Metabolomics identified new biomarkers for the precise diagnosis of pancreatic cancer and associated tissue metastasis

Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era

Pancreatic cancer (PC) is an increasing cause of cancer-related death, with a dismal prognosis caused by its aggressive biology, the lack of clinical symptoms in the early phases of the disease, and the inefficacy of treatments. PC is characterized by a complex tumor microenvironment. The interaction of its cellular components plays a crucial role in tumor development and progression, contributing to the alteration of metabolism and cellular hyperproliferation, as well as to metastatic evolution and abnormal tumor-associated immunity. Furthermore, in response to intrinsic oncogenic alterations and the influence of the tumor microenvironment, cancer cells undergo a complex oncogene-directed metabolic reprogramming that includes changes in glucose utilization, lipid and amino acid metabolism, redox balance, and activation of recycling and scavenging pathways. The advent of omics sciences is revolutionizing the comprehension of the pathogenetic conundrum of pancreatic carcinogenesis. In particular, metabolomics and genomics has led to a more precise classification of PC into subtypes that show different biological behaviors and responses to treatments. The identification of molecular targets through the pharmacogenomic approach may help to personalize treatments. Novel specific biomarkers have been discovered using proteomics and metabolomics analyses. Radiomics allows for an earlier diagnosis through the computational analysis of imaging. However, the complexity, high expertise required, and costs of the omics approach are the main limitations for its use in clinical practice at present. In addition, the studies of extracellular vesicles (EVs), the use of organoids, the understanding of host-microbiota interactions, and more recently the advent of artificial intelligence are helping to make further steps towards precision and personalized medicine. This present review summarizes the main evidence for the application of omics sciences to the study of PC and the identification of future perspectives.

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Identification of Pancreatic Metastasis Cells and Cell Spheroids by the Organelle-Targeting Sensor Array

Pancreatic cancer is a highly malignant and metastatic cancer. Pancreatic cancer can lead to liver metastases, gallbladder metastases, and duodenum metastases. The identification of pancreatic cancer cells is essential for the diagnosis of metastatic cancer and exploration of carcinoma in situ. Organelles play an important role in maintaining the function of cells, the various cells show significant differences in organelle microenvironment. Herein, six probes are synthesized for targeting mitochondria, lysosomes, cell membranes, endoplasmic reticulum, Golgi apparatus, and lipid droplets. The six fluorescent probes form an organelles-targeted sensor array (OT-SA) to image pancreatic metastatic cancer cells and cell spheroids. The homology of metastatic cancer cells brings the challenge for identification of these cells. The residual network (ResNet) model has been proven to automatically extract and select image features, which can figure out a subtle difference among similar samples. Hence, OT-SA is developed to identify pancreatic metastasis cells and cell spheroids in combination with ResNet analysis. The identification accuracy for the pancreatic metastasis cells (> 99%) and pancreatic metastasis cell spheroids (> 99%) in the test set is successfully achieved respectively. The organelles-targeting sensor array provides a method for the identification of pancreatic cancer metastasis in cells and cell spheroids.

The bacterial metabolite, lithocholic acid, has antineoplastic effects in pancreatic adenocarcinoma

Lithocholic acid (LCA) is a secondary bile acid. LCA enters the circulation after bacterial synthesis in the gastrointestinal tract, reaches distantly located cancer cells, and influences their behavior. LCA was considered carcinogenic, but recent studies demonstrated that LCA has antitumor effects. We assessed the possible role of LCA in pancreatic adenocarcinoma. At the serum reference concentration, LCA induced a multi-pronged antineoplastic program in pancreatic adenocarcinoma cells. LCA inhibited cancer cell proliferation and induced mesenchymal-to-epithelial (MET) transition that reduced cell invasion capacity. LCA induced oxidative/nitrosative stress by decreasing the expression of nuclear factor, erythroid 2-like 2 (NRF2) and inducing inducible nitric oxide synthase (iNOS). The oxidative/nitrosative stress increased protein nitration and lipid peroxidation. Suppression of oxidative stress by glutathione (GSH) or pegylated catalase (pegCAT) blunted LCA-induced MET. Antioxidant genes were overexpressed in pancreatic adenocarcinoma and decreased antioxidant levels correlated with better survival of pancreatic adenocarcinoma patients. Furthermore, LCA treatment decreased the proportions of cancer stem cells. Finally, LCA induced total and ATP-linked mitochondrial oxidation and fatty acid oxidation. LCA exerted effects through the farnesoid X receptor (FXR), vitamin D receptor (VDR), and constitutive androstane receptor (CAR). LCA did not interfere with cytostatic agents used in the chemotherapy of pancreatic adenocarcinoma. Taken together, LCA is a non-toxic compound and has antineoplastic effects in pancreatic adenocarcinoma.

Preoperative profiles of plasma amino acids and derivatives distinguish periampullary cancer and benign disease

Periampullary cancers, including pancreatic ductal adenocarcinoma, ampullary-, cholangio-, and duodenal carcinoma, are frequently diagnosed in an advanced stage and are associated with poor overall survival. They are difficult to differentiate from each other and challenging to distinguish from benign periampullary disease preoperatively. To improve the preoperative diagnostics of periampullary neoplasms, clinical or biological markers are warranted.In this study, 28 blood plasma amino acids and derivatives from preoperative patients with benign (N = 45) and malignant (N = 72) periampullary disease were analyzed by LC-MS/MS.Principal component analysis and consensus clustering both separated the patients with cancer and the patients with benign disease. Glutamic acid had significantly higher plasma expression and 15 other metabolites significantly lower plasma expression in patients with malignant disease compared with patients having benign disease. Phenylalanine was the only metabolite associated with improved overall survival (HR = 0.50, CI 0.30-0.83, P < 0.01).Taken together, plasma metabolite profiles from patients with malignant and benign periampullary disease were significantly different and have the potential to distinguish malignant from benign disease preoperatively.

HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers

Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.

Chinmedomics: a potent tool for the evaluation of traditional Chinese medicine efficacy and identification of its active components

As an important part of medical science, Traditional Chinese Medicine (TCM) attracts much public attention due to its multi-target and multi-pathway characteristics in treating diseases. However, the limitations of traditional research methods pose a dilemma for the evaluation of clinical efficacy, the discovery of active ingredients and the elucidation of the mechanism of action. Therefore, innovative approaches that are in line with the characteristics of TCM theory and clinical practice are urgently needed. Chinmendomics, a newly emerging strategy for evaluating the efficacy of TCM, is proposed. This strategy combines systems biology, serum pharmacochemistry of TCM and bioinformatics to evaluate the efficacy of TCM with a holistic view by accurately identifying syndrome biomarkers and monitoring their complex metabolic processes intervened by TCM, and finding the agents associated with the metabolic course of pharmacodynamic biomarkers by constructing a bioinformatics-based correlation network model to further reveal the interaction between agents and pharmacodynamic targets. In this article, we review the recent progress of Chinmedomics to promote its application in the modernisation and internationalisation of TCM.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Applying Untargeted Lipidomics to Evaluate the Efficacy of Combined Neoadjuvant Chemotherapy and Immunotherapy for Esophageal Squamous Carcinoma Treatment

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignant tumor with a poor prognosis due to insidious symptoms that make early diagnosis difficult. Despite the combination of multiple treatment modalities, the recurrence and mortality rates of ESCC remain high. Neoadjuvant chemotherapy combined with immunotherapy is an emerging treatment modality that improves the prognosis of patients with ESCC. However, owing to the presence of hyperprogression and pseudoprogression, the currently used methods cannot accurately evaluate the efficacy of this therapy in patients, thus creating an evaluation bias and depriving these patients of the opportunity to benefit. We used untargeted lipidomics to identify the differences in lipid composition between cancer specimens and normal tissue specimens in the neoadjuvant chemotherapy combined with the immunotherapy group and the surgery-alone group of esophageal cancer patients and constructed a prediction model based on sphingomyelin 12:1;2O/30:0 and triglyceride (TG) 60:3 | TG 18:0_24:1_18 using a machine learning approach, which helps to better evaluate the neoadjuvant efficacy of combination therapy and better guide the treatment of ESCC.

Contributions of the Microbiome-Derived Metabolome for Risk Assessment and Prognostication of Pancreatic Cancer

BACKGROUND

Increasing evidence implicates microbiome involvement in the development and progression of pancreatic ductal adenocarcinoma (PDAC). Studies suggest that reflux of gut or oral microbiota can lead to colonization in the pancreas, resulting in dysbiosis that culminates in release of microbial toxins and metabolites that potentiate an inflammatory response and increase susceptibility to PDAC. Moreover, microbe-derived metabolites can exert direct effector functions on precursors and cancer cells, as well as other cell types, to either promote or attenuate tumor development and modulate treatment response.

CONTENT

The occurrence of microbial metabolites in biofluids thereby enables risk assessment and prognostication of PDAC, as well as having potential for design of interception strategies. In this review, we first highlight the relevance of the microbiome for progression of precancerous lesions in the pancreas and, using liquid chromatography-mass spectrometry, provide supporting evidence that microbe-derived metabolites manifest in pancreatic cystic fluid and are associated with malignant progression of intraductal papillary mucinous neoplasm(s). We secondly summarize the biomarker potential of microbe-derived metabolite signatures for (a) identifying individuals at high risk of developing or harboring PDAC and (b) predicting response to treatment and disease outcomes.

SUMMARY

The microbiome-derived metabolome holds considerable promise for risk assessment and prognostication of PDAC.

Targeted therapies of curcumin focus on its therapeutic benefits in cancers and human health: Molecular signaling pathway-based approaches and future perspectives

The curry powder spices turmeric (Curcuma longa L.), which contains curcumin (diferuloylmethane), an orange-yellow chemical. Polyphenols are the most commonly used sources of curcumin. It combats oxidative stress and inflammation in diseases, such as hyperlipidemia, metabolic syndrome, arthritis, and depression. Most of these benefits are due to their anti-inflammatory and antioxidant properties. Curcumin consumption leads to decreased bioavailability, resulting in limited absorption, quick metabolism, and quick excretion, which hinders health improvement. Numerous factors can increase its bioavailability. Piperine enhances bioavailability when combined with curcumin in a complex. When combined with other enhancing agents, curcumin has a wide spectrum of health benefits. This review evaluates the therapeutic potential of curcumin with a specific emphasis on its approach based on molecular signaling pathways. This study investigated its influence on the progression of cancer, inflammation, and many health-related mechanisms, such as cell proliferation, apoptosis, and metastasis. Curcumin has a significant potential for the prevention and treatment of various diseases. Curcumin modulates several biochemical pathways and targets involved in cancer growth. Despite its limited tissue accumulation and bioavailability when administered orally, curcumin has proven useful. This review provides an in-depth analysis of curcumin's therapeutic applications, its molecular signaling pathway-based approach, and its potential for precision medicine in cancer and human health.

