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. 2025 Jun 4:13:1601908.
doi: 10.3389/fcell.2025.1601908. eCollection 2025.

Mechanistic insights curcumin's anti-inflammatory in pancreatic cancer: experimental and computational evidence implicating IL1B interference via IL10RA upregulation and NLRP3/TLR3 downregulation

Jun-Feng Cao  1   2 , Kuan Hang  1 , Hao Zhang  1 , Qingjie Xia  3 , Xiao Zhang  4 , Jie Men  2 , Jin Tian  2 , Zengliang Xia  3 , Dunshui Liao  3 , Kezhou Li  1   2
Affiliations

Mechanistic insights curcumin's anti-inflammatory in pancreatic cancer: experimental and computational evidence implicating IL1B interference via IL10RA upregulation and NLRP3/TLR3 downregulation

Jun-Feng Cao et al. Front Cell Dev Biol. .

Abstract

Purpose: Pancreatic cancer is a highly aggressive malignancy characterised by a complex tumour microenvironment and chronic inflammation. Studies found curcumin inhibited with inflammatory responses and tumour proliferation by interfering with production and activation of pro-inflammatory factors. This study investigated curcumin treated pancreatic cancer by modulating key targets in the inflammatory response and their signalling pathways.

Methods: The human pancreatic cancer PL45 cells and SUIT-2 cells were utilized to establish cellular experiments, and the effects of curcumin on proliferation, apoptosis and cell migration of PL45 cells and SUIT-2 cells were detected by CCK-8, Annexin V-FITC/PI and cell scratching experiment. PL45 cells RNA from experimental and control groups was also analyzed by transcriptome sequencing. Bioinformatics screening of differential gene targets in transcriptome sequencing was performed. Gene Ontology, KEGG and Protein-protein interaction were used to analyze the differentially expressed targets at the gene level and protein level, respectively. We validated the differential gene targets by machine learning analysis of GSE28735 data, and performed survival analysis, pan-tumor analysis, immune infiltration analysis and single-cell transcriptional analysis on the differentially expressed targets. Computer simulations were utilized to verify the stability of curcumin binding to key proteins.

Results: Results of cellular experiments suggested 30 μg/mL curcumin and 50 μg/mL curcumin significantly inhibited the proliferation and growth of PL45 and SUIT-2, respectively. The transcriptome results indicated that 2,676 genes showed differential expression in curcumin-treated group compared to control group. Bioinformatics and machine learning analyses screened 14 key targets that are closely related to the inflammatory response in pancreatic cancer. Molecular dynamics showed binding free energies for IL1B/Curcumin, IL10RA/Curcumin, NLRP3/Curcumin and TLR3/Curcumin were -12.76 ± 1.41 kcal/mol, -11.42 ± 2.57 kcal/mol, -28.16 ± 3.11 kcal/mol and -12.54 ± 4.80 kcal/mol, respectively.

Conclusion: This research findings indicated that curcumin not only directly interfered with the activation of IL1B through blocking activation of NLRP3 by TLR3, but also upregulated expression of IL10RA to activate IL-10, thereby interfering with IL1B and its downstream signalling pathway.

Keywords: cellular experiments; computer simulation; curcumin; machine learning; pancreatic cancer; transcriptome sequencing.

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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

