Background Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. Methods The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan–Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. Results A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. Conclusion Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Background. Chronic lymphocytic leukemia (CLL) is the most common type of leukemia in adults. Thus, novel reliable biomarkers need to be further explored to increase diagnostic, therapeutic, and prognostic effectiveness. Methods. Six datasets containing CLL and control samples were downloaded from the Gene Expression Omnibus database. Differential gene expression analysis, weighted gene coexpression network analysis (WGCNA), and the least absolute shrinkage and selection operator (LASSO) regression were applied to identify potential diagnostic biomarkers for CLL using R software. The diagnostic performance of the hub genes was then measured by the receiver operating characteristic (ROC) curve analysis. Functional analysis was implemented to uncover the underlying mechanisms. Additionally, correlation analysis was performed to assess the relationship between the hub genes and immunity characteristics. Results. A total number of 47 differentially expressed genes (DEGs) and 25 candidate hub genes were extracted through differential gene expression analysis and WGCNA, respectively. Based on the 14 overlapped genes between the DEGs and the candidate hub genes, LASSO regression analysis was used, which identified a final number of six hub genes as potential biomarkers for CLL: ABCA6, CCDC88A, PMEPA1, EBF1, FILIP1L, and TEAD2. The ROC curves of the six genes showed reliable predictive ability in the training and validation cohorts, with all area under the curve (AUC) values over 0.80. Functional analysis revealed an abnormal immune status in the CLL patients. A significant correlation was found between the hub genes and the immune-related pathways, indicating a possible tight connection between the hub genes and tumor immunity in CLL. Conclusion. This study was based on machine learning algorithms, and we identified six genes that could be possible CLL markers, which may be involved in CLL pathogenesis and progression through immune-related signal pathways.

Background Multiple myeloma (MM) accounts for 1% of neoplastic diseases. Cuproptosis, a copper-triggered modality of mitochondrial cell death, might be a promising therapeutic target for cancer treatment. However, the role of cuproptosis-related genes (CRGs) in MM is not well characterized. Thus, we aimed to explore the diagnostic value of CRGs in MM and further illustrate the potential mechanism. Methods The differential expression of CRGs between MM and control samples was identified and validated in the GSE6477 and GSE47552 datasets downloaded from the Gene Expression Omnibus database. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential CRGs as diagnostic biomarkers for MM. Receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic performance of the biomarkers. Functional enrichment and correlation analyses were then conducted to figure out the underlying mechanisms. Results Based on the differentially expressed CRGs by the gene expression difference of samples, LASSO and SVM-RFE algorithms were used to identify a final number of two CRGs as potential biomarkers for MM: CDKN2A and GLS. The all area under the curve (AUC) values of the 2 marker gene-based logistic regression model were 0.933 and 0.886 in the training and validation cohort, respectively, indicating a good performance in predicting MM diagnosis. Functional enrichment and correlation analyses suggested that the biomarkers may promote MM cell tumorigenesis and survival by modulating the immune cells through its immune-related pathways. Conclusion Two CRGs (CDKN2A and GLS) were identified and validated as possible MM biomarkers, which developed a diagnostic potency and provided an insight for exploring the mechanism for MM.