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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

457
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...
457
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

297
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...
297
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

276
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,...
276
Survival Curves01:18

Survival Curves

330
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.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
330
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

199
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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相关实验视频

Updated: Sep 16, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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在日本癌队列中系统识别与生存相关的eQTL.

Xiya Song1, Han Jin1, Xiangyu Li1

  • 1Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.

PLoS genetics
|July 7, 2025
PubMed
概括

表达量的特征位点 (eQTLs) 提供了对清细胞癌 (ccRCC) 的预后见解. 这项研究确定了与ccRCC存活率相关的新型eQTL,跨种族验证突出了潜在的生物标志物.

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科学领域:

  • 基因组学和生物信息学
  • 在瘤学瘤学.
  • 分子生物学分子生物学

背景情况:

  • 清细胞癌 (ccRCC) 是最常见的癌亚型.
  • 在ccRCC中表达定量特征位点 (eQTL) 的预后意义尚不清楚,特别是在亚洲人群中.

研究的目的:

  • 在日本ccRCC患者中识别eQTL.
  • 评估eQTL与患者存活率的相关性.
  • 在独立的ccRCC队列中验证发现.

主要方法:

  • 来自100名日本ccRCC患者的全外体和RNA测序数据.
  • 识别eGenes和cis-eQTLs. 这是一个很好的方法.
  • 使用多种Cox比例危险模型进行生存分析.
  • 在癌症基因组图谱 (TCGA) ccRCC队列中的验证.

主要成果:

  • 在日本队列中确定了805个eGenes和4,558个cis-eQTL.
  • 九个eGenes与整体存活率 (FDR <0.05) 有意义地相关.
  • 探索性分析揭示了223个eQTLs调节54个eGenes,具有一致的预后效应.
  • 调节11个eGenes的8个eQTL显示了跨种族的可复制的生存关联,包括ERV3-1和ANKRD20A7P的变异.

结论:

  • eQTL在ccRCC预后中发挥着重要作用.
  • 在不同人群中确定了具有一致预后价值的特定eQTL和eGenes.
  • 在ERV3-1和ANKRD20A7P中的变种显示出作为ccRCC的跨种族预后生物标志物的潜力.