New Metabolic Alterations and A Predictive Marker Pipecolic Acid in Sera for Esophageal Squamous Cell Carcinoma

Unveiling the methionine cycle: a key metabolic signature and NR4A2 as a methionine-responsive oncogene in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a deadly malignancy with notable metabolic reprogramming, yet the pivotal metabolic feature driving ESCC progression remains elusive. Here, we show that methionine cycle exhibits robust activation in ESCC and is reversely associated with patient survival. ESCC cells readily harness exogenous methionine to generate S-adenosyl-methionine (SAM), thus promoting cell proliferation. Mechanistically, methionine augments METTL3-mediated RNA m6A methylation through SAM and revises gene expression. Integrative omics analysis highlights the potent influence of methionine/SAM on NR4A2 expression in a tumor-specific manner, mediated by the IGF2BP2-dependent stabilization of methylated NR4A2 mRNA. We demonstrate that NR4A2 facilitates ESCC growth and negatively impacts patient survival. We further identify celecoxib as an effective inhibitor of NR4A2, offering promise as a new anti-ESCC agent. In summary, our findings underscore the active methionine cycle as a critical metabolic characteristic in ESCC, and pinpoint NR4A2 as a novel methionine-responsive oncogene, thereby presenting a compelling target potentially superior to methionine restriction.

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Metabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. Here, we conduct NMR- and MS-based metabolomics on 1,153 matched ESCC tissues, normal mucosae, pre- and one-week post-operative sera and urines from 560 participants across three hospitals, with machine learning and WGCNA. Aberrations in 'alanine, aspartate and glutamate metabolism' proved to be prevalent throughout the ESCC evolution, consistently identified by NMR and MS, and reflected in 16 serum and 10 urine metabolic signatures in both discovery and validation sets. NMR-based simplified panels of any five serum or urine metabolites outperform clinical serological tumor markers (AUC = 0.984 and 0.930, respectively), and are effective in distinguishing early-stage ESCC in test set (serum accuracy = 0.994, urine accuracy = 0.879). Collectively, NMR-based biofluid screening can reveal characteristic metabolic events of ESCC and be feasible for early detection (ChiCTR2300073613).

LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer

BACKGROUND

Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach.

METHODS

In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30). The multivariate data analysis, determination of the fold change and the Mann-Whitney U test were used to screen out the differential metabolites. Additionally, machine learning methods including receiver operating curve analysis and logistic regression analysis were conducted to establish diagnostic biomarker panels.

RESULTS

There were 36 metabolites found to be significantly different between BC and non-BC groups, and 12 metabolites discovered to be significantly different between TNBC and non-TNBC patients. Results also showed that four metabolites, including N-acetyl-D-tryptophan, 2-arachidonoylglycerol, pipecolic acid and oxoglutaric acid, were considered as vital biomarkers for the diagnosis of BC and non-BC with an area under the curve (AUC) of 0.995. Another two-metabolite panel of N-acetyl-D-tryptophan and 2-arachidonoylglycerol was discovered to discriminate TNBC from non-TNBC and produced an AUC of 0.965.

CONCLUSION

This study demonstrated that serum metabolomics can be used to identify BC specifically and identified promising serum metabolic markers for TNBC diagnosis.

Identification of salivary metabolic biomarker signatures for oral tongue squamous cell carcinoma

OBJECTIVE

To identify the salivary metabolites associated with squamous cell carcinoma of the tongue to develop easy and non-invasive potential biomarkers for disease diagnosis.

DESIGN

Initially, the study utilized untargeted metabolomics to analyze 20 samples of tongue squamous cell carcinoma and 10 control samples. The objective was to determine the salivary metabolites that exhibited differential expression in tongue squamous cell carcinoma. Then the selected metabolites were validated using targeted metabolomics in saliva samples of 100 patients diagnosed with squamous cell carcinoma of the tongue, as well as 30 healthy control individuals.

RESULTS

From the analysis of untargeted metabolomics, 10 metabolites were selected as potential biomarkers. In the subsequent targeted metabolomics study on these selected metabolites, it was observed that N-Acetyl-D-glucosamine, L-Pipecolic acid, L-Carnitine, Phosphorylcholine, and Deoxyguanosine exhibited significant differences. The receiver operating characteristic curve analysis indicates a combination of three important metabolites such as N-Acetyl-D-glucosamine, L-Pipecolic acid and L-Carnitine provided the best prediction with an area under the curve of 0.901.

CONCLUSIONS

The present result reveals that the N-Acetyl-D-glucosamine, L-Pipecolic acid and L-Carnitine are the signature diagnostic biomarkers for oral tongue squamous cell carcinoma. These findings can be used to develop a rapid and non-invasive method for disease monitoring and prognosis in oral tongue cancer.

