grade neuroendocrine cervical carcinoma (HGNECC) /
immune landscape /
bioinformatics /
differential genes
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基金
湖南省自然科学基金（grant. 2022JJ30415）,湖南省肿瘤医院科研攀登计划资助（ZX2020004-3）
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