Blood Serum and Drainage Microbial and Mitochondrial Metabolites in Patients after Surgery for Pancreatic Cancer

Pancreatic cancer (PC) has the highest mortality rate of all major cancers in the world despite improvements in clinical care and an understanding of the biology of pancreatic cancer. A study of 64 patients with verified pancreatic cancer who underwent surgery was included. Sampling was carried out at three points: before surgery and on days 1-3 after surgery and 5-7 days after surgery. Drainage fluid collection was taken from the drains installed intraoperatively one day after surgery. Tyrosine and phenylalanine metabolites and two mitochondrial metabolites, namely succinic and fumaric acids, were identified and quantified by GC-MS in the serum of healthy donors and patients. Differences in the metabolomic profile were found between the patients and healthy people. A statistically significant decrease in the level of p-hydroxyphenyllactic acid (p-HPhLA), the amount of sum 3 sepsis-associated metabolites (Σ 3AMM), as well as fumaric and succinic acids in patients was observed. It was also noted that p-hydroxyphenyllactic acid in the preoperative period may be considered as a predictor of complications and longer postoperative recovery.

High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network

BACKGROUND

Raman spectroscopy has been extensively utilized as a marker-free detection method in the complementary diagnosis of cancer. Multivariate statistical classification analysis is frequently employed for Raman spectral data classification. Nevertheless, traditional multivariate statistical classification analysis performs poorly when analyzing large samples and multicategory spectral data. In addition, with the advancement of computer vision, convolutional neural networks (CNNs) have demonstrated extraordinarily precise analysis of two-dimensional image processing.

RESULT

Combining 2D Raman spectrograms with automatic weighted feature fusion network (AWFFN) for bladder cancer detection is presented in this paper. Initially, the s-transform (ST) is implemented for the first time to convert 1D Raman data into 2D spectrograms, achieving 99.2% detection accuracy. Second, four upscaling techniques, including short time fourier transform (STFT), recurrence map (RP), markov transform field (MTF), and grammy angle field (GAF), were used to transform the 1D Raman spectral data into a variety of 2D Raman spectrograms. In addition, a particle swarm optimization (PSO) algorithm is combined with VGG19, ResNet50, and ResNet101 to construct a weighted feature fusion network, and this parallel network is employed for evaluating multiple spectrograms. Class activation mapping (CAM) is additionally employed to illustrate and evaluate the process of feature extraction via the three parallel network branches. The results demonstrate that the combination of a 2D Raman spectrogram along with a CNN for the diagnosis of bladder cancer obtains a 99.2% accuracy rate，which indicates that it is an extremely promising auxiliary technology for cancer diagnosis.

SIGNIFICANCE

The proposed two-dimensional Raman spectroscopy method has an improved precision than one-dimensional spectroscopic data, which presents a potential methodology for assisted cancer detection and providing crucial technical support for assisted diagnosis.

Coagulation parameters for the differential diagnosis of pancreatic cancer in the early stage: a retrospective study

BACKGROUND

In recent years, conventional coagulation (CC) and thromboelastography (TEG) parameters have been reported to be closely related to the progression of pancreatic cancer (PC). However, the potential utility of these parameters in differentiating benign and malignant pancreatic diseases is still unclear.

OBJECTIVES

A retrospective study was conducted to evaluate the efficacy of coagulation parameters in differentiating pancreatic cancer/early stage pancreatic cancer (EPC, TNM stages I and II) from benign control conditions, and to further explore whether coagulation parameters could improve the differential value of CA199.

METHODS

Receiver operating characteristic (ROC) curves and logistic regression analysis were used to identify the diagnostic value of each coagulation parameter or combination of parameters.

RESULTS

Compared with benign pancreatic disease (BPD), patients with pancreatic malignant tumors had significant coagulation disorders, specifically manifested as abnormal increases or decreases in several CC and TEG parameters (such as activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer (DD2), K time, R time, Angle, maximum amplitude (MA), coagulation index (CI), and Ly30). In the training group, ROC curve showed that FIB, DD2, Angle, MA, and CI had favorable efficacy at differentiating PC or EPC from BPD (for PC, AUC = 0.737, 0.654, 0.627, 0.602, 0.648; for EPC, AUC = 0.723, 0.635, 0.630, 0.614, 0.648). However, several combined diagnostic indicators based on FIB, DD2 and CI failed to outperform the individual coagulation indexes in diagnostic efficiency. Combinations of certain coagulation indexes with CA199 outperformed CA199 alone at identifying PC or EPC, especially FIB + CA199 (for PC, AUC = 0.904; for EPC, AUC = 0.905), FIB + DD2 + CA199 (for PC, AUC = 0.902; for EPC, AUC = 0.900), FIB + CI + CA199 (for PC, AUC = 0.906; for EPC, AUC = 0.906), and FIB + DD2 + CI + CA199 (for PC, AUC = 0.905; for EPC, AUC = 0.900). The results from a validation set also confirmed that these combinations have advantageous diagnostic value for PC and EPC.

CONCLUSIONS

A significant hypercoagulable state was common in PC. Some CC and TEG parameters are valuable in the differential diagnosis of benign and malignant pancreatic diseases. In addition, coagulation indexes combined with CA199 can further enhance the differential diagnosis efficacy of CA199 in PC and EPC.

