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Cuproptosis-related lncRNAs potentially predict prognosis and therapy sensitivity of breast cancer.

ABSTRACT: Background: Cuproptosis-related lncRNAs regulate the biological functions of various cancers. However, the role of cuproptosis-related lncRNAs in breast cancer remains unclear. In this study, we investigated the biological functions and clinical applications of cuproptosis-related lncRNAs in breast cancer. Methods: The Cancer Genome Atlas (TCGA) database and the GSE20685 dataset were used for screening cuproptosis-related lncRNAs. Colony formation and CCK-8 kit assays were performed for detecting the proliferative function of cuproptosis-related lncRNAs, whereas wound healing, migration, and invasion assays were performed for detecting the metastatic regulation of cuproptosis-related lncRNAs in breast cancer. Finally, a prognostic cuproptosis-related lncRNA model was constructed using LASSO Cox regression analysis for detecting survival and sensitivity to conventional treatment (endocrine therapy, chemotherapy, and radiotherapy) and novel therapy (PARP and CDK4/6 inhibitors). Results: In this study, we screened six cuproptosis-related lncRNAs associated with the survival of patients with breast cancer. Biofunctional experiments indicated that cuproptosis-related lncRNAs play essential roles in regulating the proliferation and metastasis of breast cancer cells. Finally, we applied a model of six cuproptosis-related lncRNAs to classify the patients into high- and low-risk groups. High-risk group patients exhibited worse survival rates (p < 0.001) and lower sensitivity to chemotherapy, endocrine therapy, and radiation therapy. Compared with high-risk patients, low-risk patients exhibited a lower expression of CDK4/6 inhibitor-resistant biomarkers (CCNE1, E2F1, and E2F2) and PARP inhibitor-resistant biomarkers (BRCA1/BRCA2), indicating that patients in the low-risk group were more suitable for PARP inhibitor and CDK4/6 inhibitor application. Conclusion: Cuproptosis-related lncRNAs are essential for regulating the biological functions of breast cancer, and they have the potential to predict prognosis and sensitivity of breast cancer to various therapies.

SUBMITTER: Wu X

PROVIDER: S-EPMC10390311 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Cuproptosis-related lncRNAs potentially predict prognosis and therapy sensitivity of breast cancer.

Wu Xiwen X Zhang Ying Y Liang Gehao G Ye Huizhen H

Frontiers in pharmacology 20230717

<b>Background:</b> Cuproptosis-related lncRNAs regulate the biological functions of various cancers. However, the role of cuproptosis-related lncRNAs in breast cancer remains unclear. In this study, we investigated the biological functions and clinical applications of cuproptosis-related lncRNAs in breast cancer. <b>Methods:</b> The Cancer Genome Atlas (TCGA) database and the GSE20685 dataset were used for screening cuproptosis-related lncRNAs. Colony formation and CCK-8 kit assays were performe ...[more]

PMID: 37529698

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Project description:BackgroundThere has been a recent discovery of a new type of cell death produced by copper-iron ions, called Cuproptosis (copper death). The purpose of this study was to identify LncRNA signatures associated with Cuproptosis in ovarian cancer that could be used as prognostic indicators.MethodsRNA sequencing (RNA-seq) profiles with clinicopathological data from TCGA database were used to select prognostic CRLs and then constructed prognostic risk model using multivariate regression analysis and LASSO algorithms. An independent dataset from GEO database was used to validate the prognostic performance. Combined with clinical factors, we further constructed a prognostic nomogram. In addition, tumor immune microenvironment, somatic mutation and drug sensitivity were analyzed using ssGSEA, GSVA, ESTIMATE and CIBERSORT algorithms.ResultA total of 129 CRLs were selected whose expression levels were significantly related to expression levels of 10 cuproptosis-related genes. The univariate Cox regression analysis showed that 12 CRLs were associated with overall survival (OS). Using LASSO algorithms and multivariate regression analysis, we constructed a four-CRLs prognostic signature in the training dataset. Patients in the training dataset could be classified into high- or low-risk subgroups with significantly different OS (log-rank p < 0.001). The prognostic performance was confirmed in TCGA-OC cohort (log-rank p < 0.001) and an independent GEO cohort (log-rank p = 0.023). Multivariate cox regression analysis proved the four-CRLs signature was an independent prognostic factor for OC. Additionally, different risk subtypes showed significantly different levels of immune cells, signal pathways, and drug response.ConclusionWe established a prognostic signature based on cuproptosis-related lncRNAs for OC patients, which will be of great value in predicting the prognosis patients and may provide a new perspective for research and individualized treatment.

