BMC Cancer (Jun 2023)

Comprehensive analysis identifies cuproptosis-related gene DLAT as a potential prognostic and immunological biomarker in pancreatic adenocarcinoma

  • Xiaoling Zhang,
  • Yuxin Zhou,
  • Jiahe Hu,
  • Xuefeng Yu,
  • Haitao Xu,
  • Zhichang Ba,
  • Haoxin Zhang,
  • Yanan Sun,
  • Rongfang Wang,
  • Xinlian Du,
  • Ruishu Mou,
  • Xuedong Li,
  • Jiuxin Zhu,
  • Rui Xie

DOI
https://doi.org/10.1186/s12885-023-11042-7
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 18

Abstract

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Abstract Background Cuproptosis is a regulated cell death form associated with tumor progression, clinical outcomes, and immune response. However, the role of cuproptosis in pancreatic adenocarcinoma (PAAD) remains unclear. This study aims to investigate the implications of cuproptosis-related genes (CRGs) in PAAD by integrated bioinformatic methods and clinical validation. Methods Gene expression data and clinical information were downloaded from UCSC Xena platform. We analyzed the expression, mutation, methylation, and correlations of CRGs in PAAD. Then, based on the expression profiles of CRGs, patients were divided into 3 groups by consensus clustering algorithm. Dihydrolipoamide acetyltransferase (DLAT) was chosen for further exploration, including prognostic analysis, co-expression analysis, functional enrichment analysis, and immune landscape analysis. The DLAT-based risk model was established by Cox and LASSO regression analysis in the training cohort, and then verified in the validation cohort. Quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays were performed to examine the expression levels of DLAT in vitro and in vivo, respectively. Results Most CRGs were highly expressed in PAAD. Among these genes, increased DLAT could serve as an independent risk factor for survival. Co-expression network and functional enrichment analysis indicated that DLAT was engaged in multiple tumor-related pathways. Moreover, DLAT expression was positively correlated with diverse immunological characteristics, such as immune cell infiltration, cancer-immunity cycle, immunotherapy-predicted pathways, and inhibitory immune checkpoints. Submap analysis demonstrated that DLAT-high patients were more responsive to immunotherapeutic agents. Notably, the DLAT-based risk score model possessed high accuracy in predicting prognosis. Finally, the upregulated expression of DLAT was verified by RT-qPCR and IHC assays. Conclusions We developed a DLAT-based model to predict patients’ clinical outcomes and demonstrated that DLAT was a promising prognostic and immunological biomarker in PAAD, thereby providing a new possibility for tumor therapy.

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