Cuproptosis-related lncRNA predict prognosis and immune response of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) accounts for 50% of lung cancers, with high mortality and poor prognosis. Long non-coding RNA (lncRNA) plays a vital role in the progression of tumors. Cuproptosis is a newly discovered form of cell death that is highly investigated. Therefore, in the present study, we aimed to investigate the role of cuproptosis-related lncRNA signature in clinical prognosis prediction and immunotherapy and the relationship with drug sensitivity. Genomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and cuproptosis-related genes were obtained from cuproptosis-related studies. The prognostic signature was constructed by co-expression analysis and Cox regression analysis. Patients were divided into high and low risk groups, and then, a further series of model validations were carried out to assess the prognostic value of the signature. Subsequently, lncRNAs were analyzed for gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes Enrichment (KEGG), immune-related functions, and tumor mutation burden (TMB). Finally, we used tumor immune dysfunction and exclusion (TIDE) algorithms on immune escape and immunotherapy of cuproptosis-related lncRNAs, thereby identifying its sensitivity toward potential drugs for LUAD. A total of 16 cuproptosis-related lncRNAs were obtained, and a prognostic signature was developed. We found that high-risk patients had worse overall survival (OS) and progression-free survival (PFS) and higher mortality. Independent prognostic analyses, ROC, C-index, and nomogram showed that the cuproptosis-related lncRNAs can accurately predict the prognosis of patients. The nomogram and heatmap showed a distinct distribution of the high- and low-risk cuproptosis-related lncRNAs. Enrichment analysis showed that the biological functions of lncRNAs are associated with tumor development. We also found that immune-related functions, such as antiviral activity, were suppressed in high-risk patients who had mutations in oncogenes. OS was poorer in patients with high TMB. TIDE algorithms showed that high-risk patients have a greater potential for immune escape and less effective immunotherapy. To conclude, the 16 cuproptosis-related lncRNAs can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

Wang F ，Lin H ，Su Q ，Li C ... -  《World Journal of Surgical Oncology》

A cuproptosis-related lncRNA signature for predicting prognosis and immunotherapy response of lung adenocarcinoma.

Copper-induced cell death (cuproptosis) is a new regulatory cell death mechanism. Long noncoding RNAs (lncRNAs) are related to tumor immunity and metastasis. However, the correlation of cuproptosis-related lncRNAs with the immunotherapy response and prognosis of lung adenocarcinoma (LUAD) patients is not clear. We obtained the clinical characteristics and transcriptome data from TCGA-LUAD dataset (containing 539 LUAD and 59 paracancerous tissues). By utilizing LASSO-penalized Cox regression analysis, we identified a prognostic signature composed of cuproptosis-related lncRNAs. This signature was then utilized to segregate patients into two different risk categories based on their respective risk scores. The identification of differentially expressed genes (DEGs) between high- and low-risk groups was carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We evaluated the immunotherapy response by analyzing tumor mutational burden (TMB), immunocyte infiltration and Tumor Immune Dysfunction and Exclusion (TIDE) web application. The "pRRophetic" R package was utilized to conduct further screening of potential therapeutic drugs for their sensitivity. We ultimately identified a prognostic risk signature that includes six cuproptosis-related lncRNAs (AP003778.1, AC011611.2, CRNDE, AL162632.3, LY86-AS1, and AC090948.1). Compared with clinical characteristics, the signature was significantly correlated with prognosis following the control of confounding variables (HR = 2.287, 95% CI = 1.648-3.174, p ˂ 0.001), and correctly predicted 1-, 2-, and 3-year overall survival (OS) rates (AUC value = 0.725, 0.715, and 0.662, respectively) in LUAD patients. In terms of prognosis, patients categorized as low risk exhibited more positive results in comparison to those in the high-risk group. The enrichment analysis showed that the two groups had different immune signaling pathways. Immunotherapy may offer a more appropriate treatment option for high-risk patients due to their higher TMB and lower TIDE scores. The higher risk score may demonstrate increased sensitivity to bexarotene, cisplatin, epothilone B, and vinorelbine. Based on cuproptosis-related lncRNAs, we constructed and validated a novel risk signature that may be used to predict immunotherapy efficacy and prognosis in LUAD patients.

Yu S ，Tang L ，Zhang Q ，Li W ，Yao S ，Cai Y ，Cheng H ... -  《HEREDITAS》

Prognostic signature construction and immunotherapy response analysis for Uterine Corpus Endometrial Carcinoma based on cuproptosis-related lncRNAs.

