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Multiomics combined with single-cell analysis shows that mitophagy-related genes could accurately predict the prognosis of patients with clear cell renal cell carcinoma.
Translational Cancer Research 2024 Februrary 30
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor that accounts for a large proportion of kidney cancer, It is prone to recurrence and metastasis, and has a high mortality rate. Although mitophagy is important for metastasis and the recurrence of various tumors, its effect on renal clear cell carcinoma is poorly understood.
METHODS: Mitophagy-related genes were obtained through the GeneCards database. We normalised the data from different sources by removing the batch effect. Next, we conducted a preliminary screening of mitophagy-related genes and obtained prognosis-related genes from differentially expressed genes. We constructed a prognostic model using least absolute shrinkage and selection operator (LASSO) regression with data from The Cancer Genome Atlas (TCGA) and GSE29609 datasets and validated it internally. International Cancer Genome Consortium (ICGC) and E-MTAB-1980 cohorts also provided double external validation. In addition, we combined multi-omics and single-cell data to comprehensively analyse mitophagy-related gene model signature (MRGMS). Combined with the mitophagy-related gene model (MRGM) score, we constructed a nomogram. Finally, we performed pathway enrichment analysis using a variety of methods.
RESULTS: Multiomics and single-cell data analysis showed that the MRGMS is important for patients with ccRCC and is expected to become a new biomarker. The construction of a nomogram was conducive to accurately predicting patient survival.
CONCLUSIONS: Mitophagy-related genes are important for predicting the prognosis of ccRCC and are conducive to the development of more personalised treatment plans for patients.
METHODS: Mitophagy-related genes were obtained through the GeneCards database. We normalised the data from different sources by removing the batch effect. Next, we conducted a preliminary screening of mitophagy-related genes and obtained prognosis-related genes from differentially expressed genes. We constructed a prognostic model using least absolute shrinkage and selection operator (LASSO) regression with data from The Cancer Genome Atlas (TCGA) and GSE29609 datasets and validated it internally. International Cancer Genome Consortium (ICGC) and E-MTAB-1980 cohorts also provided double external validation. In addition, we combined multi-omics and single-cell data to comprehensively analyse mitophagy-related gene model signature (MRGMS). Combined with the mitophagy-related gene model (MRGM) score, we constructed a nomogram. Finally, we performed pathway enrichment analysis using a variety of methods.
RESULTS: Multiomics and single-cell data analysis showed that the MRGMS is important for patients with ccRCC and is expected to become a new biomarker. The construction of a nomogram was conducive to accurately predicting patient survival.
CONCLUSIONS: Mitophagy-related genes are important for predicting the prognosis of ccRCC and are conducive to the development of more personalised treatment plans for patients.
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