Prognostic prediction and immunotherapy response analysis of the fatty acid metabolism-related genes in clear cell renal cell carcinoma

Yao, Qiong; Zhang, Xiuyuan; Wei, Chunchun; Chen, Hongjun; Xu, Qiannan; Chen, Jianghua; Chen, Dajin

doi:10.1016/j.heliyon.2023.e17224

Cited by 3 publications

(1 citation statement)

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“…Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal cell carcinoma, 1 accounting for the majority (75%) of all kidney cancer cases, 2 and its prevalence is increasing 3 . Owing to postoperative metastases and medication resistance, 4 ccRCC has one of the highest mortality rates among all malignancies, 5 and its incidence and mortality have grown fast over the last 20 years 6 .…”

Section: Introductionmentioning

confidence: 99%

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Chu,

Liu,

et al. 2023

Health Science Reports

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Background and AimsClear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.MethodsGene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein–protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.ResultsWe identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest‐scoring hub genes were screened to identify critical genes in the protein–protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para‐cancer tissues.ConclusionIdentifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.

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Section: Introductionmentioning

confidence: 99%