Abstract
The metabolic dysregulation is a hallmark of cancers including KIRC, specifically caused by alterations in metabolic genes. Currently, a lack of consensus exists between metabolic signatures in the tumor microenvironment. Here, in this study, we observed the significant correlations of differentially expressed metabolic genes (DEmGs) between KIRC and the related normal samples. Briefly, we collected sets of metabolic genes through RNA-seq data of KIRC and normal tissues from TCGA, followed by the identification of KIRC-related DEmGs. Next, patients were classified into three clusters, and using WGCNA, we identified metabolic genes involved in the survival among different clusters. Furthermore, we investigated survival and clinical parameters along with immune infiltration in the clusters. At the same time, we constructed and validated a prediction model based on these DEmGs. These analyses revealed that the patients having high expression of DEmGs showed poor survival, while infiltration of less-immune cells was associated with the metastasis of KIRC. In the end, we identified NUDT1 as a hub gene as it showed significantly high expression in KIRC samples as well as associated with the survival and prognosis of the patients. Further analysis revealed the oncogenic role of NUDT1 in 786-O and ACHN cells. Thus, we conclude that NUDT1 could be a potential diagnostic and prognostic marker for KIRC.
Highlights
Kidney renal clear cell carcinoma (KIRC) is a type of renal cell carcinoma (RCC), considered as one of the common cancers which accounts for 70–80% of cases [1], ranked as the 16th most common cause of cancer-related mortality worldwide [2]
In order to explore the metabolic dysregulation in KIRC, we explored the available The Cancer Genome Atlas (TCGA) data to get deep insights for metabolism-targeted therapeutics in the clinic
WGCNA analysis identified a group of genes correlated with the survival status of KIRC
Summary
Kidney renal clear cell carcinoma (KIRC) is a type of renal cell carcinoma (RCC), considered as one of the common cancers which accounts for 70–80% of cases [1], ranked as the 16th most common cause of cancer-related mortality worldwide [2]. Renal cell carcinoma (RCC) is the most common type of renal cancer and accounts for 90% of the kidney cancer cases [3]. Bioinformatics-based identification of the potential markers in cancers is being widely used but only few reliable biomarkers of KIRC have been identified or most of the markers are so far not fully validated. Many of the identified biomarkers have not been studied for their effect on the cellular phenotype and respective underlying molecular mechanisms in the KIRC [6].
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