Efficient Discovery of Robust Prognostic Biomarkers and Signatures in Solid Tumors.

in Cancer letters by Zaoqu Liu, Jinhai Deng, Hui Xu, Long Liu, Yuyuan Zhang, Yuhao Ba, Zhengyu Zhang, Fuchu He, Linhai Xie

TLDR

  • The SurvivalML platform was developed to support the discovery and validation of prognostic biomarkers and gene signatures in cancer.
  • The platform identified a novel biomarker for hepatocellular carcinoma and developed a simplified gene signature for glioblastoma with superior predictive performance.
  • The study demonstrates the potential of the platform to advance cancer research and clinical application.

Abstract

Recent advancements in multi-omics and big-data technologies have facilitated the discovery of numerous cancer prognostic biomarkers and gene signatures. However, their clinical application remains limited due to poor reproducibility and insufficient independent validation. Despite the availability of high-quality datasets, achieving reliable biomarker identification across multiple cohorts continues to be a significant challenge. To address these issues, we developed a comprehensive platform, SurvivalML, designed to support the discovery and validation of prognostic biomarkers and gene signatures using large-scale and harmonized data from 21 cancer types. Through SurvivalML, we identified DCLRE1B as a novel prognostic biomarker for hepatocellular carcinoma, with experimental confirmation of its role in promoting tumor progression. Additionally, we developed the Chinese glioblastoma prognostic signature (CGPS) and its simplified version, SCGPS, a three-gene model. Both demonstrated superior predictive performance compared to other glioblastoma signatures in our in-house cohort and five independent Chinese datasets. The SCGPS model was further validated in 109 clinical samples using multiplex immunofluorescence, showing strong consistency with the original CGPS model. Overall, SurvivalML provides a robust platform for the identification and validation of prognostic biomarkers and gene signatures, offering a valuable resource for advancing cancer research and clinical application.

Overview

  • The study aims to develop a comprehensive platform, SurvivalML, to support the discovery and validation of prognostic biomarkers and gene signatures using large-scale and harmonized data from 21 cancer types.
  • The platform is designed to address the challenges of poor reproducibility and insufficient independent validation in cancer biomarker identification.
  • The study focuses on identifying novel biomarkers and gene signatures that can provide accurate prognosis for cancer patients.

Comparative Analysis & Findings

  • The study identified DCLRE1B as a novel prognostic biomarker for hepatocellular carcinoma, with experimental confirmation of its role in promoting tumor progression.
  • The Chinese glioblastoma prognostic signature (CGPS) and its simplified version, SCGPS, demonstrated superior predictive performance compared to other glioblastoma signatures in multiple datasets.
  • The SCGPS model was further validated in 109 clinical samples using multiplex immunofluorescence, showing strong consistency with the original CGPS model.

Implications and Future Directions

  • The SurvivalML platform provides a robust resource for advancing cancer research and clinical application by enabling the identification and validation of prognostic biomarkers and gene signatures.
  • Future studies can utilize the platform to identify novel biomarkers and gene signatures for various cancer types, improving prognosis and treatment outcomes.
  • The development of more simplified and widely applicable biomarkers, like SCGPS, can facilitate integration into clinical practice.
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