Research Paper Volume 15, Issue 22 pp 13504—13541
Crosstalk between copper homeostasis and cuproptosis reveals a lncRNA signature to prognosis prediction, immunotherapy personalization, and agent selection for patients with lung adenocarcinoma
- 1 Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Received: April 21, 2023 Accepted: September 26, 2023 Published: November 26, 2023
https://doi.org/10.18632/aging.205281How to Cite
Copyright: © 2023 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Copper homeostasis and cuproptosis play critical roles in various biological processes of cancer; however, whether they can impact the prognosis of lung adenocarcinoma (LUAD) remain to be fully elucidated. We aimed to adopt these concepts to create and validate a lncRNA signature for LUAD prognostic prediction.
Methods: For this study, the TCGA-LUAD dataset was used as the training cohort, and multiple datasets from the GEO database were pooled as the validation cohort. Copper homeostasis and cuproptosis regulated genes were obtained from published studies, and various statistical methods, including Kaplan-Meier (KM), Cox, and LASSO, were used to train our gene signature CoCuLncSig. We utilized KM analysis, COX analysis, receiver operating characteristic analysis, time-dependent AUC analysis, principal component analysis, and nomogram predictor analysis in our validation process. We also compared CoCuLncSig with previous studies. We performed analyses using R software to evaluate CoCuLncSig's immunotherapeutic ability, focusing on eight immune algorithms, TMB, and TIDE. Additionally, we investigated potential drugs that could be effective in treating patients with high-risk scores. Additionally qRT-PCR examined the expression patterns of CoCuLncSig lncRNAs, and the ability of CoCuLncSig in pan-cancer was also assessed.
Results: CoCuLncSig containing eight lncRNAs was trained and showed strong predictive ability in the validation cohort. Compared with previous similar studies, CoCuLncSig had more prognostic ability advantages. CoCuLncSig was closely related to the immune status of LUAD, and its tight relationship with checkpoints IL10, IL2, CD40LG, SELP, BTLA, and CD28 may be the key to its potential immunotherapeutic ability. For the high CoCuLncSig score population, we found 16 drug candidates, among which epothilone-b and gemcitabine may have the most potential. The pan-cancer analysis found that CoCuLncSig was a risk factor in multiple cancers. Additionally, we discovered that some of the CoCuLncSig lncRNAs could play crucial roles in specific cancer types.
Conclusion: The current study established a powerful prognostic CoCuLncSig signature for LUAD that was also valid for most pan-cancers. This signature could serve as a potential target for immunotherapy and might help the more efficient application of drugs to specific populations.
Abbreviations
ACC: Adrenocortical carcinoma; ALL: Acute Lymphoblastic Leukemia; AUC: Area under the ROC curve; BLCA: Bladder Urothelial Carcinoma; BRCA: Breast invasive carcinoma; CDF: cumulative distribution function; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; Cl: confidence interval; Cmap: Connectivity Map; COAD: Colon adenocarcinoma; COADREAD: Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma; CoCu clusters: clusters identified by copper homeostasis and cuproptosis correlated genes; CoCu-DEGs: differentially expressed genes identified among CoCu clusters; CoCuLncSig: copper homeostasis and cuproptosis regulated lncRNA signature; DEGs: differentially expressed genes; DELs: lncRNA that were differentially expressed between the CoCu-DEG clusters; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA: Esophageal carcinoma; FDR: false discovery rate; FPPP: FFPE Pilot Phase II; GBM: Glioblastoma multiforme; GBMLGG: Glioma; GSEA: Gene Set Enrichment Analysis; HNSC: Head and Neck squamous cell carcinoma; HR: hazard ratio; IC50: half maximal inhibitory concentration; KEGG: Kyoto Encyclopedia of Genes and Genomes; KICH: Kidney Chromophobe; KIPAN: Pan-kidney cohort (KICH+KIRC+KIRP); KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; KM: Kaplan-Meier estimator; LAML: Acute Myeloid Leukemia; LASSO: least absolute shrinkage and selection operator Cox regression model; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; NB: Neuroblastoma; OS: Osteosarcoma; ov: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PC: principal component; PCA: Principal components analysis; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; qRT-PCR: quantitative real-time PCR; READ: Rectum adenocarcinoma; ROC: receiver operating characteristic; SARC: Sarcoma; SKCM: Skin Cutaneous Melanoma; STAD: Stomach adenocarcinoma; STES: Stomach and Esophageal carcinoma; TARGET: Therapeutically Applicable Research to Generate Effective Treatments; tAUC: Time-dependent AUC; TCGA: The Cancer Genome Atlas; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; TICs: tumor-infiltrating immune cells; TIDE: Tumor Immune Dysfunction and Exclusion; TMB: Tumor mutational burden; UCEC: Uterine Corpus Endometrial Carcinoma; ucs: Uterine Carcinosarcoma; UMAP: Uniform Manifold Approximation and Projection; UVM: Uveal Melanoma; WT: High-Risk Wilms Tumor.