Identification of anti-gastric cancer effects and molecular mechanisms of resveratrol: From network pharmacology and bioinformatics to experimental validation

Unveiling esophageal cancer treatment mechanisms: network pharmacology and molecular docking of Physcion

This study investigates the effects of Physcion on esophageal cancer and its possible mechanisms of action. Potential Physcion targets were identified using databases. Transcriptomic data from 17 esophageal cancer and adjacent tissues were analyzed to find differentially expressed genes, intersecting with potential targets to select 16 key genes. Their expression and distribution were evaluated in patient sequencing data. Diagnostic potential was assessed through differential gene expression and ROC curves. Pathway enrichment analysis was performed using KEGG, and molecular docking simulations were conducted to assess Physcion's binding affinity to key genes. In vitro assays complemented these findings. A total of 161 drug targets were identified, narrowing down to 16 pivotal genes. Expression patterns were examined across cell populations, and enrichment analysis showed significant PI3K/AKT pathway involvement. Molecular docking indicated strong binding of Physcion to HSP90AA1 and MMP2. In vitro assays confirmed Physcion's dose- and time-dependent impact on esophageal cancer cells, with significant DAPI staining effects. Physcion shows promise as a therapeutic agent for esophageal cancer. The study supports its potential for clinical development and future research in esophageal cancer treatment.

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

BACKGROUND

Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course.

METHODS

We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data.

RESULTS

We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922).

CONCLUSIONS

The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.