Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

Naghdibadi, Mohammadjavad; Momeni, Maryam; Yavari, Parvin; Gholaminejad, Alieh; Roointan, Amir

doi:10.1159/000529861

Cited by 2 publications

(1 citation statement)

References 65 publications

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“…Clear cell renal cell carcinoma-associated DEGs were mainly enriched in the translocation of ZAP70 to immune synapses, endosomal/vacuolar pathways, vascular wall cell surface interactions, and immune-related pathways. 49 ZAP70 , which is mainly expressed in T cells and NK cells, regulates the TCR signaling pathway. The activation of T cells promotes the CD3 (transmembrane protein)-mediated recruitment of ZAP70 to the phosphorylation domain of the TCR complex, leading to T cell activation and proliferation.…”

Section: Discussionmentioning

confidence: 99%

Identification of Immune-Related Genes as Biomarkers for Uremia

Lyu,

He,

Zhou

et al. 2023

IJGM

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Purpose Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression. Methods The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR). Results In total, four biomarkers ( PDCD1, NGF, PDGFRB , and ZAP70 ) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis. Conclusion PDCD1, NGF, PDGFRB , and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

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Section: Discussionmentioning

confidence: 99%

Identification of Immune-Related Genes as Biomarkers for Uremia

Lyu,

He,

Zhou

et al. 2023

IJGM

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Prognostic value of neutrophil extracellular trap signature in clear cell renal cell carcinoma

Jiang

Wang

et al. 2023

Front. Oncol.

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IntroductionClear cell renal cell carcinoma (ccRCC) is the most prevalent type of urological carcinoma. Although targeted therapy and immunotherapy are usually employed, they often result in primary and acquired resistance. There is currently a lack of dependable biomarkers that can accurately anticipate the prognosis of ccRCC. Recent research has indicated the critical role of neutrophil extracellular traps (NETs) in the development, metastasis, and immune evasion of cancer. The aim of this study was to explore the value of NETs in the development and prognosis of ccRCC.MethodsClinical features and genetic expression information of ccRCC patients were acquired from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and E-MTAB-1980 database. NETs-related gene set were obtained from previous studies. A NETs-related gene signature was constructed based on TCGA data and validated using ICGC and E-MTAB-1980 databases. Furthermore, the immune microenvironment and responsiveness to anticancer medications in ccRCC patients with varying levels of NETs risks were investigated.ResultsA total of 31 NET-related genes were differently expressed between normal kidney and ccRCC tissues. 17 out of 31 were significantly associated with overall survival. After LASSO Cox regression analysis, nine NETs-related genes were enrolled to construct the NETs prognosis signature, and all the ccRCC patients from TCGA were divided into low and high risk group. This signature demonstrated excellent performance in predicting the overall survival of TCGA patients as well as the validation ICGC and E-MTAB-1980 patients. Additionally, the NETs signature was significantly correlated with immune infiltration and drug sensitivity.ConclusionsThe NETs signature established by the current study has prognostic significance in ccRCC, and may serve as a useful biomarker for patient stratification and treatment decisions. Further validation and clinical studies are required to fully translate these findings into clinical practice.

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Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

Cited by 2 publications

References 65 publications

Identification of Immune-Related Genes as Biomarkers for Uremia

Identification of Immune-Related Genes as Biomarkers for Uremia

Prognostic value of neutrophil extracellular trap signature in clear cell renal cell carcinoma

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