Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. ccRCC Data Collection
2.2. Identification of CRLRs
2.3. Construction of the CRLR Prognostic Model by a Machine Learning Algorithm
2.4. Kaplan–Meier (K–M) Survival Analysis and Principal Component Analysis (PCA)
2.5. Cell Culture
2.6. qPCR and RNA Isolation
2.7. Statistical Analysis
3. Results
3.1. Identification of the Prognostic CRLR Signature in ccRCC
3.2. Construction of the Hybrid Nomogram and GO Analysis
3.3. Survival Analysis and Principal Component Analysis
3.4. TIDE Algorithm and IC50 for Assessing Therapeutic Response
3.5. Validation of CRLRs by qPCR and ICGC Database
4. Discussion
5. Conclusions
Availability of Data and Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
lncRNAs | Long noncoding RNA |
ICGC | International Cancer Genome Consortium |
TCGA | The Cancer Genome Atlas |
LASSO | Least absolute shrinkage and selection operator |
ML | Machine learning |
IC50 | Half maximal inhibitory concentration |
ccRCC | Clear cell renal cell carcinoma |
CRLR | Cuproptosis-related long noncoding RNA |
TIDE | Tumor immune dysfunction and exclusion |
TMB | Tumor mutational burden |
CRGs | Cuproptosis-related genes |
PCA | Principal component analysis |
OS | Overall survival |
AUC | Area under the curve |
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Gene | Forward Primer | Reverse Primer |
---|---|---|
LASTR | 3′-GCAAGAGAGAAGACAGTGGGTGAAG-5′ | 3′-CCAGTGAAGGGCTGAAGGGTTTAG-5′ |
FOXD2−AS1 | 3′-TGGGTTGAGGGTCTGTGACTGTAG-5′ | 3′-GCTGCCGCTGGAGTATTCTTGG-5′ |
AC026401.3 | 3′-AGTGGGAAATCTGACCTCTTTTGGC-5′ | 3′-TCCTGTTCTTAGTGGCTGCATTACC-5′ |
β-Actin | 3′-CGGGAAATCGTGCGTGAC-5′ | 3′-CAGGAAGGAAGGCTGGAAG-5′ |
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Bai, Z.; Lu, J.; Chen, A.; Zheng, X.; Wu, M.; Tan, Z.; Xie, J. Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning. Biomolecules 2022, 12, 1890. https://doi.org/10.3390/biom12121890
Bai Z, Lu J, Chen A, Zheng X, Wu M, Tan Z, Xie J. Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning. Biomolecules. 2022; 12(12):1890. https://doi.org/10.3390/biom12121890
Chicago/Turabian StyleBai, Zhixun, Jing Lu, Anjian Chen, Xiang Zheng, Mingsong Wu, Zhouke Tan, and Jian Xie. 2022. "Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning" Biomolecules 12, no. 12: 1890. https://doi.org/10.3390/biom12121890