Construction of an anoikis-associated lncRNA-miRNA-mRNA network reveals the prognostic role of β-elemene in non-small cell lung cancer

β-elemene: A promising natural compound in lung cancer therapy

Lung cancer, a leading cause of cancer-related mortality globally, presents complex challenges in treatment and disease management. This review explores β-elemene, a sesquiterpene from Curcuma wenyujin, emphasising its pharmacological effects and therapeutic mechanisms in lung cancer. Focusing on its roles in modulating cellular pathways, this study details β-elemene's influence on apoptosis, autophagy, ferroptosis, hypoxic responses, metabolic shifts, and cell cycle arrest, as well as its impact on the tumour microenvironment and regulatory pathways (including PI3K/AKT, STAT3, AMPK/MAPK) and non-coding RNAs. The potential of β-elemene as a complementary agent in chemotherapy, radiotherapy, and hyperthermia therapy is examined, underscoring its capability to bolster treatment efficacy and counter drug resistance. The review also addresses current obstacles in clinical use, notably bioavailability issues, and explores innovative delivery systems like liposomes and microemulsions designed to enhance therapeutic delivery. Although preclinical studies indicate significant anti-tumor effects, further research is needed to address clinical translation challenges. Collectively, this review highlights β-elemene's multi-targeted therapeutic potential in lung cancer, advocating for ongoing research to refine its clinical use and optimize patient outcomes.

Comparative Analysis of Volatile Components in Chi-Nan and Ordinary Agarwood Aromatherapies: Implications for Sleep Improvement

Agarwood, a precious traditional medicinal herb and fragrant material, is known for its sedative and sleep-improving properties. This study explores the mechanisms underlying the aromatherapy effects of Chi-Nan agarwood and ordinary agarwood in improving sleep. Using a combination of gas chromatography-mass spectrometry (GC-MS), network pharmacology, and molecular docking techniques, we identified and c ompared the chemical compositions and potential molecular targets of both types of agarwood. The GC-MS analysis detected 87 volatile components across six types of agarwood aromatherapy, with 51 shared between Chi-Nan and ordinary agarwood, while each type also had 18 unique components. Ordinary agarwood was found to be richer in sesquiterpenes and small aromatic molecules, whereas Chi-Nan agarwood contained higher levels of chromones. These differences in chemical composition are likely responsible for the distinct sleep-improving effects observed between the two types of agarwood. Through network pharmacology, 100, 65, and 47 non-repetitive target genes related to sleep improvement were identified for components shared by both types of agarwood (CSBTs), components unique to common agarwood (CUCMs), and components unique to Chi-Nan agarwood (CUCNs), respectively. The constructed protein-protein interaction (PPI) networks revealed that key targets such as MAOA, MAOB, SLC6A4, and ESR1 are involved in the sleep-improving mechanisms of agarwood aromatherapy. Molecular docking further confirmed the strong binding affinities of major active components, such as 5-Isopropylidene-6-methyldeca-369-trien-2-one and 2-(2-Phenylethyl)chromone, with these core targets. The results suggest that agarwood aromatherapy enhances sleep quality through both hormonal and neurotransmitter pathways, with ordinary agarwood more deeply mediating hormonal regulation, while Chi-Nan agarwood predominantly influences neurotransmitter pathways, particularly those involving serotonin and GABA. This study provides valuable insights into the distinct therapeutic potentials of Chi-Nan and ordinary agarwood, highlighting their roles in sleep improvement and offering a foundation for future research in the clinical application of agarwood-based aromatherapy.

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.