Background: Roburic acid (ROB) is a newly discovered tetracyclic triterpene acid extracted from oak galls, which has anti-inflammatory effects, but the mechanism of its anticancer effect is not clear. Our study focuses on exploring the potential mechanism of action of ROB in the treatment of lung cancer using a combination of network pharmacological prediction, molecular docking technique and experimental validation.Methods: A network pharmacology approach was used to screen the protein targets of ROB and lung cancer, and PPI network analysis and enrichment analysis were performed on the intersecting genes. The tissue and organ distribution of the targets was also evaluated based on the BioGPS database. To ensure the reliability of the network pharmacology prediction results, we proceeded to use molecular docking technique to determine the relationship between drugs and targets. Finally, in vitro experiments with cell lines were performed to further reveal the potential mechanism of ROB for the treatment of lung cancer.Results: A total of 83 potential targets of ROB in lung cancer were collected and further screened by using Cytoscape software, and 7 targets of PTGS2, CYP19A1, PTGS1, AR, CYP17A1, PTGES and SRD5A1 were obtained as hub genes and 7 hub targets had good binding energy with ROB. GO and KEGG analysis showed that ROB treatment of lung cancer mainly involves Arachidonic acid metabolism, Notch signaling pathway, cancer pathway and PPAR signaling pathway. The results of in vitro experiments indicated that ROB may inhibit the proliferation and metastasis of lung cancer cells and activate the PPARγ signaling pathway, as well as induce cellular autophagy.Conclusions: The results of this study comprehensively elucidated the potential targets and molecular mechanisms of ROB for the treatment of lung cancer, providing new ideas for further lung cancer therapy.

Background The unique stress response state of T cells (Tstr) is found in various cancers and correlates with altered Lung Adenocarcinoma (LUAD) tumor microenvironment and immunotherapy outcomes. However, fewer studies have been described on the relationship between Tstr and LUAD, and its mechanism of action in the pathogenesis of LUAD needs to be further investigated. Methods The categorization and validation of cluster types for Tstr signature genes were systematically carried out using transcriptomic data sourced from the TCGA and GEO databases. The focus was on elucidating distinct pharmacological characteristics, tumor microenvironmental traits, and immunotherapeutic benefits across various subtypes. Moreover, a predictive model was developed through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, and subsequent cellular experiments confirmed the pivotal role of CREG2 in driving LUAD cell proliferation and migration. Results Tstr signature genes represented by the heat shock protein family are highly expressed at the end of T-cell differentiation and affect cellular communication. Patients with the C1 subtype have a poorer prognosis and high expression of HSPA1A and HSPA1B, which exhibit cold tumor characteristics and define them as important Tstr subtypes. CREG2 is positioned as a promising biological indicator as it plays an important part in enhancing the migratory and proliferative capacity of LUAD cells. Discussion This study reveals the characteristics of different Tstr types. A predictive model was built to forecast the prognosis of LUAD patients based on the DEGs of the subtype. The exploration delved into the bio-function of CREG2 in the progression of LUAD.

Background: M2-like tumor-associated macrophages (TAMs) promote an immunosuppressive microenvironment and contribute to tumor progression and metastasis. However, their molecular characterization and prognostic value have not been fully explored in the field of breast cancer.Methods: Weighted gene co-expression network analysis (WGCNA) was used to identify modules significantly associated with M2-like TAMs. Consensus clustering analysis identified three molecular subtypes with distinct clinical features, and we explored potential differences in genomic mutations, pathway enrichment, and immune infiltration in patients between subtypes. Machine learning algorithms were used to screen key genes and construct M2-like macrophage-associated prognostic models. Comprehensive transcriptomic analysis and in vitro phenotyping and polarization experiments were performed on the key gene DLG3.Results: M2-like TAMs infiltration was strongly associated with the prognosis of BC patients, and the associated gene characterization revealed three molecular subtypes, of which C2 has the worst prognosis with high M2 macrophages, immune desert phenotype, and immunotherapeutic resistance; C1 had the best prognosis, rich in stromal and immune cell infiltration, and metabolic pathway activation; and C3 had a high level of TILs and genomic mutations, with a high degree of immunogenicity and immunotherapeutic Potential. Risk scores can effectively predict the prognosis and immunotherapy response of BC patients, in which DLG3 is a key gene that may be involved in shaping the immunosuppressive microenvironment of breast cancer, and down-regulation of DLG3 can inhibit M2 polarization of macrophages.Discussion: We constructed and comprehensively solved a model of M2-like TAM-related molecular subtypes and prognosis, which helps stratify and customize treatment regimens for BC patients. We also explored the role of DLG3 in BC progression and macrophage polarization.

Background: Epigenetic heterogeneity has been demonstrated in a wide range of cancers including leukemia, colorectal cancer, and glioma; in the case of low-grade glioma (LGG), its association with tumor malignant progression and immune microenvironmental regulation has been less reported. This study aimed to further explore the application of epigenetics in LGG. Methods: Epigenetic subtypes were identified based on consensus clustering of multi-omics data, and prognostic models were constructed based on 101 machine learning combinations. Biological differences among different risk groups were explored by functional enrichment analysis, and finally Jumonji Domain-Containing 8 (JMJD8) was identified as a potential molecular marker in LGGs by pan-cancer survival analysis. Results: We revealed three epigenetic molecular subtypes in LGG patients, and the chromosomal regulator-associated risk score, defined as an independent prognostic factor for LGGs, was effective in predicting the response to treatment in LGG patients. JMJD8, a key model gene, was also strongly associated with the prognosis of LGG patients. Conclusions: This study reveals the molecular pattern and prognostic model of epigenetic inheritance in LGG, and the key gene JMJD8 is a potential prognostic biomarker of LGG, which is a guide for the clinical application of epigenetics.