Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma

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.

Identification of avaC from Human Gut Microbial Isolates that Converts 5AVA to 2-Piperidone

2-piperidone is a crucial industrial raw material of high-value nylon-5 and nylon-6,5. Currently, a major bottleneck in the biosynthesis of 2-piperidone is the identification of highly efficient 2-piperidone synthases. In this study, we aimed to identify specific strains among 51 human gut bacterial strains capable of producing 2-piperidone and to elucidate its synthetic mechanism. Our findings revealed that four gut bacterial strains, namely Collinsella aerofaciens LFYP39, Collinsella intestinalis LFYP54, Clostridium bolteae LFYP116, and Clostridium hathewayi LFYP18, could produce 2-piperidone from 5-aminovaleric acid (5AVA). Additionally, we observed that 2-piperidone could be synthesized from proline through cross-feeding between Clostridium difficile LFYP43 and one of the four 2-piperidone producing strains, respectively. To identify the enzyme responsible for catalyzing the conversion of 5AVA to 2-piperidone, we utilized a gain-of-function library and identified avaC (5-aminovaleric acid cyclase) in C. intestinalis LFYP54. Moreover, homologous genes of avaC were validated in the other three bacterial strains. Notably, avaC were found to be widely distributed among environmental bacteria. Overall, our research delineated the gut bacterial strains and genes involved in 2-piperidone production, holding promise for enhancing the efficiency of industrial biosynthesis of this compound.

Genetic and molecular characterization of metabolic pathway-based clusters in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive types of squamous cell carcinoma and represents a significant proportion of esophageal cancer. Metabolic reprogramming plays a key role in the occurrence and development of ESCC. Unsupervised clustering analysis was employed to stratify ESCC samples into three clusters: MPC1-lipid type, MPC2-amino acid type, and MPC3-energy type, based on the enrichment scores of metabolic pathways extracted from the Reactome database. The MPC3 cluster exhibited characteristics of energy metabolism, with heightened glycolysis, cofactors, and nucleotide metabolism, showing a trend toward increased aggressiveness and poorer survival rates. On the other hand, MPC1 and MPC2 primarily involved lipid and amino acid metabolism, respectively. In addition, liquid chromatography‒mass spectrometry-based metabolite profiles and potential therapeutic agents were explored and compared among ESCC cell lines with different MPCs. MPC3 amplified energy metabolism markers, especially carnitines. In contrast, MPC1 and MPC2 predominantly had elevated levels of lipids (primarily triacylglycerol) and amino acids, respectively. Furthermore, MPC3 demonstrated a suboptimal clinical response to PD-L1 immunotherapy but showed increased sensitivity to the doramapimod chemotherapy regimen, as evident from drug sensitivity evaluations. These insights pave the way for a more personalized therapeutic approach, potentially enhancing treatment precision for ESCC patients.

Plasma free fatty acid levels in cervical cancer: concurrent chemoradiotherapy improves abnormal profile

Background: Epidemiology has demonstrated that plasma free fatty acids (FFAs) can prevent the development of cancer. Our study sought to evaluate the relationship between plasma (FFA) levels and cervical cancer. Methods: In recent years, metabolomics-based approaches have been recognized as an emerging tool, so we examined the plasma FFA profiles of 114 patients with cervical cancer and 151 healthy people using liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods. Results: The data results were analyzed by multifactorial binary logistic regression analysis, and it was found that palmitic acid, docosahexaenoic acid (DHA), and total ω-3 fatty acids were negatively correlated with the risk of cervical cancer; whereas tetracosanoic acid was positively correlated with the risk of cervical cancer (OR, 1.026; 95% CI, 1.013-1.040; p < 0.001). Dynamic follow-up of 40 cervical cancer patients who successfully completed CCRT revealed that most fatty acid levels tended to increase after the end of treatment, except for palmitic and stearic acid levels, which were lower than before treatment. Conclusion: Plasma FFA profiles were altered in cervical cancer patients, which may be related to abnormal fatty acid metabolism in cervical cancer. The described changes in fatty acid profiles during CCRT may be related to the good functioning of CCRT. Further studies on plasma FFA composition and its changes due to CCRT in patients with cervical cancer are warranted.

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.

Discovering metabolic vulnerability using spatially resolved metabolomics for antitumor small molecule-drug conjugates development as a precise cancer therapy strategy

Against tumor-dependent metabolic vulnerability is an attractive strategy for tumor-targeted therapy. However, metabolic inhibitors are limited by the drug resistance of cancerous cells due to their metabolic plasticity and heterogeneity. Herein, choline metabolism was discovered by spatially resolved metabolomics analysis as metabolic vulnerability which is highly active in different cancer types, and a choline-modified strategy for small molecule-drug conjugates (SMDCs) design was developed to fool tumor cells into indiscriminately taking in choline-modified chemotherapy drugs for targeted cancer therapy, instead of directly inhibiting choline metabolism. As a proof-of-concept, choline-modified SMDCs were designed, screened, and investigated for their druggability in vitro and in vivo. This strategy improved tumor targeting, preserved tumor inhibition and reduced toxicity of paclitaxel, through targeted drug delivery to tumor by highly expressed choline transporters, and site-specific release by carboxylesterase. This study expands the strategy of targeting metabolic vulnerability and provides new ideas of developing SMDCs for precise cancer therapy.

