Construction of a model for predicting the prognosis of liver cancer patients based on CuProtosis-related LncRNA
Construcción de un modelo para predecir el pronóstico de pacientes con cáncer de hígado basado en LncRNA relacionado con CuProtosis
Yiyang Chen, Wanbang Zhou, Yiju Gong, Xi Ou
Abstract
Introduction: Liver cancer is one of the most common malignant tumors in the world, and patients with liver cancer are often in the middle and late stages of cancer when they are diagnosed. Copper death is a newly discovered new cell death method. It is a copper-dependent and regulated cell death method. At the same time, Long noncoding RNAs (LncRNAs) also play an important regulatory role in the pathological process of tumors such as liver cancer.
Materials and methods: First, the expression levels of CuProtosis-related genes in liver cancer samples were extracted, and a CuProtosis- related LncRNA prognostic model was constructed. C-index curve and ROC curve were drawn by survival analysis, PFS analysis, and independent prognosis analysis. The model was also validated by clinical grouping and PCA principal component analysis. To ensure its accuracy, enrichment analysis, immune analysis and tumor mutational burden analysis further explored the potential function of this model, and finally discussed potential drugs targeting this model.
Results: A prognostic model for predicting survival was constructed and its high predictive ability in liver cancer patients was validated. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment showed that the differential genes were mainly enriched in 5 pathways. Meanwhile, six differentially expressed immune functions were found in the high-risk and low-risk groups. The survival rate of patients in the high mutation group was significantly lower than that of the patients with liver cancer in the low mutation group. Twelve drugs with significant differences in drug sensitivity between high- and low-risk groups were explored.
Conclusions: The risk-prognosis model based on CuProtosis LncRNA established in this study is expected to be used to predict the prognosis and immunotherapy response of liver cancer patients. It provides new clues and methods for predicting the survival time of liver cancer patients, and also provides new ideas for guiding individualized immunotherapy strategies for liver cancer patients in the future.
Keywords
Resumen
Introducción: El cáncer de hígado es uno de los tumores malignos más comunes en el mundo, y los pacientes con cáncer de hígado a menudo se encuentran en las etapas intermedia y tardía del cáncer cuando se les diagnostica. La muerte por cobre es un nuevo método de muerte celular recientemente descubierto. Es un método de muerte celular regulado y dependiente del cobre. Al mismo tiempo, los ARN no codificantes largos (LncRNA) también juegan un papel regulador importante en el proceso patológico de tumores como el cáncer de hígado.
Materiales y métodos: En primer lugar, se extrajeron los niveles de expresión de genes relacionados con CuProtosis en muestras de cáncer de hígado y se construyó un modelo pronóstico de LncRNA relacionado con CuProtosis. La curva de índice C y la curva ROC se dibujaron mediante análisis de supervivencia, análisis de PFS y análisis de pronóstico independiente. El modelo también fue validado por agrupación clínica y análisis de componentes principales PCA. Para garantizar su precisión, el análisis de enriquecimiento, el análisis inmunitario y el análisis de la carga mutacional del tumor exploraron más a fondo la función potencial de este modelo y, finalmente, discutieron los posibles fármacos dirigidos a este modelo.
Resultados: Se construyó un modelo pronóstico para predecir la supervivencia y se validó su alta capacidad predictiva en pacientes con cáncer de hígado. El enriquecimiento de Gene Ontology (GO) y el enriquecimiento de Kyoto Encyclopedia of Genes and Genomes (KEGG) mostraron que los genes diferenciales se enriquecieron principalmente en 5 vías. Mientras tanto, se encontraron seis funciones inmunes expresadas diferencialmente en los grupos de alto y bajo riesgo. La tasa de supervivencia de los pacientes en el grupo de alta mutación fue significativamente menor que la de los pacientes con cáncer de hígado en el grupo de baja mutación. Se exploraron doce medicamentos con diferencias significativas en la sensibilidad a los medicamentos entre los grupos de alto y bajo riesgo.
