BMC Bioinformatics (May 2023)

Identification of cuproptosis-related lncRNAs to predict prognosis and immune infiltration characteristics in alimentary tract malignancies

  • Yangyang Xie,
  • Xue Song,
  • Danwei Du,
  • Zhongkai Ni,
  • Hai Huang

DOI
https://doi.org/10.1186/s12859-023-05314-z
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 18

Abstract

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Abstract Background Alimentary tract malignancies (ATM) caused nearly one-third of all tumor-related death. Cuproptosis is a newly identified cell death pattern. The role of cuproptosis-associated lncRNAs in ATM is unknown. Method Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to identify prognostic lncRNAs by Cox regression and LASSO. Then a predictive nomogram was constructed based on seven prognostic lncRNAs. In addition, the prognostic potential of the seven-lncRNA signature was verified via survival analysis, the receiver operating characteristic (ROC) curve, calibration curve, and clinicopathologic characteristics correlation analysis. Furthermore, we explored the associations between the signature risk score and immune landscape, and somatic gene mutation. Results We identified 1211 cuproptosis-related lncRNAs and seven survival-related lncRNAs. Patients were categorized into high-risk and low-risk groups with significantly different prognoses. ROC and calibration curve confirmed the good prediction capability of the risk model and nomogram. Somatic mutations between the two groups were compared. We also found that patients in the two groups responded differently to immune checkpoint inhibitors and immunotherapy. Conclusion The proposed novel seven lncRNAs nomogram could predict prognosis and guide treatment of ATM. Further research was required to validate the nomogram.

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