PLAG Exerts Anti-Metastatic Effects by Interfering with Neutrophil Elastase/PAR2/EGFR Signaling in A549 Lung Cancer Orthotopic Model

1-palmitoyl-2-linoleoyl-3-acetyl-rac-glycerol treatment inhibits abnormal tumor growth by regulating neutrophil infiltration in a non-small cell lung carcinoma mouse model

Excessive neutrophil infiltration into the tumor microenvironment (TME) is an important factor that contributes to tumor overgrowth and limited immunotherapy efficacy. Neutrophils activate various receptors involved in tumor progression, while suppressing the infiltration and activity of cytotoxic T cells and creating optimal conditions for tumor growth. Therefore, the appropriate control of neutrophil infiltration is an effective strategy for tumor treatment. In the present study, 1-palmitoyl-2-linoleoyl-3-acetyl-rac-glycerol (PLAG) inhibited tumor overgrowth by suppressing excessive neutrophil infiltration, resulting in >74.97 % reduction in tumor size in a Lewis lung carcinoma (LLC-1) mouse model. All subjects in the positive control group died during the 90-day survival period, whereas only four subjects in the PLAG treatment group survived. PLAG had a significantly higher tumor growth inhibitory effect and survival rate than other neutrophil infiltration-targeting inhibitors (e.g., Navarixin, lymphocyte antigen 6 complex locus G6D antibody [aLy6G]). The ability of PLAG to regulate neutrophil infiltration and inhibit tumor growth depends on thioredoxin-interacting protein (TXNIP). In tumors lacking TXNIP expression, PLAG failed to control neutrophil infiltration and infiltration-related factor release, and the inhibitory effect of PLAG on tumor growth was reduced. PLAG-mediated inhibition of neutrophil infiltration enhances the efficacy of immune checkpoint inhibitors (ICIs), increasing the antitumor efficacy and survival rate by 30 %. In conclusion, PLAG could be a novel alternative to anti-tumor drugs that effectively targets excessive neutrophil infiltration into cancer tissues.

Comprehensive analysis of resistance mechanisms to EGFR-TKIs and establishment and validation of prognostic model

PURPOSE

Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the first-line therapy for patients with lung adenocarcinoma (LUAD) harboring activating EGFR mutations. However, the emergence of drug resistance to EGFR-TKIs remains a critical obstacle for successful treatment and is associated with poor patient outcomes. The overarching objective of this study is to apply bioinformatics tools to gain insights into the mechanisms underlying resistance to EGFR-TKIs and develop a robust predictive model.

METHODS

The genes associated with gefitinib resistance in the LUAD cell Gene Expression Omnibus (GEO) database were identified using gene chip expression data. Functional enrichment analysis, gene set enrichment analysis (GSEA), and immune infiltration analysis were performed to comprehensively explore the mechanism of gefitinib resistance. Furthermore, a GRRG_score was constructed by integrating genes related to LUAD prognosis from The Cancer Genome Atlas (TCGA) database with the screened Gefitinib Resistant Related differentially expressed genes (GRRDEGs) using the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses. Furthermore, we conducted an in-depth analysis of the tumor microenvironment (TME) features and their association with immune infiltration between different GRRG_score groups. A prognostic model for LUAD was developed based on the GRRG_score and validated. The HPA database was used to validate protein expression. The CTR-DB database was utilized to validate the results of drug therapy prediction based on the relevant genes.

RESULTS

A total of 110 differentially expression genes were identified. Pathway enrichment analysis of DEGs showed that the differentially expressed genes were mainly enriched in Mucin type O-glycan biosynthesis, Cytokine-cytokine receptor interaction, Sphingolipid metabolism. Gene set enrichment analysis showed that biological processes strongly correlated with gefitinib resistance were cell proliferation and immune-related pathways, EPITHELIAL_MESENCHYMAL_TRANSITION, APICAL_SURFACE, and APICAL_JUNCTION were highly expressed in the drug-resistant group; KRAS_SIGNALING_DN, HYPOXIA, and HEDGEHOG_SIGNALING were highly expressed in the drug-resistant group. The GRRG_score was constructed based on the expression levels of 13 genes, including HSPA2, ATP8B3, SPOCK1, EIF6, NUP62CL, BCAR3, PCSK9, NT5E, FLNC, KRT8, FSCN1, ANGPTL4, and ID1. We further screened and validated two key genes, namely, NUP62CL and KRT8, which exhibited predictive value for both prognosis and drug resistance.

CONCLUSIONS

Our study identified several novel GRRDEGs and provided insight into the underlying mechanisms of gefitinib resistance in LUAD. Our results have implications for developing more effective treatment strategies and prognostic models for LUAD patients.

