Identification of an immune signature to predict poor clinical outcome in cervical cancer

Abstract

Aim: To explore tumor immune microenvironment and identify immune prognostic-related circRNAs in cervical cancer. Materials & methods: RNA-seq in combination with bioinformatics were performed to establish a prognostic risk model and a circRNAs-miRNAs- CXCL8 network. Results: High-risk group correlated with poor survival outcome, and had lower PD-1 immunogenicity. Additionally, CXCL8 could distinguish normal tissue, low- and high-risk tumor tissues, the expression of which showed an increasing trend among the three groups. RNA-seq and bioinformatics indicated that circRNAs like hsa_circ_0025721 might upregulate CXCL8 through sponging miRNAs including hsa-miR-4428. Conclusion: We constructed an immune risk model related with CD8 T cells to predict the cervical cancer patients’ prognosis and explored the abnormal expression mechanism of CXCL8 through the ceRNA mechanism.

Fanconi Anemia Pathway Genes Advance Cervical Cancer via Immune Regulation and Cell Adhesion

“…It has been reported that CXCL8 inhibits the cell communication between cancer tissue and stroma, as well as the angiogenesis activity of endothelial cells ( Lee et al, 2015 ). As an immune-associated signature, CXCL8 has been revealed to affect the prognosis of CC through the circRNAs - miRNAs - CXCL8 network ( Yan et al, 2021 ). Our study illustrated the outstanding prediction effects of 10-immune signature, revealing the essential role of FARGs and immune regulation in CC.…”

Cohort Profile: Chinese Cervical Cancer Clinical Study

“…The effect of different management on patients with specific clinical stages of cervical cancer remains controversial (12)(13)(14)(15). Prognostic factors, including tumor characteristics, medical condition, sociodemographic characteristics, lifestyle behaviors, biomarkers, diagnosis, treatment, and care, are suggested for further detailed exploration (16)(17)(18)(19)(20)(21)(22). This may provide a potential way to reduce residual disease after treatment, as well as minimize recurrence, decrease the complications, and improve survival.…”

“…The Chinese Cervical Cancer Clinical (Four-C) Study was created in 2014 with the aim of collecting clinical and prognostic information on patients diagnosed with cervical cancer in mainland China since 2004. Its research objectives currently focus on four main themes: (i) to explore the associations of therapeutic strategies with complications as well as mid-and long-term clinical outcomes, including comparative effectiveness research based on marginal structural models or propensity scores (25-27); (ii) to widely evaluate the prognostic factors of cervical cancer (such as late access to care and the influence of nutritional status) and then guide treatment as well as care options, and to precisely predict the prognosis of patients so as to develop much more effective program of personalized followup and intervention (21,22); (iii) to utilize artificial intelligence (AI) and machine learning (ML) approaches for multimodal data aggregation and multifactorial examination in order to develop a knowledge base of cervical clinical auxiliary diagnosis and prognostic prediction (28)(29)(30). What' more, as the Four-C Study relatively represents the occurrence of cervical cancer across mainland China in terms of age, geographical origin, year of diagnosis, clinical stage, gross type, and histological type, it can also serve to map the burden of cervical cancer in different districts and monitor trends in incidence of cervical cancer, which could potentially inform prevention and control strategies (31).…”