Identification and validation of a novel cuproptosis-related stemness signature to predict prognosis and immune landscape in lung adenocarcinoma by integrating single-cell and bulk RNA-sequencing

Abstract

BackgroundCancer stem cells (CSCs) play vital roles in lung adenocarcinoma (LUAD) recurrence, metastasis, and drug resistance. Cuproptosis has provided a novel insight into the treatment of lung CSCs. However, there is a lack of knowledge regarding the cuproptosis-related genes combined with the stemness signature and their roles in the prognosis and immune landscape of LUAD.MethodsCuproptosis-related stemness genes (CRSGs) were identified by integrating single-cell and bulk RNA-sequencing data in LUAD patients. Subsequently, cuproptosis-related stemness subtypes were classified using consensus clustering analysis, and a prognostic signature was constructed by univariate and least absolute shrinkage operator (LASSO) Cox regression. The association between signature with immune infiltration, immunotherapy, and stemness features was also investigated. Finally, the expression of CRSGs and the functional roles of target gene were validated in vitro.ResultsWe identified six CRSGs that were mainly expressed in epithelial and myeloid cells. Three distinct cuproptosis-related stemness subtypes were identified and associated with the immune infiltration and immunotherapy response. Furthermore, a prognostic signature was constructed to predict the overall survival (OS) of LUAD patients based on eight differently expressed genes (DEGs) with cuproptosis-related stemness signature (KLF4, SCGB3A1, COL1A1, SPP1, C4BPA, TSPAN7, CAV2, and CTHRC1) and confirmed in validation cohorts. We also developed an accurate nomogram to improve clinical applicability. Patients in the high-risk group showed worse OS with lower levels of immune cell infiltration and higher stemness features. Ultimately, further cellular experiments were performed to verify the expression of CRSGs and prognostic DEGs and demonstrate that SPP1 could affect the proliferation, migration, and stemness of LUAD cells.ConclusionThis study developed a novel cuproptosis-related stemness signature that can be used to predict the prognosis and immune landscape of LUAD patients, and provided potential therapeutic targets for lung CSCs in the future.

CACNA1E, C4BPA is causally associated with gastric cancer: A Mendelian randomization study

“…Upregulation of the C4BP gene as an inhibitor of the complement system is a possible strategy adopted by the tumor cells to evade the immune system [32] . Constructed a risk score based on the 129 intersecting differentially expressed genes by LASSO and univariate regression, it was found that the higher expression of C4BPA, the lower the overall survival rate of lung adenocarcinoma patients [33] .…”

Commentary: Copper and cuproptosis-related genes in hepatocellular carcinoma: therapeutic biomarkers targeting tumor immune microenvironment and immune checkpoints

“…Moreover, how to detect the intensity of cuproptosis and how to assess the effects of genes on cuproptosis are the other critical issues needed to be addressed. Regretfully, several recent studies in Frontiers in Immunology have failed to eliminate the above-mentioned concerns well (3)(4)(5). Therefore, we performed the following discussion which aims to provide some insights into further CR research.…”

Construction of a prognostic model based on memory CD4+ T cell–associated genes for lung adenocarcinoma and its applications in immunotherapy

“…Accurate prediction of patients' prognosis plays a crucial role in providing beneficial treatment decisions and improving patients' quality of life. Currently, several LUAD prognostic models have been developed, including the basement membrane‐related gene prognostic model, 6 lipid metabolism‐related gene prognostic model, 7 hypoxia‐related gene prognostic model, 8 cuproptosis‐related feature prognostic model, 9 and antigen‐presenting cell/T/NK cell‐related gene prognostic model, among others. Although these existing LUAD prognostic models have provided useful information for predicting patient survival and prognosis assessment, they still have significant limitations as a result of issues such as small sample sizes, disease heterogeneity, and model feasibility.…”