Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma

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.

Construction and validation of serum Metabolic Risk Score for early warning of malignancy in esophagus

Using noninvasive biomarkers to identify high-risk individuals prior to endoscopic examination is crucial for optimization of screening strategies for esophageal squamous cell carcinoma (ESCC). We conducted a nested case-control study based on two community-based screening cohorts to evaluate the warning value of serum metabolites for esophageal malignancy. The serum samples were collected at enrollment when the cases had not been diagnosed. We identified 74 differential metabolites and two prominent perturbed metabolic pathways, and constructed Metabolic Risk Score (MRS) based on 22 selected metabolic predictors. The MRS generated an area under the receiver operating characteristics curve (AUC) of 0.815. The model performed well for the within-1-year interval (AUC: 0.868) and 1-to-5-year interval (AUC: 0.845) from blood draw to diagnosis, but showed limited ability in predicting long-term cases (>5 years). In summary, the MRS could serve as a potential early warning and risk stratification tool for establishing a precision strategy of ESCC screening.

Latest insights into the global epidemiological features, screening, early diagnosis and prognosis prediction of esophageal squamous cell carcinoma

As a highly invasive carcinoma, esophageal cancer (EC) was the eighth most prevalent malignancy and the sixth leading cause of cancer-related death worldwide in 2020. Esophageal squamous cell carcinoma (ESCC) is the major histological subtype of EC, and its incidence and mortality rates are decreasing globally. Due to the lack of specific early symptoms, ESCC patients are usually diagnosed with advanced-stage disease with a poor prognosis, and the incidence and mortality rates are still high in many countries, especially in China. Therefore, enormous challenges still exist in the management of ESCC, and novel strategies are urgently needed to further decrease the incidence and mortality rates of ESCC. Although the key molecular mechanisms underlying ESCC pathogenesis have not been fully elucidated, certain promising biomarkers are being investigated to facilitate clinical decision-making. With the advent and advancement of high-throughput technologies, such as genomics, proteomics and metabolomics, valuable biomarkers with high sensitivity, specificity and stability could be identified for ESCC. Herein, we aimed to determine the epidemiological features of ESCC in different regions of the world, especially in China, and focused on novel molecular biomarkers associated with ESCC screening, early diagnosis and prognosis prediction.

MiR-21-5p modulates LPS-induced acute injury in alveolar epithelial cells by targeting SLC16A10

Sepsis is a systemic inflammatory response syndrome resulting from the invasion of the human body by bacteria and other pathogenic microorganisms. One of its most prevalent complications is acute lung injury, which places a significant medical burden on numerous countries and regions due to its high morbidity and mortality rates. MicroRNA (miRNA) plays a critical role in the body's inflammatory response and immune regulation. Recent studies have focused on miR-21-5p in the context of acute lung injury, but its role appears to vary in different models of this condition. In the LPS-induced acute injury model of A549 cells, there is differential expression, but the specific mechanism remains unclear. Therefore, our aim is to investigate the changes in the expression of miR-21-5p and SLC16A10 in a type II alveolar epithelial cell injury model induced by LPS and explore the therapeutic effects of their targeted regulation. A549 cells were directly stimulated with 10 µg/ml of LPS to construct a model of LPS-induced cell injury. Cells were collected at different time points and the expression of interleukin 1 beta (IL-1β), tumor necrosis factor-α (TNF-α) and miR-21-5p were measured by RT-qPCR and western blot. Then miR-21-5p mimic transfection was used to up-regulate the expression of miR-21-5p in A549 cells and the expression of IL-1β and TNF-α in each group of cells was measured by RT-qPCR and western blot. The miRDB, TargetScan, miRWalk, Starbase, Tarbase and miR Tarbase databases were used to predict the miR-21-5p target genes and simultaneously, the DisGeNet database was used to search the sepsis-related gene groups. The intersection of the two groups was taken as the core gene. Luciferase reporter assay further verified SLC16A10 as the core gene with miR-21-5p. The expression of miR-21-5p and SLC16A10 were regulated by transfection or inhibitors in A549 cells with or without LPS stimulation. And then the expression of IL-1β and TNF-α in A549 cells was tested by RT-qPCR and western blot in different groups, clarifying the role of miR-21-5p-SLC16A10 axis in LPS-induced inflammatory injury in A549 cells. (1) IL-1β and TNF-α mRNA and protein expression significantly increased at 6, 12, and 24 h after LPS stimulation as well as the miR-21-5p expression compared with the control group (P < 0.05). (2) After overexpression of miR-21-5p in A549 cells, the expression of IL-1β and TNF-α was significantly reduced after LPS stimulation, suggesting that miR-21-5p has a protection against LPS-induced injury. (3) The core gene set, comprising 51 target genes of miR-21-5p intersecting with the 1448 sepsis-related genes, was identified. This set includes SLC16A10, TNPO1, STAT3, PIK3R1, and FASLG. Following a literature review, SLC16A10 was selected as the ultimate target gene. Dual luciferase assay results confirmed that SLC16A10 is indeed a target gene of miR-21-5p. (4) Knocking down SLC16A10 expression by siRNA significantly reduced the expression of IL-1β and TNF-α in A549 cells after LPS treatment (P < 0.05). (5) miR-21-5p inhibitor increased the expression levels of IL-1β and TNF-α in A549 cells after LPS stimulation (P < 0.05). In comparison to cells solely transfected with miR-21-5p inhibitor, co-transfection of miR-21-5p inhibitor and si-SLC6A10 significantly reduced the expression of IL-1β and TNF-α (P < 0.05). MiR-21-5p plays a protective role in LPS-induced acute inflammatory injury of A549 cells. By targeting SLC16A10, it effectively mitigates the inflammatory response in A549 cells induced by LPS. Furthermore, SLC16A10 holds promise as a potential target for the treatment of acute lung injury.

