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. 2023 Apr 6:14:1147366.
doi: 10.3389/fendo.2023.1147366. eCollection 2023.

Identification of the cell cycle characteristics of non-small cell lung cancer and its relationship with tumor immune microenvironment, cell death pathways, and metabolic reprogramming

Affiliations

Identification of the cell cycle characteristics of non-small cell lung cancer and its relationship with tumor immune microenvironment, cell death pathways, and metabolic reprogramming

Shengji Cao et al. Front Endocrinol (Lausanne). .

Abstract

Background: The genes related to the cell cycle progression could be considered the key factors in human cancers. However, the genes involved in cell cycle regulation in non-small cell lung cancer (NSCLC) have not yet been reported. Therefore, it is necessary to evaluate the genes related to the cell cycle in all types of cancers, especially NSCLC.

Methods: This study constituted the first pan-cancer landscape of cell cycle signaling. Cluster analysis based on cell cycle signaling was conducted to identify the potential molecular heterogeneity of NSCLC. Further, the discrepancies in the tumor immune microenvironment, metabolic remodeling, and cell death among the three clusters were investigated. Immunohistochemistry was performed to validate the protein levels of the ZWINT gene and examine its relationship with the clinical characteristics. Bioinformatics analyses and experimental validation of the ZWINT gene were also conducted.

Results: First, pan-cancer analysis provided an overview of cell cycle signaling and highlighted its crucial role in cancer. A majority of cell cycle regulators play risk roles in lung adenocarcinoma (LUAD); however, some cell cycle genes play protective roles in lung squamous cell carcinoma (LUSC). Cluster analysis revealed three potential subtypes for patients with NSCLC. LUAD patients with high cell cycle activities were associated with worse prognosis; while, LUSC patients with high cell cycle activities were associated with a longer survival time. Moreover, the above three subtypes of NSCLC exhibited distinct immune microenvironments, metabolic remodeling, and cell death pathways. ZWINT, a member of the cell signaling pathway, was observed to be significantly associated with the prognosis of LUAD patients. A series of experiments verified the higher expression levels of ZWINT in NSCLC compared to those in paracancerous tissues. The activation of epithelial-mesenchymal transition (EMT) induced by ZWINT might be responsible for tumor progression.

Conclusion: This study revealed the regulatory function of the cell cycle genes in NSCLC, and the molecular classification based on cell cycle-associated genes could evaluate the different prognoses of patients with NSCLC. ZWINT expression was found to be significantly upregulated in NSCLC tissues, which might promote tumor progression via activation of the EMT pathway.

Keywords: cell cycle; cell death pathways; metabolic reprogramming; non-small cell lung cancer; pan-cancer analysis; tumor immune microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Expression traits and prognostic values of the cell cycle-related genes in pan cancer. (A) mRNA expression levels of the 93 cell cycle-related genes in other human tumors (P < 0.05). (B) Clinical outcomes of the cell cycle in pan cancer. White color (P > 0.05) indicates no statistical difference. Red color indicates the risk factor, while the blue color indicates the protective factor.
Figure 2
Figure 2
Genomics traits of cell cycle-related genes in pan cancer. (A) CNV outcomes of the 93 cell cycle-related genes in different types of cancers. The length line represents the wave frequency of the cell cycle-related genes in human malignant tumors. (B) Heatmap representing the SNV mutations for the 93 cell cycle-related genes.
Figure 3
Figure 3
Methylation levels and pathway correlation of the cell cycle-related genes in pan cancer. (A) DNA methylation of the 93 cell cycle-related genes in different types of cancers (red to blue represents high to low). (B) 93 cell cycle-related genes were correlated with several classical tumor-related pathways (red to blue represents high to low).
Figure 4
Figure 4
Cluster analysis based on the cell cycle-related genes. The patients with NSCLC in the TCGA-LUAD and TCGA-LUSC cohorts were successfully grouped into 3 clusters for LUAD (A) and LUSC (B). Pathway enrichment scores followed the trend C1 > C2 > C3 in LUAD (C) and C2 > C1 > C3 in LUSC (D). Three different clusters showed different survival curves. Cluster 1 has the worse survival rate in LUAD (E). However, Cluster 1 has the worse survival rate in LUSC (F). x indicates survival time and y indicates survival rate.
Figure 5
Figure 5
Correlation of the cell cycle-related gene scores with metabolic reprogramming, immunological microenvironment, and cell death pathways in LUAD. (A) Activity of well-recognized metabolic reprogramming in the three clusters for LUAD. (B) Activity of well-recognized immune pathways in the three clusters for LUAD. (C) Correlation between the cell cycle-related gene scores and cell death pathways for LUAD. * indicates p < 0.05; ** indicates p < 0.01; *** indicates p < 0.001; and **** indicates p < 0.0001.
Figure 6
Figure 6
Correlation of the cell cycle-related gene scores with metabolic reprogramming, immunological microenvironment, and cell death pathways in LUSC. (A) Activity of well-recognized metabolic reprogramming in the three clusters for LUSC. (B) Activity of well-recognized immune pathways in the three clusters for LUSC. (C) Correlation between the cell cycle-related gene scores and cell death pathways for LUSC. * indicates p < 0.05; ** indicates p < 0.01; *** indicates p < 0.001; and **** indicates p < 0.0001.
Figure 7
Figure 7
Correlation between the expression levels of ZWINT gene and immune cell infiltration in pan cancer. (Red to blue color represents high to low expression levels).
Figure 8
Figure 8
Pathway enrichment analysis of the hub ZWINT gene by GO and KEGG in LUAD and LUSU. (A, B) GO and KEGG analysis for investigating the relationship between the classic cancer pathways and the hub ZWINT gene in LUAD. (C, D) GO and KEGG analysis for evaluating the relationship between the classic cancer pathway and the hub ZWINT gene in LUSC.
Figure 9
Figure 9
(A) Genomic mutation data of LUAD patients with varying ZWINT expression levels. (B) Genomic mutation data of LUSC patients with varying ZWINT expression levels. * indicates p < 0.05; ** indicates p < 0.01; *** indicates p < 0.001; and **** indicates p < 0.0001.
Figure 10
Figure 10
Expression validation of the ZWINT gene in NSCLC. (A) RT-qPCR analysis of 30 paired frozen cancerous and paracancerous tissues. (B) IHC experiments verified the expression levels of the ZWINT gene in cancerous and paracancerous tissues. (C) IHC experiments verified the expression levels of EMT pathway-associated markers in cancerous and paracancerous tissues. *** indicates p < 0.001.

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