A New NT4 Peptide-Based Drug Delivery System for Cancer Treatment

Progress of near-infrared-II fluorescence in precision diagnosis and treatment of colorectal cancer

Colorectal cancer is a malignant tumour with high incidence and mortality worldwide; therefore, improving the early diagnosis of colorectal cancer and implementing a targeted "individualized treatment" strategy is of great concern. NIR-II fluorescence imaging is a large-depth, high-resolution optical bioimaging tool. Around the NIR-II window, researchers have developed a variety of luminescent probes, imaging systems, and treatment methods with colorectal cancer targeting capabilities, which can be visualized and image-guided in clinical surgery. This article aims to overcome the difficulties in diagnosing and treating colorectal cancer. The present review summarizes the latest results on using NIR-II fluorescence for targeted colorectal cancer imaging, expounds on the application prospects of NIR-II optical imaging for colorectal cancer, and discusses the imaging-guided multifunctional diagnosis and treatment platforms.

Combining Solid-State NMR with Structural and Biophysical Techniques to Design Challenging Protein-Drug Conjugates

Several protein-drug conjugates are currently being used in cancer therapy. These conjugates rely on cytotoxic organic compounds that are covalently attached to the carrier proteins or that interact with them via non-covalent interactions. Human transthyretin (TTR), a physiological protein, has already been identified as a possible carrier protein for the delivery of cytotoxic drugs. Here we show the structure-guided development of a new stable cytotoxic molecule based on a known strong binder of TTR and a well-established anticancer drug. This example is used to demonstrate the importance of the integration of multiple biophysical and structural techniques, encompassing microscale thermophoresis, X-ray crystallography and NMR. In particular, we show that solid-state NMR has the ability to reveal effects caused by ligand binding which are more easily relatable to structural and dynamical alterations that impact the stability of macromolecular complexes.

Heparan sulfate proteoglycans in cancer: Pathogenesis and therapeutic potential

The heparan sulfate proteoglycans (HSPGs) are glycoproteins that consist of a proteoglycan "core" protein and covalently attached heparan sulfate (HS) chain. HSPGs are ubiquitously expressed in mammalian cells on the cell surface and in the extracellular matrix (ECM) and secretory vesicles. Within HSPGs, the protein cores determine when and where HSPG expression takes place, and the HS chains mediate most of HSPG's biological roles through binding various protein ligands, including cytokines, chemokines, growth factors and receptors, morphogens, proteases, protease inhibitors, and ECM proteins. Through these interactions, HSPGs modulate cell proliferation, adhesion, migration, invasion, and angiogenesis to display essential functions in physiology and pathology. Under physiological conditions, the expression and localization of HSPGs are finely regulated to orchestrate their physiological functions, and this is disrupted in cancer. The HSPG dysregulation elicits multiple oncogenic signaling, including growth factor signaling, ECM and Integrin signaling, chemokine and immune signaling, cancer stem cell, cell differentiation, apoptosis, and senescence, to prompt cell transformation, proliferation, tumor invasion and metastasis, tumor angiogenesis and inflammation, and immunotolerance. These oncogenic roles make HSPGs an attractive pharmacological target for anti-cancer therapy. Several therapeutic strategies have been under development, including anti-HSPG antibodies, peptides and HS mimetics, synthetic xylosides, and heparinase inhibitors, and shown promising anti-cancer efficacy. Therefore, much progress has been made in this line of study. However, it needs to bear in mind that the roles of HSPGs in cancer can be either oncogenic or tumor-suppressive, depending on the HSPG and the cancer cell type with the underlying mechanisms that remain obscure. Further studies need to address these to fill the knowledge gap and rationalize more efficient therapeutic targeting.

Advances in Self-Assembled Peptides as Drug Carriers

In recent years, self-assembled peptide nanotechnology has attracted a great deal of attention for its ability to form various regular and ordered structures with diverse and practical functions. Self-assembled peptides can exist in different environments and are a kind of medical bio-regenerative material with unique structures. These materials have good biocompatibility and controllability and can form nanoparticles, nanofibers and hydrogels to perform specific morphological functions, which are widely used in biomedical and material science fields. In this paper, the properties of self-assembled peptides, their influencing factors and the nanostructures that they form are reviewed, and the applications of self-assembled peptides as drug carriers are highlighted. Finally, the prospects and challenges for developing self-assembled peptide nanomaterials are briefly discussed.

