Potentiality of raloxifene loaded melittin functionalized lipidic nanovesicles against pancreatic cancer cells

Graphene oxide nanoribbons conjugated with 1, 2-distearoyl-sn-glycero-3 phosphoethanolamine-poly (ethylene glycol)-transferrin enhanced targeted delivery and cytotoxicity of raloxifene against breast cancer

The clinical utility of raloxifene (RLX), a selective estrogen receptor modulator (SERM), has been compromised by severe side effects and unfavorable drug properties. To address these, a transferrin (Tf) conjugated graphene oxide nanoribbon (GONR) platform was tried for RLX. The stability of GONRs in biological media was improved by surface modification with 1, 2-Distearoyl-sn-glycero-3 phosphoethanolamine-Poly (ethylene glycol) (DSPE-PEG). The Tf molecule was covalently attached to DSPE-PEG (DPT) using EDC-NHS chemistry. The surface of GONR was then modified with DSPE-PEG (DP) or DPT and loaded with RLX (GDP-RLX and GDPT-RLX). The final formulations were characterized for drug loading and stability. The anticancer activities of pure RLX, GDP-RLX, and GDPT-RLX were evaluated and compared in all the in vitro and in vivo studies. In vitro cell line studies showed that GDPT-RLX have significantly high cytotoxicity, cellular uptake, apoptosis induction, G2/M phase arrest, anti-migration properties, and apoptotic protein expression, followed by GDP-RLX and RLX. Pharmacokinetics and tumor biodistribution were also found to be excellent with GDPT-RLX. The in vivo tumor therapy and tumor evaluation outcomes were also consistent with the in vitro data. The Tf conjugated GDPT-RLX represents a promising approach for targeted and sustained delivery of RLX with enhanced therapeutic efficacy.

Melittin and phospholipase A2: Promising anti-cancer candidates from bee venom

As the research on cancer-related treatment deepens, integrating traditional therapies with emerging interventions reveals new therapeutic possibilities. Melittin and phospholipase A2, the primary anti-cancer components of bee venom, are currently gaining increasing attention. This article reviews the various formulations of melittin in cancer therapy and its potential applications in clinical treatments. The reviewed formulations include melittin analogs, hydrogels, adenoviruses, fusion toxins, fusion peptides/proteins, conjugates, liposomes, and nanoparticles. The article also explored the collaborative therapeutic effects of melittin with natural products, synthetic drugs, radiotherapy, and gene expression regulatory strategies. Phospholipase A2 plays a key role in bee venom anti-cancer strategy due to its unique biological activity. Using an extensive literature review and the latest scientific results, this paper explores the current state and challenges of this field, with the aim to provide new perspectives that guide future research and potential clinical applications. This will further promote the application of bee venom in cancer therapy.

Lysosome-related biomarkers in preeclampsia and cancers: Machine learning and bioinformatics analysis

BACKGROUND

Lysosomes serve as regulatory hubs, and play a pivotal role in human diseases. However, the precise functions and mechanisms of action of lysosome-related genes remain unclear in preeclampsia and cancers. This study aimed to identify lysosome-related biomarkers in preeclampsia, and further explore the biomarkers shared between preeclampsia and cancers.

MATERIALS AND METHODS

We obtained GSE60438 and GSE75010 datasets from the Gene Expression Omnibus database, pre-procesed them and merged them into a training cohort. The limma package in R was used to identify the differentially expressed mRNAs between the preeclampsia and normal control groups. Differentially expressed lysosome-related genes were identified by intersecting the differentially expressed mRNAs and lysosome-related genes obtained from Gene Ontology and GSEA databases. Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed using the DAVID database. The CIBERSORT method was used to analyze immune cell infiltration. Weighted gene co-expression analyses and three machine learning algorithm were used to identify lysosome-related diagnostic biomarkers. Lysosome-related diagnostic biomarkers were further validated in the testing cohort GSE25906. Nomogram diagnostic models for preeclampsia were constructed. In addition, pan-cancer analysis of lysosome-related diagnostic biomarkers were identified by was performed using the TIMER, Sangebox and TISIDB databases. Finally, the Drug-Gene Interaction, TheMarker and DSigDB Databases were used for drug-gene interactions analysis.