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.

Active post-transcriptional regulation and ACLY-mediated acetyl-CoA synthesis as a pivotal target of Shuang-Huang-Sheng-Bai formula for lung adenocarcinoma treatment

BACKGROUND

New therapeutic approaches are required to improve the outcomes of lung cancer (LC), a leading cause of cancer-related deaths worldwide. Chinese herbal medicine formulae widely used in China provide a unique opportunity for improving LC treatment, and the Shuang-Huang-Sheng-Bai (SHSB) formula is a typical example. However, the underlying mechanisms of action remains unclear.

PURPOSE

This study aimed to confirm the efficacy of SHSB against lung adenocarcinoma (LUAD), which is a major histological type of LC, unveil the downstream targets of this formula, and assess the clinical relevance and biological roles of the newly identified target.

METHODS

An experimental metastasis mouse model and a subcutaneous xenograft mouse model were used to evaluate the anti-cancer activity of SHSB. Multi-omics profiling of subcutaneous tumors and metabolomic profiling of sera were performed to identify downstream targets, especially the metabolic targets of SHSB. A clinical trial was conducted to verify the newly identified metabolic targets in patients. Next, the metabolites and enzymes engaged in the metabolic pathway targeted by SHSB were measured in clinical samples. Finally, routine molecular experiments were performed to decipher the biological functions of the metabolic pathways targeted by SHSB.

RESULTS

Oral SHSB administration showed overt anti-LUAD efficacy as revealed by the extended overall survival of the metastasis model and impaired growth of implanted tumors in the subcutaneous xenograft model. Mechanistically, SHSB administration altered protein expression in the post-transcriptional layer and modified the metabolome of LUAD xenografts. Integrative analysis demonstrated that SHSB markedly inhibited acetyl-CoA synthesis in tumors by post-transcriptionally downregulating ATP-citrate lyase (ACLY). Consistently, our clinical trial showed that oral SHSB administration declined serum acetyl-CoA levels of patients with LC. Moreover, acetyl-CoA synthesis and ACLY expression were both augmented in clinical LUAD tissues of patients, and high intratumoral ACLY expression predicted a detrimental prognosis. Finally, we showed that ACLY-mediated acetyl-CoA synthesis is essential for LUAD cell growth by promoting G1/S transition and DNA replication.

CONCLUSION

Limited downstream targets of SHSB for LC treatment have been reported in previous hypothesis-driven studies. In this study, we conducted a comprehensive multi-omics investigation and demonstrated that SHSB exerted its anti-LUAD efficacy by actively and post-transcriptionally modulating protein expression and particularly restraining ACLY-mediated acetyl-CoA synthesis.

Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics

OBJECTIVE

This study aimed to construct a new staging system for patients with esophageal squamous cell carcinoma (ESCC) based on combined pathological TNM (pTNM) stage, radiomics, and proteomics.

METHODS

This study collected patients with radiomics and pTNM stage (Cohort 1, n = 786), among whom 103 patients also had proteomic data (Cohort 2, n = 103). The Cox regression model with the least absolute shrinkage and selection operator, and the Cox proportional hazards model were used to construct a nomogram and predictive models. Concordance index (C-index) and the integrated area under the time-dependent receiver operating characteristic (ROC) curve (IAUC) were used to evaluate the predictive models. The corresponding staging systems were further assessed using Kaplan-Meier survival curves.

RESULTS

For Cohort 1, the RadpTNM4c staging systems, constructed based on combined pTNM stage and radiomic features, outperformed the pTNM4c stage in both the training dataset 1 (Train1; IAUC 0.711 vs. 0.706, p < 0.001) and the validation dataset 1 (Valid1; IAUC 0.695 vs. 0.659, p < 0.001; C-index 0.703 vs. 0.674, p = 0.029). For Cohort 2, the ProtRadpTNM2c staging system, constructed based on combined pTNM stage, radiomics, and proteomics, outperformed the pTNM2c stage in both the Train2 (IAUC 0.777 vs. 0.610, p < 0.001; C-index 0.898 vs. 0.608, p < 0.001) and Valid2 (IAUC 0.746 vs. 0.608, p < 0.001; C-index 0.889 vs. 0.641, p = 0.009) datasets.

CONCLUSIONS

The ProtRadpTNM2c staging system, based on combined pTNM stage, radiomic, and proteomic features, improves the predictive performance of the classical pTNM staging system.