None
The mechanisms of curcumin inhibits inflammatory response thereby treating pancreatic cancer.
FIGURE 1
FIGURE 1
Results of cellular experiments. (A) The results of cell viability by Cell Counting Kit-8 (CCK-8) (n = 6), the effects of curcumin on cell proliferation were detected after treating PL45 and SUIT-2 cells with different concentrations of curcumin for 24 h, respectively, (B) Results of cell scratch assay (n = 8), after 24 h of treatment, 30 μg/mL curcumin and 50 μg/mL curcumin inhibited the migration of PL45 cells and SUIT-2 cells, respectively, (C) Results of Annexin V-FITC/PI cell apoptosis assay.
FIGURE 2
FIGURE 2
The results of transcriptome sequencing, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. (A) The heat map of microarray (n = 3), (B) The Volcano plot of microarray (n = 3), (C) The GO enrichment analysis of upregulated gene, (D) The KEGG enrichment analysis of upregulated gene, (E) The GO enrichment analysis of downregulated gene, (F) The KEGG enrichment analysis of downregulated gene.
FIGURE 3
FIGURE 3
Intersection gene targets and Protein-protein interaction (PPI) network. (A) Intersection gene targets of transcriptome sequencing, pancreatic cancer and inflammatory response, (B) PPI network of protein targets, (C) PPI network of key protein targets (confidence >0.95).
FIGURE 4
FIGURE 4
Analysis of GSE28735 and machine Learning for random forest, LASSO and SVM-RFE. (A) Volcano map of differentially expressed genes in GSE28735, (B) Heatmap of differentially expressed in GSE28735, (C) Random forest, black line indicated the training error, red line indicated the cross-validation error and green line indicated the standard deviation of the error, (D) Feature importance analysis, (E) LASSO regression coefficients plot, coefficients of different features in the LASSO regression model as a function of the number of L1 norms, (F) Binomial deviation of logistic regression, the binomial deviation of the logistic regression model as the logarithm λ varies, (G) The cross-validation accuracy of SVM-RFE, the best number of features was five and the accuracy was 0.811, (H) The cross-validation error of SVM-RFE, the cross-validation error has a best eigenvalue of five and an error of 0.189.
FIGURE 5
FIGURE 5
Expression and prognostic analysis of key differentially expressed genes. (A) Results of Survival analysis, (B) Results of pan-tumor analysis, (C) Results of immune infiltration analysis. (D) Result of single-cell transcriptome in CRA001160 and GSE154778.
FIGURE 6
FIGURE 6
Expression of the key differential target mRNA (n = 3). (A) The mRNA expression of CASP1, (B) The mRNA expression of CCL2, (C) The mRNA expression of CSF2, (D) The mRNA expression of HMGB1, (E) The mRNA expression of IL1A, (F) The mRNA expression of IL1B, (G) The mRNA expression of IL1R1, (H) The mRNA expression of IL1RN, (I) The mRNA expression of IL6, (J) The mRNA expression of IL10RA, (K) The mRNA expression of NLRP3, (L) The mRNA expression of S100A9, (M) The mRNA expression of TLR3, (N) The mRNA expression of TLR4.
FIGURE 7
FIGURE 7
Molecular docking of curcumin and protein targets. (A) CASP1/Curcumin, (B) CCL2/Curcumin, (C) CSF2/Curcumin, (D) HMGB1/Curcumin, (E) IL1A/Curcumin, (F) IL1B/Curcumin, (G) IL1R1/Curcumin, (H) IL1RN/Curcumin, (I) IL6/Curcumin, (J) IL10RA/Curcumin, (K) NLRP3/Curcumin, (L) S100A9/Curcumin, (M) TLR3/Curcumin, (N) TLR4/Curcumin.
FIGURE 8
FIGURE 8
The result of molecular dynamics. (A) Binding states of curcumin and protein in molecular dynamics simulations, (B) Root mean square deviation (RMSD), (C) Binding free energies and energy components predicted by MM/GBSA (kcal/mol), (D) Hydrogen bonding, (E) Radius of gyration (RoG), (F) Root mean square fluctuations (RMSF), (G) Solvent accessible surface area (SASA).
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References

    1. Afshari H., Noori S., Zarghi A. (2023). Curcumin potentiates the anti-inflammatory effects of Tehranolide by modulating the STAT3/NF-κB signaling pathway in breast and ovarian cancer cell lines. Inflammopharmacology 31 (5), 2541–2555. 10.1007/s10787-023-01281-2 - DOI - PubMed
    1. Aggarwal S., Ichikawa H., Takada Y., Sandur S. K., Shishodia S., Aggarwal B. B. (2006). Curcumin (diferuloylmethane) down-regulates expression of cell proliferation and antiapoptotic and metastatic gene products through suppression of IkappaBalpha kinase and Akt activation. Mol. Pharmacol. 69 (1), 195–206. 10.1124/mol.105.017400 - DOI - PubMed
    1. Ahmed A., Köhler S., Klotz R., Giese N., Hackert T., Springfeld C., et al. (2023). Sex differences in the systemic and local immune response of pancreatic cancer patients. Cancers 15 (6), 1815. 10.3390/cancers15061815 - DOI - PMC - PubMed
    1. Akl L., Abd El-Hafeez A. A., Ibrahim T. M., Salem R., Marzouk H. M. M., El-Domany R. A., et al. (2022). Identification of novel piperazine-tethered phthalazines as selective CDK1 inhibitors endowed with in vitro anticancer activity toward the pancreatic cancer. Eur. J. Med. Chem. 243, 114704. 10.1016/j.ejmech.2022.114704 - DOI - PubMed
    1. Alfaro C., Sanmamed M. F., Rodríguez-Ruiz M. E., Teijeira Á., Oñate C., González Á., et al. (2017). Interleukin-8 in cancer pathogenesis, treatment and follow-up. Cancer Treat. Rev. 60, 24–31. 10.1016/j.ctrv.2017.08.004 - DOI - PubMed

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