GC-SMS

The software tool 'GC-SMS' utilizes genetic algorithm-based support vector machine (GA-SVM) and GA-based Cox regression methods to identify site-specific transcriptomic prognostic biomarkers for gastric cancer (GC). Analyzing the Cancer Genome Atlas (TCGA) database, the tool distinguishes between cardia and non-cardia cancer, constructing prognostic models with 10 and 13 biomarkers, respectively. Evaluation using time-dependent ROC curves and concordance index demonstrates improved predictive ability compared to traditional models. The addition of site-specific biomarkers significantly enhances model preference, leading to the development of combined nomograms with superior performance in cardia and non-cardia GC survival prediction.

Topic

Biomarkers;Oncology;Public health and epidemiology;Transcriptomics;Transcription factors and regulatory sites

Detail

  • Operation: Regression analysis;Incident curve plotting

  • Software interface: Web user interface,Library

  • Language: -

  • License: -

  • Cost: Free

  • Version name: -

  • Credit: the National Key R&D Program of China.

  • Input: -

  • Output: -

  • Contact: Mulong Du drdumulong@njmu.edu.cn ,Zhengdong Zhang drzdzhang@njmu.edu.cn

  • Collection: -

  • Maturity: -

Publications

  • A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.
  • Xin J, et al. A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods. A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods. 2020; 24:9457-9465. doi: 10.1111/jcmm.15618
  • https://doi.org/10.1111/JCMM.15618
  • PMID: 32649057
  • PMC: PMC7417703

Download and documentation

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