As a general female malignant tumor, Uterine Corpus Endometrial Carcinoma (UCEC) has high mortality and relapses. Cuproptosis was found to play an essential role in tumor by more and more researches. However, it is still unclear of the prognostic value and function of cuproptosis related Long non-coding RNA (lncRNA) in UCEC. Sequencing data with the corresponding clinical data and cuproptosis-related genes (CRGs) data were obtained from the Cancer Gene Atlas (TCGA) database and cuproptosis related studies. Pearson test was applied to select cuproptosis-related lncRNAs (CRLs). Prognosis associated CRLs was identified by univariate Cox analysis and the predictors were determined by least absolute shrinkage and selection operator (Lasso)-Cox and multivariate Cox analyses to construct the cuproptosis-related lncRNA prognostic signature (CRLPS). The performance of the CRLPs was evaluated by consistency index (C-index) and Kaplan-Meier analysis. A nomogram model was constructed for survival prediction and the accuracy of the model was evaluated by calibration curve. Finally, immune related analyses were applied to predict immune responses and identify drugs with potential efficacy for the overall survival (OS). A total of 734 CRLs were found and 29 of them were identified as prognosis related lncRNAs. 12 CRLs were finally determined to build the CRLPS which revealed good ability on prognosis predicting. Subsequently, risk score of the CRLPS and grade were assessed as independent prognosis factors for UCEC, based on which the prognostic model provided the highest prediction accuracy of 99.7%. The calibration curve suggested that the prediction results consisted well with the observation. Enrichment analysis showed the CRLPS was mainly associated with tumor development and immune response. Patients in low tumor mutation burden (TMB) group had poorer OS. Significant difference was found in tumor immune dysfunction and exclusion (TIDE) score between different risk score groups. Finally, based on the CRLPs, drug sensitivity analysis identified nine anticancer drugs with potential efficacy on prognosis. Cuproptosis-related lncRNA prognostic signature was constructed for UCEC for the first time. Its high reliability and accuracy on predicting prognosis and immunotherapy response provided new perspective to explore the tumor mechanism and improve clinical prognosis. Nine discovered sensitive drugs provided important clues for personalized treatment of UCEC.

Zhang X ，Ye Z ，Xiao G ，He T ... -  《-》

A Cuproptosis-Related LncRNA Risk Model for Predicting Prognosis and Immunotherapeutic Efficacy in Patients with Hepatocellular Carcinoma.

Cuproptosis is a novel programmed cell death pathway that is initiated by direct binding of copper to lipoylated tricarboxylic acid (TCA) cycle proteins. Recent studies have demonstrated that cuproptosis-related genes regulate tumorigenesis. However, the potential role and clinical significance of cuproptosis-related long noncoding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) have not been established. We performed a bioinformatics analyses of RNA-sequencing data of HCC patients extracted from The Cancer Genome Atlas (TCGA) dataset to identify and validate a cuproptosis-related lncRNA prognostic signature. Furthermore, we analyzed the clinical significance of the prognostic signature of cuproptosis-related lncRNA in predicting the immunotherapeutic efficacy and the status of the tumor immune microenvironment. The RNA-sequencing data, genomic mutations, and clinical information were downloaded for 374 HCC samples and 50 normal liver samples from TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. Co-expression analysis of Gene-lncRNA pairs with 49 known cuproptosis-related prognostic genes was used to define cuproptosis-related prognostic lncRNAs. We performed the LASSO algorithm and univariate and multivariate Cox regression analysis, respectively, to gradually identify the prognostic risk models of cuproptosis-related lncRNA based on the TCGA-LIHC dataset. Subsequently, the predictive performance of the model was evaluated using receiver operation characteristic (ROC) curves, Kaplan-Meier survival curves, and prognostic nomogram. The analysis of gene-lncRNA co-expression with 49 known cuproptosis-related genes identified 1359 cuproptosis-related lncRNAs in the TCGA-LIHC data set. A prognostic model was constructed with nine cuproptosis-related prognostic lncRNAs (AC007998.3, AC003086.1, AC009974.2, IQCH-AS1, LINC0256 1, AC105345.1, ZFPM2-AS1, AL353708.1 and WAC-AS1) using LASSO regression and Cox regression analyses. Risk scores were calculated for all HCC patient samples based on the four cuproptosis-related lncRNA prognostic models. All HCC patients were divided into high-risk and low-risk subgroups according to a 1:1 ratio column. The Kaplan-Meier survival curve analysis showed that the overall survival rate (OS) of the high-risk group patients was significantly lower than that of the low-risk group. The principal component analysis (PCA) confirmed that the prognostic lncRNA model accurately distinguished between high- and low-risk HCC patients. Furthermore, regression analysis as well as ROC curves confirmed the prognostic value of the risk score. A nomogram with risk scores and other clinicopathological characteristics was constructed. The nomogram accurately predicted the probability of 1-, 3-, and 5-year OS in HCC patients. Tumor mutation burden (TMB) scores were higher for high-risk patients than for low-risk patients. HCC patients in the low-risk group showed lower TIDE scores and greater sensitivity to antitumor drugs than those in the high-risk group. Tumor immune responses and tumor immune cell infiltration were significantly different between the high-risk and low-risk groups of patients with HCC. Our study identified a 9-cuproptosis-related lncRNA signature that accurately predicted prognosis, immunotherapeutic efficacy, and the status of the tumor immune microenvironment in HCC patients. Therefore, this cuproptosis-related lncRNA risk model is a potential prognostic biometric feature in HCC and shows high clinical value in identifying HCC patients who are potentially responsive to immunotherapy.