Conclusiones: Se espera que el modelo de riesgo-pronóstico basado en CuProtosis LncRNA establecido en este estudio se utilice para predecir el pronóstico y la respuesta a la inmunoterapia de los pacientes con cáncer de hígado. Brinda nuevas pistas y métodos para predecir el tiempo de supervivencia de los pacientes con cáncer de hígado y también brinda nuevas ideas para guiar estrategias de inmunoterapia individualizadas para pacientes con cáncer de hígado en el futuro.
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References
1. Zhao T, Zhao C, Lu Y, Lin J, Tian Y, Ma Y, et al. Noxa and Puma genes regulated by hTERT promoter can mitigate growth and induce apoptosis in hepatocellular carcinoma mouse model. J Cancer. 2022 28;13(6):2001-13. doi: 10.7150/jca.70282.
2. Zhang X, Chen F, Huang P, Wang X, Zhou K, Zhou C, et al. Exosome-depleted MiR-148a-3p derived from Hepatic Stellate Cells Promotes Tumor Progression via ITGA5/PI3K/Akt Axis in Hepatocellular Carcinoma. Int J Biol Sci. 2022;18(6):2249-60. doi: 10.7150/ijbs.66184.
3. Kong Y, Chen H, Chen M, Li Y, Li J, Liu Q, et al. Abnormal ECA-Binding Membrane Glycans and Galactosylated CAT and P4HB in Lesion Tissues as Potential Biomarkers for Hepatocellular Carcinoma Diagnosis. Front Oncol. 2022;12:855952. doi: 10.3389/fonc.2022.855952.
4. Huang C, Zhou J, Nie Y, Guo G, Wang A, Zhu X. A new finding in the key prognosis-related proto-oncogene FYN in hepatocellular carcinoma based on the WGCNA hub-gene screening trategy. BMC Cancer. 2022;22(1):380. doi: 10.1186/s12885-022-09388-5.
5. Chen S, Zhang Y, Ding X, Li W. Identification of lncRNA/circRNA-miRNA-mRNA ceRNA Network as Biomarkers for Hepatocellular Carcinoma. Front Genet. 2022;13:838869. doi: 10.3389/fgene.2022.838869.
6. Tang W, Lu Q, Zhu J, Zheng X, Fang N, Ji S, et al. Identification of a Prognostic Signature Composed of GPI, IL22RA1, CCT6A and SPOCK1 for Lung Adenocarcinoma Based on Bioinformatic Analysis of lncRNA-Mediated ceRNA Network and Sample Validation. Front Oncol. 2022;12:844691. doi: 10.3389/fonc.2022.844691.
7. Ye X, Wang LP, Han C, Hu H, Ni CM, Qiao GL, et al. Increased m6A modification of lncRNA DBH-AS1 suppresses pancreatic cancer growth and gemcitabine resistance via the miR-3163/USP44 axis. Ann Transl Med. 2022;10(6):304. doi: 10.21037/atm-22-556.
8. Liu X, Zhao T, Yuan Z, Ge S. MIR600HG sponges miR-125a-5p to regulate glycometabolism and cisplatin resistance of oral squamous cell carcinoma cells via mediating RNF44. Cell Death Discov. 2022;8(1):216. doi: 10.1038/s41420-022-01000-w.
9. Peng S, Chen L, Yuan Z, Duan S. Suppression of MIR31HG affects the functional properties of thyroid cancer cells depending on the miR-761/MAPK1 axis. BMC Endocr Disord. 2022;22(1):107. doi: 10.1186/s12902-022-00962-3.
10. Wang Y, Zou Y, Zhang Q, Chen D, Lin L. LncRNA SBF2-AS1 Facilitates Nonsmall Cell Lung Cancer Progression by Targeting miR-520a-3p. J Healthc Eng. 2022;2022:2223149. doi: 10.1155/2022/2223149.