Role of neutrophils on cancer cells and other immune cells in the tumor microenvironment

The notion that neutrophils only perform a specific set of single functions in the body has changed with the advancement of research methods. As the most abundant myeloid cells in human blood, neutrophils are currently emerging as important regulators of cancer. Given the duality of neutrophils, neutrophil-based tumor therapy has been clinically carried out in recent years and has made some progress. But due to the complexity of the tumor microenvironment, the therapeutic effect is still not satisfactory. Therefore, in this review, we discuss the direct interaction of neutrophils with the five most common cancer cells and other immune cells in the tumor microenvironment. Also, this review covered current limitations, potential future possibilities, and therapeutic approaches targeting neutrophil function in cancer therapy.

Improving anticancer effect of aPD-L1 through lowering neutrophil infiltration by PLAG in tumor implanted with MB49 mouse urothelial carcinoma

BACKGROUND

The PD-L1 antibody is an immune checkpoint inhibitor (ICI) attracting attention. The third-generation anticancer drug has been proven to be very effective due to fewer side effects and higher tumor-specific reactions than conventional anticancer drugs. However, as tumors produce additional resistance in the host immune system, the effectiveness of ICI is gradually weakening. Therefore, it is very important to develop a combination therapy that increases the anticancer effect of ICI by removing anticancer resistance factors present around the tumor.

METHODS

The syngeneic model was used (n = 6) to investigate the enhanced anti-tumor effect of PD-L1 antibody with the addition of PLAG. MB49 murine urothelial cancer cells were implanted into the C57BL/6 mice subcutaneously. PLAG at different dosages (50/100 mpk) was daily administered orally for another 4 weeks with or without 5 mpk PD-L1 antibody (10F.9G2). PD-L1 antibody was delivered via IP injection once a week.

RESULTS

The aPD-L1 monotherapy group inhibited tumor growth of 56% compared to the positive group, while the PLAG and aPD-L1 co-treatment inhibited by 89%. PLAG treatment effectively reduced neutrophils infiltrating localized in tumor and converted to a tumor microenvironment with anti-tumor effective T-cells. PLAG increased tumor infiltration of CD8 positive cytotoxic T-cell populations while effectively inhibiting the infiltration of neoplastic T-cells such as CD4/FoxP3. Eventually, neutrophil-induced tumor ICI resistance was resolved by restoring the neutrophil-to-lymphocyte ratio to the normal range. In addition, regulation of cytokine and chemokine factors that inhibit neutrophil infiltration and increase the killing activity of cytotoxic T cells was observed in the tumors of mice treated with PLAG + aPD-L1.

CONCLUSIONS

PLAG effectively turned the tumor-promoting microenvironment into a tumor-suppressing microenvironment. As a molecule that increases the anti-tumor effectiveness of aPD-L1, PLAG has the potential to be an essential and effective ICI co-therapeutic agent.

PLAG co-treatment increases the anticancer effect of Adriamycin and cyclophosphamide in a triple-negative breast cancer xenograft mouse model

Chemotherapy induces tumor cell death and inhibits tumor progression, but the accompanying immune responses in the surrounding dying tissue cause significant inflammation. These responses, such as excessive neutrophil infiltration into tumor tissue, are the main causes of resistance to anticancer treatment. The development of drugs that reduce neutrophil infiltration into tumors is necessary to increase the anticancer effect of chemotherapy. Here, we show that the antitumor effect of the chemotherapy AC regimen (Adriamycin and cyclophosphamide) was increased by 1-palmitoyl-2-linoleoyl-3-acetyl-rac-glycerol (PLAG) cotreatment in the MDA-MB-231 triple-negative breast cancer xenograft mouse model. Tumor growth was inhibited up to 56% in mice treated with AC and inhibited up to 94% in mice cotreated with AC and PLAG. Side effects of chemotherapy, such as a reduction in body weight, were alleviated in mice cotreated with AC and PLAG. Excessive neutrophil infiltration caused by the AC regimen was successfully cleared in mice cotreated with AC and PLAG. We conclude that PLAG inhibits excessive neutrophil infiltration that aids tumor growth. Reduced neutrophils and increased lymphocytes in PLAG-treated mice can maximize the antitumor effect of the AC regimen and inhibit tumor growth.

Machine learning analysis of DNA methylation in a hypoxia-immune model of oral squamous cell carcinoma

BACKGROUND

Hypoxia status and immunity are related with the development and prognosis of oral squamous cell carcinoma (OSCC). Here, we constructed a hypoxia-immune model to explore its upstream mechanism and identify potential CpG sites.

METHODS

The hypoxia-immune model was developed and validated by the iCluster algorithm. The LASSO, SVM-RFE and GA-ANN were performed to screen CpG sites correlated to the hypoxia-immune microenvironment.

RESULTS

We found seven hypoxia-immune related CpG sites. Lasso had the best classification performance among three machine learning algorithms.

CONCLUSION

We explored the clinical significance of the hypoxia-immune model and found seven hypoxia-immune related CpG sites by multiple machine learning algorithms. This model and candidate CpG sites may have clinical applications to predict the hypoxia-immune microenvironment.