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. 2024 Sep;7(9):e70010.
doi: 10.1002/cnr2.70010.

Identification of PI3K-AKT Pathway-Related Genes and Construction of Prognostic Prediction Model for ccRCC

Shaowen Hu  1 , Xiaoli Zhang  1 , Huiru Xin  2 , Mingjie Guo  3 , Yafei Xiao  4 , Zhongwei Chang  4 , Qingyang Luo  1 , Yang Li  1 , Chaoyang Zhu  1
Affiliations

Identification of PI3K-AKT Pathway-Related Genes and Construction of Prognostic Prediction Model for ccRCC

Shaowen Hu et al. Cancer Rep (Hoboken). 2024 Sep.

Abstract

Background: Clear cell renal cell carcinoma (ccRCC), the predominate histological type of renal cell carcinoma (RCC), has been extensively studied, with poor prognosis as the stage increases. Research findings consistently indicated that the PI3K-Akt pathway is commonly dysregulated across various cancer types, including ccRCC. Targeting the PI3K-Akt pathway held promise as a potential therapeutic approach for treating ccRCC. Development and validation of PI3K-Akt pathway-related genes related biomarkers can enhance healthcare management of patients with ccRCC.

Purpose: This study aimed to identify the key genes in the PI3K-Akt pathway associated with the diagnosis and prognosis of CCRCC using data mining from the Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO) datasets.

Methods: The purpose of this study is to use bioinformatics methods to screen data sets and clinicopathological characteristics associated with ccRCC patients. The exhibited significantly differential expressed genes (DEGs) associated with the PI3K-Akt pathway were examined by KEGG. In addition, Kaplan-Meier (KM) analysis used to estimate the survival function of the differential genes by using the UALCAN database and graphPad Prism 9.0. And exploring the association between the expression levels of the selected genes and the survival status and time of patients with ccRCC based on SPSS22.0. Finally, a multigene prognostic model was constructed to assess the prognostic risk of ccRCC patients.

Results: A total of 911 genes with common highly expressed were selected based on the GEO and TCGA databases. According to the KEGG pathway analysis, there were 42 genes enriched in PI3K-Akt signalling pathway. And seven of highly expressed genes were linked to a poor prognosis in ccRCC. And a multigene prognostic model was established based on IL2RG, EFNA3, and MTCP1 synergistic expression might be utilized to predict the survival of ccRCC patients.

Conclusions: Three PI3K-Akt pathway-related genes may be helpful to identify the prognosis and molecular characteristics of ccRCC patients and to improve therapeutic regimens, and these risk characteristics might be further applied in the clinic.

Keywords: EFNA3; IL2RG; MTCP1; bioinformatics; clear cell renal cell carcinoma; prognostic model; the PI3K‐Akt signaling pathway.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Volcano plots of the expressed genes in the three datasets. Red dots represented up‐regulated genes, blue dots represented down‐regulated genes, and grey dots represented genes not differentially expressed with |log2FC| > 1and p < 0.05. (A) The volcano plot drawn based on the dataset GES53757. (B) The volcano plot drawn based on the dataset GES66270. (C) The volcano plot drawn based on the dataset TCGA‐ccRCC.
FIGURE 2
FIGURE 2
Venn diagram of highly expressed genes in the three datasets. Each color represented highly expressed genes in different datasets, where the crossover regions were genes commonly highly expressed in the different datasets.
FIGURE 3
FIGURE 3
Pathway bubble mapping and expression visualization analysis of differential genes. (A) The signaling pathway enrichment analysis of 911 highly DEGs based on KEGG. The PI3K‐Akt signaling pathway ranked the third based on the gene counts ranking. (B) Analysis of 42 genes involved in the PI3K‐Akt signaling pathway. All of these 42 genes showed highly expressed status in ccRCC.
FIGURE 4
FIGURE 4
Ranking of the AUC values for the top 12 genes.
FIGURE 5
FIGURE 5
Seven genes clearly associated with poor prognosis in ccRCC.
FIGURE 6
FIGURE 6
Prognostic analysis of novel genes based on the TCGA‐ccRCC dataset.
FIGURE 7
FIGURE 7
Prognostic analysis of novel genes based on the prognostic index in ccRCC.
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