Multi-omics characterization of esophageal squamous cell carcinoma identifies molecular subtypes and therapeutic targets

Esophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal cancer and is characterized by an unfavorable prognosis. To elucidate the distinct molecular alterations in ESCC and investigate therapeutic targets, we performed a comprehensive analysis of transcriptomics, proteomics, and phosphoproteomics data derived from 60 paired treatment-naive ESCC and adjacent nontumor tissue samples. Additionally, we conducted a correlation analysis to describe the regulatory relationship between transcriptomic and proteomic processes, revealing alterations in key metabolic pathways. Unsupervised clustering analysis of the proteomics data stratified patients with ESCC into 3 subtypes with different molecular characteristics and clinical outcomes. Notably, subtype III exhibited the worst prognosis and enrichment in proteins associated with malignant processes, including glycolysis and DNA repair pathways. Furthermore, translocase of inner mitochondrial membrane domain containing 1 (TIMMDC1) was validated as a potential prognostic molecule for ESCC. Moreover, integrated kinase-substrate network analysis using the phosphoproteome nominated candidate kinases as potential targets. In vitro and in vivo experiments further confirmed casein kinase II subunit α (CSNK2A1) as a potential kinase target for ESCC. These underlying data represent a valuable resource for researchers that may provide better insights into the biology and treatment of ESCC.

A glutamine tug-of-war between cancer and immune cells: recent advances in unraveling the ongoing battle

Glutamine metabolism plays a pivotal role in cancer progression, immune cell function, and the modulation of the tumor microenvironment. Dysregulated glutamine metabolism has been implicated in cancer development and immune responses, supported by mounting evidence. Cancer cells heavily rely on glutamine as a critical nutrient for survival and proliferation, while immune cells require glutamine for activation and proliferation during immune reactions. This metabolic competition creates a dynamic tug-of-war between cancer and immune cells. Targeting glutamine transporters and downstream enzymes involved in glutamine metabolism holds significant promise in enhancing anti-tumor immunity. A comprehensive understanding of the intricate molecular mechanisms underlying this interplay is crucial for developing innovative therapeutic approaches that improve anti-tumor immunity and patient outcomes. In this review, we provide a comprehensive overview of recent advances in unraveling the tug-of-war of glutamine metabolism between cancer and immune cells and explore potential applications of basic science discoveries in the clinical setting. Further investigations into the regulation of glutamine metabolism in cancer and immune cells are expected to yield valuable insights, paving the way for future therapeutic interventions.

Characteristics of Transcriptome and Metabolome Concerning Intramuscular Fat Content in Beijing Black Pigs

To study the characteristics of genes and metabolites related to intramuscular fat (IMF) content with less influence by breed background and individual differences, the skeletal muscle samples from 40 Beijing black pigs with either high or low IMF content were used to perform transcriptome and metabolome analyses. About 99 genes (twofold-change) were differentially expressed. Up-regulated genes in the high IMF pigs were mainly related to fat metabolism. The key genes in charge of IMF deposition are ADIPOQ, CIDEC, CYP4B1, DGAT2, LEP, OPRL1, PLIN1, SCD, and THRSP. KLHL40, TRAFD1, and HSPA6 were novel candidate genes for the IMF trait due to their high abundances. In the low IMF pigs, the differentially expressed genes involved in virus resistance were up-regulated. About 16 and 18 differential metabolites (1.5 fold-change) were obtained in the positive and negative modes, respectively. Pigs with low IMF had weaker fatty acid oxidation due to the down-regulation of various carnitines. Differentially expressed genes were more important in determining IMF deposition than differential metabolites because relatively few differential metabolites were obtained, and they were merely the products under the physiological status of diverged IMF content. This study provided valuable information for further studies on IMF deposition.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Rethinking glutamine metabolism and the regulation of glutamine addiction by oncogenes in cancer

