Zhou, Q.; Liu, Y.; Duan, H.; Quan, L.; Gao, Y.; Wang, L.; Liu, J.; Abbas, Z.; Nzeyimana, J.B.; Qamer, S.; Ibrahim, N.; Khan, M.S.; Mohammed, A.H.; Ameen, A. Prognosis Prediction Based on Cuproptosis-Related lncRNAs and Immune Responses in Patients with LUAD. Preprints2022, 2022110526. https://doi.org/10.20944/preprints202211.0526.v1
APA Style
Zhou, Q., Liu, Y., Duan, H., Quan, L., Gao, Y., Wang, L., Liu, J., Abbas, Z., Nzeyimana, J.B., Qamer, S., Ibrahim, N., Khan, M.S., Mohammed, A.H., & Ameen, A. (2022). Prognosis Prediction Based on Cuproptosis-Related lncRNAs and Immune Responses in Patients with LUAD. Preprints. https://doi.org/10.20944/preprints202211.0526.v1
Chicago/Turabian Style
Zhou, Q., Amal Hussain Mohammed and Ayesha Ameen. 2022 "Prognosis Prediction Based on Cuproptosis-Related lncRNAs and Immune Responses in Patients with LUAD" Preprints. https://doi.org/10.20944/preprints202211.0526.v1
Abstract
Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of the heterogeneity of programmed cell death. The purpose of this work is to investigate and develop a cuproptosis-associated lncRNA-based LUAD prediction marker. We firstly performed bioinformatic analysis of the Cuprotosis database and The Cancer Genome Atlas (TCGA) database to obtain 19 cuprotosis-related gene datasets and transcriptional data for LUAD. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were utilized to construct cuproptosis-associated lncRNA modes. LUAD patients were thus classified into high-risk and low-risk categories based on prognostic risk values, with a median of It acted as a boundary. Risk models were evaluated and validated using Kaplan-Meier analysis, principal component analysis (PCA), gene set enrichment analysis (GSEA) and nomograms. Utilizing the TCGA-LUAD dataset, we identified seven predicted cuproptosis-associated lncRNAs in tumor microenvironment to create the risk model. 95.54% (214/224) of high-risk category tumor samples included cuproptosis-associated gene alterations, compared to 85.65% (203/237) of low-risk category tumor samples, with TP53 accounting for the bulk of occurrences. According to these findings, risk value was superior to other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival (OS). The predictive validity of the cuproptosis-associated lncRNA-based risk model for LUAD is high, and this may have implications for how lung cancer patients are treated individually.
Biology and Life Sciences, Immunology and Microbiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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