Insight into RNA-based Therapies for Ovarian Cancer

Ovarian cancer (OC) is one of the most common malignancies in women and is associated with poor outcomes. The treatment for OC is often associated with resistance to therapies and hence this has stimulated the search for alternative therapeutic approaches, including RNA-based therapeutics. However, this approach has some challenges that include RNA degradation. To solve this critical issue, some novel delivery systems have been proposed. In current years, there has been growing interest in the improvement of RNAbased therapeutics as a promising approach to target ovarian cancer and improve patient outcomes. This paper provides a practical insight into the use of RNA-based therapeutics in ovarian cancers, highlighting their potential benefits, challenges, and current research progress. RNA-based therapeutics offer a novel and targeted approach to treat ovarian cancer by exploiting the unique characteristics of RNA molecules. By targeting key oncogenes or genes responsible for drug resistance, siRNAs can effectively inhibit tumor growth and sensitize cancer cells to conventional therapies. Furthermore, messenger RNA (mRNA) vaccines have emerged as a revolutionary tool in cancer immunotherapy. MRNA vaccines can be designed to encode tumor-specific antigens, stimulating the immune system to distinguish and eliminate ovarian cancer cells. A nano-based delivery platform improves the release of loaded RNAs to the target location and reduces the off-target effects. Additionally, off-target effects and immune responses triggered by RNA molecules necessitate careful design and optimization of these therapeutics. Several preclinical and clinical researches have shown promising results in the field of RNA-based therapeutics for ovarian cancer. In a preclinical study, siRNA-mediated silencing of the poly (ADP-ribose) polymerase 1 (PARP1) gene, involved in DNA repair, sensitized ovarian cancer cells to PARP inhibitors, leading to enhanced therapeutic efficacy. In clinical trials, mRNA-based vaccines targeting tumor-associated antigens have demonstrated safety and efficacy in stimulating immune responses in ovarian cancer patients. In aggregate, RNA-based therapeutics represent a promising avenue for the therapy of ovarian cancers. The ability to specifically target oncogenes or stimulate immune responses against tumor cells holds great potential for improving patient outcomes. However, further research is needed to address challenges related to delivery, permanence, and off-target effects. Clinical trials assessing the care and effectiveness of RNAbased therapeutics in larger patient cohorts are warranted. With continued advancements in the field, RNAbased therapeutics have the potential to develop the management of ovarian cancer and provide new hope for patients.

Peptide-Based Bioconjugates and Therapeutics for Targeted Anticancer Therapy

With rapidly growing knowledge in bioinformatics related to peptides and proteins, amino acid-based drug-design strategies have recently gained importance in pharmaceutics. In the past, peptide-based biomedicines were not widely used due to the associated severe physiological problems, such as low selectivity and rapid degradation in biological systems. However, various interesting peptide-based therapeutics combined with drug-delivery systems have recently emerged. Many of these candidates have been developed for anticancer therapy that requires precisely targeted effects and low toxicity. These research trends have become more diverse and complex owing to nanomedicine and antibody–drug conjugates (ADC), showing excellent therapeutic efficacy. Various newly developed peptide–drug conjugates (PDC), peptide-based nanoparticles, and prodrugs could represent a promising therapeutic strategy for patients. In this review, we provide valuable insights into rational drug design and development for future pharmaceutics.

The therapeutic potential of γ-Al2O3 nanoparticle containing 5-fluorouracil in the treatment of colorectal cancer

5-Fluorouracil (5-FU) is being used in the treatment of several malignancies, but side effects are often reported and include: diarrhea, vomiting, nausea, poor appetite, watery eyes, and photophobia. We have developed and tested the cytotoxic activity of nanocrystalline powder of γ-alumina (γ-Al₂O₃) containing 5-FU in two-dimensional and three-dimensional (3D) CRC cell culture. γ-Al₂O₃ was prepared using a facile sol-gel method. The physicochemical properties of nanoparticles were investigated by Fourier Transform Infrared (FTIR) analysis, Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray Analysis (EDXA). Moreover, the particle size was monitored by Transmission Electron Microscopy (TEM). We used MTT and a scratch assay to assess the antiproliferative and anti-migratory of this agent. The effect of γ-Al₂O₃-5-FU on SOD, MDA, and total-thiols levels were evaluated. We assessed the expression of apoptotic markers in mRNA or proteins by RT-PCR and ELISA respectively. γ-Al₂O₃-5-FU inhibited cell growth in two-dimensional (2D) and three-dimensional (3D) cell culture and increased apoptosis as detected by DAPI stainning via modulation of caspases, BAx, BCl2 and cyclinD1. γ-Al₂O₃-5-FU also reduced the migratory activity of CRC cells relative to untreated controls. γ-Al₂O₃-5-FU increased the level of MDA, while reducing the level of SOD and total-thiols as well as inflamatory markers (e.g., TNF-s and IL-6). Our study demonstrated that γ-Al₂O₃-5-FU inhibited cell growth and migration, indicating its potential value in the treatment of colorectal cancer.

Changes and Prognostic Value of lncRNA CASC9 in Patients with Advanced Colon Cancer after Chemotherapy

Objective

Colon cancer (CC) shows a gradual increasing incidence in recent years, and chemotherapy is a frequently adopted treatment for patients with middle or advanced colon cancer (ACC), but it lacks prognostic markers after CC.

Methods

The changes of lncRNA CASC9 in 58 patients with CC were determined using a real-time quantitative PCR (qRT-PCR) assay before and after chemotherapy, and the correlation of serum lncRNA CASC9 with efficacy of FOLFOX4 regimen (oxaliplatin + calcium folinate + fluorouracil) was analyzed. The patients were followed up to understand the association of lncRNA CASC9 with overall survival (OS) and progression-free survival (PFS).

