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. 2023 Mar 23:14:1038651.
doi: 10.3389/fimmu.2023.1038651. eCollection 2023.

Identification of berberine as a potential therapeutic strategy for kidney clear cell carcinoma and COVID-19 based on analysis of large-scale datasets

Zhihua Zheng  1 , Xiushen Li  2 , Kechao Nie  3 , Xiaoyu Wang  4 , Wencong Liang  1 , Fuxia Yang  1 , Kairi Zheng  5 , Yihou Zheng  1
Affiliations

Identification of berberine as a potential therapeutic strategy for kidney clear cell carcinoma and COVID-19 based on analysis of large-scale datasets

Zhihua Zheng et al. Front Immunol. .

Abstract

Background: Regarding the global coronavirus disease 2019 (COVID)-19 pandemic, kidney clear cell carcinoma (KIRC) has acquired a higher infection probability and may induce fatal complications and death following COVID-19 infection. However, effective treatment strategies remain unavailable. Berberine exhibits significant antiviral and antitumour effects. Thus, this study aimed to provide a promising and reliable therapeutic strategy for clinical decision-making by exploring the therapeutic mechanism of berberine against KIRC/COVID-19.

Methods: Based on large-scale data analysis, the target genes, clinical risk, and immune and pharmacological mechanisms of berberine against KIRC/COVID-19 were systematically investigated.

Results: In total, 1,038 and 12,992 differentially expressed genes (DEGs) of COVID-19 and KIRC, respectively, were verified from Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively, and 489 berberine target genes were obtained from official websites. After intersecting, 26 genes were considered potential berberine therapeutic targets for KIRC/COVID-19. Berberine mechanism of action against KIRC/COVID-19 was revealed by protein-protein interaction, gene ontology, and Kyoto Encyclopedia of Genes and Genomes with terms including protein interaction, cell proliferation, viral carcinogenesis, and the PI3K/Akt signalling pathway. In COVID-19 patients, ACOX1, LRRK2, MMP8, SLC1A3, CPT1A, H2AC11, H4C8, and SLC1A3 were closely related to disease severity, and the general survival of KIRC patients was closely related to ACOX1, APP, CPT1A, PLK1, and TYMS. Additionally, the risk signature accurately and sensitively depicted the overall survival and patient survival status for KIRC. Numerous neutrophils were enriched in the immune system of COVID-19 patients, and the lives of KIRC patients were endangered due to significant immune cell infiltration. Molecular docking studies indicated that berberine binds strongly to target proteins.

Conclusion: This study demonstrated berberine as a potential treatment option in pharmacological, immunological, and clinical practice. Moreover, its therapeutic effects may provide potential and reliable treatment options for patients with KIRC/COVID-19.

Keywords: berberine; coronavirus disease 2019; immune mechanism; kidney clear cell carcinoma; molecular docking.

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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
Flow chart.
Figure 2
Figure 2
Identification of therapeutic target genes of berberine against KIRC/COVID-19. (A, B) Volcano plot and heatmap of DEGs from GSE157103 data set, and heatmap shown the top 50 DEGs (C, D) DEGs from GSE171110 data set. (E, F) DEGs from TCGA data set. (G) The intersection of GSE157103 and GSE171110 data set. (H) The union set of berberine target genes of each website. (I) The intersection of COVID-19 DEGs, KIRC DEGs and berberine target genes.
Figure 3
Figure 3
Exploration of the potential mechanism of berberine against KIRC/COVID-19. (A) The PPI network based on therapeutic target genes of berberine against KIRC/COVID-19. (B) the core network of PPI. (C) GO analysis of target genes. (D) KEGG pathway analysis of target genes.
Figure 4
Figure 4
Clinical significance of target genes in COVID-19 patients. (A) Boxplot of target genes associated with mechanical ventilation. (B) Boxplot of target genes associated with ICU admission. (C) The correlation analysis between genes after screening. *p <0.05, **p <0.01, ***p <0.005.
Figure 5
Figure 5
Assessment of target gene signatures for KIRC patients. (A) The forest plot of univariate Cox proportional analysis. (B, C) The LASSO Cox regression analysis for detecting the representative gene. (D) Heatmap of target gene signatures. (E) The correlation analysis between target gene signatures. (F–J) Kaplan-Meier survival analysis of target gene signatures.
Figure 6
Figure 6
Clinical significance of risk signature for KIRC patients. (A) Distribution of risk scores to be divided into high- and low- risk groups. (B) Distribution of the OS to depict the relationship between OS and risk signature in dead and alive KIRC patients. (C) Kaplan-Meier survival analysis of risk signature. (D) AUC in ROC analysis for risk signature at 1‐, 3‐and 5‐years survival. When AUC is greater than 0.7, the prediction model has reliable accuracy. (E–J) Boxplot reveals the relationship between risk signature and clinical information, containing in age, grade, tumor stage, tumor, lymph node and metastasis deteriorated.
Figure 7
Figure 7
Immune response of target genes for COVID-19 patients. (A–E) Boxplot of target genes associated with 22 immune cell infiltration. (F) Boxplot of neutrophil infiltration associated with mechanical ventilation. (G) Boxplot of neutrophil infiltration associated with ICU admission. *p <0.05, **p <0.01, ***p <0.005.
Figure 8
Figure 8
Immune response of target genes for KIRC patients. (A–F) Boxplot of target gene signatures associated with 22 immune cell infiltration. (G) The analysis of immunological functions. (H) the tumor microenvironment score. *p <0.05, **p <0.01, ***p <0.005.
Figure 9
Figure 9
Molecular docking of berberine and target genes. The top 5 docking affinity score. (A) Berberine binding to ACOX1. (B) Berberine binding to PLK. (C) Berberine binding to H4C8. (D) Berberine binding to H2AC11. (E) Berberine binding to MMP8.
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