Dataset Information

An immunoscore nomogram for predicting survival in patients with oesophageal cancer.

ABSTRACT: This study aimed to construct and validate an immunoscore nomogram that may be used to predict the prognosis of oesophageal cancer. With the gene expression data of oesophageal cancer in a public database, we used CIBERSORT to estimate the fractions of 22 infiltrating immune cell types. We then built an immunoscore signature based on 12 types of infiltrating immune cells using the least absolute shrinkage and selection operator (LASSO) model. This immunoscore was used as an independent predictor in the prognostic model (training cohort: [hazard ratio (HR), 4.78; 95% confidence interval (CI), 2.64-8.67; P < 0.001], validation cohort: [HR, 2.15; 95% CI, 1.04-4.45; P = 0.040]). Subgroup analysis by clinical features showed that overall survival was significantly different between the high-immunoscore group and the low-immunoscore group. The predictors that constituted the individualized prediction nomogram were immunoscore, age, and tumour stage. The nomogram had good discrimination and calibration. Decision curve analysis showed that the immunoscore nomogram was clinically useful. Therefore, the novel immunoscore signature based on infiltrating immune cells can be used as a reliable predictor of the prognosis of oesophageal cancer, and the immunoscore nomogram is a convenient tool for predicting the survival of individual patients.

SUBMITTER: Li G

PROVIDER: S-EPMC8034926 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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An immunoscore nomogram for predicting survival in patients with oesophageal cancer.

Li Gaoming G Song Qiuyue Q Cai Angsong A Wei Zeliang Z Zhang Rui R Yi Dali D Chen Jia J Li Fang F Zhang Yanqi Y Liu Ling L Wu Yazhou Y Yi Dong D

Aging 20210310 6

This study aimed to construct and validate an immunoscore nomogram that may be used to predict the prognosis of oesophageal cancer. With the gene expression data of oesophageal cancer in a public database, we used CIBERSORT to estimate the fractions of 22 infiltrating immune cell types. We then built an immunoscore signature based on 12 types of infiltrating immune cells using the least absolute shrinkage and selection operator (LASSO) model. This immunoscore was used as an independent predictor ...[more]

PMID: 33714960

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Project description:ObjectivesThe Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.MethodsA total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method.ResultsThe collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients.ConclusionsThe collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.

Project description:BackgroundGallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients.MethodWe extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram.ResultA total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system.ConclusionWe constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions.

Project description:PurposeThis study aimed to explore a visual model for predicting the prognosis of patients with parathyroid carcinoma (PC) and analyze related biochemistries in different groups of stage.MethodsThe training dataset of 342 patients with PC was obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and the validation dataset included 59 patients from The First Affiliated Hospital of Zhengzhou University. Univariate and multivariate Cox regression analyses were performed to evaluate significant independent prognostic factors. Based on those factors, nomograms and Web-based probability calculators were constructed to evaluate the overall survival (OS) and the cancer-specific survival (CSS) at 3, 5, and 8 years. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. Moreover, biochemistries from the validation set were retrospectively analyzed in different groups of stage by Kruskal-Wallis test.ResultsAge, marital status, tumor size, stage, lymph node status, and radiation were identified as prognostic factors of OS. In contrast, only tumor size and stage were predictive for CSS. The nomogram was developed based on these independent factors. The C-index, ROC curve, calibration curve, and DCA of the nomogram in both training and validation sets showed that the nomogram had good predictive value, stability, and clinical benefit in predicting 3-, 5-, and 8-year OS and CSS in PC patients. Among the 59 PC patients from our hospital, lower albumin (ALB) levels and higher postoperative parathyroid hormone (PTH) levels were found in patients with distant metastasis (Distant vs. Regional ALB levels: p = 0.037; Distant vs. Local ALB levels: p = 0.046; Distant vs. Regional postoperative PTH levels: p = 0.002; Distant vs. Local postoperative PTH: p = 0.002).ConclusionThe established nomogram application can provide accurate prognostics for patients with PC in the Chinese population, but it must be validated on prospectively collected real-world data.

Dataset Information

An immunoscore nomogram for predicting survival in patients with oesophageal cancer.

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An immunoscore nomogram for predicting survival in patients with oesophageal cancer.

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