RESULTS

A total of 11 differentially expressed lysosome-related genes were identified between the preeclampsia and control groups. Three molecular clusters connected to lysosome were identified, and enrichment analysis demonstrated their strong relevance to the development and progression of preeclampsia. Immune infiltration analysis revealed significant immunity heterogeneity among different clusters. GBA, OCRL, TLR7 and HEXB were identified as lysosome-related diagnostic biomarkers with high AUC values, and validated in the testing cohort GSE25906. Nomogram, calibration curve, and decision curve analysis confirmed the accuracy of predicting the occurrence of preeclampsia based on OCRL and HEXB. Pan-cancer analysis showed that GBA, OCRL, TLR7 and HEXB were associated with the prognosis of patients with various tumors and tumor immune cell infiltration. Twelve drugs were identified as potential drugs for the treatment of preeclampsia and cancers.

CONCLUSION

This study identified GBA, OCRL, TLR7 and HEXB as potential lysosome-related diagnostic biomarkers shared between preeclampsia and cancers.

Investigation of the potential effects of estrogen receptor modulators on immune checkpoint molecules

Immune checkpoints regulate the immune system response. Recent studies suggest that flavonoids, known as phytoestrogens, may inhibit the PD-1/PD-L1 axis. We explored the potential of estrogens and 17 Selective Estrogen Receptor Modulators (SERMs) as inhibiting ligands for immune checkpoint proteins (CTLA-4, PD-L1, PD-1, and CD80). Our docking studies revealed strong binding energy values for quinestrol, quercetin, and bazedoxifene, indicating their potential to inhibit PD-1 and CTLA-4. Quercetin and bazedoxifene, known to modulate EGFR and IL-6R alongside estrogen receptors, can influence the immune checkpoint functionality. We discuss the impact of SERMs on PD-1 and CTLA-4, suggesting that these SERMs could have therapeutic effects through immune checkpoint inhibition. This study highlights the potential of SERMs as inhibitory ligands for immune checkpoint proteins, emphasizing the importance of considering PD-1 and CTLA-4 inhibition when evaluating SERMs as therapeutic agents. Our findings open new avenues for cancer immunotherapy by exploring the interaction between various SERMs and immune checkpoint pathways.

Melittin: a possible regulator of cancer proliferation in preclinical cell culture and animal models

BACKGROUND

Melittin is a water-soluble cationic peptide derived from bee venom that has been thoroughly studied for the cure of different cancers. However, the unwanted interactions of melittin produce hemolytic and cytotoxic effects that hinder their therapeutic applications. To overcome the shortcomings, numerous research groups have adopted different approaches, including conjugation with tumor-targeting proteins, gene therapy, and encapsulation in nanoparticles, to reduce the non-specific cytotoxic effects and potentiate their anti-cancerous activity.

PURPOSE

This article aims to provide mechanistic insights into the chemopreventive activity of melittin and its nanoversion in combination with standard anti-cancer drugs for the treatment of cancer.

METHODS

We looked over the pertinent research on melittin's chemopreventive properties in online databases such as PubMed and Scopus.

CONCLUSION

In the present article, the anti-cancerous effects of melittin on different cancers have been discussed very nicely, as have their possible mechanisms of action to act against different tumors. Besides, it interacts with different signal molecules that regulate the diverse pathways of cancerous cells, such as cell cycle arrest, apoptosis, metastasis, angiogenesis, and inflammation. We also discussed the recent progress in the synergistic combination of melittin with standard anti-cancer drugs and a nano-formulated version of melittin for targeted delivery to improve its anticancer potential.

Recent advances in melittin-based nanoparticles for antitumor treatment: from mechanisms to targeted delivery strategies

As a naturally occurring cytolytic peptide, melittin (MLT) not only exhibits a potent direct tumor cell-killing effect but also possesses various immunomodulatory functions. MLT shows minimal chances for developing resistance and has been recognized as a promising broad-spectrum antitumor drug because of this unique dual mechanism of action. However, MLT still displays obvious toxic side effects during treatment, such as nonspecific cytolytic activity, hemolytic toxicity, coagulation disorders, and allergic reactions, seriously hampering its broad clinical applications. With thorough research on antitumor mechanisms and the rapid development of nanotechnology, significant effort has been devoted to shielding against toxicity and achieving tumor-directed drug delivery to improve the therapeutic efficacy of MLT. Herein, we mainly summarize the potential antitumor mechanisms of MLT and recent progress in the targeted delivery strategies for tumor therapy, such as passive targeting, active targeting and stimulus-responsive targeting. Additionally, we also highlight the prospects and challenges of realizing the full potential of MLT in the field of tumor therapy. By exploring the antitumor molecular mechanisms and delivery strategies of MLT, this comprehensive review may inspire new ideas for tumor multimechanism synergistic therapy.

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.