A Mitochondrial Dysfunction and Oxidative Stress Pathway‐Based Prognostic Signature for Clear Cell Renal Cell Carcinoma

Wu, Yue; Zhang, Xi; Xian, Wa; Feng, Huan; Hu, Bintao; Deng, Zhiyao; Liu, Bo; Luan, Yang; Ruan, Yajun; Liu, Xiaohui; Liu, Zhuo; Liu, Jihong; Wang, Tao

doi:10.1155/2021/9939331

Cited by 23 publications

(24 citation statements)

References 56 publications

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“…Pyrroline-5-Carboxylate Reductase 1 (PYCR1) is a mitochondrial enzyme that is the final step in the proline biosynthetic pathway. Currently, PYCR1 has been reported to regulate tumour cell proliferation in hepatocellular carcinoma and cloud be an effective therapeutic target for multiple myeloma 58,59 , as well as inhibit the development of clear cell renal cell carcinoma by causing mitochondrial dysfunction and interfering with oxidative stress pathways 60 . Thus, according to the available studies, the eight biomarkers mentioned above play an important role in tumorigenesis and progression, but their exact mechanisms in NSCLC remain unknown.…”

Section: Discussionmentioning

confidence: 99%

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Wang

2022

Sci Rep

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Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.

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Section: Discussionmentioning

confidence: 99%

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Wang

2022

Sci Rep

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“…Several studies revealed that NNMT was up-regulated, depending on the stage of progression in renal cell carcinoma, and its expression played a critical role in the invasive potential of human ccRCC cells (Tang et al, 2011;Holstein et al, 2019). Yue Wu et al demonstrated that mitochondrial gene signatures, including ACADSB, could accurately predict OS, and ACADSB had good diagnostic and prognostic abilities in ccRCC (Liu et al, 2021;Wu et al, 2021). Yeda Chen et al indicated that the expression levels of GLDC were significantly decreased in RCC cell lines compared to the normal cell lines (Chen Y. et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Novel amino acid metabolism‐related gene signature to predict prognosis in clear cell renal cell carcinoma

et al. 2022

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Background: Amino acid metabolism (AAM) deregulation, an emerging metabolic hallmark of malignancy, plays an essential role in tumour proliferation, invasion, and metastasis. However, the expression of AAM-related genes and their correlation with prognosis in clear cell renal cell carcinoma (ccRCC) remain elusive. This study aims to develop a novel consensus signature based on the AAM-related genes.Methods: The RNA-seq expression data and clinical information for ccRCC were downloaded from the TCGA (KIRC as training dataset) and ArrayExpress (E-MTAB-1980 as validation dataset) databases. The AAM‐related differentially expressed genes were screened via the “limma” package in TCGA cohorts for further analysis. The machine learning algorithms (Lasso and stepwise Cox (direction = both)) were then utilised to establish a novel consensus signature in TCGA cohorts, which was validated by the E-MTAB-1980 cohorts. The optimal cutoff value determined by the “survminer” package was used to categorise patients into two risk categories. The Kaplan-Meier curve, the receiver operating characteristic (ROC) curve, and multivariate Cox regression were utilised to evaluate the prognostic value. The nomogram based on the gene signature was constructed, and its performance was analysed using ROC and calibration curves. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were conducted on its potential mechanisms. The relationship between the gene signature and key immune checkpoint, N6-methyladenosine (m6A)-related genes, and sensitivity to chemotherapy was assessed.Results: A novel consensus AMM‐related gene signature consisting of IYD, NNMT, ACADSB, GLDC, and PSAT1 is developed to predict prognosis in TCGA cohorts. Kaplan-Meier survival shows that overall survival in the high-risk group was more dismal than in the low-risk group in the TCGA cohort, validated by the E-MTAB-1980 cohort. Multivariate regression analysis also demonstrates that the gene signature is an independent predictor of ccRCC. Immune infiltration analysis highlighted that the high-risk group indicates an immunosuppressive microenvironment. It is also closely related to the level of key immune checkpoints, m6A modification, and sensitivity to chemotherapy drugs.Conclusion: In this study, a novel consensus AAM-related gene signature is developed and validated as an independent predictor to robustly predict the overall survival from ccRCC, which would further improve the clinical outcomes.