Positive regulation of oxidative phosphorylation by nuclear myosin 1 protects cells from metabolic reprogramming and tumorigenesis in mice

Metabolic reprogramming is one of the hallmarks of tumorigenesis. Here, we show that nuclear myosin 1 (NM1) serves as a key regulator of cellular metabolism. NM1 directly affects mitochondrial oxidative phosphorylation (OXPHOS) by regulating mitochondrial transcription factors TFAM and PGC1α, and its deletion leads to underdeveloped mitochondria inner cristae and mitochondrial redistribution within the cell. These changes are associated with reduced OXPHOS gene expression, decreased mitochondrial DNA copy number, and deregulated mitochondrial dynamics, which lead to metabolic reprogramming of NM1 KO cells from OXPHOS to aerobic glycolysis.This, in turn, is associated with a metabolomic profile typical for cancer cells, namely increased amino acid-, fatty acid-, and sugar metabolism, and increased glucose uptake, lactate production, and intracellular acidity. NM1 KO cells form solid tumors in a mouse model, suggesting that the metabolic switch towards aerobic glycolysis provides a sufficient carcinogenic signal. We suggest that NM1 plays a role as a tumor suppressor and that NM1 depletion may contribute to the Warburg effect at the onset of tumorigenesis.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Prediction of Pathologic Change Development in the Pancreas Associated with Diabetes Mellitus Assessed by NMR Metabolomics

Nuclear magnetic resonance (NMR) metabolomics was used for identification of metabolic changes in pancreatic cancer (PC) blood plasma samples when compared to healthy controls or diabetes mellitus patients. An increased number of PC samples enabled a subdivision of the group according to individual PC stages and the construction of predictive models for finer classification of at-risk individuals recruited from patients with recently diagnosed diabetes mellitus. High-performance values of orthogonal partial least squares (OPLS) discriminant analysis were found for discrimination between individual PC stages and both control groups. The discrimination between early and metastatic stages was achieved with only 71.5% accuracy. A predictive model based on discriminant analyses between individual PC stages and the diabetes mellitus group identified 12 individuals out of 59 as at-risk of development of pathological changes in the pancreas, and four of them were classified as at moderate risk.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Metabolomics in oncology

BACKGROUND

Oncogenic transformation alters intracellular metabolism and contributes to the growth of malignant cells. Metabolomics, or the study of small molecules, can reveal insight about cancer progression that other biomarker studies cannot. Number of metabolites involved in this process have been in spotlight for cancer detection, monitoring, and therapy.

RECENT FINDINGS

In this review, the "Metabolomics" is defined in terms of current technology having both clinical and translational applications. Researchers have shown metabolomics can be used to discern metabolic indicators non-invasively using different analytical methods like positron emission tomography, magnetic resonance spectroscopic imaging etc. Metabolomic profiling is a powerful and technically feasible way to track changes in tumor metabolism and gauge treatment response across time. Recent studies have shown metabolomics can also predict individual metabolic changes in response to cancer treatment, measure medication efficacy, and monitor drug resistance. Its significance in cancer development and treatment is summarized in this review.

CONCLUSION

Although in infancy, metabolomics can be used to identify treatment options and/or predict responsiveness to cancer treatments. Technical challenges like database management, cost and methodical knowhow still persist. Overcoming these challenges in near further can help in designing new treatment régimes with increased sensitivity and specificity.

Biomarkers for pancreatic cancer based on tissue and serum metabolomics analysis in a multicenter study

BACKGROUND

Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis.

METHODS

We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis.

RESULTS

Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr (odds ratio = 2.17, [95% confidence interval 1.34-3.53]). The three markers showed area under the receiver-operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19-9 (CA19-9) was added to the model.

CONCLUSION

The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19-9.

Unravelling the enigma of siRNA and aptamer mediated therapies against pancreatic cancer

Pancreatic cancer (PC) is a fatal disease that has a poor 5-year survival rate. The poor prognosis can be attributed to both troublesome detections at the initial stage, which makes the majority of the treatment options largely unsuccessful and leads to extensive metastasis, as well as to its distinct pathophysiological characteristics, such as rich desmoplastic tumours bounded by dysplastic and hypo perfused vessels restricting the mobility of therapeutic agents. Continued attempts have been made to utilise innovative measures for battling PC to increase the therapeutic effectiveness of therapies and overcome their cytotoxicity. Combined cancer targeting and gene silencing approach has shown improved outcomes in patients' survival rates and quality of life, offering a potential solution to therapeutic complications. It particularly targets various barriers to alleviate delivery problems and diminish tumour recurrence and metastasis. While aptamers, a type of single-stranded nucleic acids with strong binding affinity and specificity to target molecules, have recently surfaced as a viable PC strategy, siRNA can interfere with the expression of certain genes. By concurrently suppressing genes and boosting targeted approach, the cocktail of siRNA/Aptamer and other therapeutic drugs can circumvent the multi-drug resistance phenomena. Additionally, combination therapy with additive or synergistic effects can considerably increase the therapeutic efficacy of anti-cancer medications. This study outlines the primary difficulties in treating PC, along with recent developments in siRNA/Aptamer mediated drug delivery to solve the major hiccup of oncology field.

