According to statistics by the World Health Organization, the incidence and death rates of CC are > 500 000/year and > 10 000/year, respectively 2. CD8+ and CD4+ T cells are closely related to the occurrence and development of CC.Ĭervical cancer (CC) is one of the most common gynecologic malignancies in the clinical setting, and its incidence rate ranks fourth in female malignant tumors 1. CEP55, MCM2, RFC4, and RRM2 can be used as diagnostic markers for CC.
The survival analysis of the hub gene showed that CEP55, MCM2, RFC4, and RRM2 had high diagnostic value. The immune cell infiltration analysis showed that T cells were the main infiltrating immune cells in CC, especially T cells CD8+ and CD4+ . The enrichment pathway is closely related to the interaction between viral proteins and cytokines and cytokine receptors, the interleukin 17 signaling pathway, and chemokine signaling pathway. DEGs are mainly involved in molecular functions such as serine-peptidase activity, serine-hydrolase activity, and chemokine activity. A total of 144 DEGs and 12 hub genes were identified. Immune cell infiltration in CC was analyzed via scientific reverse convolution algorithm (CIBERSORT), and the hub gene was analyzed via survival analysis to screen the diagnostic biomarkers of CC. A protein–protein interaction network was constructed to screen the hub gene.
CYTOSCAPE ONLINE SOFTWARE
The differentially expressed genes (DEGs) between CC and normal cervical tissues were identified via R software (version 4.1.1), and their functions and pathways were enriched and analyzed. The expression profile datasets of CC were downloaded from the GEO. In this study, CIBERSORT was used to analyze the immune cell infiltration in CC patients, to evaluate the proportion of immune cell types in CC samples, to quantify the cell composition of the immune response, and to analyze its prognostic value. At present, many studies are exploring the safety and efficacy of immunotherapy for advanced or recurrent CC. Immunotherapy has become a new model for the treatment of CC, especially advanced and recurrent cancer.
All rights reserved.Cervical cancer (CC) is the most common gynecological malignant tumor. This makes CytoNCA an excellent tool for calculating centrality, evaluating and visualizing biological networks.Ĭopyright © 2014 Elsevier Ireland Ltd. (v) Besides current eight centrality measures, the biological characters from other sources could also be analyzed and assessed by CytoNCA. (iv) It can be utilized to quantitatively assess the calculation results, and evaluate the accuracy by statistical measures. (iii) CytoNCA offers multiple potent visualization analysis modules, which generate various forms of output such as graph, table, and chart, and analyze associations among all measures. (ii) It allows users to upload biological information of both nodes and edges in the network, to integrate biological data with topological data to detect specific nodes. (i) CytoNCA supports eight different centrality measures and each can be applied to both weighted and unweighted biological networks. Here we present CytoNCA, a Cytoscape plugin integrating calculation, evaluation and visualization analysis for multiple centrality measures. Nowadays, centrality analysis has become a principal method for identifying essential proteins in biological networks.
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