Natural product fargesin interferes with H3 histone lactylation via targeting PKM2 to inhibit non-small cell lung cancer tumorigenesis.

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors. There is an urgent need to find more effective drugs that inhibit NSCLC. Fargesin (FGS) has demonstrated anti-tumor effects; however, its efficacy and the molecular mechanism of inhibiting NSCLC are unclear. Herein, we investigated FGS' inhibitory effects on NSCLC by CCK8 and EdU assays and cell cycle analysis of A549 cells in vitro and in a nude mouse tumor transplantation model in vivo. FGS (10-50 µM) significantly inhibited cell proliferation and down-regulated expression levels of CDK1 and CCND1. Transcriptomic analysis showed that FGS regulated the cell metabolic process pathway. Differential metabolites with FGS treatment were enriched in glycolysis and pyruvate pathways. Cell metabolism assay were used to evaluate the oxygen consumption rate (OCR), Extracellular acidification rate (ECAR) in A549 cells. FGS also inhibited the production of cellular lactate and the expression of LDHA, LDHB, PKM2, and SLC2A1. These genes were identified as important oncogenes in lung cancer, and their binding to FGS was confirmed by molecular docking simulation. Notably, the over-expression and gene silencing experiments signified PKM2 as the molecular target of FGS for anti-tumorigenesis. Moreover, the H3 histone lactylation, were correlated with tumorigenesis, were inhibited with FGS treatment. Conclusively, FGS inhibited the aerobic glycolytic and H3 histone lactylation signaling pathways in A549 NSCLC cells by targeting PKM2. These findings provide evidence of the therapeutic potential of FGS in NSCLC.

Endoplasmic Reticulum Stress-Related Four-Biomarker Risk Classifier for Survival Evaluation in Esophageal Cancer.

Purpose: Esophageal cancer (EC) is a lethal digestive tumor worldwide with a dismal clinical outcome. Endoplasmic reticulum (ER) stress poses essential implications for a variety of tumor malignant behaviors. Here, we set up an ER stress-based risk classifier for assessing patient outcome and exploiting robust targets for medical decision-making of EC cases. Methods: 340 EC cases with transcriptome and survival data from two independent public datasets (TCGA and GEO) were recruited for this project. Cox regression analyses were employed to create a risk classifier based on ER stress-related genes (ERGs) which were strongly linked to EC cases' outcomes. Then, we detected and confirmed the predictive ability of our proposed classifier via a host of statistical methods, including survival analysis and ROC method. In addition, immune-associated algorithm was implemented to analyze the immune activity of EC samples. Results: Four EGRs (BCAP31, HSPD1, PDHA1, and UBE2D1) were selected to build an EGR-related classifier (ERC). This classifier could distinguish the patients into different risky subgroups. The remarkable differences in patient outcome between the two groups were observed, and similar results were also confirmed in GEO cohort. In terms of the immune analysis, the ERC could forecast the infiltration level of immunocytes, such as Tregs and NK cells. Conclusion: We created a four-ERG risk classifier which displays the powerful capability of survival evaluation for EC cases.