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Differential gene expression and immune infiltration landscape in high-grade neuroendocrine carcinoma of cervix |
KANG Jiawen1, XIANG Xiaoqing1, JIANG Jingwen1, GUO Sihui1, ZHANG Yong1, LI Lesai2 |
1. School of Medicine, Medical College of Hunan Normal University, Changsha 410013, China; 2. Hunan Cancer Hospital, Changsha 410013, China |
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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.
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Received: 03 November 2022
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