Identification of a prognostic signature based on ammonia metabolism-related genes in clear cell renal cell carcinoma: an integrated analysis of bulk and single-cell transcriptomics.
Clear cell renal cell carcinoma (ccRCC) is an angiogenic tumor originating from proximal tubule epithelial cells. Ammonia induced cell death is closely associated with carcinogenesis, but its potential mechanism in ccRCC remains unclear and requires further investigation.
The transcriptomic data of ccRCC and the genes related to ammonia induced cell death were retrieved from public resources. Candidate genes were ascertained by taking the common part of the Differentially Expressed Genes (DEGs) and the Ammonia death-related genes (ADRGs). Prognostic genes were filtered using machine learning and a prognostic model was established. ccRCC patients were segmented into a high-risk group (HRG) and a low-risk group (LRG) in accordance with the risk score values. Functional enrichment, immune infiltration analysis, somatic mutation and Reverse transcription quantitative PCR (RT-qPCR) were also executed. Key cells were identified at the single-cell level, and analyses including cell communication and pseudo-temporal analysis were conducted.
The risk model was constructed from 6 prognostic genes (RGS20, ADA, AICDA, SLC12A5, RUFY4 and CDK5RAP3). RGS20, ADA, AICDA, SLC12A5, RUFY4 and CDK5RAP3 were significantly upregulated in ccRCC group, and the RT - qPCR analysis results were consistent (p < 0.05). Enrichment analysis suggested that HRG and LRG were linked to pathways, such as "olfactory transduction" signaling pathway (p < 0.05). Sixteen immune cells with differential abundance between HRG and LRG were identified, such as activated B cells. The most frequent type of mutation between HRG and LRG was missense mutation. Malignant cells (MCs) were identified as the key cells. Moreover, frequent interactions between MCs and T cells were observed in ccRCC, and 6 prognostic genes were involved in the regulation of MCs' expression.
This study identified 6 prognostic genes for ccRCC, MCs were identified as the key cells. We explored potential mechanisms and prognostic associations in ccRCC, providing new insights for potential therapeutic strategies.
The transcriptomic data of ccRCC and the genes related to ammonia induced cell death were retrieved from public resources. Candidate genes were ascertained by taking the common part of the Differentially Expressed Genes (DEGs) and the Ammonia death-related genes (ADRGs). Prognostic genes were filtered using machine learning and a prognostic model was established. ccRCC patients were segmented into a high-risk group (HRG) and a low-risk group (LRG) in accordance with the risk score values. Functional enrichment, immune infiltration analysis, somatic mutation and Reverse transcription quantitative PCR (RT-qPCR) were also executed. Key cells were identified at the single-cell level, and analyses including cell communication and pseudo-temporal analysis were conducted.
The risk model was constructed from 6 prognostic genes (RGS20, ADA, AICDA, SLC12A5, RUFY4 and CDK5RAP3). RGS20, ADA, AICDA, SLC12A5, RUFY4 and CDK5RAP3 were significantly upregulated in ccRCC group, and the RT - qPCR analysis results were consistent (p < 0.05). Enrichment analysis suggested that HRG and LRG were linked to pathways, such as "olfactory transduction" signaling pathway (p < 0.05). Sixteen immune cells with differential abundance between HRG and LRG were identified, such as activated B cells. The most frequent type of mutation between HRG and LRG was missense mutation. Malignant cells (MCs) were identified as the key cells. Moreover, frequent interactions between MCs and T cells were observed in ccRCC, and 6 prognostic genes were involved in the regulation of MCs' expression.
This study identified 6 prognostic genes for ccRCC, MCs were identified as the key cells. We explored potential mechanisms and prognostic associations in ccRCC, providing new insights for potential therapeutic strategies.
Authors
Jiang Jiang, Zhang Zhang, Fan Fan, Su Su, Qiao Qiao, Ji Ji, Hu Hu, Zhou Zhou, Wei Wei, Du Du, Yang Yang, Zhang Zhang
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