Project description:Background: Cuproptosis is a novel form of cell death discovered in recent. A great quantity of researches has confirmed the close relationships and crucial roles between long non-coding RNAs (lncRNAs) with the progression of colorectal cancer (CRC). However, the relationship between cuproptosis and lncRNAs remains unclear in CRC. Methods: 1,111 co-expressed lncRNAs with 16 cuproptosis regulators were retrieved from CRC samples of The Cancer Genome Atlas (TCGA) database. Through univariate Cox and least absolute shrinkage and selection operator regression analysis, a prognosis model was constructed with 15 lncRNAs. The Kaplan-Meier, receiver operating characteristic curve, C-index and principal component analysis identified the prognostic power. Furthermore, a cuproptosis-related cluster was generated based on the 15 lncRNAs by unsupervised methods. The correlations between the cuproptosis-related signatures with immune cell infiltration and anti-tumor therapy were explored by multiple algorithms. Results: A risk score and nomogram with great prediction ability were constructed for CRC prognosis evaluation. The immune activate pathways, immune infiltration cells, immune functions, immune score and immune activation genes were remarkably enriched in the high risk group. The cuproptosis-related cluster was generated, of which the cluster 2 showed longer overall survival. The immune cell infiltration analysis indicated the similar results of cluster 2 with the high risk group, implying a significant marker for "hot tumor." The cluster 2 also presented high expression of immune checkpoint molecules, MSI-H status and higher susceptibility to multiple immunotherapy drugs. Conclusion: We appraised a novel cuproptosis-related prognosis model and molecular signature associated with prognosis, immune infiltration and immunotherapy. The identification of cuproptosis-related lncRNAs improved our understanding of immune infiltration and provided a significant marker for prognosis and immunotherapy in CRC.

Project description:BackgroundCuproptosis-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.MethodsCuproptosis-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.ResultsThe 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.ConclusionsThe 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.

Project description:Breast cancer is the most common diagnosed cancer, the HER2-positive subtype account for 15% of all breast cancer. HER2-targeted therapy is the mainstay treatment for HER2-positive breast cancer. Cuproptosis is a novel form of programmed cell death, and is caused by mitochondrial lipoylation and destabilization of iron-sulfur proteins triggered by copper, which was considered as a key player in various biological processes. However, the roles of cuproptosis-related genes in HER2-positive breast cancer remain largely unknown. In the present study, we constructed a prognostic prediction model of HER2-positive breast cancer patients using TCGA database. Dysregulated genes for cells resistant to HER2-targeted therapy were analyzed in the GEO dataset. KEGG pathway, GO enrichment and GSEA was performed respectively. The immune landscape of DLAT was analyzed by CIBERSORT algorithm and TIDE algorithm. HER2-positive breast cancer patients with high CRGs risk score showed shorter OS. DLAT was downregulated and correlated with better survival of HER2-positive breast cancer patients (HR = 3.30, p = 0.022). High expressed DLAT was associated with resistant to HER2-targeted therapy. Knocking down DLAT with siRNA increased sensitivity of breast cancer to trastuzumab. KEGG pathway and GO enrichment of DEGs indicated that DLAT participates in various pathways correlated with organelle fission, chromosome segregation, nuclear division, hormone-mediated signaling pathway, regulation of intracellular estrogen receptor signaling pathway, condensed chromosome and PPAR signaling pathway. There was a negative correlation between TIDE and DLAT expression (r = - 0.292, p < 0.001), which means high DLAT expression is an indicator of sensitivity to immunotherapy. In conclusion, our study constructed a four CRGs signature prognostic prediction model and identified DLAT as an independent prognostic factor and associated with resistant to HER2-targeted therapy for HER2-positive breast cancer patients.

Project description:BackgroundCuproptosis, one of the newest forms of cell death induction, is attracting mounting attention. However, the role of cuproptosis in lung cancer is currently unclear. In this study, we constructed a prognostic signature utilizing cuproptosis-related long noncoding RNAs (CRL) in lung adenocarcinoma (LUAD) and researched its clinical and molecular function.MethodsRNA-related and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed CRLs were screened using the 'limma' package of R software. We used coexpression analysis and univariate Cox analysis to further identify prognostic CRLs. Applying least absolute shrinkage and selection operator (LASSO) regression and Cox regression models, a prognostic risk model based on 16 prognostic CRLs was constructed. To validate prognostic CRL function in LUAD, vitro experiments were conducted to explore the expression of GLIS2-AS1, LINC01230, and LINC00592 in LUAD. Subsequently, according to a formula, patients in the training, test, and overall groups were split into high- and low-risk groups. Kaplan-Meier and receiver operating characteristic (ROC) analyses were applied to assess the predictability of the risk model. Finally, the associations between risk signature and immunity-related analysis, somatic mutation, principal component analysis (PCA), enriched molecular pathways, and drug sensitivity was investigated.ResultsA cuproptosis-related long noncoding RNAs (lncRNAs) signature was constructed. Using quantitative polymerase chain reaction (qPCR) trial, we verified that the expressions of GLIS2-AS1, LINC01230, and LINC00592 in LUAD cell lines and tissues were consistent with the above screening results. Based on this signature, a total of 471 LUAD samples from TCGA data set were split into two risk groups based on the computed risk score. The risk model showed a better capacity in predicting prognosis than traditional clinicopathological features. Moreover, significant differences were found in immune cell infiltration, drug sensitivity, and immune checkpoint expression between the two risk groups.ConclusionsThe CRLs signature was shown to be a prospective biomarker to forecast prognosis in patients with LUAD and presents new insights for personalized treatment of LUAD.

Project description:BackgroundCuproptosis is the recently defined regulatory cell death (RCD) that plays essential roles in tumorigenesis and progression. Long noncoding RNAs (lncRNAs) regulate the gene expression through various means. However, the clinical value of cuproptosis-related lncRNAs in bladder cancer (BLCA) remains poorly described.MethodsWe downloaded the transcriptome sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and lasso Cox regression analyses were performed to construct the prognostic risk signature, the predictive accuracy of which was validated in the subsequent independence and stratification analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the underlying molecular mechanisms involved in the signature to explore therapeutic vulnerabilities and potential targets in BLCA. Tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) were used to estimate the response to immune checkpoint inhibitors (ICIs). We further explored the potential new drug-target candidates based on the half maximal inhibitory concentration for this patient population.ResultsFifteen cuproptosis-related lncRNAs significantly associated with survival were identified to construct the risk signature based on the normalized expression level and regression coefficient of each gene. The patients with BLCA and high-risk scores defined by the signature were associated with worse survival outcomes. The differentially expressed genes (DEGs) between the 2 risk groups had different biological activity. Furthermore, the patients in the low-risk group exhibited a higher TMB index and a lower TIDE score. The sensitivity of multiple antitumor drugs was negatively related to risk score, including AR-42, AS605240, FK866, TAK-715, and tubastatin A, while the sensitivity of some antitumor drugs, such as AMG-706, BX-795, and RO-3306, were positively correlated with risk score.ConclusionsOur study established and verified a novel clinical risk signature with cuproptosis-related lncRNAs that may predict therapy response and prognosis with robust and stable accuracy in patients with BLCA and enhance the personalized management of this patient population.

Project description:Background: Given the high incidence and high mortality of cervical cancer (CC) among women in developing countries, identifying reliable biomarkers for the prediction of prognosis and therapeutic response is crucial. We constructed a prognostic signature of cuproptosis-related long non-coding RNAs (lncRNAs) as a reference for individualized clinical treatment. Methods: A total of seven cuproptosis-related lncRNAs closely related to the prognosis of patients with CC were identified and used to construct a prognostic signature via least absolute shrinkage and selection operator regression analysis in the training set. The predictive performance of the signature was evaluated by Kaplan-Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, and univariate and multivariate Cox analyses. Functional enrichment analysis and single-sample gene set enrichment analysis were conducted to explore the potential mechanisms of the prognostic signature, and a lncRNA-microRNA-mRNA network was created to investigate the underlying regulatory relationships between lncRNAs and cuproptosis in CC. The associations between the prognostic signature and response to immunotherapy and targeted therapy were also assessed. Finally, the prognostic value of the signature was validated using the CC tissues with clinical information in my own center. Results: A prognostic signature was developed based on seven cuproptosis-related lncRNAs, including five protective factors (AL441992.1, LINC01305, AL354833.2, CNNM3-DT, and SCAT2) and two risk factors (AL354733.3 and AC009902.2). The ROC curves confirmed the superior predictive performance of the signature compared with conventional clinicopathological characteristics in CC. The ion transport-related molecular function and various immune-related biological processes differed significantly between the two risk groups according to functional enrichment analysis. Furthermore, we discovered that individuals in the high-risk group were more likely to respond to immunotherapy and targeted therapies including trametinib and cetuximab than those in the low-risk group. Finally, CC tissues with clinical data from my own center further verify the robustness of the seven-lncRNA risk signature. Conclusion: We generated a cuproptosis-related lncRNA risk signature that could be used to predict prognosis of CC patients. Moreover, the signature could be used to predict response to immunotherapy and chemotherapy and thus could assist clinicians in making personalized treatment plans for CC patients.

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Cuproptosis-related lncRNAs potentially predict prognosis and therapy sensitivity of breast cancer.

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Cuproptosis-related lncRNAs potentially predict prognosis and therapy sensitivity of breast cancer.

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