Abstract
Background: Esophageal carcinoma (ESCA) is one of the most prevalent malignant tumors in the world. The prognosis of patients has significantly improved with the development of surgery, targeted therapy and immunotherapy. But the 5-year survival rate of ESCA patients is still incredibly low. Cuproptosis is a type of mitochondrial cell death induced by copper. It is unclear how cuproptosis-related lncRNAs (CRLs) affect ESCA prognosis. Methods: In this study, we obtained the clinical data of ESCA patients, the transcriptome data from TCGA and identified CRLs by co-expression analysis, lasso regression, and cox regression analysis, to build a prognostic model. Then we validated the prognostic model using the Kaplan-Meier curve, cox regression analysis, and ROC, to create a nomogram based on risk score to forecast the prognosis of ESCA. Next, the immune escape of the CRLs was examined using the TIDE algorithm to assess its sensitivity to possible ESCA medications. Results: To predict the prognosis of ESCA patients, we created a predictive model using 6 CRLs (AC034199.1, AC125437.1, AC107032.2, CTBP1-DT, AL024508.1, and AC008610.1), validated by the Kaplan-Meier and ROC curves. The model has a higher diagnostic value compared to other clinical features. The 6 CRLs expressed high in TCGA and ESCA specimens. Enrichment analysis revealed CRLs largely contributed to the interaction between cytokines and their receptors as well as complement coagulation cascades. The immunity escape analysis demonstrated that immunotherapy had a worse effect in the low-risk group than in the high-risk group. Additionally, we screened out potential antineoplastic drugs according to the results of the immunoassay and obtained 5 drugs, including CP-466722, crizotinib, MS-275, KIN001-135, and CP-466722. Conclusion: The prognosis of patients with ESCA can be correctly predicted by the 6 CRLs chosen from this investigation. It lays the groundwork for more investigation into the ESCA mechanism and the identification of novel therapeutic targets.
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