Dataset Information

A Novel Pyroptosis-Related lncRNAs Signature for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma and Its Associations with Immunity.

ABSTRACT: The most common kind of kidney cancer with poor prognosis is clear cell renal cell carcinoma (ccRCC). Pyroptosis is shown to be an inflammatory type of programmed cell death in recent years. In this research, we utilized pyroptosis-related differentially expressed lncRNAs in ccRCC to develop a predictive multi-lncRNA signature. We uncovered 14 lncRNAs with different expression patterns that were linked to ccRCC prognosis. Kaplan-Meier analysis identified a signature of high-risk lncRNAs related to poor prognosis for ccRCC. Furthermore, the AUC of the lncRNA signature was 0.771, indicating that they can be used to predict ccRCC prognosis. In predicting ccRCC prognosis, our risk analysis approach outperformed standard clinicopathological characteristics. In the low-risk group, GSEA indicated tumor-related pathways. T-cell functions such as T-cell coinhibition and T-cell costimulation were found to be expressed differently in two groups. Immune checkpoints including PD-1, LAG3, CTLA4, and BTLA were also differently expressed between the two groups. In patients with ccRCC, we created a 14-lncRNA-based predictor as a robust prognostic and predictive tool for OS.

SUBMITTER: Tang X

PROVIDER: S-EPMC8577956 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A Novel Pyroptosis-Related lncRNAs Signature for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma and Its Associations with Immunity.

Tang Xinfang X Tang Xinfang X Zhang Anna A Feng Yuanyuan Y Su Ying Y Wang Xiaoyu X Jiang Feng F Ma Jianxin J

Journal of oncology 20211018

The most common kind of kidney cancer with poor prognosis is clear cell renal cell carcinoma (ccRCC). Pyroptosis is shown to be an inflammatory type of programmed cell death in recent years. In this research, we utilized pyroptosis-related differentially expressed lncRNAs in ccRCC to develop a predictive multi-lncRNA signature. We uncovered 14 lncRNAs with different expression patterns that were linked to ccRCC prognosis. Kaplan-Meier analysis identified a signature of high-risk lncRNAs related ...[more]

PMID: 34764994

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Project description:BackgroundThe tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio-informative analysis was adopted to construct a TME-related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients.MethodsThe TME mRNAs were screened following the immune and stromal scores with the data from GSE15641, GSE29609, GSE36895, GSE46699, GSE53757, and The Cancer Genome Atlas (TCGA)-kidney renal clear cell carcinoma (KIRC). And the TME-related lncRNAs were recognized using correlation analysis. The TME-related lncRNAs prognostic model was constructed using the training dataset. Kaplan-Meier analysis, principal-component analysis, and time-dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single-sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model.ResultsIn the current study, 364 TME-related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME-related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high-risk group exhibited poor overall survival and disease-free survival than those in the low-risk group. The 1-, 3-, and 5-year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high-risk group was concentrated on immune-related pathways. The high-risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS-PD-1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high-risk group.ConclusionThe TME-related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients' therapy.

Project description:PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.

Dataset Information

A Novel Pyroptosis-Related lncRNAs Signature for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma and Its Associations with Immunity.

Publications

A Novel Pyroptosis-Related lncRNAs Signature for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma and Its Associations with Immunity.

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