Background Immune-related long non-coding RNAs (irlncRNAs) are known to hold great promise as superior biomarkers for cervical cancer-related immunotherapeutic response and the tumor immune microenvironment. Here, we constructed a prognostic signature based on irlncRNA pairs (IRLPs). Methods The samples were downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. The least absolute shrinkage and selection operator Cox regression was performed to construct the prognostic model. Receiver operating characteristic (ROC) curve and nomogram were plotted to validate accuracy of the model. Next, we estimated the immune cell infiltration and the correlation between risk score and the expression of genes related to immune checkpoint. Finally, we calculated the score of the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the half maximal inhibitory concentration of the chemotherapeutic agent to evaluate the response to immunotherapy and chemotherapy. Results We constructed a prognostic signature that consisted of 11 irlncRNAs. The area under the curve values of the 1-, 3-, and 5-year ROC curves were 0.844, 0.891, and 0.871, respectively. The expression of CTLA-4, HAVCR2, IDO1, LAG3, and PDCD1 were negatively correlated with risk scores. The score of TIDE in the high-risk group was significantly higher than in the low-risk group ( P < 0.01). Patients in the low-risk subgroup were more sensitive to chemotherapeutic agents, such as axitinib and docetaxel, whereas patients in the low-risk subgroup were more sensitive to mitomycin C. Conclusion Our study highlighted the value of the 11 IRLPs signatures to predict the prognosis and the response to immunotherapy and chemotherapeutics for patients with cervical cancer.

Introduction: Cervical cancer with lymph node metastasis (LNM) has a poor prognosis, but the prognosis of patients varies among individuals to a great extent and depends on diverse factors. This study attempted to develop and externally validate a prognostic model based on risk factors to predict the probability of survival of patients with cervical cancer with LNM. Methods A population-based cohort with 4238 participants diagnosed with cervical cancer with LNM between 2000 and 2016 from the Surveillance, Epidemiology, and End Results database was used to select prognostic variables for inclusion in our model. Model performance was validated internally and externally using the concordance index (C-index), areas under the curve (AUC) of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). Kaplan–Meier survival curve was used to validate the risk stratification capability of the established model. Results Prognostic factors included marital status, age, pathological subtype, clinical stage, tumor size, surgical treatment, radiotherapy, and chemotherapy (all P < .05). The C-index (0.736, 0.727, and 0.701 for the training, internal validation, and external validation cohorts) and AUC values of the 3- and 5-year ROC curves (0.781 and 0.777 for the training cohort, 0.78 and 0.759 for the internal validation cohort, and 0.728 and 0.74 for the external validation cohort) demonstrated the satisfactory discrimination and excellent accuracy of the nomogram. Calibration plots showed the favorable agreement between the predicted and observed probabilities, and DCA indicated good clinical benefits. The nomogram-based risk stratification successfully discriminated patients into low-, intermediate-, and high-risk populations. Conclusion An easy-to-use online website of the dynamic nomogram was provided which could help predict overall survival of cervical cancer with LNM.

Background Oxidative stress is closely correlated with tumor development and progression, which can act as a latent treatment target for cancer. The purpose of this study was to identified the oxidative stress-related gene (OSRG) profile of cervical cancer and established a novel prognostic prediction model. Methods Differentially expressed OSRGs between cervical cancer and paired normal tissues were extracted from the GeneCards and GEPIA databases. Clinical information was collected from patients with cervical cancer in TCGA cohort. Univariate Cox analysis together with the LASSO algorithm were used to determine prognosis-related genes, construct an OSRG-signature, and derive risk scores. Kaplan–Meier (K-M) survival analysis and receiver operating characteristic (ROC) curves were used to verify the predictive ability of the risk scores. At the same time, the correlation between risk scores and tumor immune cell infiltration and chemosensitivity was observed. Results An 10-OSRG signature was constructed. Patients with cervical cancer were categorized as high-risk or low-risk through the median risk score derived from the 10-OSRG signature. As shown by survival analysis, the median overall survival (OS) time of high-risk patients was significantly shorter than that of low-risk patients. The ROC curves also demonstrated the usefulness of the 10-OSRG signature for predicting the prognosis of cervical cancer patients. The risk scores derived from the 10-OSRG signature and 5 clinical variables were used to develop a nomogram, which can be used to predict 1-, 3-, and 5-year survival rates in patients with cervical cancer. Immunological analysis suggested that the tumor killer immune cells in the low-risk group were higher than those in the high-risk group. The sensitivity of the two subgroups to various chemotherapy drugs were different. Conclusion A novel 10-OSRG signature was constructed and verified to forecast the prognosis of patients with cervical cancer and provide novel insights and directions for cervical carcinoma.