Therapeutic Peptide RF16 Derived from CXCL8 Inhibits MDA-MB-231 Cell Invasion and Metastasis

Peptides in breast cancer therapy: From mechanisms to emerging drug delivery and immunotherapy strategies

Breast cancer therapy can be improved by the application of multifunctional peptides and they have unique features, such as high specificity, minimized toxicity, and the capability to influence diverse processes. The role of peptides in breas cancer therapy is highlighted in the present review, examining their functions as therapeutic agents, diagnostic tools, and drug delivery application. Therapeutic peptides have displayed the ability to regulate key pathways in breast tumor, including HER2, VEGF, and EGFR, providing ideal alternatives to the conventional chemotherapy with reduced adverse effects. Additionally, peptide-based vaccines and immune-modulating peptides have demonstrated the capacity in enhancing anti-cancer immunity. The incorporation of peptides into nanoparticles has improved the delivery of drugs and genes, enhanced anti-cancer efficacy while minimizing side impacts. The progresses in the peptide engineering, including stapled peptides, peptide-drug conjugates, and cell-penetrating peptides, have remarkably increased their therapeutic efficacy and stability, elevating their applications in breast cancer therapy. Peptides can be developed using bioinformatics and high-throughput screening technologies to optimize pharmacokinetics and bioavailability. Despite their promise, peptides demonstrate challenges such as enzymatic degradation, limited stability, and high production costs. These obstacles can be addressed through strategies such as peptide cyclization, the employement of non-natural amino acids, and nanoparticle encapsulation. This review explores these recent advancements and strategies, providing ideal insights into the clinical potential of peptides in breast tumor therapy.

Expression of CXCL8 and its relationship with prognosis in patients with non-small cell lung cancer

To determine the expression of chemokine 8 (CXCL8) in non-small cell lung cancer (NSCLC) patients and analyze its correlation with tumor characteristics and patient prognosis. We conducted a retrospective analysis of 149 NSCLC patients treated between January 2016 and April 2018, measuring serum CXCL8 expression upon admission or prior to treatment. The clinical characteristics, including lymph node metastasis and staging, based on CXCL8 expression levels, were analyzed. Receiver Operating Characteristic (ROC) curves was drawn to assess its predictive value for lymph node metastasis and staging in NSCLC patients. Furthermore, the Kaplan-Meier curve was plotted to assess the impact of CXCL8 on 5-year survival in NSCLC Patients. NSCLC patients exhibited significantly higher serum CXCL8 levels than those with benign tumors (P<0.001), with the high CXCL8 expression group showing a higher incidence of lymph node metastasis or stage III NSCLC (P<0.01). CXCL8 was identified as an independent predictor of lymph node metastasis (AUC=0.730) and higher TNM stage (AUC=0.708), as well as a validated biomarker for predicting five-year survival in NSCLC patients. This study highlights the strong association between CXCL8 expression in NSCLC and patient prognosis, particularly regarding lymph node metastasis and clinical staging, suggesting the need for further research to explore CXCL8's specific role in the tumor microenvironment and its impact on different NSCLC subtypes.

Computational design of anti-cancer peptides tailored to target specific tumor markers

Anti-cancer peptides (ACPs) are short peptides known for their ability to inhibit tumor cell proliferation, migration, and the formation of tumor blood vessels. In this study, we designed ACPs to target receptors often overexpressed in cancer using a systematic in silico approach. Three target receptors (CXCR1, DcR3, and OPG) were selected for their significant roles in cancer pathogenesis and tumor cell proliferation. Our peptide design strategy involved identifying interacting residues (IR) of these receptors, with their natural ligands serving as a reference for designing peptides specific to each receptor. The natural ligands of these receptors, including IL8 for CXCR1, TL1A for DcR3, and RANKL for OPG, were identified from the literature. Using the identified interacting residues (IR), we generated a peptide library through simple permutation and predicted the structure of each peptide. All peptides were analyzed using the web-based prediction server for Anticancer peptides, AntiCP. Docking simulations were then conducted to analyze the binding efficiencies of peptides with their respective target receptors, using VEGA ZZ and Chimera for interaction analysis. Our analysis identified HPKFIKELR as the interacting residues (IR) of CXCR-IL8. For DcR3, we utilized three domains from TL1A (TDSYPEP, TKEDKTF, LGLAFTK) as templates, along with two regions (SIKIPSS and PDQDATYP) from RANKL, to generate a library of peptide analogs. Subsequently, peptides for each receptor were shortlisted based on their predicted anticancer properties as determined by AntiCP and were subjected to docking analysis. After docking, peptides that exhibited the least binding energy were further analyzed for their detailed interaction with their respective receptors. Among these, peptides C9 (HPKFELY) and C7 (HPKFEWL) for CXCR1, peptides D6 (ADSYPQP) and D18 (AFSYPFP) for DcR3, and peptides P19 (PDTYPQDP) and p16 (PDQDATYP) for OPG, demonstrated the highest affinity and stronger interactions compared to the other peptides. Although in silico predictions indicated a favorable binding affinity of the designed peptides with target receptors, further experimental validation is essential to confirm their binding affinity, stability and pharmacokinetic characteristics.