Nomogram model for predicting postoperative survival of patients with stage IB-IIA cervical cancer.
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ABSTRACT:
SUBMITTER: Yang HS
PROVIDER: S-EPMC8640795 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Publications
Nomogram model for predicting postoperative survival of patients with stage IB-IIA cervical cancer.
Yang Huan-Song HS Li Bin B Liu Shuang-Huan SH Ao Miao M
American journal of cancer research 20211115 11
To establish a prediction model based on clinical and pathological information for the long-term survival of patients with cervical cancer, we retrospectively analyzed the clinical data of patients pathologically diagnosed with stage IB-IIA cervical cancer between July 2007 and September 2017 in the Chinese Academy of Medical Sciences Cancer Hospital. Factors affecting the overall survival of the patients were analyzed using a Cox model, and a cervical cancer patient prediction nomogram model wa ...[more]
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Project description:Previous studies have shown that the C-reactive protein/albumin ratio (CAR) is a prognostic indicator in multiple types of carcinomas. This study is the first to evaluate the prognostic significance of CAR in stage IB-IIA cervical cancer patients treated with radical surgery, as well as that of several other inflammation-based factors, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI). A total of 235 patients were enrolled in this study. The optimal cut-off values of CAR and other inflammation-based factors were determined by receiver operating characteristic curves. The Kaplan-Meier method and Cox regression model analysis were performed to determine the independent predictors of progression-free survival (PFS) and overall survival (OS). At a cut-off value of 0.15, patients with a high CAR had significantly shorter PFS and OS than those with a lower CAR (P < 0.001). A higher CAR was significantly associated with elevated scores of NLR and PLR and a decreased PNI (P < 0.001). Univariate analyses showed that elevated CAR preoperatively was significantly associated with poor survival; a similar trend was also noted for the NLR, PLR, and PNI. Multivariate analyses demonstrated that only CAR was an independent indicator for PFS (hazard ratio [HR]: 5.164; 95% confidence interval [CI]: 2.495-10.687; P < 0.001) and OS (HR: 4.729; 95% CI: 2.263-9.882; P < 0.001). In conclusion, preoperative CAR is a novel and superior predictor of poor survival in patients with stage IB-IIA cervical cancer.
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Project description:Objectives: The present study aimed to identify the predictive value of inflammatory indexes stratified according to human papillomavirus (HPV) infection status in women with FIGO 2018 stage IB∼IIA cervical cancer. We also explored the influences of HPV infection status on the survival of cervical cancer patients. Methods: We collected data for 583 women with stage IB∼IIA cervical cancer in Sun Yat-sen University Cancer Center between 2009 and 2017. The t-test, chi-squared (χ2) test and Fisher's exact test were applied to compare the differences of inflammatory indexes and clinicopathological features between HPV-positive and HPV-negative groups. Univariate and multivariate analyses were used to identify clinicopathological factors that were associated with the prognosis of cervical cancer patients. Results: There were no differences in overall survival (OS) and progression-free survival (PFS) between HPV-positive and HPV-negative groups. In HPV-positive group, the maximum tumor size, neoadjuvant chemotherapy and the body mass index (BMI) correlated significantly with C-reactive protein/albumin ratio (CAR). The maximum tumor size and the prognostic nutritional index (PNI) correlated significantly with the platelet-lymphocyte ratio (PLR). The maximum tumor size, neoadjuvant chemotherapy and PLR correlated significantly with PNI. Univariate and multivariate analyses showed that the depth of tumor invasion (HR: 3.651, 95% CI: 1.464-9.103, p = 0.005; HR: 2.478, 95% CI: 1.218-5.043, p = 0.012) and CAR (HR: 5.201, 95% CI: 2.080-13.004, p < 0.0001; HR: 2.769, 95% CI: 1.406-5.455, p = 0.003) were independent predictors of poor OS and PFS. PNI was an independent protective factor of OS (HR: 0.341, 95% CI: 0.156-0.745, p = 0.007). PLR was an independent factor of PFS (HR: 1.991, 95% CI: 1.018-3.894, p = 0.044). In HPV-negative group, BMI correlated significantly with CAR. Only depth of invasion (HR: 9.192, 95% CI: 1.016-83.173, p = 0.048) was the independent predictor of poor OS, and no inflammation indexes were independent predictors of prognosis. Conclusion: In patients with HPV-positive cervical cancer, depth of invasion, PNI and CAR are independent factors of OS, and depth of invasion, PLR and CAR are independent factors for PFS. For patients with HPV-negative disease, no inflammation indexes had predictive value for prognosis. The predictive value of inflammation indexes on prognosis is more significant in patients with HPV-positive cervical cancer. Stratification of HPV infection status promotes a more precise clinical application of inflammation indexes, thus improving their accuracy and feasibility.
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Project description:BackgroundVarious studies reported that the prognosis of patients with cervical cancer (CC) was significantly associated with immunity, whereas limited studies have explored whether immune-associated genes could be classifiers for recurrence-free survival (RFS) of stage I CC. Thus, an improved immune-related gene signature for stage I CC patients' prognosis is urgently required.Materials and methodsWe retrospectively analyzed the gene expression profiles of stage I CC patients in the GSE44001 set from the Gene Expression Omnibus (GEO) database. The stage I CC patients were randomly divided into the training group and the internal validation group. The training patients were adopted to develop a prognostic immune gene-based signature; meanwhile, the internal validation patients were used to validate the power of the selected immune gene-related signature using univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The accuracy and reliability of the immune gene-related signature were evaluated based on Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves.ResultsHigh power of the 8-immune gene signature was found on the basis of ROC analysis (AUC at 1, 3, and 5 years were exhibited in the internal validation group (0.702, 0.715, and 0.728, respectively), external validation group (0.702, 0.825, and 0.842, respectively), and entire GEO dataset (0.840, 0.894, and 0.852, respectively)). Besides, C-index, ROC, calibration plots, and decision curve analysis (DCA) also acted well in our nomogram, suggestive of a high ability of the nomogram to elevate the prognostic prediction of stage I CC patients.ConclusionsIn this study, we successfully constructed an integrated 8-immune gene-based signature which could accurately identify patients with low prognostic risk from those with high prognostic risk. In addition, we developed an immune-related nomogram which can elevate the prognostic prediction of stage I CC patients.
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