integrating-network-pharmacology-to-investigate-cinnamaldehyde-s

Integrating Network Pharmacology to Investigate Cinnamaldehyde's Protective Effect Against Oxaliplatin-induced Neurotoxicity

This study investigates the protective effects of cinnamaldehyde (CA) against oxaliplatin (OXA)-induced damage in rat dorsal root ganglion (DRG) cells. ...

Liaoning Cancer Hospital & Institute

Fatty acid oxidation (FAO) plays a crucial role in glioma metabolism and its interaction with the immune microenvironment. The aim of the present study was to investigate the relationship between FAO-related genes and glioma by constructing gene clusters using a glioma cohort. A total of 287 differentially expressed genes related to FAO were identified and the top 50 genes were selected based on their P-values. Subsequently, patients were classified into two distinct gene subtypes (A and B) based on these genes. Scores for each patient were calculated using the 50 genes and the patients were divided into the high and low-score groups accordingly. Patients in subtype B exhibited higher tumor grades and poor prognostic factors such as older age and worse survival rates. The high-score subgroup had unfavorable indicators, including isocitrate dehydrogenase 1 wild-type, high tumor grade and 1p19q non-codeleted, while immune checkpoint expression was generally higher in the high-score subgroup. The constructed scoring model was validated using an external dataset, and the tissue inhibitor of metalloproteinase 1 gene was identified through protein interaction analysis, suggesting its potential involvement in glioma malignancy and promotion of glioblastoma proliferation. In conclusion, FAO-related genes may contribute to tumor development through immune mechanisms and the present study may provide novel insights for potential therapeutic strategies in glioma treatment.

Identification and validation of prognostic genes and immune landscape signatures based on a fatty acid oxidation‑related risk score model in glioma.

Colorectal cancer (CRC) is a prevalent malignant tumor in the digestive system. As reported in the 2020 global cancer statistics, CRC accounted for more than 1.9 million new cases and 935,000 deaths, making it the third most common cancer worldwide in terms of incidence and the second leading cause of cancer-related deaths globally. This poses a significant threat to global public health. Early screening methods, such as fecal occult blood tests, colonoscopies, and imaging techniques, are crucial for detecting early lesions and enabling timely intervention before cancer becomes invasive. Early detection greatly enhances treatment possibilities, such as surgery, radiation therapy, and chemotherapy, with surgery being the main approach for treating early-stage CRC. In this context, artificial intelligence (AI) has shown immense potential in revolutionizing CRC management, serving as one of the most effective screening tools. AI, utilizing machine learning (ML) and deep learning (DL) algorithms, improves early detection, diagnosis, and treatment by processing large volumes of medical data, uncovering hidden patterns, and forecasting disease development. DL, a more advanced form of ML, simulates the brain's processing power, enhancing the accuracy of tumor detection, differentiation, and prognosis predictions. These innovations offer the potential to revolutionize cancer care by boosting diagnostic accuracy, refining treatment approaches, and ultimately enhancing patient outcomes.

Advancements in the application of artificial intelligence in the field of colorectal cancer.

Oxaliplatin, a third-generation platinum-based chemotherapeutic agent, has shown substantial efficacy in cancer treatment. However, its associated side effects, particularly chemotherapy-induced peripheral neuropathic pain (CIPNP), continue to challenge cancer survivors globally. Clinically, it frequently presents as numbness, coldness, and discomfort in the limbs and extremities. Duloxetine is advised for analgesic purposes. Despite its clinical relevance, both the application methods and the underlying mechanisms of oxaliplatin-induced CINP warrant further investigation. Consequently, more precise animal models are needed to explore the mechanisms and progression of this condition. This review consolidates recent advancements in rat and mouse models of oxaliplatin-induced CINP, with the aim of enhancing modeling success rates and developing models that more accurately mirror disease progression. Such models are essential for advancing clinical research and drug development.

Article	Year
Identification and validation of prognostic genes and immune landscape signatures based on a fatty acid oxidation‑related risk score model in glioma. Oncology letters\| PubMed ID: 38249808	2024
Advancements in the application of artificial intelligence in the field of colorectal cancer. Frontiers in oncology\| PubMed ID: 40071094	2025
Oxaliplatin-induced neuropathic pain in cancer: animal models and related research progress. Frontiers in pharmacology\| PubMed ID: 40520182	2025

Zhenzhu Zhai

Biography

JoVE Journal Publications

Other Publications