Dataset Information

Causes of Death and Conditional Survival of Renal Cell Carcinoma.

ABSTRACT: Background: As conditional survival could provide more relevant prognostic information at each follow-up time, the present study aimed to assess conditional overall survival (COS) based on two cohorts and assess the risks of death due to renal cell carcinoma (RCC) vs. other causes. Methods: The Fudan University Shanghai Cancer Center (FUSCC) and Surveillance, Epidemiology, and End Results (SEER) database were used as the source of data for our analysis. COS and cancer-specific survival were evaluated using the Kaplan-Meier method. Results: A total of 90,927 patients (SEER cohort = 88,807, FUSCC cohort = 2,120) were enrolled. Our results suggest that hazards of other causes-related death were always higher than that of cancer-specific death in low-risk RCC patients, but lower in metastatic RCC patients. It exceeded that of cancer-specific death by 8 years in high-risk RCC patients. Only in metastatic RCC patients, the COS improved markedly with survivorship increasing. After surviving 1, 2, 3, 4, and 5 years, the 5 years COS increased by +10, +18, +23, +29, and 35% (the observed 5 years OS: 12%), respectively. Conclusions: COS can better help patients with metastatic RCC rather than other RCC patients. Additionally, COS brings optimism for metastatic RCC patients with expected poorer prognosis psychologically.

SUBMITTER: Shao N

PROVIDER: S-EPMC6644417 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Causes of Death and Conditional Survival of Renal Cell Carcinoma.

Shao Ning N Wan Fangning F Abudurexiti Mierxiati M Wang Jun J Zhu Yao Y Ye Dingwei D

Frontiers in oncology 20190715

<b>Background:</b> As conditional survival could provide more relevant prognostic information at each follow-up time, the present study aimed to assess conditional overall survival (COS) based on two cohorts and assess the risks of death due to renal cell carcinoma (RCC) vs. other causes. <b>Methods:</b> The Fudan University Shanghai Cancer Center (FUSCC) and Surveillance, Epidemiology, and End Results (SEER) database were used as the source of data for our analysis. COS and cancer-specific surv ...[more]

PMID: 31380266

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Project description:Immunogenic cell death (ICD) is the trigger of adaptive immune responses. However, the role of ICD-related genes in clear cell renal carcinoma (ccRCC) remains unclear. We aimed to identify biomarkers associated with ICD and develop an ICD-related predictive model that predicts the immune microenvironment, prognosis, and response to immunotherapy in ccRCC. Our study included 739 patients (603 in the training set and 136 in the validation set) with clinicopathologic information and transcriptome sequencing data. Consensus clustering, principal component analysis (PCA), weighted gene co-expression network analysis (WGCNA), univariate COX analysis, multivariate COX analysis, and the Lasso-Cox algorithm were applied to shrink predictors and construct a predictive signature of overall survival (OS). We used CIBERSORT, ESTIMATE, and TIMER in the R package IOBR to evaluate the tumor microenvironment and immune infiltration pattern of each sample. Finally, the single cell sequencing results of immune cells in ccRCC were used to verify the results of immune infiltration analysis, and the performance of the prognostic model was evaluated by calibration curves and c-index. This study revealed that inability of the initial immune response and primary immunodeficiency were significantly enriched in the ICD subgroup with poor prognosis. We found that the ten candidate ICD genes (CALR, ENTPD1, FOXP3, HSP90AA1, IFNB1, IFNG, IL6, LY96, PIK3CA, and TLR4) could affect the prognosis of ccRCC (p < 0.05). The prediction model (PRE) we constructed can not only predict the long-term survival probability but also evaluate the landscape of immune infiltration in ccRCC. Our study demonstrated that low infiltration of dendritic cells in ccRCC implies a poor prognosis, whereas the degree of CTL infiltration is less important. An individualized prediction model was created to predict the 1-, 2-, 3-, and 5-year survival and responsiveness of ccRCC patients to immunotherapy, which may serve as a potent tool for clinicians to make better treatment decisions and thus improve the overall survival (OS) of ccRCC patients in the future.

Dataset Information

Causes of Death and Conditional Survival of Renal Cell Carcinoma.

Publications

Causes of Death and Conditional Survival of Renal Cell Carcinoma.

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