Glutamine, the most abundant non-essential amino acid in human blood, is crucial for cancer cell growth and cancer progression. Glutamine mainly functions as a carbon and nitrogen source for biosynthesis, energy metabolism, and redox homeostasis maintenance in cancer cells. Dysregulated glutamine metabolism is a notable metabolic characteristic of cancer cells. Some carcinogen-driven cancers exhibit a marked dependence on glutamine, also known as glutamine addiction, which has rendered the glutamine metabolic pathway a breakpoint in cancer therapeutics. However, some cancer cells can adapt to the glutamine unavailability by reprogramming metabolism, thus limiting the success of this therapeutic approach. Given the complexity of metabolic networks and the limited impact of inhibiting glutamine metabolism alone, the combination of glutamine metabolism inhibition and other therapeutic methods may outperform corresponding monotherapies in the treatment of cancers. This review summarizes the uptake, transport, and metabolic characteristics of glutamine, as well as the regulation of glutamine dependence by some important oncogenes in various cancers to emphasize the therapeutic potential of targeting glutamine metabolism. Furthermore, we discuss a glutamine metabolic pathway, the glutaminase II pathway, that has been substantially overlooked. Finally, we discuss the applicability of polytherapeutic strategies targeting glutamine metabolism to provide a new perspective on cancer therapeutics.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Knockdown of DEAD-box 51 inhibits tumor growth of esophageal squamous cell carcinoma via the PI3K/AKT pathway

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent malignancies that seriously threaten people’s health worldwide. DEAD-box helicase 51 (DDX51) is a member of the DEAD-box (DDX) RNA helicase family, and drives or inhibits tumor progression in multiple cancer types.

AIM

To determine whether DDX51 affects the biological behavior of ESCC.

METHODS

The expression of DDX51 in ESCC tumor tissues and adjacent normal tissues was detected by Immunohistochemistry (IHC) analyses and quantitative PCR (qPCR). We knocked down DDX51 in ESCC cell lines by using a small interfering RNA (siRNA) transfection. The proliferation, apoptosis, and mobility of DDX51 siRNA-transfected cells were detected. The effect of DDX51 on the phosphoinositide 3-kinase (PI3K)/AKT pathway was investigated by western blot analysis. A mouse xenograft model was established to investigate the effects of DDX51 knockdown on ESCC tumor growth.

RESULTS

DDX51 exhibited high expression in ESCC tissues compared with normal tissues and represented a poor prognosis in patients with ESCC. Knockdown of DDX51 induced inhibition of ESCC cell proliferation and promoted apoptosis. Moreover, DDX51 siRNA-expressing cells also exhibited lower migration and invasion rates. Investigations into the underlying mechanisms suggested that DDX51 knockdown induced inactivation of the PI3K/AKT pathway, including decreased phosphorylation levels of phosphate and tensin homolog, PI3K, AKT, and mammalian target of rapamycin. Rescue experiments demonstrated that the AKT activator insulin-like growth factor 1 could reverse the inhibitory effects of DDX51 on ESCC malignant development. Finally, we injected DDX51 siRNA-transfected TE-1 cells into an animal model, which resulted in slower tumor growth.

CONCLUSION

Our study suggests for the first time that DDX51 promotes cancer cell proliferation by regulating the PI3K/AKT pathway; thus, DDX51 might be a therapeutic target for ESCC.

New Advances in Tissue Metabolomics: A Review

Metabolomics offers a hypothesis-generating approach for biomarker discovery in clinical medicine while also providing better understanding of the underlying mechanisms of chronic diseases. Clinical metabolomic studies largely rely on human biofluids (e.g., plasma, urine) as a more convenient specimen type for investigation. However, biofluids are non-organ specific reflecting complex biochemical processes throughout the body, which may complicate biochemical interpretations. For these reasons, tissue metabolomic studies enable deeper insights into aberrant metabolism occurring at the direct site of disease pathogenesis. This review highlights new advances in metabolomics for ex vivo analysis, as well as in situ imaging of tissue specimens, including diverse tissue types from animal models and human participants. Moreover, we discuss key pre-analytical and post-analytical challenges in tissue metabolomics for robust biomarker discovery with a focus on new methodological advances introduced over the past six years, including innovative clinical applications for improved screening, diagnostic testing, and therapeutic interventions for cancer.