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Background

Previous studies have identified MYBL1 as a cancer-promoting molecule in numerous types of cancer. Nevertheless, the role of MYBL in renal cancer remains unclear.

Methods

Genomic and clinical data of clear cell renal cell carcinoma (ccRCC) was get from the Cancer Genome Atlas (TCGA) database. CCK8, colony formation, and 5-ethynyl-2'-deoxyuridine assay were utilized to evaluate the performance of cell proliferation. Cell apoptosis was detected using the flow cytometric analysis. The protein level of MYBL1 in different tissues was evaluated using immunohistochemistry. A machine learning algorithm was utilized to identify the prognosis signature based on MYBL1-derived molecules.

Results

Here, we comprehensively investigated the role of MYBL1 in ccRCC. Here, we noticed a higher level of MYBL1 in ccRCC patients in both RNA and protein levels. Further analysis showed that MYBL1 was correlated with progressive clinical characteristics and worse prognosis performance. Biological enrichment analysis showed that MYBL1 can activate multiple oncogenic pathways in ccRCC. Moreover, we found that MYBL1 can remodel the immune microenvironment of ccRCC and affect the immunotherapy response. In vitro and in vivo assays indicated that MYBL1 was upregulated in ccRCC cells and can promote cellular malignant behaviors of ccRCC. Ultimately, an machine learning algorithm - LASSO logistics regression was utilized to identify a prognosis signature based on the MYBL1-derived molecules, which showed satisfactory prediction ability on patient prognosis in both training and validation cohorts.

Conclusions

Our result indicated that MYBL1 is a novel biomarker of ccRCC, which can remodel the tumor microenvironment, affect immunotherapy response and guide precision medicine in ccRCC."xsd:string
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http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Dong Y."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Li Q."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Meng Y."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Zhang C."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Wang T."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Xue W."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Xu S."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Xia Z."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Mao A."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/author"Jian W."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/date"2022"xsd:gYear
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/name"Front Immunol"xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/pages"1080403"xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/title"Integration analysis identifies MYBL1 as a novel immunotherapy biomarker affecting the immune microenvironment in clear cell renal cell carcinoma: Evidence based on machine learning and experiments."xsd:string
http://purl.uniprot.org/citations/36591240http://purl.uniprot.org/core/volume"13"xsd:string
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