Exploring the mechanism of Erteng-Sanjie capsule in treating gastric and colorectal cancers via network pharmacology and in-vivo validation.

ETHNOPHARMACOLOGICAL RELEVANCE: The Erteng-Sanjie capsule (ETSJC) has therapeutic effects against gastric cancer (GC) and colorectal cancer (CRC). However, its underlying pharmacological mechanism remains unclear. AIM OF THE STUDY: To explore the pharmacological mechanism of ETSJC against GC and CRC via network pharmacology and in-vivo validation. MATERIALS AND METHODS: Data on the ingredients of ETSJC were obtained from the TCMSP and HERB databases. Further, details on the related targets of the active ingredients were collected from the HERB and SwissTargetPrediction databases. The targets in GC and CRC, which were screened from the OMIM, GeneCards, and TTD databases, were uploaded to STRING for a separate protein-protein interaction network analysis. The common targets shared by ETSJC, GC, and CRC were then screened. Cytoscape and STRING were used to construct the networks of herbs-compounds-targets and PPI. Metascape was utilized to analyze the enrichment of the GO and KEGG pathways. Molecular docking was used to validate the potential binding mode between the core ingredients and targets. Finally, the predicted results were verified with animal experiment. RESULTS: Eight core ingredients (resveratrol, quercetin, luteolin, baicalein, delphinidin, kaempferol, pinocembrin, and naringenin) and six core targets (TP53, SRC, PIK3R1, AKT1, MAPK3, and STAT3) were filtered via network analysis. The molecular mechanism mainly involved the positive regulation of various processes such as cell migration, protein phosphorylation, and the PI3K-Akt signaling pathway. Molecular docking revealed that the core ingredients could be significantly combined with all core targets. The animal experiment revealed that ETSJC could suppress proliferation and promote apoptosis of both GC and CRC tumor cells by regulating the PI3K/Akt signaling pathway. CONCLUSIONS: Multiple targets (TP53, SRC, AKT1, and STAT3) were important in GC and CRC. ETSJC could act on these targets and engage in different pathways against GC and CRC. Simultaneously, inhibiting the PI3K/Akt signaling pathway was a promising therapeutic mechanism for treating GC and CRC.

Evaluation of Small Molecules in Blank EDTA Plasma Tubes and Optimization of Metabolomic Workflow for Biomarker Studies Using Plasma Samples.

As the first step of metabolomic analysis in biomarker identification studies, various types of blood collection tubes are used in clinical practice. However, little attention is paid to potential contamination caused by the blank tube itself. Here, we evaluated small molecules in blank EDTA plasma tubes through LC-MS-based untargeted metabolomic analysis and identified small molecules with markedly varied levels among different production batches or specifications. Our data demonstrate possible contamination and data interference caused by blank EDTA plasma tubes when employing large clinical cohorts for biomarker identification. Therefore, we propose a workflow of filtering metabolites in blank tubes prior to statistical analysis to improve the fidelity of biomarker identification.

Identification of Plasma Metabolites Associated with Lung Cancer Survival.

Limited knowledge has been reported regarding the performance of plasma metabolomics for predicting lung cancer prognosis. In this chapter, we compared the plasma metabolomics of lung cancer patients with differential disease-free survival (DFS, <3 years vs. >4 years) using liquid chromatography-mass spectrometry. We identified 29 survival-related aqueous metabolites but no lipid metabolites. Amino acids and organic acids constitute the majority of these metabolites. The metabolic pathways of these metabolites were cysteine and methionine metabolism and arginine biosynthesis. The Cox proportional hazards regression models confirmed the predictive values of 18 metabolites for DFS, while the phosphocholine and xanthine showed independent predictive values. Regarding cancer phenotypes, thelephoric acid, phosphocholine, inosine, 3-hydroxyanthranilic acid, hypoxanthine, xanthine, and 4-hydroxybenzoic acid showed good correction with lymph node metastasis. Taken together, plasma metabolomics is a powerful tool for identifying prognostic metabolites of lung cancer.

Development of a DNA aptamer targeting IDO1 with anti-tumor effects.

Immune checkpoint blockade has become an effective approach to reverse the immune tolerance of tumor cells. Indoleamine 2,3-dioxygenase 1 (IDO1) is frequently upregulated in many types of cancers and contributes to the establishment of an immunosuppressive cancer microenvironment, which has been thought to be a potential target for cancer therapy. However, the development of IDO1 inhibitors for clinical application is still limited. Here, we isolated a DNA aptamer with a strong affinity and inhibitory activity against IDO1, designated as IDO-APT. By conjugating with nanoparticles, in situ injection of IDO-APT to CT26 tumor-bearing mice significantly suppresses the activity of regulatory T cells and promotes the function of CD8+ T cells, leading to tumor suppression and prolonged survival. Therefore, this functional IDO1-specific aptamer with potent anti-tumor effects may serve as a potential therapeutic strategy in cancer immunotherapy. Our data provide an alternative way to target IDO1 in addition to small molecule inhibitors.

