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BACKGROUND: Non-small cell lung cancer (NSCLC) is the primary form of lung cancer, and the combination of chemotherapy with immunotherapy offers promising treatment options for patients suffering from this disease. However, the emergence of drug resistance significantly limits the effectiveness of these therapeutic strategies. Consequently, it is imperative to devise methods for accurately detecting and evaluating the efficacy of these treatments. AIM: To identify the metabolic signatures associated with neutrophil extracellular traps (NETs) and chemoimmunotherapy efficacy in NSCLC patients. METHODS: In total, 159 NSCLC patients undergoing first-line chemoimmunotherapy were enrolled. We first investigated the characteristics influencing clinical efficacy. Circulating levels of NETs and cytokines were measured by commercial kits. Liquid chromatography tandem mass spectrometry quantified plasma metabolites, and differential metabolites were identified. Least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest algorithms were employed. By using plasma metabolic profiles and machine learning algorithms, predictive metabolic signatures were established. RESULTS: First, the levels of circulating interleukin-8, neutrophil-to-lymphocyte ratio, and NETs were closely related to poor efficacy of first-line chemoimmunotherapy. Patients were classed into a low NET group or a high NET group. A total of 54 differential plasma metabolites were identified. These metabolites were primarily involved in arachidonic acid and purine metabolism. Three key metabolites were identified as crucial variables, including 8,9-epoxyeicosatrienoic acid, L-malate, and bis(monoacylglycerol)phosphate (18:1/16:0). Using metabolomic sequencing data and machine learning methods, key metabolic signatures were screened to predict NET level as well as chemoimmunotherapy efficacy. CONCLUSION: The identified metabolic signatures may effectively distinguish NET levels and predict clinical benefit from chemoimmunotherapy in NSCLC patients.
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BACKGROUND: Chaihu-Shugan-San (CHSGS), a traditional Chinese medicinal herbal formula, registered in Jingyue Quanshu, has been indicated that oral administration of the extract from it can remit depressive disorder. C-Jun amino-terminal kinase (JNK/SAPK) signal transduction plays a key role in the apoptosis of nerve cells, be reported closely correlated with depression. This study was designed to investigate CHSGS antidepressant-like effects in rat models of depression and probe its possible mechanism. MATERIALS AND METHODS: The classical experimental depression model chronic mild unpredictable stress (CMUS) was used to evaluate the antidepressant-like effects of CHSGS. The extracts were administered orally for 14 days, while the parallel positive control was given at the same time using fluoxetine hydrochloride. The expressions of JNK in the hippocampus were detected by real-time fluorescent quantitation PCR and Western blot assay. RESULTS: Intragastric administration of CHSGS for 14 days caused a significant improvement of weight and locomotor activity in the open-field test. In addition, CHSGS treatment inhibited the expressions of JNK in the hippocampus tissue in CMUS rats. CONCLUSION: CHSGS could obviously improve the depressive state of the model rats and its mechanism may be correlated with regulating the expressions of JNK in the hippocampus.