Results

Patients with CC showed notably higher lncRNA CASC9 expression than controls, and lncRNA CASC9 presented an association with the clinical stage of the patients. In addition, lncRNA CASC9 demonstrated a clinical value in predicting efficacy on patients and acted as one independent prognostic factor for PFS in patients with ACC.

Conclusions

With increased expression of serum lncRNA CASC9, patients with ACC suffered an unfavorable chemotherapy effect. In addition, serum lncRNA CASC9 is a promising sensitive indicator for prediction of ACC and is related to the clinical efficacy and prognosis of patients.

Active Semisupervised Model for Improving the Identification of Anticancer Peptides

Cancer is one of the most dangerous threats to human health. Accurate identification of anticancer peptides (ACPs) is valuable for the development and design of new anticancer agents. However, most machine-learning algorithms have limited ability to identify ACPs, and their accuracy is sensitive to the amount of label data. In this paper, we construct a new technology that combines active learning (AL) and label propagation (LP) algorithm to solve this problem, called (ACP-ALPM). First, we develop an efficient feature representation method based on various descriptor information and coding information of the peptide sequence. Then, an AL strategy is used to filter out the most informative data for model training, and a more powerful LP classifier is cast through continuous iterations. Finally, we evaluate the performance of ACP-ALPM and compare it with that of some of the state-of-the-art and classic methods; experimental results show that our method is significantly superior to them. In addition, through the experimental comparison of random selection and AL on three public data sets, it is proved that the AL strategy is more effective. Notably, a visualization experiment further verified that AL can utilize unlabeled data to improve the performance of the model. We hope that our method can be extended to other types of peptides and provide more inspiration for other similar work.

Application and Importance of Theranostics in the Diagnosis and Treatment of Cancer

The number of cancer cases worldwide in terms of morbidity and mortality is a serious concern, despite the presence of therapeutic interventions and supportive care. Limitations in the current available diagnosis methods and treatments methods may contribute to the increase in cancer mortality. Theranostics, is a novel approach that has opened avenues for the simultaneous precise diagnosis and treatment for cancer patients. Although still in the early development stage, theranostic agents such as quantum dots, radioisotopes, liposomes and plasmonic nanobubbles can be bound to anticancer drugs, cancer cell markers and imaging agents, with the support of available imaging techniques, provide the potential to facilitate diagnosis, treatment and management of cancer patients. Herein, we discuss the potential benefits of several theranostic tools for the management of cancer. Specifically, quantum dots, radio-labelled isotopes, liposomes and plasmonic nanobubbles coupled with targeting agents and/or anticancer molecules and imaging agents as theranostic agents are deliberated upon in this review. Overall, the use of theranostic agents shows promise in cancer management. Nevertheless, intensive research is required to realize these expectations.

EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides

As cancer remains one of the main threats of human life, developing efficient cancer treatments is urgent. Anticancer peptides, which could overcome the significant side effects and poor results of traditional cancer treatments, have become a new potential alternative these years. However, identifying anticancer peptides by experimental methods is time consuming and resource consuming, it is of great significance to develop effective computational tools to quickly and accurately identify potential anticancer peptides from amino acid sequences. For most current computational methods, feature representation plays a key role in their final successes. This study proposes a novel fast and accurate approach to identify anticancer peptides using diversified feature representations and ensemble learning method. For the feature representations, the information is encoded from multidimensional feature spaces, including sequence composition, sequence-order, physicochemical properties, etc. In order to better model the potential relationships of peptides, multiple ensemble classifiers, LightGBMs, are applied to detect the different feature sets at first. Then the obtained multiple outputs are used as inputs of the support vector machine classifier, which effectively identifies anticancer peptides. Experimental results on cross validation and independent test sets demonstrate that our method can achieve better or comparable performances compared with other state-of-the-art methods.

Extracellular Vesicles-Based Drug Delivery Systems: A New Challenge and the Exemplum of Malignant Pleural Mesothelioma

Research for the most selective drug delivery to tumors represents a fascinating key target in science. Alongside the artificial delivery systems identified in the last decades (e.g., liposomes), a family of natural extracellular vesicles (EVs) has gained increasing focus for their potential use in delivering anticancer compounds. EVs are released by all cell types to mediate cell-to-cell communication both at the paracrine and the systemic levels, suggesting a role for them as an ideal nano-delivery system. Malignant pleural mesothelioma (MPM) stands out among currently untreatable tumors, also due to the difficulties in achieving an early diagnosis. Thus, early diagnosis and treatment of MPM are both unmet clinical needs. This review looks at indirect and direct evidence that EVs may represent both a new tool for allowing an early diagnosis of MPM and a potential new delivery system for more efficient therapeutic strategies. Since MPM is a relatively rare malignant tumor and preclinical MPM models developed to date are very few and not reliable, this review will report data obtained in other tumor types, suggesting the potential use of EVs in mesothelioma patients as well.