Evaluation of the antitumor effects of PP242 in a colon cancer xenograft mouse model using comprehensive metabolomics and lipidomics

The Utility of Lipidomic Analysis in Colorectal Cancer Diagnosis and Prognosis-A Systematic Review of Recent Literature

Colorectal cancer (CRC) is among the most prevalent and lethal malignancies. Lipidomic investigations have revealed numerous disruptions in lipid profiles across various cancers. Studies on CRC exhibit potential for identifying novel diagnostic or prognostic indicators through lipidomic signatures. This review examines recent literature regarding lipidomic markers for CRC. PubMed database was searched for eligible articles concerning lipidomic biomarkers of CRC. After selection, 36 articles were included in the review. Several studies endeavor to establish sets of lipid biomarkers that demonstrate promising potential to diagnose CRC based on blood samples. Phosphatidylcholine, phosphatidylethanolamine, ceramides, and triacylglycerols (TAGs) appear to offer the highest diagnostic accuracy. In tissues, lysophospholipids, ceramides, and TAGs were among the most altered lipids, while unsaturated fatty acids also emerged as potential biomarkers. In-depth analysis requires both cell culture and animal studies. CRC involves multiple lipid metabolism alterations. Although numerous lipid species have been suggested as potential diagnostic markers, the establishment of standardized methods and the conduct of large-scale studies are necessary to facilitate their clinical application.

Roles of Rictor alterations in gastrointestinal tumors (Review)

Gastrointestinal tumors account for five of the top 10 causes of mortality from all cancers (colorectal, liver, stomach, esophageal and pancreatic cancer). Mammalian target of rapamycin (mTOR) signaling is commonly dysregulated in various human cancers. As a core component of the mTOR complex 2 (mTORC2), Rictor is a key effector molecule of the PI3K/Akt pathway. A high alteration rate of Rictor has been observed in gastrointestinal tumors, and such Rictor alterations are often associated with resistance to chemotherapy and related adverse clinical outcomes. However, the exact roles of Rictor in gastrointestinal tumors remain elusive. The aim of the present study was to critically discuss the following: i) Mutation and biological characteristics of Rictor in tumors with a detailed overview of Rictor in cell proliferation, angiogenesis, apoptosis, autophagy and drug resistance; ii) the role of Rictor in tumors of the digestive system, particularly colorectal, hepatobiliary, gastric, esophageal and pancreatic cancer and cholangiocarcinoma; and iii) the current status and prospects of targeted therapy for Rictor by inhibiting Akt activation. Despite the growing realization of the importance of Rictor/mTORC2 in cancer, the underlying mechanistic details remain poorly understood; this needs to change in order for the development of efficient targeted therapies and re‑sensitization of therapy‑resistant cancers to be made possible.

MNK, mTOR or eIF4E-selecting the best anti-tumor target for blocking translation initiation

Overexpression of eIF4E is common in patients with various solid tumors and hematologic cancers. As a potential anti-cancer target, eIF4E has attracted extensive attention from researchers. At the same time, mTOR kinases inhibitors and MNK kinases inhibitors, which are directly related to regulation of eIF4E, have been rapidly developed. To explore the optimal anti-cancer targets among MNK, mTOR, and eIF4E, this review provides a detailed classification and description of the anti-cancer activities of promising compounds. In addition, the structures and activities of some dual-target inhibitors are briefly described. By analyzing the different characteristics of the inhibitors, it can be concluded that MNK1/2 and eIF4E/eIF4G interaction inhibitors are superior to mTOR inhibitors. Simultaneous inhibition of MNK and eIF4E/eIF4G interaction may be the most promising anti-cancer method for targeting translation initiation.

Biomarker Discovery for Hepatocellular Carcinoma in Patients with Liver Cirrhosis Using Untargeted Metabolomics and Lipidomics Studies

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the disease. In addition, conventional biomarkers perform suboptimally when identifying HCC in its early stages, heightening the need for the identification of new and more effective biomarkers. Using metabolomics and lipidomics approaches, this study aims to identify serum biomarkers for identification of HCC in patients with liver cirrhosis (LC). Serum samples from 20 HCC cases and 20 patients with LC were analyzed using ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q-Exactive-MS). Metabolites and lipids that are significantly altered between HCC cases and patients with LC were identified. These include organic acids, amino acids, TCA cycle intermediates, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids. The most significant variability was observed in the concentrations of bile acids, fatty acids, and glycerophospholipids. In the context of HCC cases, there was a notable increase in the levels of phosphatidylethanolamine and triglycerides, but the levels of fatty acids and phosphatidylcholine exhibited a substantial decrease. In addition, it was observed that all of the identified metabolites exhibited a superior area under the receiver operating characteristic (ROC) curve in comparison to alpha-fetoprotein (AFP). The pathway analysis of these metabolites revealed fatty acid, lipid, and energy metabolism as the most impacted pathways. Putative biomarkers identified in this study will be validated in future studies via targeted quantification.

