Cuproptosis-related LncRNAs are potential prognostic and immune response markers for patients with HNSCC via the integration of bioinformatics analysis and experimental validation

lncRNAs as prognostic markers and therapeutic targets in cuproptosis-mediated cancer

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in various cellular processes, including cancer progression and stress response. Recent studies have demonstrated that copper accumulation induces a unique form of cell death known as cuproptosis, with lncRNAs playing a key role in regulating cuproptosis-associated pathways. These lncRNAs may trigger cell-specific responses to copper stress, presenting new opportunities as prognostic markers and therapeutic targets. This paper delves into the role of lncRNAs in cuproptosis-mediated cancer, underscoring their potential as biomarkers and targets for innovative therapeutic strategies. A thorough review of scientific literature was conducted, utilizing databases such as PubMed, Google Scholar, and ScienceDirect, with search terms like 'lncRNAs,' 'cuproptosis,' and 'cancer.' Studies were selected based on their relevance to lncRNA regulation of cuproptosis pathways and their implications for cancer prognosis and treatment. The review highlights the significant contribution of lncRNAs in regulating cuproptosis-related genes and pathways, impacting copper metabolism, mitochondrial stress responses, and apoptotic signaling. Specific lncRNAs are potential prognostic markers in breast, lung, liver, ovarian, pancreatic, and gastric cancers. The objective of this article is to explore the role of lncRNAs as potential prognostic markers and therapeutic targets in cancers mediated by cuproptosis.

Ubiquitination-Related Gene Signature, Nomogram and Immune Features for Prognostic Prediction in Patients with Head and Neck Squamous Cell Carcinoma

The objective of this research was to create a prognostic model focused on genes related to ubiquitination (UbRGs) for evaluating their clinical significance in head and neck squamous cell carcinoma (HNSCC) patients. The transcriptome expression data of UbRGs were obtained from The Cancer Genome Atlas (TCGA) database, and weighted gene co-expression network analysis (WGCNA) was used to identify specific UbRGs within survival-related hub modules. A multi-gene signature was formulated using LASSO Cox regression analysis. Furthermore, various analyses, including time-related receiver operating characteristics (ROCs), Kaplan-Meier, Cox regression, nomogram prediction, gene set enrichment, co-expression, immune, tumor mutation burden (TMB), and drug sensitivity, were conducted. Ultimately, a prognostic signature consisting of 11 gene pairs for HNSCC was established. The Kaplan-Meier curves indicated significantly improved overall survival (OS) in the low-risk group compared to the high-risk group (p < 0.001), suggesting its potential as an independent and dependable prognostic factor. Additionally, a nomogram with AUC values of 0.744, 0.852, and 0.861 at 1-, 3-, and 5-year intervals was developed. Infiltration of M2 macrophages was higher in the high-risk group, and the TMB was notably elevated compared to the low-risk group. Several chemotherapy drugs targeting UbRGs were recommended for low-risk and high-risk patients, respectively. The prognostic signature derived from UbRGs can effectively predict prognosis and provide new personalized therapeutic targets for HNSCC.

Identification of cuproptosis-related lncRNAs with the significance in prognosis and immunotherapy of oral squamous cell carcinoma

Cuproptosis, a recently characterized programmed cell death mechanism, has emerged as a potential contributor to tumorigenesis, metastasis, and immune modulation. Long non-coding RNAs (lncRNAs) have demonstrated diverse regulatory roles in cancer and hold promise as biomarkers. However, the involvement and prognostic significance of cuproptosis-related lncRNAs (CRLs) in oral squamous cell carcinoma (OSCC) remain poorly understood. Based on TCGA-OSCC data, we integrated single-sample gene set enrichment analysis (ssGSEA), the LASSO algorithm, and the tumor immune dysfunction and exclusion (TIDE) algorithm. We identified 11 CRLs through differential expression, Spearman correlation, and univariate Cox regression analyses. Two distinct CRL-related subtypes were unveiled, delineating divergent survival patterns, tumor microenvironments (TME), and mutation profiles. A robust CRL-based signature (including AC107027.3, AC008011.2, MYOSLID, AC005785.1, AC019080.5, AC020558.2, AC025265.1, FAM27E3, and LINC02367) prognosticated OSCC outcomes, immunotherapy responses, and anti-tumor strategies. Superior predictive power compared to other lncRNA models was demonstrated. Functional assessments confirmed the influence of FAM27E3, LINC02367, and MYOSLID knockdown on OSCC cell behaviors. Remarkably, the CRLs-based signature maintained stability across OSCC patient subgroups, underscoring its clinical potential for survival prediction. This study elucidates CRLs' roles in TME of OSCC and establishes a potential signature for precision therapy.