Functional metabolomics revealed the dual-activation of cAMP-AMP axis is a novel therapeutic target of pancreatic cancer

Pancreatic cancer (PC) is one of the most malignant cancers, owing to extremely high aggressiveness and mortality. Yet, this condition currently incurs widely drug resistance and therapeutic deficiency. In this study, we proposed a novel functional metabolomics strategy as Spatial Temporal Operative Real Metabolomics (STORM) to identify the determinant functional metabolites in a dynamic and visualized pattern whose level changes are mechanistically associated with therapeutic efficiency of gemcitabine against PC. Integrating quantitative analysis and spatial-visualization characterization of functional metabolites in vivo, we identified that the AMP-cAMP axis was a novel therapeutic target of PC to intermediate therapeutic efficiency of gemcitabine. Gemcitabine could induce the dual accumulation of cyclic AMP (cAMP) and AMP in tumor tissues. Quantitative analysis of associated biosynthetic enzymes and genes revealed that two independent intracellular ATP derived biosynthetic pathways to promote the dual activation of AMP-cAMP axis in a lower-level energetic environment. Then, gemcitabine induced the dual accumulation of AMP and cAMP can separately activate signaling pathways of AMPK and PKA, leading to the inhibition of tumor growth by the upregulation of the downstream tumor suppressor GADD45A. Collectively, our new STORM strategy was the first time to identify novel target of PC from a metabolic perspective as the dual activation of AMP-cAMP axis induced by gemcitabine can efficiently suppress PC tumor growth. In addition, such discovery has the capability to lower drug resistance of gemcitabine by specifically interacting with novel target, contributing to the improvement of therapeutic efficiency.

Serum untargeted metabolomics reveal metabolic alteration of non-small cell lung cancer and refine disease detection

This study was performed to characterize the metabolic alteration of non-small-cell lung cancer (NSCLC) and discover blood-based metabolic biomarkers relevant to lung cancer detection. An untargeted metabolomics-based approach was applied in a case-control study with 193 NSCLC patients and 243 healthy controls. Serum metabolomics were determined by using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method. We screened differential metabolites based on univariate and multivariate analysis, followed by identification of the metabolites and related pathways. For NSCLC detection, machine learning was employed to develop and validate the model based on the altered serum metabolite features. The serum metabolic pattern of NSCLC was definitely different from the healthy condition. In total, 278 altered features were found in the serum of NSCLC patients comparing with healthy people. About one-fifth of the abundant differential features were identified successfully. The altered metabolites were enriched in metabolic pathways such as phenylalanine metabolism, linoleic acid metabolism, and biosynthesis of bile acids. We demonstrated a panel of 10 metabolic biomarkers which representing excellent discriminating capability for NSCLC discrimination, with a combined area under the curve (AUC) in the validation set of 0.95 (95% CI: 0.91-0.98). Moreover, this model showed a desirable performance for the detection of NSCLC at an early stage (AUC = 0.95, 95% CI: 0.92-0.97). Our study offers a perspective on NSCLC metabolic alteration. The finding of the biomarkers might shed light on the clinical detection of lung cancer, especially for those cancers in an early stage in Chinese population.

Isotopic Tracer for Absolute Quantification of Metabolites of the Pentose Phosphate Pathway in Bacteria

The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites.

Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30–40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS.

Objective

The objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS.

Methods

Untargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.

Results

Metabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993).

Conclusions

Our study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy.

Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer

Pancreatic cancer (PC) is burdened with a low 5-year survival rate and high mortality due to a severe lack of early diagnosis methods and slow progress in treatment options. To improve clinical diagnosis and enhance the treatment effects, we applied metabolomics using ultra-high-performance liquid chromatography with a high-resolution mass spectrometer (UHPLC-HRMS) to identify and validate metabolite biomarkers from paired tissue samples of PC patients. Results showed that the metabolic reprogramming of PC mainly featured enhanced amino acid metabolism and inhibited sphingolipid metabolism, which satisfied the energy and biomass requirements for tumorigenesis and progression. The altered metabolism results were confirmed by the significantly changed gene expressions in PC tissues from an online database. A metabolites biomarker panel (six metabolites) was identified for the differential diagnosis between PC tumors and normal pancreatic tissues. The panel biomarker distinguished tumors from normal pancreatic tissues in the discovery group with an area under the curve (AUC) of 1.0 (95%CI, 1.000−1.000). The biomarker panel cutoff was 0.776. In the validation group, an AUC of 0.9000 (95%CI = 0.782–1.000) using the same cutoff, successfully validated the biomarker signature. Moreover, this metabolites panel biomarker had a great capability to predict the overall survival (OS) of PC. Taken together, this metabolomics method identifies and validates metabolite biomarkers that can diagnose the onsite progression and prognosis of PC precisely and sensitively in a clinical setting. It may also help clinicians choose proper therapeutic interventions for different PC patients and improve the survival of PC patients.

Meta-Analysis Reveals Both the Promises and the Challenges of Clinical Metabolomics

Clinical metabolomics is a rapidly expanding field focused on identifying molecular biomarkers to aid in the efficient diagnosis and treatment of human diseases. Variations in study design, metabolomics methodologies, and investigator protocols raise serious concerns about the accuracy and reproducibility of these potential biomarkers. The explosive growth of the field has led to the recent availability of numerous replicate clinical studies, which permits an evaluation of the consistency of biomarkers identified across multiple metabolomics projects. Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related death and has the lowest five-year survival rate primarily due to the lack of an early diagnosis and the limited treatment options. Accordingly, PDAC has been a popular target of clinical metabolomics studies. We compiled 24 PDAC metabolomics studies from the scientific literature for a detailed meta-analysis. A consistent identification across these multiple studies allowed for the validation of potential clinical biomarkers of PDAC while also highlighting variations in study protocols that may explain poor reproducibility. Our meta-analysis identified 10 metabolites that may serve as PDAC biomarkers and warrant further investigation. However, 87% of the 655 metabolites identified as potential biomarkers were identified in single studies. Differences in cohort size and demographics, p-value choice, fold-change significance, sample type, handling and storage, data collection, and analysis were all factors that likely contributed to this apparently large false positive rate. Our meta-analysis demonstrated the need for consistent experimental design and normalized practices to accurately leverage clinical metabolomics data for reliable and reproducible biomarker discovery.

Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Biological Mechanisms of Unsaponifiable Matter in Kanglaite Injection for Pancreatic Ductal Adenocarcinoma

Background

Kanglaite injection (KLTi) has shown good clinical efficacy in the treatment of pancreatic ductal adenocarcinoma (PDAC). While previous studies have demonstrated the antitumor effects of the oil compounds in KLTi, it is unclear whether the unsaponifiable matter (USM) also has antitumor effects. This study used network pharmacology, molecular docking, and database verification methods to investigate the molecular biological mechanisms of USM.

Methods

Compounds of USM were obtained from GC-MS, and targets from DrugBank. Next, the GEO database was searched for differentially expressed genes in cancerous tissues and healthy tissues of PDAC to identify targets. Subsequently, the protein-protein interaction of USM and PDAC targets was constructed by BisoGenet to extract candidate genes. The candidate genes were enriched using GO and KEGG by Metascape, and the gene-pathway network was constructed to screen the key genes. Molecular docking and molecular dynamic simulations of core compound targets were finally performed and to explore the diagnostic, survival, and prognosis value of targets.

Results

A total of 10 active compounds and 36 drug targets were screened for USM, 919 genes associated with PDAC, and 139 USM candidate genes against PDAC were excavated. The enrichment predicted USM by acting on RELA, NFKB1, IKBKG, JUN, MAPK1, TP53, and AKT1. Molecular docking and dynamic simulations confirmed the screened core targets had good affinity and stability with the corresponding compounds. In diagnostic ROC validation, the above targets have certain accuracy for diagnosing PDAC, and the combined diagnosis is more advantageous. As the most diagnostic value of RELA, it is equally significant in predicting disease-specific survival and progression-free interval.

Conclusions

USM in KLTi plays an anti-PDAC role by intervening in the cell cycle, inducing apoptosis, and downregulating the NF-κB, MAPK, and PI3K-Akt pathways. It might participate in the pancreatic cancer pathway, and core target groups have diagnostic, survival, and prognosis value biomarker significance.

L-norvaline affects the proliferation of breast cancer cells based on the microbiome and metabolome analysis

AIMS

The altered fecal metabolites and microbiota might be involved in the development of breast cancer. We aimed to investigate the effect of differential metabolites on the proliferative activity of breast cancer cells.

METHODS AND RESULTS

We collected fecal samples from 14 breast cancer patients and 14 healthy subjects. Untargeted metabolomics analysis, short-chain fatty acid (SCFA) targeted analysis, and 16S rDNA sequencing was performed. The gut metabolite composition of patients changed significantly. Levels of norvaline, glucuronate, and galacturonate were lower in the Cancer group than in the Control (p < 0.05). 4-Methylcatechol and guaiacol increased (p < 0.05). Acetic acid and butyric acid were lower in the Cancer group than in the Control group (p < 0.05). Isobutyric acid and pentanoic acid were higher in the Cancer group than in the Control (p < 0.05). In the genus, the abundance of Rothia and Actinomyces increased in the Cancer group, compared with the Control group (p < 0.05). The differential microbiotas were clearly associated with differential metabolites but weakly with SCFAs. The abundance of Rothia and Actinomyces was markedly positively correlated with 4-methylcatechol and guaiacol (p < 0.05) and negatively correlated with norvaline (p < 0.05). L-norvaline inhibited the content of Arg-1 in a concentration-dependent manner. Compared with the L-norvaline or doxorubicin hydrochloride (DOX) group, the proliferation abilities of 4T1 cells were the lowest in the L-norvaline combined with DOX (p < 0.05). The apoptosis rate increased (p < 0.05).

CONCLUSIONS

Fecal metabolites and microbiota were significantly altered in breast cancer. Levels of differential metabolites (i.e., Norvaline) were significantly correlated with the abundance of differential microbiota. L-norvaline combined with DOX could clearly inhibit the proliferation activity of breast cancer cells.

SIGNIFICANCE AND IMPACT OF STUDY

This might provide clues to uncover potential biomarkers for breast cancer diagnosis and treatment.

1H-NMR Based Metabolomics Technology Identifies Potential Serum Biomarkers of Colorectal Cancer Lung Metastasis in a Mouse Model

Background

Lung metastasis is a common metastasis site of colorectal cancer which largely reduces the quality of life and survival rates of patients. The discovery of potential novel diagnostic biomarkers is very meaningful for the early diagnosis of colorectal cancer with lung metastasis.

Methods

In the present study, the metabonomic profiling of serum samples of lung metastasis mice was analyzed by ¹H-nuclear magnetic resonance (¹H-NMR). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to elucidate the distinguishing metabolites between different groups, and all achieved excellent separations, which indicated that metastatic mice could be differentiated from control mice based on the metabolic profiles at serum levels. Furthermore, during lung metastasis of colorectal cancer, metabolic phenotypes changed significantly, and some of metabolites were identified.