Endoplasmic reticulum stress: molecular mechanism and therapeutic targets

The endoplasmic reticulum (ER) functions as a quality-control organelle for protein homeostasis, or "proteostasis". The protein quality control systems involve ER-associated degradation, protein chaperons, and autophagy. ER stress is activated when proteostasis is broken with an accumulation of misfolded and unfolded proteins in the ER. ER stress activates an adaptive unfolded protein response to restore proteostasis by initiating protein kinase R-like ER kinase, activating transcription factor 6, and inositol requiring enzyme 1. ER stress is multifaceted, and acts on aspects at the epigenetic level, including transcription and protein processing. Accumulated data indicates its key role in protein homeostasis and other diverse functions involved in various ocular diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, retinitis pigmentosa, achromatopsia, cataracts, ocular tumors, ocular surface diseases, and myopia. This review summarizes the molecular mechanisms underlying the aforementioned ocular diseases from an ER stress perspective. Drugs (chemicals, neurotrophic factors, and nanoparticles), gene therapy, and stem cell therapy are used to treat ocular diseases by alleviating ER stress. We delineate the advancement of therapy targeting ER stress to provide new treatment strategies for ocular diseases.

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis

There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.

The crosstalk of the human microbiome in breast and colon cancer: A metabolomics analysis

The human microbiome's role in colon and breast cancer is described in this review. Understanding how the human microbiome and metabolomics interact with breast and colon cancer is the chief area of this study. First, the role of the gut and distal microbiome in breast and colon cancer is investigated, and the direct relationship between microbial dysbiosis and breast and colon cancer is highlighted. This work also focuses on the many metabolomic techniques used to locate prospective biomarkers, make an accurate diagnosis, and research new therapeutic targets for cancer treatment. This review clarifies the influence of anti-tumor medications on the microbiota and the proactive measures that can be taken to treat cancer using a variety of therapies, including radiotherapy, chemotherapy, next-generation biotherapeutics, gene-based therapy, integrated omics technology, and machine learning.

Identification of Key Pathways Involved in White Strain of Hypsizygus marmoreus Extracts-Induced Cell Death of Human Hepatoma Hep3B Cells by Next Generation Sequencing

White strain of Hypsizygus marmoreus is named as white genius mushroom (WGM) and is a popular food in Taiwan. We have confirmed the cytotoxicity of WGM extracts on human Hep3B liver cancer cells. A total of 8711 significantly differential genes were identified through large-scale transcriptome sequencing. According to the KEGG pathway enrichment analysis, autophagy, mitophagy and apoptosis pathways were identified as significant in WGM extracts-treated cells. WGM extracts induced a dose-dependent generation of reactive oxygen species (ROS) and membrane-enclosed vacuoles in Hep3B cells. The inhibition of ROS by the ROS scavengers blocked the induction of cell death and vacuoles formation. We suggested that the cell death and membrane-enclosed vacuoles induced by WGM extracts are dependent on ROS production in Hep3B cells. (2E,6E)-3,7,11,15,19,23,27,31,35-Nonamethylhexatriaconta-2,6,34-triene-1,11,15,19,23,27,31-heptol and (18:2) lysophosphatidylcholine were identified in WGM extracts. In addition to being a very popular edible mushrooms, WGM may be developed into a dietary supplement or dietary chemopreventive agent for the cancer treatment.

Geniposide Ameliorates Liver Fibrosis Through Reducing Oxidative Stress and Inflammatory Respose, Inhibiting Apoptosis and Modulating Overall Metabolism

Liver fibrosis is a progressive liver damage condition caused by various factors and may progress toward liver cirrhosis, and even hepatocellular carcinoma. Many studies have found that the disfunction in metabolism could contribute to the development of liver fibrosis. Geniposide, derived from Gardenia jasminoides J. Ellis, has been demonstrated with therapeutic effects on liver fibrosis. However, the exact molecular mechanisms of such liver-protection remain largely unknown. The aim of this study was to explored the effect of geniposide on metabolic regulations in liver fibrosis. We used carbon tetrachloride (CCl₄) to construct a mouse model of liver fibrosis and subsequently administered geniposide treatment. Therapeutic effects of geniposide on liver fibrosis were accessed through measuring the levels of hepatic enzymes in serum and the pathological changes in liver. We also investigated the effects of geniposide on inflammatory response, oxidative stress and apoptosis in liver. Furthermore, serum untargeted metabolomics were used to explore the metabolic regulatory mechanisms behind geniposide on liver fibrosis. Our results demonstrated that geniposide could reduce the levels of hepatic enzymes in serum and ameliorate the pathological changes in liver fibrosis mice. Geniposide enhanced the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) and decreased methane dicarboxylic aldehyde (MDA) levels in liver. Geniposide treatment also decreased the levels of interleukin (IL)-6, IL-1β, and tumor necrosis factor-alpha (TNF-a) in liver tissue homogenate. Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay (TUNEL) staining demonstrated that geniposide could reduce the apoptosis of hepatocytes. Geniposide increased the protein expression of B-cell lymphoma-2 (Bcl-2) and downregulated the protein expression of Bcl-2 Associated X (Bax), cleaved-Caspase 3, and cleaved-Caspase 9. Serum untargeted metabolomics analysis demonstrated that geniposide treatment improved the metabolic disorders including glycerophospholipid metabolism, arginine and proline metabolism, and arachidonic acid (AA) metabolism. In conclusion, our study demonstrated the protective effects of geniposide on liver fibrosis. We found that geniposide could treat liver fibrosis by inhibiting oxidative stress, reducing inflammatory response and apoptosis in the liver, and modulating glycerophospholipid, and arginine, proline, and AA metabolism processes.