Wang S ，Bai H ，Fei S ，Miao B ... -  《-》

Cuproptosis-related lncRNA scoring system to predict the clinical outcome and immune landscape in pancreatic adenocarcinoma.

Cuproptosis is a recently discovered novel programmed cell death pathway that differs from traditional programmed cell death and has an important role in cancer and immune regulation. Long noncoding RNA (lncRNA) is considered new potential prognostic biomarkers in pancreatic adenocarcinoma (PAAD). However, the prognostic role and immune landscape of cuproptosis-related lncRNA in PAAD remain unclear. The transcriptome and clinical data of PAAD were obtained from The Cancer Genome Atlas (TCGA) database. Cuproptosis-related lncRNA was identified using Pearson correlation analysis. The optimal lncRNA was screened by Cox and the Least Absolute Shrinkage and Selection Operator (LASSO) regression mode, and for the construction of risk scoring system. PAAD patients were divided into high- and low-risk groups according to the risk score. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to explore differences in biological function between different risk groups. Single-sample gene set enrichment analysis (ssGSEA) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were used to analyze the differences in tumor immune microenvironment (TIME) in different risk groups of PAAD. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict immunotherapy response and identify potential immune beneficiaries. Immune checkpoints and tumor mutation burden (TMB) were also systematically analyzed. Finally, drug sensitivity analysis was used to explore the reactivity of different drugs in high- and low-risk groups to provide a reference for the selection of precise therapeutic drugs. Six cuproptosis-related lncRNAs (AL117335.1, AC044849.1, AL358944.1, ZNF236-DT, Z97832.2, and CASC8) were used to construct risk model. Survival analysis showed that overall survival and progression-free survival in the low-risk group were better than those in the high-risk group, and it is suitable for PAAD patients with different clinical characteristics. Univariate and multifactorial Cox regression analysis showed that risk score was an independent prognostic factor in PAAD patients. ROC analysis showed that the AUC values of the risk score in 1 year, 3 years and 5 years were 0.707,0.762 and 0.880, respectively. Nomogram showed that the total points of PAAD patients at 1 year, 3 years, and 5 years were 0.914,0.648, and 0.543. GO and KEGG analyses indicated that the differential genes in the high- and low-risk groups were associated with tumor proliferation and metastasis and immune regulatory pathway. Immune correlation analysis showed that the amount of pro-inflammatory cells, including CD8+ T cells, was significantly higher in the low-risk group than in the high-risk group, and the expression of immune checkpoint genes, including PD-1 and CTLA-4, was increased in the low-risk group. TIDE analysis suggests that patients in the low-risk group may benefit from immunotherapy. Finally, there was significant variability in multiple chemotherapeutic and targeted drugs across the risk groups, which informs our clinical drug selection. Our cuproptosis-related lncRNA scoring system (CRLss) could predict the clinical outcome and immune landscape of PAAD patients, identify the potential beneficiaries of immunotherapy, and provide a reference for precise therapeutic drug selection.

Huang Y ，Gong P ，Su L ，Zhang M ... -  《Scientific Reports》