11. Wu S, Yang X, Tang W, Familiari G, Relucenti M, Aschner M, et al. Chemotherapeutic Risk lncRNA-PVT1 SNP Sensitizes Metastatic Colorectal Cancer to FOLFOX Regimen. Front Oncol. 2022;12:808889. doi: 10.3389/fonc.2022.808889.
12. Zhu J, Huang Q, Peng X, Luo C, Liu S, Liu Z, et al. Identification of LncRNA Prognostic Signature Associated With Genomic Instability in Pancreatic Adenocarcinoma. Front Oncol. 2022;12:799475. doi: 10.3389/fonc.2022.799475.
13. Han Y, Jiang W, Wang Y, Zhao M, Li Y, Ren L. Serum long non-coding RNA SCARNA10 serves as a potential diagnostic biomarker for hepatocellular carcinoma. BMC Cancer. 2022;22(1):431. doi: 10.1186/s12885-022-09530-3.
14. Ruiz LM, Libedinsky A, Elorza AA. Role of Copper on Mitochondrial Function and Metabolism. Front Mol Biosci. 2021;8:711227. doi: 10.3389/fmolb.2021.711227.
15. Oliveri V. Selective Targeting of Cancer Cells by Copper Ionophores: An Overview. Front Mol Biosci. 2022;9:841814. doi: 10.3389/fmolb.2022.841814.
16. Tsvetkov P, Coy S, Petrova B, Dreishpoon M, Verma A, Abdusamad M, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science. 2022;375(6586):1254-61. doi: 10.1126/science.abf0529.
17. Tang D, Chen X, Kroemer G. Cuproptosis: a copper-triggered modality of mitochondrial cell death. Cell Res. 2022;32(5):417-8. doi: 10.1038/s41422-022-00653-7.
18. Burcher KM, Lantz JW, Gavrila E, Abreu A, Burcher JT, Faucheux AT, et al. Relationship between Tumor Mutational Burden, PD-L1, Patient Characteristics, and Response to Immune Checkpoint Inhibitors in Head and Neck Squamous Cell Carcinoma. Cancers (Basel). 2021;13(22):5733. doi: 10.3390/cancers13225733.
19. Kim H, Kim H, Kim R, Jo H, Kim HR, Hong J, et al. Tumor Mutational Burden as a Biomarker for Advanced Biliary Tract Cancer. Technol Cancer Res Treat. 2021;20:15330338211062324. doi: 10.1177/15330338211062324.
20. Chan TA, Wolchok JD, Snyder A. Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma. N Engl J Med. 2015;373(20):1984. doi: 10.1056/NEJMc1508163.
21. Zeng T, Chen G, Qiao X, Chen H, Sun L, Ma Q, et al. NUSAP1 Could be a Potential Target for Preventing NAFLD Progression to Liver Cancer. Front Pharmacol. 2022;13:823140. doi: 10.3389/fphar.2022.823140.
22. Guo X, Yu X, Li F, Xia Q, Ren H, Chen Z, et al. Identification of Survival-Related Metabolic Genes and a Novel Gene Signature Predicting the Overall Survival for Patients with Uveal Melanoma. Ophthalmic Res. 2022;65(5):516-28. doi: 10.1159/000524505.
23. Dousset L, Poizeau F, Robert C, Mansard S, Mortier L, Caumont C, et al. Positive Association Between Location of Melanoma, Ultraviolet Signature, Tumor Mutational Burden, and Response to Anti-PD-1 Therapy. JCO Precis Oncol. 2021;5:PO.21.00084. doi: 10.1200/PO.21.00084.
24. Allgäuer M, Budczies J, Christopoulos P, Endris V, Lier A, Rempel E, et al. Implementing tumor mutational burden (TMB) analysis in routine diagnostics-a primer for molecular pathologists and clinicians. Transl Lung Cancer Res. 2018;7(6):703-15. doi: 10.21037/tlcr.2018.08.14.
Submitted date:
09/05/2022
Reviewed date:
09/27/2022
Accepted date:
10/20/2022
Publication date:
10/22/2022
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