Bioinformatic analysis and experimental validation of cuproptosis-related LncRNA as a novel biomarker for prognosis and immunotherapy of oral squamous cell carcinoma

BACKGROUND

The novel form of regulatory cell death, cuproptosis, is characterized by proteotoxicity, which ultimately leads to cell death. Its targeting has emerged as a promising therapeutic approach for oral squamous cell carcinoma (OSCC). Long noncoding RNAs (lncRNAs) participate in epigenetic regulation and have been linked to the progression, prognosis, and treatment of OSCC. Thus, this study aimed to identify new cuproptosis-related lncRNAs (CRLs), establish predictive models for clinical prognosis, immune response, and drug sensitivity, and provide novel insights into immune escape and tumor drug resistance.

METHODS

The present study screened eight CRLs (THAP9-AS1, STARD4-AS1, WDFY3-AS2, LINC00847, CDKN2A-DT, AL132800.1, GCC2-AS1, AC005746.1) using Lasso Cox regression analysis to develop an eight-CRL prognostic model. Patients were categorized into high- and low-risk groups using risk scores. To evaluate the predictive ability of the model, Kaplan-Meier analysis, ROC curves, and nomograms were employed. Furthermore, the study investigated the differences in immune function and anticancer drug sensitivity between the high- and low-risk groups. To validate the expression of CRLs in the model, OSCC cell lines were subjected to quantitative real-time fluorescence PCR (qRT-PCR).

RESULTS

The results of the study showed that the high-risk group had a shorter overall survival (OS) time in OSCC patients. Cox regression analysis demonstrated that the high-risk score was an independent risk factor for a poor prognosis. The validity of the model was confirmed using ROC curve analysis, and a nomogram was developed to predict the prognosis of OSCC patients. Furthermore, patients in the high-risk group with high TMB had a poorer prognosis. Patients in the low-risk group responded better to immunotherapy than those in the high-risk group. Additionally, the risk scores were significantly associated with drug sensitivity in OSCC patients. Finally, the findings of qRT-PCR supported the reliability of the proposed risk model.

CONCLUSION

The study identified and established the 8-CRL model, which represents a novel pathway of lncRNA regulation of cuproptosis in OSCC. This model provides guidance for the prognosis and treatment of OSCC and offers a new insight into immune escape and tumor drug resistance.

Novel cuproptosis-related lncRNAs can predict the prognosis of patients with multiple myeloma

Background

Cuproptosis-related long-stranded non-coding RNAs (lncRNAs) have several implications for the prognosis of multiple myeloma (MM). This research aimed to construct a prognostic risk model for MM patients and explore the potential signaling pathways in the risk group.

Methods

Cuproptosis-related lncRNAs were obtained from the co-expression analysis of cuproptosis-related genes and lncRNAs. Subsequently, twelve cuproptosis-related lncRNAs were selected to construct a prognostic risk model of MM patients by the least absolute shrinkage and selection operator (LASSO) regression. Then, the clinical data of these patients were randomly divided into the training group and the testing group. Next, patients were divided into the low- and high-risk groups according to the median risk score. The Kaplan-Meier survival analysis was performed to clarify the prognostic differences between risk subtypes. Besides, the Cox analysis was conducted to identify whether the risk score can be used as an independent prognostic factor. In addition, the receiver operating characteristic (ROC) curve analysis and the concordance index (C-index) curve analysis were performed to elucidate the value of risk score as a prognostic indicator. Finally, the differential risk analysis and functional enrichment analysis were carried out to identify the potential signaling pathways in the low- and high-risk groups.

Results

The results demonstrated that the overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group. There were significant differences in the expression of genes in MM patients between the high- and low-risk groups. The Gene Ontology (GO) analysis results showed that the differentially expressed risk-related genes (DERGs) were mainly concentrated on the collagen-containing extracellular matrix. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results, the DERGs may be related to the neuroactive ligand-receptor interaction and mitogen-activated protein kinase (MAPK) signaling pathway, indicating that they may be involved in the progression of tumors.

Conclusions

The findings of this study suggest that cuproptosis-related lncRNAs may be effective biomarkers for predicting the prognosis of MM patients, which is anticipated to contribute to the improvement of clinical outcomes.

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package "compound.Cox". The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications.