Dataset Information

Construction and Validation of a Novel Cuproptosis-Related Seven-lncRNA Signature to Predict the Outcomes, Immunotherapeutic Responses, and Targeted Therapy in Patients with Clear Cell Renal Cell Carcinoma.

ABSTRACT:

Background

Cuproptosis was recently recognized as a novel form of cell death, linked closely to the occurrence and progression of cancer. We aimed to identify prognostic cuproptosis-related long noncoding RNAs (lncRNAs) and build a risk signature to predict the prognosis and treatment responses of clear cell renal cell carcinoma (ccRCC) in this work.

Methods

LASSO-Cox regression was conducted to construct the signature based on prognostic cuproptosis-related lncRNAs (CR-lncRNAs). The signature's reliability and sensitivity were assessed by the Kaplan-Meier survival analysis and receiver operating characteristic analysis. External validation was performed via data from the International Cancer Genome Consortium database. On the basis of CR-lncRNAs, an lncRNA-microRNA-mRNA regulatory network was created, and functional enrichment analysis was used to investigate the underlying biological roles of these genes. In addition, the relationship between the risk signature and immunotherapy and targeted therapy responses was examined. Finally, the expression levels of seven candidate lncRNAs between tumor and normal cells were compared in vitro using quantitative real-time PCR.

Results

A seven-CR-lncRNA risk signature was constructed, which showed a stronger potential for survival prediction than standard clinicopathological features in patients with kidney cancer. Functional enrichment analysis showed that the CR-lncRNA risk signature was enriched in ion transport-related molecular functions as well as various immune-related biological processes. Furthermore, we discovered that individuals in the high-risk group were more likely than those in the low-risk group to respond to immunotherapy and targeted therapies with medications like sunitinib and pazopanib. Finally, quantitative real-time PCR revealed that the expression levels of seven candidate lncRNAs differed significantly between RCC and healthy kidney cells.

Conclusion

In summary, we generated a CR-lncRNA risk signature that may be utilized to predict outcomes in patients with ccRCC and responsiveness to immunotherapy and targeted treatment, potentially serving as a reference for clinical personalized medicine.

SUBMITTER: Zhang H

PROVIDER: S-EPMC9893525 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Construction and Validation of a Novel Cuproptosis-Related Seven-lncRNA Signature to Predict the Outcomes, Immunotherapeutic Responses, and Targeted Therapy in Patients with Clear Cell Renal Cell Carcinoma.

Zhang Haoxun H Ning Zikuan Z Wang Bowen B Liu Yiwen Y Tao Boju B Zhang Guoling G Liu Hua H Wang Chunyang C

Disease markers 20230125

<h4>Background</h4>Cuproptosis was recently recognized as a novel form of cell death, linked closely to the occurrence and progression of cancer. We aimed to identify prognostic cuproptosis-related long noncoding RNAs (lncRNAs) and build a risk signature to predict the prognosis and treatment responses of clear cell renal cell carcinoma (ccRCC) in this work.<h4>Methods</h4>LASSO-Cox regression was conducted to construct the signature based on prognostic cuproptosis-related lncRNAs (CR-lncRNAs). ...[more]

PMID: 36741910

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Project description:Background Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancers. As cuproptosis, a new cell death mechanism proposed recently, differs from all other known mechanisms regulating cell death, we aimed to create prognostic markers using cuproptosis-related long non-coding ribonucleic acids (RNAs; lncRNAs) and elucidate the molecular mechanism. Methods Data from transcriptome RNA sequencing of ccRCC samples and the relevant clinical data were downloaded from The Cancer Genome Atlas, and Pearson's correlation analysis was implemented to obtain the cuproptosis-related lncRNAs. Then, univariate Cox, multivariate Cox, and Least Absolute Shrinkage and Selection Operator Cox analyses were performed to construct the risk signatures. The cuproptosis-related lncRNAs predictive signature was evaluated with receiver operating characteristic curves and subgroup analysis. Finally, Gene Set Enrichment Analysis (GSEA), single-sample GSEA (ssGSEA), tumor immune microenvironment (TIME), and immune checkpoints were performed to explore the relationship between immunity and patient prognosis. Results Five cuproptosis-related lncRNAs, including FOXD2-AS1, LINC00460, AC091212.1, AC007365.1, and AC026401.3, were used to construct the signature. In the training and test sets, low-risk groups (as identified by a risk score lower than the median) demonstrated a better prognosis with an area under the curve for 1-, 3-, and 5-year survival being 0.793, 0.716, and 0.719, respectively. GSEA analysis suggested significant enrichment of the tricarboxylic acid cycle and metabolism-related pathways in the low-risk group. Besides, both ssGSEA and TIME suggested that the high-risk group exhibited more active immune infiltration. Conclusion We proposed a cuproptosis-related lncRNAs signature, which had the potential for prognoses and prediction. Our findings might contribute to elucidating potential genomic biomarkers and targets for future therapies in the cuproptosis-related signaling pathways.

Project description:BackgroundRecent studies have demonstrated that long non-coding RNAs (lncRNAs) are involved in regulating tumor cell ferroptosis. However, prognostic signatures based on ferroptosis-related lncRNAs (FRLs) and their relationship to the immune microenvironment have not been comprehensively explored in clear cell renal cell carcinoma (ccRCC).MethodsIn the present study, the expression profiles of ccRCC were acquired from The Cancer Genome Atlas (TCGA) database; 459 patient specimens and 69 adjacent normal tissues were randomly separated into training or validation cohorts at a 7:3 ratio. We identified 7 FRLs that constitute a prognostic signature according to the differential analysis, correlation analysis, univariate regression, and least absolute shrinkage and selection operator (LASSO) Cox analysis. To identify the independence of risk score as a prognostic factor, univariate and multivariate regression analyses were also performed. Furthermore, CIBERSORT was conducted to analyze the immune infiltration of patients in the high-risk and low-risk groups. Subsequently, the differential expression of immune checkpoint and m6A genes was analyzed in the two risk groups.ResultsA 7-FRLs prognostic signature of ccRCC was developed to distinguish patients into high-risk and low-risk groups with significant survival differences. This signature has great prognostic performance, with the area under the curve (AUC) for 1, 3, and 5 years of 0.713, 0.700, 0.726 in the training set and 0.727, 0.667, and 0.736 in the testing set, respectively. Moreover, this signature was significantly associated with immune infiltration. Correlation analysis showed that risk score was positively correlated with regulatory T cells (Tregs), activated CD4 memory T cells, CD8 T cells and follicular helper T cells, whereas it was inversely correlated with monocytes and M2 macrophages. In addition, the expression of fourteen immune checkpoint genes and nine m6A-related genes varied significantly between the two risk groups.ConclusionWe established a novel FRLs-based prognostic signature for patients with ccRCC, containing seven lncRNAs with precise predictive performance. The FRLs prognostic signature may play a significant role in antitumor immunity and provide a promising idea for individualized targeted therapy for patients with ccRCC.

Project description:BackgroundKidney renal clear cell carcinoma (KIRC) lacks effective prognostic biomarkers and the role and mechanism of N6-methyladenosine (m6A) modification of long noncoding RNAs (lncRNAs) in KIRC remain unclear.MethodsWe extracted standard mRNA-sequencing and clinical data from the TCGA database. The prognostic risk model was obtained by Lasso regression and Cox regression. We randomly divided the samples into training and test sets, each taking half of the cases. Based on Lasso regression and Cox regression for training set, the prognostic risk signature was constructed; risk scores were calculated with the R package "glmnet." Based on the median value of the prognostic risk score, risk scores were calculated for each patient and we divided all KIRC samples into high-risk and low-risk groups. Then, high- and low-risk subtypes were established and their prognosis, clinical features, and immune infiltration microenvironment were evaluated in test set and the entire sampled data set. The reliability of the prognostic model was confirmed by receiver operating characteristic curve analysis.ResultsWe found 28 prognostic m6A-related lncRNAs and established a m6A-related lncRNAs prognostic signature. Risk score=AC015813.1∗(0.0086)+EMX2OS∗(-0.0101)+LINC00173∗(0.0309)+PWAR5∗(-0.0146)+SNHG1∗(0.0043). The signature showed a better predictive ability than other clinical indicators, including tumor node metastasis classification (TNM), histological, and pathological stages. In the high-risk group, M0 macrophages, CD8+ T cells, and regulatory T cells had significantly higher scores. Contrarily, in the low-risk group, activated dendritic cells, M1 macrophages, mast resting cells, and monocytes had significantly higher scores. In the high-risk group, LSECtin was overexpressed. In the low-risk group, PD-L1 was overexpressed. Moreover, high-risk patients may benefit more from AZ628.ConclusionsIn conclusion, prognosis prediction of patients with KIRC and new insights for immunotherapy are provided by the m6A-related lncRNA prognostic signature.

Project description:Cuproptosis is a new form of cell death, the second form of metal ion-induced cell death defined after ferroptosis. Recently, cuproptosis has been suggested to be associated with tumorigenesis. However, the relationship between cuproptosis and patient prognosis in clear cell renal cell carcinoma (ccRCC) in the context of immunotherapy remains unknown. The aim of this study was to investigate the correlation between cuproptosis-related long non-coding RNA (lncRNA) and ccRCC in terms of immunity as well as prognosis. Clinical information on lncRNAs associated with differences in cuproptosis genes in ccRCC and normal tissues was collected from The Cancer Genome Atlas (TCGA) dataset. Univariate Cox regression was used to screen lncRNAs. A total of 11 lncRNAs closely associated with cuproptosis were further screened and established using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression, and the samples were randomly divided into training and test groups. A risk prognostic model was constructed using the training group, and the model was validated using the test group. We investigated the predictive ability of the prognostic risk model in terms of clinical prognosis, tumor mutation, immune escape, immunotherapy, tumor microenvironment, immune infiltration levels, and tumor drug treatment of ccRCC. Using the median risk score, patients were divided into low and high-risk groups. Kaplan-Meier curves showed that the overall survival (OS) of patients in the high-risk group was significantly worse than low-risk group (p < 0.001). Receiver operating characteristic (ROC) curves further validated the reliability of our model. The model consistently and accurately predicted prognosis at 1, 3, and 5 years, with an AUC above 0.7. Tumor cell genes generally precede morphological abnormalities; therefore, the model we constructed can effectively compensate for the traditional method of evaluating the prognosis of patients with renal cancer, and our model was also clinically meaningful in predicting ccRCC staging. In addition, lower model risk scores determined by mutational load indicated a good chance of survival. The high-risk group had greater recruitment of immune cells, while the anti-immune checkpoint immunotherapy was less efficacious overall than that of the low-risk group. Tumor and immune-related pathways were enriched, and anti-tumor agents were selected to improve the survival of ccRCC. This prognostic risk model is based on the levels of cuproptosis-associated lncRNAs and provides a new perspective in the clinical assessment and precise treatment of ccRCC.

Dataset Information

Construction and Validation of a Novel Cuproptosis-Related Seven-lncRNA Signature to Predict the Outcomes, Immunotherapeutic Responses, and Targeted Therapy in Patients with Clear Cell Renal Cell Carcinoma.

Background

Methods

Results

Conclusion

Publications

Construction and Validation of a Novel Cuproptosis-Related Seven-lncRNA Signature to Predict the Outcomes, Immunotherapeutic Responses, and Targeted Therapy in Patients with Clear Cell Renal Cell Carcinoma.

Similar Datasets

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