The Effect of Supplemental Concentrate Feeding on the Morphological and Functional Development of the Pancreas in Early Weaned Yak Calves

Simple Summary

This study aimed to investigate the nutritional intake deficiency on rearing yak calves. We investigated supplemental concentrate feeding effects on the morphological and functional development of the pancreas in early weaned yak calves. In the study, we determined the apparent digestibility of nutrients by digestion trail, the morphological development of the pancreas in yak calves by tissue sectioning, the activity of main digestive enzymes and hormone levels by ELISA kits, and the content of major small molecule metabolites in the pancreas by non-targeted metabolomics techniques. The morphological and functional development of the pancreas and its small molecule metabolites are mainly presented in graphical form, which had positive regulatory effects on the development of the pancreas in early weaned yak calves. In summary, we found that supplemental concentrate feeding was crucial for the high-quality growth and development of early weaned yak calves and had a positive influence on the intrinsic relationship between the overall development level and physiological functions of the pancreas, which could provide an important reference for scientific rearing of early weaned yak calves.

Abstract

This experiment was conducted to investigate the effect of supplemental concentrate feeding on the pancreatic development of yak calves. Twenty one-month-old yak calves with healthy body condition and similar body weight were selected as experimental animals and randomly divided into two groups, five replicates in each group. The control group yak calves were fed milk replacer and alfalfa hay, the experimental group yak calves were fed milk replacer, alfalfa hay and concentrate. The pre-feeding period of this experiment was thirty days, the trial period was one hundred days. At the end of feeding trail, five yak calves from each group were selected and slaughtered and the pancreas tissues of yak calves were collected and determined. The results showed that: (1) Dry matter and body weight of yak calves in the test group were significantly higher than those of the control group. (2) The apparent nutrient digestibility of crude protein, crude fat, calcium and phosphorus in the test group of yak calves was significantly higher than that of the control group, while the apparent nutrient digestibility of neutral detergent fiber and acid detergent fiber in the test group was significantly lower than that of the control group. (3) Pancreatic weight, organ index, total ratio of exocrine part area and total ratio of endocrine area of yak calves in the test group were significantly higher than those in the control group, while the ratio of exocrine area was significantly lower in the test group than that of the control group. (4) The activities of the main pancreatic digestive enzymes: pancreatic amylase, pancreatic lipase, pancreatic protease and chymotrypsin were significantly higher in the test group than those of the control group, as were the hormonal contents of glucagon, insulin and pancreatic polypeptide. (5) The main differential metabolites of the pancreas in the test group were significantly higher than those of the control group, such as D-proline, hypoxanthine, acetylcysteine, gamma-glutamylcysteine, thiazolidine-4-carboxylic acid, piperidinic acid, ellagic acid, nicotinamide, tropolone, D-serine, ribulose-5-phosphate, (+/-)5(6)-epoxyeicosatrienoic acid(EET), 2-hydroxycinnamic acid, L-phenylalanine, creatinine, tetrahydrocorticosterone, pyridoxamine, xanthine, 5-oxoproline, asparagine, DL-tryptophan, in-dole-3-acrylic acid, thymine, trehalose, docosapentaenoic acid, docosahexaenoic acid, fatty acid esters of hydroxy fatty acids(FAHFA) (18:1/20:3), fatty acid esters of hydroxy fatty acids(FAHFA) (18:2/20:4), adrenic acid and xanthosine. In conclusion, supplemental concentrate feeding promoted the good development of morphological and functional properties of the pancreas in early weaned yak calves to improve the digestion and absorption of feed nutrients, so as to enhance the growth and development quality of early weaned yak calves.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with esophageal cancer

Metabolomics has been reported as an efficient tool to screen biomarkers that are related to esophageal cancer. However, the metabolic biomarkers identifying malignant degrees and therapeutic efficacy are still largely unknown in the disease. Here, GC-MS-based metabolomics was used to understand metabolic alteration in 137 serum specimens from patients with esophageal cancer, which is approximately two- to fivefold as many plasma specimens as the previous reports. The elevated amino acid metabolism is in sharp contrast to the reduced carbohydrate as a characteristic feature of esophageal cancer. Comparative metabolomics showed that most metabolic differences were determined between the early stage (0–II) and the late stage (III and IV) among the 0–IV stages of esophageal cancer and between patients who received treatment and those who did not receive treatment. Glycine, serine, and threonine metabolism and glycine were identified as the potentially overlapped metabolic pathway and metabolite, respectively, in both disease progress and treatment effect. Glycine, fructose, ornithine, and threonine can be a potential array for the evaluation of disease prognosis and therapy in esophageal cancer. These results highlight the means of identifying previously unknown biomarkers related to esophageal cancer by a metabolomics approach.

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Background

Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014–2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients.

Methods

Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, “caret” R package, “e1071” R package and “Tensorflow” Python package, respectively.

Results

Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83–93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent.

Conclusions

Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07694-8.