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BMC Cancer

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Open Access 01-12-2021 | Gastric Cancer | Research

A stable gene set for prediction of prognosis and efficacy of chemotherapy in gastric cancer

Authors: Rui Wu, Sixuan Guo, Shuhui Lai, Guixing Pan, Linyi Zhang, Huanbing Liu

Published in: BMC Cancer | Issue 1/2021

Abstract

Background

Gastric cancer (GC) is a primary reason for cancer death in the world. At present, GC has become a public health issue urgently to be solved to. Prediction of prognosis is critical to the development of clinical treatment regimens. This work aimed to construct the stable gene set for guiding GC diagnosis and treatment in clinic.

Methods

A public microarray dataset of TCGA providing clinical information was obtained. Dimensionality reduction was carried out by selection operator regression on the stable prognostic genes discovered through the bootstrap approach as well as survival analysis.

Findings

A total of 2 prognostic models were built, respectively designated as stable gene risk scores of OS (SGRS-OS) and stable gene risk scores of PFI (SGRS-PFI) consisting of 18 and 21 genes. The SGRS set potently predicted the overall survival (OS) along with progression-free interval (PFI) by means of univariate as well as multivariate analysis, using the specific risk scores formula. Relative to the TNM classification system, the SGRS set exhibited apparently higher predicting ability. Moreover, it was suggested that, patients who had increased SGRS were associated with poor chemotherapeutic outcomes.

Interpretation

The SGRS set constructed in this study potentially serves as the efficient approach for predicting GC patient survival and guiding their treatment.

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Title: A stable gene set for prediction of prognosis and efficacy of chemotherapy in gastric cancer
Authors: Rui Wu
Sixuan Guo
Shuhui Lai
Guixing Pan
Linyi Zhang
Huanbing Liu
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Gastric Cancer
Gastric Cancer
Cytostatic Therapy
Published in: BMC Cancer / Issue 1/2021
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-021-08444-w

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Abstract

Background

Methods

Findings

Interpretation

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