Integrated multi-omics characterization reveals a distinctive metabolic signature and the role of NDUFA4L2 in promoting angiogenesis, chemoresistance, and mitochondrial dysfunction in clear cell renal cell carcinoma

Lucarelli, Giuseppe; Rutigliano, Monica; Fabio, Sallustio; Ribatti, Doménico; Giglio, Andrea; Signorile, Martina Lepore; Grossi, Valentina; Sanese, Paola; Napoli, Anna; Maiorano, Eugenio; Bianchi, Cristina; Perego, R; Ferro, Matteo; Ranieri, Elena; Serino, Grazia; Bell, Lauren N.; Ditonno, Pasquale; Simone, Cristiano; Battaglia, Michele

doi:10.18632/aging.101685

Cited by 146 publications

(130 citation statements)

References 29 publications

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“…25,27 Moreover, ccRCC is a metabolic disease, characterized by metabolic reprogramming. 28,29 The metabolic flux through glycolysis is partitioned and mitochondrial bioenergetics and oxidative phosphorylation are impaired in ccRCC, as well as lipid metabolism, 30 as supported by the results of GO and KEGG analyses in this study, which indicated a role for CLDN8 in oxidative phosphorylation. GSEA analyses showed enrichment in several tumor-related pathways.…”

Section: Discussionsupporting

confidence: 81%

<p>Prognostic Value and Potential Biological Functions of CLDN8 in Patients with Clear Cell Renal Cell Carcinoma</p>

Zhu¹,

Lin³

et al. 2020

OTT

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Purpose: Clear cell renal cell carcinoma (ccRCC) is among the most common malignant tumors worldwide, with a high incidence rate and poor prognosis. Currently, there are no biomarkers that can accurately guide prognostic evaluation and therapeutic strategy for ccRCC. The prognostic value and potential biological function of claudin-8 (CLDN8), a critical component of tight junctions in ccRCC, remain unclear. Methods: Sequencing data were obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, and Gene Expression Omnibus databases. R packages were used to explore CLDN8 mRNA expression levels and analyze differentially expressed genes. Results were validated in clinical specimens and cell lines, and bioinformatics analyses were conducted to explore the potential biological functions of CLDN8. Finally, functional analyses were carried out using 786-O ccRCC cell line. Results: Both CLDN8 mRNA and protein expression levels were significantly lower in ccRCC compared with the normal control tissues. Kaplan-Meier analyses showed that low CLDN8 expression levels were associated with the poor overall survival, while univariate and multivariate Cox regression indicated that CLDN8 could serve as an independent prognostic factor in patient with ccRCC. Bioinformatic and Western blot analyses showed that CLDN8 suppressed proliferation, migration, and invasion of 786-O ccRCC cells through the epithelial-mesenchymal transition and AKT pathways. Conclusion: CLDN8 could serve as an independent prognostic factor in ccRCC, in which it suppresses 786-O proliferation, migration, and invasion through EMT and AKT pathways.

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Section: Discussionsupporting

confidence: 81%

<p>Prognostic Value and Potential Biological Functions of CLDN8 in Patients with Clear Cell Renal Cell Carcinoma</p>

Zhu¹,

Lin³

et al. 2020

OTT

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“…The underlying biopathological changes associated with specific radiomic feature profiles of renal tumor subtypes are not fully understood yet. Some authors hypothesized that radiomic features correlate with the degree of microvessel density [ 22 ] and changes in renal cell metabolism [ 23 , 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Radiomic Features and Machine Learning for the Discrimination of Renal Tumor Histological Subtypes: A Pragmatic Study Using Clinical-Routine Computed Tomography

Uhlig

Leha

Delonge

et al. 2020

Cancers

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This study evaluates the diagnostic performance of radiomic features and machine learning algorithms for renal tumor subtype assessment in venous computed tomography (CT) studies from clinical routine. Patients undergoing surgical resection and histopathological assessment of renal tumors at a tertiary referral center between 2012 and 2019 were included. Preoperative venous-phase CTs from multiple referring imaging centers were segmented, and standardized radiomic features extracted. After preprocessing, class imbalance handling, and feature selection, machine learning algorithms were used to predict renal tumor subtypes using 10-fold cross validation, assessed as multiclass area under the curve (AUC). In total, n = 201 patients were included (73.7% male; mean age 66 ± 11 years), with n = 131 clear cell renal cell carcinomas (ccRCC), n = 29 papillary RCC, n = 11 chromophobe RCC, n = 16 oncocytomas, and n = 14 angiomyolipomas (AML). An extreme gradient boosting algorithm demonstrated the highest accuracy (multiclass area under the curve (AUC) = 0.72). The worst discrimination was evident for oncocytomas vs. AML and oncocytomas vs. chromophobe RCC (AUC = 0.55 and AUC = 0.45, respectively). In sensitivity analyses excluding oncocytomas, a random forest algorithm showed the highest accuracy, with multiclass AUC = 0.78. Radiomic feature analyses from venous-phase CT acquired in clinical practice with subsequent machine learning can discriminate renal tumor subtypes with moderate accuracy. The classification of oncocytomas seems to be the most complex with the lowest accuracy.

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“…Despite its increasing incidence in the past two decades, the mortality of ccRCC patients has not decreased (3,18,19). Metabolic alterations are considered a central feature of ccRCC (20) with the reporting of potential interactions of ccRCC with various metabolic reprogramming components (21,22). Additionally, several studies have examined the role of metabolic genes in the RCC processes.…”

Section: Discussionmentioning

confidence: 99%

A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

2020

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Metabolic alterations play crucial roles in carcinogenesis, tumor progression, and prognosis in clear cell renal cell carcinoma (ccRCC). A risk score (RS) model for ccRCC consisting of disease-associated metabolic genes remains unidentified. Here, we utilized gene set enrichment analysis to analyze expression data from normal and tumor groups from the cancer genome atlas. Out of 70 KEGG metabolic pathways, we found seven and two pathways to be significantly enriched in our normal and tumor groups, respectively. We identified 113 genes enriched in these nine pathways. We further filtered 47 prognostic-related metabolic genes and used Least absolute shrinkage and selection operator (LASSO) analysis to construct a three-metabolic-genes RS model composed of ALDH3A2, B3GAT3, and CPT2. We further tested the RS by mapping Kaplan-Meier plots and receiver operating characteristic curves, the results were promising. Additionally, multivariate Cox analysis revealed the RS to be an independent prognostic factor. Thereafter, we considered all the independent factors and constructed a nomogram model, which manifested in better prediction capability. We validated our results using a dataset from ArrayExpress and through qRT-PCR. In summary, our study provided a metabolic gene-based RS model that can be used as a prognostic predictor for patients with ccRCC.

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Integrated multi-omics characterization reveals a distinctive metabolic signature and the role of NDUFA4L2 in promoting angiogenesis, chemoresistance, and mitochondrial dysfunction in clear cell renal cell carcinoma

Cited by 146 publications

References 29 publications

<p>Prognostic Value and Potential Biological Functions of CLDN8 in Patients with Clear Cell Renal Cell Carcinoma</p>

<p>Prognostic Value and Potential Biological Functions of CLDN8 in Patients with Clear Cell Renal Cell Carcinoma</p>

Radiomic Features and Machine Learning for the Discrimination of Renal Tumor Histological Subtypes: A Pragmatic Study Using Clinical-Routine Computed Tomography

A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

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