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“…Additionally, ccRCC is reported to be a highly immuneinfiltrated tumor, and immunotherapy-based combination therapeutics have been shown to have increased clinical benefits in terms of their efficacy and the overall survival (OS) of patients with metastatic RCC (3,4). Recently, the general metabolism mitochondrial functions of RCC were reported to be correlated with clinical prognosis (5,6); however, the mechanisms of this correlation are not clear. Thus, investigating the mechanisms that link metabolism and immune infiltration is important for the pathogenesis and treatment of ccRCC.…”

Section: Introductionmentioning

confidence: 99%

PANK1 associates with cancer metabolism and immune infiltration in clear cell renal cell carcinoma: a retrospective prognostic study based on the TCGA database

Wang¹,

Liu²,

Luo³

et al. 2022

Transl Cancer Res TCR

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Background: Identify key biomarkers to improve the clinical prognosis of patients with advanced and metastatic clear cell renal cell carcinoma (ccRCC) remains an important research topic. Recently, ccRCC has been regarded as a metabolic disease. Pantothenate kinase-1 (PANK1) has been shown to play an important regulatory role in global metabolism and associates with the pathogenesis of hepatocellular carcinoma. Therefore, we aimed to investigate the role of PANK1 in the prognosis of ccRCC and in metabolism and immunity. Methods: PANK1 messenger ribonucleic acid (RNA) expression patterns in ccRCC using The Cancer Genome Atlas (TCGA) database. The clinical prognostic significance of PANK1 in ccRCC and a Cox regression was performed to evaluate the clinical factors associated with prognosis with confounding factors adjusted. The signaling pathways related to PANK1 expression were identified by Gene Ontology (GO) investigation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Tumor Immune Estimation Resource database was used to analyze the correlation between PANK1 and tumor-infiltrating immune cells.Results: A total of 539 ccRCC patients and corresponding clinical samples and data from TCGA were included in this analysis. Significant differences were observed in PANK1 expression levels between tumor tissues and adjacent normal tissues in both TCGA-Kidney Renal Clear Cell Carcinoma cohort (4.40 vs.2.94, P<0.001). PANK1 expression was found to be correlated with pathological stage, histological grade, age, sex, and clinical prognosis. Specifically, the low expression of PANK1 was found to be closely related to poor overall survival (OS), disease-specific survival (DSS), and the progression-free survival (PFS) in ccRCC patients. The receiver operating characteristic curve suggested that PANK1 could be a potential prognostic biomarker (area under the curve =0.880), and that the promoter methylation levels of PANK1 were correlated with clinical factors. Further, PANK1 expression was found to be associated with multiple immune cell types and correlated with the enrichment of these cells. Finally, we further investigated the role of PANK1 in tumor growth and mitochondrial metabolism using ccRCC cells.Conclusions: PANK1 correlates with ccRCC prognosis, tumor immune status and metabolism using the TGCA data. PANK1 might be a prognostic marker of clinical prognosis for ccRCC patients. the following article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/ article/view/10.21037/tcr-22-1488/rc). Methods Study design and research proceduresThis study is a retrospective prognostic study analyzing the expression difference of PANK1 between tumor and adjacent normal tissues, correlations with the clinical factors, overall

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A Mitochondrial Dysfunction and Oxidative Stress Pathway‐Based Prognostic Signature for Clear Cell Renal Cell Carcinoma

Cited by 23 publications

References 56 publications

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Novel amino acid metabolism‐related gene signature to predict prognosis in clear cell renal cell carcinoma

PANK1 associates with cancer metabolism and immune infiltration in clear cell renal cell carcinoma: a retrospective prognostic study based on the TCGA database

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