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Introduction
Renal cell carcinoma (RCC) is a highly prevalent malignancy occurring frequently in both men and women [1]. The majority of cases of clear cell RCC (ccRCC) fall under this RCC subtype. The majority of cases of clear cell RCC (ccRCC) fall under this RCC subtype. At present, surgical resection continues to be the primary treatment modality for ccRCC, given the limited efficacy of conventional radiotherapy attributed to the tumor's intrinsic resistance [2]. Therefore, identifying a robust prognostic biomarker is critical to improve treatment efficacy and patients’ quality of life.
The Forkhead protein family consists of proteins containing a winged-helix DNA-binding domain similar to that of mammalian FOXM1 [3]. In healthy cells, FOXM1 is a crucial regulator of cell proliferation [4]. Inhibition of FOXM1 expression through RNA interference (RNAi) leads to a reduction in cell proliferation, migration, invasion, and angiogenesis in tumor cells. These findings indicate that FOXM1 plays a regulatory role in the growth of cancer cells [5]. Additionally, FOXM1 can evade tumor suppressor mechanisms [6]. Studies in patient samples have verified a strong correlation between FOXM1 and various cancer cell types, with a clear association between FOXM1 levels and tumor stage [7, 8–9]. However, the related role of FOXM1 and its relationship with antitumor immunity in ccRCC remain unclear.
This study focuses on examining FOXM1 in ccRCC using an analysis of The Cancer Genome Atlas (TCGA) database. The goal is to look into how FOXM1 affects overall survival (OS) and how antitumor immunity is related to it in ccRCC. Our findings could inform novel treatment options for patients diagnosed with ccRCC.
Method and materials
Data acquisition
The Cancer Genome Atlas (TCGA) database was mined for 539 clear cell renal cell carcinoma (ccRCC) tumor tissues and 72 normal tissues to get the transcriptome profiles and pertinent clinical information. We only retain samples with complete clinical information. The "limma" package in R software was used to identify differentially expressed genes (DEGs) between ccRCC and adjacent tissues [10]. The sample with missing values is deleted. DEGs were defined as genes with |log2 fold change (FC)|≥ 1 and adjusted p-values < 0.05. The DESeq2 package in R is used to standardize the data, and log2 transformation is performed on the expression data to make the data distribution closer to the...