In Silico Drug Screening Analysis against the Overexpression of PGAM1 Gene in Different Cancer Treatments

Phytochemicals Showing Antiangiogenic Effect in Pre-clinical Models and their Potential as an Alternative to Existing Therapeutics

Angiogenesis, the formation of new blood vessels from a pre-existing vascular network, is an important hallmark of several pathological conditions, such as tumor growth and metastasis, proliferative retinopathies, including proliferative diabetic retinopathy and retinopathy of prematurity, age-related macular degeneration, rheumatoid arthritis, psoriasis, and endometriosis. Putting a halt to pathology-driven angiogenesis is considered an important therapeutic strategy to slow down or reduce the severity of pathological disorders. Considering the attrition rate of synthetic antiangiogenic compounds from the lab to reaching the market due to severe side effects, several compounds of natural origin are being explored for their antiangiogenic properties. Employing pre-clinical models for the evaluation of novel antiangiogenic compounds is a promising strategy for rapid screening of antiangiogenic compounds. These studies use a spectrum of angiogenic model systems that include HUVEC two-dimensional culture, nude mice, chick chorioallantoic membrane, transgenic zebrafish, and dorsal aorta from rats and chicks, depending upon available resources. The present article emphasizes the antiangiogenic activity of the phytochemicals shown to exhibit antiangiogenic behavior in these well-defined existing angiogenic models and highlights key molecular targets. Different models help to get a quick understanding of the efficacy and therapeutics mechanism of emerging lead molecules. The inherent variability in assays and corresponding different phytochemicals tested in each study prevent their immediate utilization in clinical studies. This review will discuss phytochemicals discovered using suitable preclinical antiangiogenic models, along with a special mention of leads that have entered clinical evaluation.

Comparative proteomics analysis in different stages of urothelial bladder cancer for identification of potential biomarkers: highlighted role for antioxidant activity

BACKGROUND

Non-muscle-invasive bladder cancer (NMIBC) has a high recurrence rate and muscle-invasive bladder cancer (MIBC) has unfavorable outcomes in urothelial bladder cancer (UBC) patients. Complex UBC-related protein biomarkers for outcome prediction may provide a more efficient management approach with an improved clinical outcome. The aim of this study is to recognize tumor-associated proteins, which are differentially expressed in different stages of UBC patients compared non-cancerous tissues.

METHODS

The proteome of tissue samples of 42 UBC patients (NMIBC n = 25 and MIBC n = 17) was subjected to two-dimensional electrophoresis (2-DE) combined with Liquid chromatography-mass spectrometry (LC-MS) system to identify differentially expressed proteins. The intensity of protein spots was quantified and compared with Prodigy SameSpots software. Functional, pathway, and interaction analyses of identified proteins were performed using geneontology (GO), PANTHER, Reactome, Gene MANIA, and STRING databases.

RESULTS

Twelve proteins identified by LC-MS showed differential expression (over 1.5-fold, p < 0.05) by LC-MS, including 9 up-regulated in NMIBC and 3 up-regulated in MIBC patients. Proteins involved in the detoxification of reactive oxygen species and cellular responses to oxidative stress showed the most significant changes in UBC patients. Additionally, the most potential functions related to these detected proteins were associated with peroxidase, oxidoreductase, and antioxidant activity.

CONCLUSION

We identified several alterations in protein expression involved in canonical pathways which were correlated with the clinical outcomes suggested might be useful as promising biomarkers for early detection, monitoring, and prognosis of UBC.

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine

BACKGROUND

Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE

This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN

We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS

The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT

Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS

The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

In silico investigations identified Butyl Xanalterate to competently target CK2α (CSNK2A1) for therapy of chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is an incurable malignancy of B-cells. In this study, bioinformatics analyses were conducted to identify possible pathogenic roles of CK2α, which is a protein encoded by CSNK2A1, in the progression and aggressiveness of CLL. Furthermore, various computational tools were used to search for a competent inhibitor of CK2α from fungal metabolites that could be proposed for CLL therapy. In CLL patients, high-expression of CSNK2A1 was associated with early need for therapy (n = 130, p < 0.0001) and short overall survival (OS; n = 107, p = 0.005). Consistently, bioinformatics analyses showed CSNK2A1 to associate with/play roles in CLL proliferation and survival-dependent pathways. Furthermore, PPI network analysis identified interaction partners of CK2α (PPI enrichment p value = 1 × 10^-16) that associated with early need for therapy (n = 130, p < 0.003) and have been known to heavily impact on the progression of CLL. These findings constructed a rational for targeting CK2α for CLL therapy. Consequently, computational analyses reported 35 fungal metabolites out of 5820 (filtered from 19,967 metabolites) to have lower binding energy (ΔG: - 10.9 to - 11.7 kcal/mol) and better binding affinity (Kd: 9.77 × 10⁷ M^-1 to 3.77 × 10⁸ M^-1) compared with the native ligand (ΔG: - 10.8, Kd: 8.3 × 10⁷ M-^-1). Furthermore, molecular dynamics simulation study established that Butyl Xanalterate-CK2α complex continuously remained stable throughout the simulation time (100 ns). Moreover, Butyl Xanalterate interacted with most of the catalytic residues, where complex was stabilized by more than 65% hydrogen bond interactions, and a significant hydrophobic interaction with residue Phe113. Here, high-expression of CSNK2A1 was implicated in the progression and poor prognosis of CLL, making it a potential therapeutic target in the disease. Butyl Xanalterate showed stable and strong interactions with CK2α, thus we propose it as a competitive inhibitor of CK2α for CLL therapy.

Phosphoglycerate mutase 1 that is essential for glycolysis may act as a novel metabolic target for predicating poor prognosis for patients with gastric cancer

BACKGROUND

To identify a novel marker for gastric cancer, we examined the usefulness of phosphoglycerate mutase 1 (PGAM1) as a potential diagnostic marker using isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics and evaluated its clinical significance.

METHODS

Proteins from a discovery group of four paired gastric cancer tissues and adjacent gastric tissues were labeled with iTRAQ reagents and then identified and quantified using LC-MS/MS. The expression of PGAM1 was further validated in 139 gastric cancer patients using immunohistochemistry. Furthermore, the correlation of PGAM1 expression with clinical parameters was analyzed. Gene set enrichment analysis (GSEA) was performed to identify gene sets that were activated in PGAM1-overexpressing patients with gastric cancer.

RESULTS

PGAM1 was significantly overexpressed in most cancers but particularly so in gastric cancer, with a sensitivity of 82.01% (95% confidence interval [CI]: 75.5%-88.5%) and specificity of 79.13% (95% CI: 72.3%-86%). Its expression was significantly associated with histological grade II and III tumors (p = 0.033), lymph node metastasis (p = 0.031), and TNM III-IV staging (p = 0.025). The area under the receiver operating characteristic (ROC) curve for the detection of PGAM1 overexpression in gastric cancer was 0.718 (p < 0.01). Furthermore, GSEA revealed that several important pathways such as glycolysis pathway and immune pathways were significantly enriched in patients with gastric cancer with PGAM1 overexpression.

CONCLUSIONS

This study provided a sensitive method for detecting PGAM1, which may serve as a novel indicator for poor prognosis of gastric cancer, as well as a potent drug target for gastric cancer.