Early identification of STEMI patients with emergency chest pain using lipidomics combined with machine learning.

OBJECTIVES: To analyze the differential expression of lipid spectrum between ST-segment elevated myocardial infarction (STEMI) and patients with emergency chest pain and excluded coronary artery disease (CAD), and establish the predictive model which could predict STEMI in the early stage. METHODS: We conducted a single-center, nested case-control study using the emergency chest pain cohort of Peking University Third Hospital. Untargeted lipidomics were conducted while LASSO regression as well as XGBoost combined with greedy algorithm were used to select lipid molecules. RESULTS: Fifty-two STEMI patients along with 52 controls were enrolled. A total of 1925 lipid molecules were detected. There were 93 lipid molecules in the positive ion mode which were differentially expressed between the STEMI and the control group, while in the negative ion mode, there were 73 differentially expressed lipid molecules. In the positive ion mode, the differentially expressed lipid subclasses were mainly diacylglycerol (DG), lysophophatidylcholine (LPC), acylcarnitine (CAR), lysophosphatidyl ethanolamine (LPE), and phosphatidylcholine (PC), while in the negative ion mode, significantly expressed lipid subclasses were mainly free fatty acid (FA), LPE, PC, phosphatidylethanolamine (PE), and phosphatidylinositol (PI). LASSO regression selected 22 lipids while XGBoost combined with greedy algorithm selected 10 lipids. PC (15: 0/18: 2), PI (19: 4), and LPI (20: 3) were the overlapping lipid molecules selected by the two feature screening methods. Logistic model established using the three lipids had excellent performance in discrimination and calibration both in the derivation set (AUC: 0.972) and an internal validation set (AUC: 0.967). In 19 STEMI patients with normal cardiac troponin, 18 patients were correctly diagnosed using lipid model. CONCLUSIONS: The differentially expressed lipids were mainly DG, CAR, LPC, LPE, PC, PI, PE, and FA. Using lipid molecules selected by XGBoost combined with greedy algorithm and LASSO regression to establish model could accurately predict STEMI even in the more earlier stage.

Identification of a new small molecule that initiates ferroptosis in cancer cells by inhibiting the system Xc- to deplete GSH.

Ferroptosis is a non-apoptotic cell death characterized by iron-mediated ROS accumulation and increasing lipid peroxidation. The activation of ferroptosis results in the destruction of cancer cells and overcoming the drug resistance associated with existing chemotherapeutic agents. It is essential to develop new ferroptosis inducers to provide new opportunities for cancer therapy. In this study, we found a small molecule Compound 8 which we had demonstrated to inhibit tumor growth in vivo initiated ferroptosis. Compound 8 treatment elevated the ferroptosis-related genes PTGS2 and CHAC1 mRNA levels in tumor cells. Ferroptosis inhibitors but not the necroptosis inhibitor or the apoptosis inhibitor can suppress the cell death induced by Compound 8. Compound 8 causes overall greater quantity of lipid peroxidation than the classic ferroptosis inducer Erastin through Flow cytometry analysis. The non-targeted lipidomic analysis also showed Compound 8 treatment resulted in oxidized lipid metabolites, similar to Erastin. The mechanism research showed that Compound 8 initiated ferroptosis by inhibiting the system Xc- to deplete GSH. Based on our previous study that Compound 8 blocked the interaction of PKM2 and VDAC3 (a regulator of ferroptosis) to inhibit tumor growth in vivo, Compound 8 may also trigger ferroptosis by regulating VADC3. Thus, Compound 8 not only will offer a potential tumor therapeutic alternative, but also provide an entrance to explore the new mechanism of ferroptosis.

FAM69C, a kinase critical for synaptic function and memory, is defective in neurodegenerative dementia.

Synapse loss and memory decline are the primary features of neurodegenerative dementia. However, the molecular underpinnings that drive memory loss remain largely unknown. Here, we report that FAM69C is a kinase critically involved in neurodegenerative dementia. Biochemical analyses uncover that FAM69C is a serine/threonine kinase. We generate the Fam69c knockout mice and show by single-cell RNA sequencing that FAM69C deficiency drives cell-type-specific transcriptional changes relevant to synapse dysfunction. Electrophysiological, morphological, and behavioral experiments demonstrate impairments in synaptic plasticity, dendritic spine density, and memory in Fam69c knockout mice, as well as stress-induced neuronal death. Phosphoproteomic characterizations reveal that FAM69C substrates are involved in synaptic structure and function. Finally, reduced levels of FAM69C are found in postmortem brains of Alzheimer's disease patients. Our study demonstrates that FAM69C is a protective regulator of memory and suggests FAM69C as a potential therapeutic target for memory loss in neurodegenerative dementia.

Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning.

BACKGROUND: Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs. METHODS: Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers. FINDINGS: A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866. INTERPRETATION: The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by rapid progression, metastasis, and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large external validation cohort and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics, and mass spectrometry imaging were applied and revealed notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC.

PTENα is responsible for protection of brain against oxidative stress during aging.

Neural cells are continuously subjected to oxidative stress arising from electrochemical activity, and cellular protection systems can turn on the oxidative stress response to detect and alleviate adverse conditions. However, the function and mechanism of the protective systems are complicated and remain largely elusive. We report that PTENα, an isoform of the PTEN family, mediates defense signaling in response to oxidative stress during brain aging. We show that genetic ablation of Ptenα in mice increases oxidative stress and results in neuronal cell death, culminating in accelerated decline of cognition and motor coordination as age increases. PTENα maintains COX activity and promotes energy metabolism through abrogating NEDD4L-mediated degradation of COX4 in response to oxidative stress. In the presence of Parkinson's disease-associated mutation, PTENα loses the capability to protect COX4 and ameliorate defects caused by Ptenα deletion. Our study reveals an important role of PTENα in response to oxidative stress. We propose that dysregulation of PTENα signaling may accelerate the rate of brain aging and promote the development of neurodegenerative disorders.

Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning.

BACKGROUND: Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients. METHODS: A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation. RESULTS: Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism. CONCLUSIONS: We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.

PTENα regulates endocytosis and modulates olfactory function.

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) α is the first identified isoform of the well-known tumor suppressor PTEN. PTENα has an evolutionarily conserved 173-aa N terminus compared with canonical PTEN. Recently, PTENα has been shown to play roles in multiple biologic processes including learning and memory, cardiac homeostasis, and antiviral immunity. Here, we report that PTENα maintains mitral cells in olfactory bulb (OB), regulates endocytosis in OB neurons, and controls olfactory behaviors in mice. We show that PTENα directly dephosphorylates the endocytic protein amphiphysin and promotes its binding to adaptor-related protein complex 2 subunit ß1 (Ap2b1). In addition, we identified mutations in the N terminus of PTENα in patients with Parkinson disease and Lewy-body dementia, which are neurodegenerative disorders with early olfactory loss. Overexpression of PTENα mutant H169N in mice OB reduces odor sensitivity. Our data demonstrate a role of PTENα in olfactory function and provide insight into the mechanism of olfactory dysfunction in neurologic disorders.-Yuan, Y., Zhao, X., Wang, P., Mei, F., Zhou, J., Jin, Y., McNutt, M. A., Yin, Y. PTENα regulates endocytosis and modulates olfactory function.

Crotonoside exhibits selective post-inhibition effect in AML cells via inhibition of FLT3 and HDAC3/6.

Targeted therapies for the treatment of acute myeloid leukemia (AML), specifically the FLT3 inhibitors, have shown promising results. Nevertheless, it is very unlikely that inhibitors which target a single pathway will provide long-term disease control. Here, we report the characterization of crotonoside, a natural product extracted from Chinese medicinal herb, Croton, for the treatment of AML via inhibition of FLT3 and HDAC3/6. In vitro, crotonoside exhibited selective inhibition in AML cells. In vivo, crotonoside treatment at 70 and 35 mg/kg/d produced significant AML tumor inhibition rates of 93.5% and 73.6%, respectively. Studies on the anti-AML mechanism of crotonoside demonstrated a significant inhibition of FLT3 signaling, cell cycle arrest in G0/G1 phase, and apoptosis. In contrast to classic FLT3 inhibitor; sunitinib, crotonoside was able to selectively suppress the expression of HDAC3 and HDAC6 without altering the expression of other HDAC isoforms. Inhibitors of HDAC3 and HDAC6; RGFP966 and HPOB, respectively, also exhibited selective inhibition in AML cells. Furthermore, we established novel signaling pathways including HDAC3/NF-κB-p65 and HDAC6/c-Myc besides FLT3/c-Myc which are aberrantly regulated in the progression of AML. In addition, crotonoside alone or the combination of sunitinib/RFP966/HPOB exhibited a significant post-inhibition effect in AML cells by the inhibition of FLT3 and HDAC3/6. Inhibitors targeting the FLT3 and HDAC3/6 might provide a more effective treatment strategy for AML. Taken together, the present study suggests that crotonoside could be a promising candidate for the treatment of AML, and deserves further investigations.