Oral Lycopene Administration Attenuates Inflammation and Oxidative Stress by Regulating Plasma Lipids in Rats with Lipopolysaccharide-Induced Epididymitis

Purpose

Epididymitis histological alterations and related long-term reproductive issues cannot be cured by antibiotics alone. Few studies have been done on the effect of lycopene on epididymitis, despite the fact that it is an efficient antioxidant. The objective of this study was to assess the impact of lycopene on Lipopolysaccharide (LPS)-induced epididymis and lipid metabolism.

Methods

Thirty-one 260–290g rats were separated into the blank control group (n=10), the oil-control group (n=10), the single intraperitoneal injection of 5 mg/kg LPS (n=5), and the continuous intragastric of 5 mg/kg lycopene (n=6). The animals were euthanized after four weeks, and blood and the epididymis were removed for analysis.

Results

Lycopene significantly decreased IL-1α, IL-1β, TNF-α, MCP-1, IL-6 and lipid peroxidation product Malondialdehyde in serum and epididymis. It significantly increased the epididymis’s antioxidant enzyme and total antioxidant capacity. According to LC-MS plasma lipidomics, lycopene increased phosphatidylcholine, lysophosphatidylcholine, decreased phosphatidylethanolamine, triacylglycerol, and diacylglycerol levels, changed the composition of lipids, altered metabolic pathways, and these changes were related to the mechanism of anti-inflammatory and oxidative stress. 20 lipids, including PC (20:5e) and LPC (14:0), were identified through additional Spearman correlation analysis as being related to cytokines and oxidation indices. They served as possible lipid markers that may be utilized to gauge the severity of inflammation.

Conclusion

Lycopene has anti-inflammatory and antioxidant properties that improve histopathological and functional damage in LPS-induced epididymitis and is an alternate supplement for treating epididymitis. Lipidomics provide new perspectives on the possible mechanism of lycopene in protecting against LPS-induced epididymitis by integrating lipid metabolism and inflammation.

Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer

Simple Summary

Neoadjuvant chemotherapy (NACT) is offered to breast cancer (BC) patients to downstage the disease. However, some patients may not respond to NACT, being resistant. We used the serum metabolic profile by Nuclear Magnetic Resonance (NMR) combined with disease characteristics to differentiate between sensitive and resistant BC patients. We obtained accuracy above 80% for the response prediction and showcased how NMR can substantially enhance the prediction of response to NACT.

Abstract

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

Untargeted metabolomics analysis of esophageal squamous cell cancer progression

90% of esophageal cancer are esophageal squamous cell carcinoma (ESCC) and ESCC has a very poor prognosis and high mortality. Nevertheless, the key metabolic pathways associated with ESCC progression haven’t been revealed yet. Metabolomics has become a new platform for biomarker discovery over recent years. We aim to elucidate dominantly metabolic pathway in all ESCC tumor/node/metastasis (TNM) stages and adjacent cancerous tissues. We collected 60 postoperative esophageal tissues and 15 normal tissues adjacent to the tumor, then performed Liquid Chromatography with tandem mass spectrometry (LC–MS/MS) analyses. The metabolites data was analyzed with metabolites differential and correlational expression heatmap according to stage I vs. con., stage I vs. stage II, stage II vs. stage III, and stage III vs. stage IV respectively. Metabolic pathways were acquired by Kyoto Encyclopedia of Genes and Genomes. (KEGG) pathway database. The metabolic pathway related genes were obtained via Gene Set Enrichment Analysis (GSEA). mRNA expression of ESCC metabolic pathway genes was detected by two public datasets: gene expression data series (GSE)23400 and The Cancer Genome Atlas (TCGA). Receiver operating characteristic curve (ROC) analysis is applied to metabolic pathway genes. 712 metabolites were identified in total. Glycerophospholipid metabolism was significantly distinct in ESCC progression. 16 genes of 77 genes of glycerophospholipid metabolism mRNA expression has differential significance between ESCC and normal controls. Phosphatidylserine synthase 1 (PTDSS1) and Lysophosphatidylcholine Acyltransferase1 (LPCAT1) had a good diagnostic value with Area under the ROC Curve (AUC) > 0.9 using ROC analysis. In this study, we identified glycerophospholipid metabolism was associated with the ESCC tumorigenesis and progression. Glycerophospholipid metabolism could be a potential therapeutic target of ESCC progression.

Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [hyodeoxycholic acid, (2S,3S)-3-methylphenylalanine, carnitine C9:1, and indole-3-carboxylic acid] were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed that perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.

Combination of Plasma-Based Metabolomics and Machine Learning Algorithm Provides a Novel Diagnostic Strategy for Malignant Mesothelioma

BACKGROUND

Malignant mesothelioma (MM) is an aggressive and incurable carcinoma that is primarily caused by asbestos exposure. However, the current diagnostic tool for MM is still under-developed. Therefore, the aim of this study is to explore the diagnostic significance of a strategy that combined plasma-based metabolomics with machine learning algorithms for MM.

METHODS

Plasma samples collected from 25 MM patients and 32 healthy controls (HCs) were randomly divided into train set and test set, after which analyzation was performed by liquid chromatography-mass spectrometry-based metabolomics. Differential metabolites were screened out from the samples of the train set. Subsequently, metabolite-based diagnostic models, including receiver operating characteristic (ROC) curves and Random Forest model (RF), were established, and their prediction accuracies were calculated for the test set samples.

RESULTS

Twenty differential plasma metabolites were annotated in the train set; 10 of these metabolites were validated in the test set. The seven most prevalent diagnostic metabolites were taurocholic acid), 0.7142 (uracil), 0.7142 (biliverdin), 0.8571 (histidine), 0.5000 (tauroursodeoxycholic acid), 0.8571 (pyrroline hydroxycarboxylic acid), and 0.7857 (phenylalanine). Furthermore, RF based on 20 annotated metabolites showed a prediction accuracy of 0.9286, and its optimized version achieved 1.0000 in the test set. Moreover, the comparison between the samples of peritoneal MM (n = 8) and pleural MM (n = 17) illustrated a significant increase in levels of taurocholic acid and tauroursodeoxycholic acid, as well as an evident decrease in biliverdin.

CONCLUSIONS

Our results revealed the potential diagnostic value of plasma-based metabolomics combined with machine learning for MM. Further research with large sample size is worthy conducting. Moreover, our data demonstrated dysregulated metabolism pathways in MM, which aids in better understanding of molecular mechanisms related to the initiation and development of MM.

An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

BACKGROUND

The essence of energy metabolism has spread to the field of esophageal cancer (ESC) cells. Herein, we tried to develop a prognostic prediction model for patients with ESC based on the expression profiles of energy metabolism associated genes.

MATERIALS AND METHODS

The overall survival (OS) predictive gene signature was developed, internally and externally validated based on ESC datasets including The Cancer Genome Atlas (TCGA), GSE54993 and GSE19417 datasets. Hub genes were identified in each energy metabolism related molecular subtypes by weighted gene correlation network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, nomogram, decision curve analysis (DCA), and restricted mean survival time (EMST) were used to assess the performance of the gene signature.

RESULTS

A novel energy metabolism based eight-gene signature (including UBE2Z, AMTN, AK1, CDCA4, TLE1, FXN, ZBTB6 and APLN) was established, which could dichotomize patients with significantly different OS in ESC. The eight-gene signature demonstrated independent prognostication potential in patient with ESC. The prognostic nomogram constructed based on the gene signature showed excellent predictive performance, whose robustness and clinical usability were higher than three previous reported prognostic gene signatures.

CONCLUSIONS

Our study established a novel energy metabolism based eight-gene signature and nomogram to predict the OS of ESC, which may help in precise clinical management.

Indoleamine 2,3-Dioxygenase 1 Inhibitor-Loaded Nanosheets Enhance CAR-T Cell Function in Esophageal Squamous Cell Carcinoma

In chimeric antigen receptor (CAR)-T cell therapy, the role and mechanism of indoleamine 2, 3 dioxygenase 1 (IDO1) in enhancing antitumor immunity require further study. IDO1 is one of the most important immunosuppressive proteins in esophageal squamous cell carcinoma (ESCC). However, the IDO1 inhibitor, epacadostat, has failed in phase III clinical trials; its limited capacity to inhibit IDO1 expression at tumor sites was regarded as a key reason for clinical failure. In this study, we innovatively loaded the IDO1 inhibitor into hyaluronic acid-modified nanomaterial graphene oxide (HA-GO) and explored its potential efficacy in combination with CAR-T cell therapy. We found that inhibition of the antitumor effect of CAR-T cells in ESCC was dependent on the IDO1 metabolite kynurenine. Kynurenine could suppress CAR-T cell cytokine secretion and cytotoxic activity. Inhibiting IDO1 activity significantly enhanced the antitumor effect of CAR-T cells in vitro and in vivo. Our findings suggested that IDO1 inhibitor-loaded nanosheets could enhance the antitumor effect of CAR-T cells compared with free IDO1 inhibitor. Nanosheet-loading therefore provides a promising approach for improving CAR-T cell therapeutic efficacy in solid tumors.