| Background Cervical cancer is one of the most common malignant tumors in gynecology,and high-risk human papillomaviruses(HPVs)are the main pathogens of it.Patients with early-stage cervical cancer can be treated with surgery to achieve a positive outcome,while for patients with advanced cervical cancer,there is a wide variation in outcomes even when standardized therapies such as radiation and chemotherapy recommended by guidelines are applied.In recent years,tumor immunotherapy has emerged as an emerging therapeutic tool and is an important topic in the field of cancer treatment.The use of bioinformatics analysis to achieve more accurate immunotherapy for cervical cancer by high-throughput sequencing technology is more helpful to assist in the development and formulation of individualized treatment strategies for cervical cancer patients and helps in prognosis assessment and treatment plan development for cervical cancer patients.Objective Our study developed immune-related long noncoding RNAs(lnc RNAs)for risk stratification and explored factors of prognosis and the immune microenvironment in cervical cancer.Methods The RNA-seq data and clinical information on cervical cancer were collected from the TCGA TARGET GTEx database and the TCGA database.lnc RNAs and immune-related signatures were obtained from the GENCODE database and the Im Port database,respectively.Immune-related lnc RNAs were screened by univariate Cox,LASSO and multivariate Cox regression methods,establishing an immune-related risk model of hub immune-related lnc RNAs to evaluate whether the risk score was an independent prognostic predictor.The x Cell and CIBERSORTx algorithms were employed to appraise the value of risk scores,which are in competition with tumor-infiltrating immune cell abundances.Tumor immunotherapy response was estimated by the TIDE algorithm as well as drug IC50 predictive values to predict and recommend chemotherapeutic agents targeting immune-related risk models.Results We successfully established six immune-related lnc RNAs(AC006126.4,EGFR-AS1,RP4-647J21.1,LINC00925,EMX2 OS,and BZRAP1-AS1)to carry out prognostic prediction of cervical cancer.The immune-related risk model was constructed,and we observed that high-risk groups were strongly linked with poor survival outcomes.Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affected the prognosis of cervical cancer.The x Cell algorithm revealed that hub immune-related signatures were relevant to immune cells,especially mast cells,DCs,megakaryocytes,memory B cells,NK cells,and Th1 cells.The CIBERSORTx algorithm revealed that in the immune microenvironment of cervical cancer tumors,naive B cells(p < 0.01),activated dendritic cells(p < 0.05),activated mast cells(p < 0.0001),CD8+ T cells(p < 0.001),and regulatory T cells(p < 0.01)were significantly lower in the high-risk group,while macrophages M0(p < 0.001),macrophages M2(p < 0.05),resting mast cells(p <0.0001)and neutrophils(p < 0.01)were significantly higher than in the low-risk group.Compared to the high-risk group(94/137),the result of TIDE indicated that the number of immunotherapy responders in the low-risk group(124/137)increased significantly(p= 0.00000022),suggesting a negative correlation between immunotherapy response and risk score in cervical cancer patients.Finally,we compared the difference in predicted IC50 values between the high-and low-risk groups,predicting 12 drugs that could be used as future drugs for the treatment of cervical cancer patients.Conclusion In this study,we identified six immune-related hub lnc RNA(AC006126.4,EGFR AS1,RP4-647J21.1,LINC00925,EMX2 OS,and BZRAP1-AS1),which are conducive to the prognosis prediction and immunotherapy regimen formulation of CC patients. |
PDF Full Text Request