ArticleDiscover oncology2024
Prognostic determinants in cancer survival: a multidimensional evaluation of clinical and genetic factors across 10 cancer types in the participants of Genomics England's 100,000 Genomes Project.
Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Pan-cancer predictive survival model development and evaluation using electronic health record and genetic data across 10 cancer types.Discover oncology · 2025Article
- Case Report: Blood single-cell analysis of a IVB high-grade serous ovarian cancer patient presenting a favorable prognosis.Frontiers in oncology · 2025Article
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2 authors.
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Abstract
backgroundCancer is a complex disease, caused and impacted by a combination of genetic, demographic, clinical, environmental and lifestyle factors. Analysis of cancer characteristics, risk factors, treatment options and the heterogeneity across cancer types has been the focus of medical research for years. The aim of this study is to describe and summarise genetic, clinicopathological, behavioural and demographic characteristics and their differences across ten common cancer types and evaluate their impact on overall survival outcomes.
methodsThis study included data from 9977 patients with bladder, breast, colorectal, endometrial, glioma, leukaemia, lung, ovarian, prostate, and renal cancers. Genetic data collected through the 100,000 Genomes Project was linked with clinical and demographic data provided by the National Cancer Registration and Analysis Service (NCRAS), Hospital Episode Statistics (HES) and Office for National Statistics (ONS). Descriptive and Kaplan Meier survival analyses were performed to visualise similarities and differences across cancer types. Cox proportional hazards regression models were applied to identify statistically significant prognostic factor associations with overall survival.
results161 clinical and 124 genetic factors were evaluated for prognostic association with overall survival. Of these, 116 unique factors were found to have significant prognostic effect for overall survival across ten cancer types when adjusted for age, sex and stage. The findings confirmed prognostic associations with overall survival identified in previous studies in factors such as multimorbidity, tumour mutational burden, and mutations in genes BRAF, CDH1, NF1, NRAS, PIK3CA, PTEN, TP53. The results also identified new prognostic associations with overall survival in factors such as mental health conditions, female health-related conditions, previous hospital encounters and mutations in genes FANCE, FBXW7, GATA3, MSH6, PTPN11, RB1, RNF43.
conclusionThis study provides a comprehensive view of clinicopathological and genetic prognostic factors across different cancer types and draws attention to less commonly known factors which might help produce more precise prognosis and survival estimates. The results from this study contribute to the understanding of cancer disease and could be used by researchers to develop complex prognostic models, which in turn could help predict cancer prognosis more accurately and improve patient outcomes.
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