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Identification of a Four Hypoxia-Associated Long Non-Coding RNA Signature and Establishment of a Nomogram Predicting Prognosis of Clear Cell Renal Cell Carcinoma.

ABSTRACT: To identify novel hypoxia-associated long non-coding RNAs (lncRNAs) as potential biomarkers, we developed a risk stratification signature and constructed a prognosis prediction nomogram of clear cell renal cell carcinoma (ccRCC). Hypoxia-related lncRNAs were identified through Pearson correlation analysis between the expression profiles of hypoxia-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Then, a signature of four key lncRNAs (COMETT, EMX2OS, AC026462.3, and HAGLR) was developed. The four lncRNAs were downregulated in high-grade, advanced stage, and high-risk ccRCC. The signature had an independent and long-standing prognosis prediction ability up to a 10-year follow-up. Notably, the risk score was significantly positively correlated with the infiltration abundances of six immune cells from the Tumor IMmune Estimation Resource (TIMER). The gene set enrichment analysis (GSEA) also suggested that the signature was involved in metabolism and tumorigenesis, which were closely related to the hypoxic tumor microenvironment. Ultimately, a nomogram of signature, age, stage, and grade, was built to predict the individual long-term survival possibility. Finally, the expressions of four lncRNAs were validated by quantitative real-time PCR (qRT-PCR). Our study identified a four-lncRNA signature and established a prognostic nomogram that reliably predicts survival in ccRCC. The findings may be beneficial to therapeutic customization and medical decision-making.

SUBMITTER: Chen H

PROVIDER: S-EPMC8353455 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeThe present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC).MethodsA total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer-Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan-Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis.ResultsA total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation.ConclusionsThe nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.

Project description:Background: Ferroptosis is a form of regulated cell death that follows cell membrane damage and mostly depends on iron-mediated oxidative. Long non-coding RNAs (LncRNAs) are associated with the development of a variety of tumors. Till date, LncRNAs have been reported to intervene in ferroptosis. Therefore, we intended to provide a prognostic ferroptosis-related-lncRNA signature in stomach adenocarcinoma (STAD). Methods: We downloaded ferroptosis-related genes from the FerrDb database and RNA sequencing data and clinicopathological characteristics from The Cancer Genome Atlas. Gene differential expression analysis was performed using the "limma" package. We used Cox regression analysis to determine the ferroptosis-related lncRNAs signature with the lowest AIC value. The Kaplan-Meier curve, ROC curve, and nomogram were used to evaluate the prognostic value of the risk score. Gene set enrichment analysis (GSEA) was used to explore the biologic functions of the three ferroptosis-related lncRNAs. LINC01615 expression in gastric cancer cell lines and tissues was measured by real-time PCR. A nuclear-cytoplasmic fractionation assay was used to analyze the subcellular localization for LINC01615. Furthermore, we used bioinformatics to predict potential target microRNAs (miRNAs) of LINC01615 and their target ferroptosis-related mRNAs. Results: Three ferroptosis-related-lncRNA signatures (AP000695.2, AL365181.3, and LINC01615) were identified, and then Kaplan-Meier, Cox regression analyses, and ROC curve confirmed that the ferroptosis-related-lncRNA model could predict the prognosis of STAD. The GSEA indicated that the three ferroptosis-related lncRNAs might be related to the extracellular matrix and cellular activities. LINC01615 is highly expressed in gastric cancer cell lines and tissues. A nuclear-cytoplasmic fractionation assay confirmed that in gastric cancer cell lines, most LINC01615 was enriched in the cytoplasm. Bioinformatics further predicts four potential target miRNAs of LINC01615 and then figured out 26 target ferroptosis-related mRNAs. Conclusion: We established a three-ferroptosis-related-lncRNA model (AP000695.2, AL365181.3, and LINC01615) that can predict the prognosis of STAD patients. We also expected to provide a promising target for LINC01615 for research in the future, which was highly expressed in gastric cancer and cell lines and acted as a ceRNA to get involved in ferroptosis.

Project description:Clear cell renal cell carcinoma (ccRCC) is the most common histological and devastating subtype of renal cell carcinoma. Necroptosis is a form of programmed cell death that causes prominent inflammatory responses. miRNAs play a significant role in cancer progression through necroptosis. However, the prognostic value of necroptosis-related miRNAs remains ambiguous. In this study, 39 necroptosis-related miRNAs (NRMs) were extracted and 17 differentially expressed NRMs between normal and tumor samples were identified using data form The Cancer Genome Atlas (TCGA). After applying univariate Cox proportional hazard regression analysis and LASSO Cox regression model, six necroptosis-related miRNA signatures were identified in the training cohort and their expression levels were verified by qRT-PCR. Using the expression levels of these miRNAs, all patients were divided into the high- and low-risk groups. Patients in the high-risk group showed poor overall survival (P < 0.0001). Time-dependent ROC curves confirmed the good performance of our signature. The results were verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression models demonstrated that the risk score was an independent prognostic factor. Additionally, a predictive nomogram with good performance was constructed to enhance the implementation of the constructed signature in a clinical setting. We then employed miRBD, miRTarBase, and TargetScan to predict the target genes of six necroptosis-related miRNAs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that 392 potential target genes were enriched in cell proliferation-related biological processes. Six miRNAs and 59 differentially expressed target genes were used to construct an miRNA-mRNA interaction network, and 11 hub genes were selected for survival and tumor infiltration analysis. Drug sensitivity analysis revealed potential drugs that may contribute to cancer management. Hence, necroptosis-related genes play an important role in cancer biology. We developed, for the first time, a necroptosis-related miRNA signature to predict ccRCC prognosis.

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Identification of a Four Hypoxia-Associated Long Non-Coding RNA Signature and Establishment of a Nomogram Predicting Prognosis of Clear Cell Renal Cell Carcinoma.

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