Results

Among these metabolites, approximately 15 were closely associated with the lung metastasis process. Pathway enrichment analysis results showed deregulation of metabolic pathways participating in the process of lung metastasis, such as synthesis and degradation of ketone bodies pathway, amino acid metabolism pathway and pyruvate metabolism pathway.

Conclusion

The present study demonstrated the metabolic disturbances of serum samples of mice during the lung metastasis process of colorectal cancer and provides potential diagnostic biomarkers for the disease.

Cytochalasin Q exerts anti-melanoma effect by inhibiting creatine kinase B

Due to the pivotal role of microfilament in cancer cells, targeting microfilaments with cytochalasins is considered a promising anticancer strategy. Here, we obtained cytochalasin Q (CQ) from Xylaria sp. DO1801, the endophytic fungi from the root of plant Damnacanthus officinarum, and discovered its anti-melanoma activity in vivo and in vitro attributing to microfilament depolymerization. Mechanistically, CQ directly bound to and inactivated creatine kinase B (CKB), an enzyme phosphorylating creatine to phosphocreatine (PCr) and regenerating ATP to cope with high energy demand, and then inhibited the creatine metabolism as well as cytosolic glycolysis in melanoma cells. Preloading PCr recovered ATP generation, reversed microfilament depolymerization and blunted anti-melanoma efficacy of CQ. Knockdown of CKB resulted in reduced ATP level, perturbed microfilament, inhibited proliferation and induced apoptosis, and manifested lower sensitivity to CQ. Further, we found that either CQ or CKB depletion suppressed the PI3K/AKT/FoxO1 pathway, whereas 740Y-P, a PI3K agonist, elevated protein expression of CKB suppressed by CQ. Taken together, our study highlights the significant anti-melanoma effect and proposes a PI3K/AKT/FoxO1/ CKB feedback circuit for the activity of CQ, opening new opportunities for current chemotherapy.

High-performance Collective Biomarker from Liquid Biopsy for Diagnosis of Pancreatic Cancer Based on Mass Spectrometry and Machine Learning

Background: Most pancreatic cancers are found at progressive stages when they cannot be surgically removed. Therefore, a highly accurate early detection method is urgently needed.

Methods: This study analyzed serum from Japanese patients who suffered from pancreatic ductal adenocarcinoma (PDAC) and aimed to establish a PDAC-diagnostic system with metabolites in serum. Two groups of metabolites, primary metabolites (PM) and phospholipids (PL), were analyzed using liquid chromatography/electrospray ionization mass spectrometry. A support vector machine was employed to establish a machine learning-based diagnostic algorithm.

Results: Integrating PM and PL databases improved cancer diagnostic accuracy and the area under the receiver operating characteristic curve. It was more effective than the algorithm based on either PM or PL database, or single metabolites as a biomarker. Subsequently, 36 statistically significant metabolites were fed into the algorithm as a collective biomarker, which improved results by accomplishing 97.4% and was further validated by additional serum. Interestingly, specific clusters of metabolites from patients with preoperative neoadjuvant chemotherapy (NAC) showed different patterns from those without NAC and were somewhat comparable to those of the control.

Conclusion: We propose an efficient screening system for PDAC with high accuracy by liquid biopsy and potential biomarkers useful for assessing NAC performance.

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.

Liquid biopsy approach to pancreatic cancer

Pancreatic cancer (PC) continues to pose a major clinical challenge. There has been little improvement in patient survival over the past few decades, and it is projected to become the second leading cause of cancer mortality by 2030. The dismal 5-year survival rate of less than 10% after the diagnosis is attributable to the lack of early symptoms, the absence of specific biomarkers for an early diagnosis, and the inadequacy of available chemotherapies. Most patients are diagnosed when the disease has already metastasized and cannot be treated. Cancer interception is vital, actively intervening in the malignization process before the development of a full-blown advanced tumor. An early diagnosis of PC has a dramatic impact on the survival of patients, and improved techniques are urgently needed to detect and evaluate this disease at an early stage. It is difficult to obtain tissue biopsies from the pancreas due to its anatomical position; however, liquid biopsies are readily available and can provide useful information for the diagnosis, prognosis, stratification, and follow-up of patients with PC and for the design of individually tailored treatments. The aim of this review was to provide an update of the latest advances in knowledge on the application of carbohydrates, proteins, cell-free nucleic acids, circulating tumor cells, metabolome compounds, exosomes, and platelets in blood as potential biomarkers for PC, focusing on their clinical relevance and potential for improving patient outcomes.

Accurate diagnosis of lung tissues for 2D Raman spectrogram by deep learning based on short-time Fourier transform

Multivariate statistical analysis methods have an important role in spectrochemical analyses to rapidly identify and diagnose cancer and the subtype. However, utilizing these methods to analyze lager amount spectral data is challenging, and poses a major bottleneck toward achieving high accuracy. Here, a new convolutional neural networks (CNN) method based on short-time Fourier transform (STFT) to diagnose lung tissues via Raman spectra readily is proposed. The models yield that the accuracies of the new method are higher than the conventional methods (principal components analysis -linear discriminant analysis and support vector machine) for validation group (95.2% vs 85.5%, 94.4%) and test group (96.5% vs 90.4%, 93.9%) after cross-validation. The results illustrate that the new method which converts one-dimensional Raman data into two-dimensional Raman spectrograms improve the discriminatory ability of lung tissues and can achieve automatically accurate diagnosis of lung tissues.

