Objective. To identify and validate effective clinical predictors for the long-term prognosis of patients with cervical cancer. Methods. Cervical cancer patients were retrieved from the TCGA database, and patients’ clinical data were collected and analyzed for the predictive value of long-term prognosis. In the other branch of the study, patients with cervical cancer and admitted to our hospital between January 1, 2016, and December 31, 2016, were retrieved and followed up for prognosis analysis. Results. In the database patient cohort of our study, 607 cases with cervical cancer were analyzed. Aneuploidy score ( p = 0.012 ), Buffa hypoxia score ( p = 0.013 ), histologic grade ( p = 0.01 ), fraction genome altered >0.4 ( p < 0.001 ), weight > 60 kg ( p < 0.001 ), height > 160 cm ( p = 0.047 ), BMI <18.5 ( p = 0.023 ), Winter hypoxia score ( p = 0.002 ), and adjuvant postoperative radiotherapy were good predictors for disease-free survival (DFS), while aneuploidy score ( p = 0.001 ), MSI sensor score > 0.5 ( p = 0.035 ), person neoplasm status ( p < 0.001 ), race ( p = 0.006 ), Ragnum hypoxia score ( p = 0.012 ), weight ( p < 0.001 ), height ( p < 0.001 ), and BMI < 18.5 ( p = 0.04 ) were good predictors for overall survival (OS). In the admitted patient cohort, age over 60 years old at the time of diagnosis was the only clinical factor influencing the long-term DFS ( p = 0.004 ). TNM stage above III ( p = 0.004 ), body weight > 70 kg ( p < 0.001 ), and complicated with other cancer ( p < 0.001 ) were clinical factor influencing the long-term OS. Conclusions. Clinical factors, especially common to both cohorts, could be used to show the long-term prognosis of cervical cancer.
Objective. To determine the efficacy of clinical characteristics in the prediction of prognosis in patients with ovarian cancer. Methods. Clinical data were collected from 3 datasets from TCGA database, including 1680 cases of ovarian serous cystadenocarcinoma, and were analyzed. Patients with ovarian cancer admitted to our hospital in 2016 were retrieved and followed up for prognosis analysis. Results. From the datasets, for patients > 75 years old at the time of diagnosis, histologic grade and mutation count were good predictors for disease-free survival, while for patients > 50 years old at the time of diagnosis, histologic grade, race, fraction genome altered, and mutation count were good predictors for overall survival. In the patients ( n = 38 ) retrieved from our hospital, the longest dimension of lesion (cm) and body weight at admission were good predictors for overall survival. Conclusions. Those clinical factors, together with the two predictive equations, could be used to comprehensively predict the long-term prognosis of patients with ovarian cancer.
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