Multiomics analysis reveals that GLS and GLS2 differentially modulate the clinical outcomes of cancer

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Saha S. K., Riazul Islam S., Abdullah-Al-Wadud M., Islam S., Ali F., Park K. S.

Journal of Clinical Medicine, vol.8, no.3, 2019 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 3
  • Publication Date: 2019
  • Doi Number: 10.3390/jcm8030355
  • Journal Name: Journal of Clinical Medicine
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: glutaminase, GLS, GLS2, multiomics, prognostic biomarkers, clinical outcomes, cancer therapy
  • TED University Affiliated: No


Kidney-type glutaminase (GLS) and liver-type glutaminase (GLS2) are dysregulated in many cancers, making them appealing targets for cancer therapy. However, their use as prognostic biomarkers is controversial and remains an active area of cancer research. Here, we performed a systematic multiomic analysis to determine whether glutaminases function as prognostic biomarkers in human cancers. Glutaminase expression and methylation status were assessed and their prominent functional protein partners and correlated genes were identified using various web-based bioinformatics tools. The cross-cancer relationship of glutaminases with mutations and copy number alterations was also investigated. Gene ontology (GO) and pathway analysis were performed to assess the integrated effect of glutaminases and their correlated genes on various cancers. Subsequently, the prognostic roles of GLS and GLS2 in human cancers were mined using univariate and multivariate survival analyses. GLS was frequently over-expressed in breast, esophagus, head-and-neck, and blood cancers, and was associated with a poor prognosis, whereas GLS2 overexpression implied poor overall survival in colon, blood, ovarian, and thymoma cancers. Both GLS and GLS2 play oncogenic and anti-oncogenic roles depending on the type of cancer. The varying prognostic characteristics of glutaminases suggest that GLS and GLS2 expression differentially modulate the clinical outcomes of cancers.