Antimetastatic Effects of Curcumin in Oral and Gastrointestinal Cancers

Gastrointestinal (GI) cancers are known as frequently occurred solid malignant tumors that can cause the high rate mortality in the world. Metastasis is a significant destructive feature of tumoral cells, which directly correlates with decreased prognosis and survival. Curcumin, which is found in turmeric, has been identified as a potent therapeutic natural bioactive compound (Curcuma longa). It has been traditionally applied for centuries to treat different diseases, and it has shown efficacy for its anticancer properties. Numerous studies have revealed that curcumin inhibits migration and metastasis of GI cancer cells by modulating various genes and proteins, i.e., growth factors, inflammatory cytokines and their receptors, different types of enzymes, caspases, cell adhesion molecules, and cell cycle proteins. Herein, we summarized the antimetastatic effects of curcumin in GI cancers, including pancreatic cancer, gastric cancer, colorectal cancer, oral cancer, and esophageal cancer.

Functional metabolomics innovates therapeutic discovery of traditional Chinese medicine derived functional compounds

Traditional Chinese medicines (TCMs) produce chemically diverse functional compounds that are importantly chemical resource for facilitating new drug discovery and development against a diversity of diseases. However, modern exploration of TCM derived functional compounds is significantly hindered by the inefficient elucidation of pharmacological functions over past decades, because conventional research methods are incapable of efficiently elucidating therapeutic potential of TCM conferred by multiple functional compounds. Functional metabolomics has the priority-capacity to characterize systems therapeutic actions of TCM by precisely capturing molecular interactions between disease response metabolite biomarkers (DRMB) and functional compounds (secondary metabolites), which underline pharmacological efficiency and associated therapeutic mechanisms. In this critical review, we innovatively summarize systems therapeutic feature of TCM derived functional compounds from a functional-metabolism perspective, then systems metabolic targets (SMT) identified by functional metabolomics method are strategically proposed to better understanding of therapeutic discovery of TCM derived functional compounds. In addition, we propose the perspective strategy as Spatial Temporal Operative Real Metabolomics (STORM) to considerably improve analytical capacity of functional metabolomics method by selectively incorporating the cutting edge technologies of mass spectrometry imaging, isotope-metabolic fluxomics, synthetic and biosynthetic chemistry, which could considerably enhance the precision and resolution of elucidating pharmacological efficiency and associated therapeutic mechanisms of TCM derived functional compounds. Collectively, such critical review is expected to provide novel perspective-strategy that could significantly improve modern exploration and exploitation of TCM derived functional compounds that further promote new drug discovery and development against the complex diseases.

Berberine improves colitis by triggering AhR activation by microbial tryptophan catabolites

Inflammatory bowel diseases (IBD) are kind of recurrent inflammatory issues that occur in the gastrointestinal tract, and currently clinical treatment is still unideal due to the complex pathogenesis of IBD. Basically, gut barrier dysfunction is triggered by gut microbiota dysbiosis that is closely associated with the development of IBD, we thus investigated the therapeutic capacity of berberine (BBR) to improve the dysregulated gut microbiota, against IBD in rats, using a combinational strategy of targeted metabolomics and 16 s rDNA amplicon sequencing technology. Expectedly, our data revealed that BBR administration could greatly improve the pathological phenotype, gut barrier disruption, and the colon inflammation in rats with dextran sulfate sodium (DSS)-induced colitis. In addition, 16S rDNA-based microbiota analysis demonstrated that BBR could alleviate gut dysbiosis in rats. Furthermore, our targeted metabolomics analysis illustrated that the levels of microbial tryptophan catabolites in the gastrointestinal tract were significantly changed during the development of the colitis in rats, and BBR treatment can significantly restore such changes of the tryptophan catabolites accordingly. At last, our in vitro mechanism exploration was implemented with a Caco-2 cell monolayer model, which verified that the modulation of the dysregulated gut microbiota to change microbial metabolites coordinated the improvement effect of BBR on gut barrier disruption in the colitis, and we also confirmed that the activation of AhR induced by microbial metabolites is indispensable to the improvement of gut barrier disruption by BBR. Collectively, BBR has the capacity to treat DSS-induced colitis in rats through the regulation of gut microbiota associated tryptophan metabolite to activate AhR, which can greatly improve the disrupted gut barrier function. Importantly, our finding elucidated a novel mechanism of BBR to improve gut barrier function, which holds the expected capacity to promote the BBR derived drug discovery and development against the colitis in clinic setting.

New insights into molecules and pathways of cancer metabolism and therapeutic implications

Cancer cells are abnormal cells that can reproduce and regenerate rapidly. They are characterized by unlimited proliferation, transformation and migration, and can destroy normal cells. To meet the needs for cell proliferation and migration, tumor cells acquire molecular materials and energy through unusual metabolic pathways as their metabolism is more vigorous than that of normal cells. Multiple carcinogenic signaling pathways eventually converge to regulate three major metabolic pathways in tumor cells, including glucose, lipid, and amino acid metabolism. The distinct metabolic signatures of cancer cells reflect that metabolic changes are indispensable for the genesis and development of tumor cells. In this review, we report the unique metabolic alterations in tumor cells which occur through various signaling axes, and present various modalities available for cancer diagnosis and clinical therapy. We further provide suggestions for the development of anti‐tumor therapeutic drugs.