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Objective: This study involved evaluating the efficacy of the Feijinsheng formula in the therapeutic management of patients with advanced non-small cell lung cancer (NSCLC). Methods: We extracted the medical records of patients with advanced NSCLC undergoing treatment in the oncology department at the Second Affiliated Hospital of Zhejiang Chinese Medicine University from the medical record system. After applying inclusion and exclusion criteria, clinical data of 150 patients were collected. The patients were stratified into two groups based on their usage of the Feijinsheng formula, comprising 69 cases in the Exposed group and 81 cases in the Control group. A comparative analysis of the survival time difference between the two groups was conducted. Results: The data between the two groups exhibited similarity (p > 0.05). Following treatment, the Exposed group demonstrated a notably prolonged overall survival time compared to the Control group (p < 0.05). While the Exposed group displayed a higher objective remission rate than the Control group, this disparity did not reach statistical significance (p > 0.05). Conclusion: The Feijinsheng formula extended the duration of survival of patients with advanced NSCLC.

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The aim of this paper was to reveal the correlation between the expression of ELOVL fatty acid elongase 6 (ELOVL6) gene in lung adenocarcinoma (LUAD) and its clinical significance, immune cell infiltration level and prognosis. Expression profile data of ELOVL6 mRNA were collected from the cancer genome atlas database to analyze the differences in ELOVL6 mRNA expression in LUAD tissues and normal lung tissues, and to analyze the correlation between ELOVL6 and information on clinicopathological features. Based on TIMER database, TISDIB database and GEPIA2 database, the correlation between ELOVL6 expression and tumor immune cell infiltration in LUAD was analyzed. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of ELOVL6-related co-expressed genes were performed to identify the involved signaling pathways and to construct their co-expressed gene protein interaction networks. Drugs affected by ELOVL6 expression were screened based on the Cell Miner database. These findings suggest that ELOVL6 plays an important role in the course of LUAD, and the expression level of this gene has a close relationship with clinicopathological characteristics and survival prognosis, and has the potential to become a prognostic marker and therapeutic target for LUAD.

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BACKGROUND: Fei Jin Sheng Formula (FJSF) is widely used in clinical treatment of lung cancer. But the underlying active ingredients and mechanisms are unclear. OBJECTIVE: To investigate the active components and functional mechanisms of FJSF in treating lung cancer using a network pharmacology approach and molecular docking combined with vitro experiments Methods: Based on the TCMSP and related literature, the chemical components of related herbs in FJSF were collected. The active components of FJSF were screened by ADME parameters, and the targets were predicted by the Swiss Target Prediction database. The "drug-active ingredient-target" network was constructed by Cytoscape. Disease-related targets of lung cancer were acquired from GeneCards, OMIM, and TTD databases. Then drug-disease intersection target genes were obtained through the Venn tool. GO analysis and KEGG pathway enrichment analysis were performed via the Metascape database. Cytoscape was used to construct a PPI network and perform topological analysis. Kaplan-Meier Plotter was used to analyze the relationship between DVL2 and the prognosis of lung cancer patients. xCell method was used to estimate the relationship between DVL2 and immune cell infiltration in lung cancer. Molecular docking was performed by AutoDockTools-1.5.6. The results were verified by experiments in vitro. RESULTS: FJSF contained 272 active ingredients and 52 potential targets for lung cancer. GO enrichment analysis is mainly related to cell migration and movement, lipid metabolism, and protein kinase activity. KEGG pathway enrichment analysis mainly involves PI3K-Akt, TNF, HIF-1, and other pathways. Molecular docking shows that the compound Xambioona, quercetin and methyl palmitate in FJSF has a strong binding ability with NTRK1, APC, and DVL2. Analysis of the data in UCSC to analyze the expression of DVL2 in lung cancer shows that DVL2 was overexpressed in lung adenocarcinoma tissues. Kaplan-Meier analysis shows that the higher DVL2 expression in lung cancer patients was associated with poorer overall survival and poorer survival in stage I patients. It was negatively correlated with the infiltration of various immune cells in the lung cancer microenvironment. Vitro Experiment showed that Methyl Palmitate (MP) can inhibit the proliferation, migration, and invasion of lung cancer cells, and its mechanism of action may be to downregulate the expression of DVL2. CONCLUSION: FJSF may play a role in inhibiting the occurrence and development of lung cancer by downregulating the expression of DVL2 in A549 cells through its active ingredient Methyl Palmitate. These results provide scientific evidence for further investigations into the role of FJSF and Methyl Palmitate in the treatment of lung cancer.

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Single-cell RNA sequencing (scRNA-seq) is increasingly used in studies on gastrointestinal cancers. This study investigated the prognostic value of epithelial cell-associated biomarkers in colorectal cancer (CRC) using scRNA-seq data. We downloaded and analysed scRNA-seq data from four CRC samples from the Gene Expression Omnibus (GEO), and we identified marker genes of malignant epithelial cells (MECs) using CRC transcriptome and clinical data downloaded from The Cancer Genome Atlas (TCGA) and GEO as training and validation cohorts, respectively. In the TCGA training cohort, weighted gene correlation network analysis, univariate Cox proportional hazard model (Cox) analysis, and least absolute shrinkage and selection operator regression analysis were performed on the marker genes of MEC subsets to identify a signature of nine prognostic MEC-related genes (MECRGs) and calculate a risk score based on the signature. CRC patients were divided into high- and low-risk groups according to the median risk score. We found that the MECRG risk score was significantly correlated with the clinical features and overall survival of CRC patients, and that CRC patients in the high-risk group showed a significantly shorter survival time. The univariate and multivariate Cox regression analyses showed that the MECRG risk score can serve as an independent prognostic factor for CRC patients. Gene set enrichment analysis revealed that the MECRG signature genes are involved in fatty acid metabolism, p53 signalling, and other pathways. To increase the clinical application value, we constructed a MECRG nomogram by combining the MECRG risk score with other independent prognostic factors. The validity of the nomogram is based on receiver operating characteristics and calibration curves. The MECRG signature and nomogram models were well validated in the GEO dataset. In conclusion, we established an epithelial cell marker gene-based risk assessment model based on scRNA-seq analysis of CRC samples for predicting the prognosis of CRC patients.

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