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A Novel Cuproptosis-Related LncRNA Signature Predicts Prognosis in Patients with Esophageal Carcinoma

Shang Peng, Haipeng Li, Jingting Min, Ran An, Nana Du1, Zhenghong Li*

Introduction: Esophageal Cancer (ESCA) is a significant cause of tumor-related mortality worldwide. Cuproptosis is a novel cell death which is different from other regulate cell death, including ferroptosis, pyroptosis and apoptosis. However, the role of cuproptosis in the initiation and progression of ESCA remains unknown.

Materials and methods: The transcriptome data and clinical data of 173 patients with esophageal cancer in The Cancer Genome Atlas (TCGA) database were sorted and extracted with Perl software. Pearson correlation analysis was performed on cuproptosis related genes and all LncRNA’s. The prognostic related LncRNA’s were determined by univariate and multivariate Cox regression analysis, and a new prognostic model was constructed to calculate the risk score of each patient. C-Index curve, Principal Component Analysis (PCA) analysis and Receiver Operating Characteristic (ROC) curve analysis were used to evaluate the prognosis prediction performance of 3-cuproptosis related LncRNA’s (CRLs) model. In addition, multivariate Cox analysis was used to assess the prognostic value of the model in the entire cohort and in different subgroups.

Results and discussion: The 3-CRLs risk scoring criteria including EWSAT1, AC125437.1 and GK-IT1 was established to evaluate the Overall Survival (OS) of ESCA. Survival analysis and ROC curve showed that the score had good prediction performance in TCGA train group and test group. The coefficients of each LncRNAs were analyzed using Lasso regression and lambda values were determined. Principal component analysis was used to determine whether 3-CRLs can clearly distinguish the gap between high and low risk samples. Multivariate Cox regression showed that 3-CRLs characteristics were independent prognostic factors of OS. Norman map showed robust effectiveness in prognosis prediction.

Conclusion: The risk characteristics based on 3-CRLs may be used to predict the prognosis of esophageal carcinoma patients.