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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Survival Curves01:18

Survival Curves

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Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Kidney Cancer Biomarker Selection Using Regularized Survival Models.

Carolina Peixoto1, Marta Martins2, Luís Costa2,3

  • 1INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029 Lisbon, Portugal.

Cells
|August 12, 2022
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Summary

Researchers identified key genes linked to clear cell renal cell carcinoma (ccRCC) survival using network-based methods. These prognostic biomarkers can help stratify patients and improve clinical decision-making for this common kidney cancer.

Keywords:
Cox regressionbiomarker selectiongene ontologykidney cancerregularization

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Clear cell renal cell carcinoma (ccRCC) is the most prevalent and deadly subtype of renal cell carcinoma.
  • Identifying specific biomarkers for early detection and prognosis in ccRCC is crucial for improving patient outcomes.
  • High-throughput transcriptomics offers a powerful tool for molecular profiling and discovering survival-associated genes, but high dimensionality presents challenges.

Purpose of the Study:

  • To identify novel prognostic biomarkers for clear cell renal cell carcinoma (ccRCC) by analyzing gene expression and patient survival data.
  • To address the challenge of high dimensionality in transcriptomics data using network-based regularizers for robust gene signature selection.
  • To explore the functional roles of identified genes and pathways in ccRCC progression and patient outcomes.

Main Methods:

  • Applied two network-based regularizers, Elastic Net (EN) and Trended Cox (TCox), to Cox regression models for gene selection.
  • Analyzed gene expression data from ccRCC patients to identify genes correlated with survival outcomes.
  • Utilized Gene Set Enrichment Analysis (GSEA) to investigate over- or under-represented biological mechanisms and pathways associated with gene signatures.

Main Results:

  • Identified consistently selected genes, including COPS7B, DONSON, GTF2E2, HAUS8, PRH2, and ZNF18, some with known roles in cancer.
  • Discovered common enriched ontologies such as nuclear division, microtubule/tubulin binding, and chromosome regions across different gene sets.
  • Developed a refined gene set that significantly stratified ccRCC patients into high- and low-risk groups.

Conclusions:

  • Network-based approaches effectively identify robust gene signatures for ccRCC prognosis despite data dimensionality.
  • The identified genes and pathways, particularly those related to cell division and chromosome organization, are critical for ccRCC progression.
  • These findings highlight the potential of selected genes as prognostic biomarkers for improved patient stratification and clinical management in ccRCC.