Altered L-Arginine Metabolic Pathways in Gastric Cancer: Potential Therapeutic Targets and Biomarkers

Emerging roles of long noncoding RNAs in enzymes related intracellular metabolic pathways in cancer biology

Metabolic reprogramming plays critical roles in the development and progression of tumor by providing cancer cells with a sufficient supply of nutrients and other factors needed for fast-proliferating. Emerging evidence indicates that long noncoding RNAs (lncRNAs) are involved in the initiation of metastasis via regulating the metabolic reprogramming in various cancers. In this paper, we aim to summarize that lncRNAs could participate in intracellular nutrient metabolism including glucose, amino acid, lipid, and nucleotide, regardless of whether lncRNAs have tumor-promoting or tumor-suppressor function. Meanwhile, modulation of lncRNAs in glucose metabolic enzymes in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle (TCA) in cancer is reviewed. We also discuss therapeutic strategies targeted at interfering with enzyme activity to decrease the utilization of glucoses, amino acid, nucleotide acid and lipid in tumor cells. This review focuses on our current understanding of lncRNAs participating in cancer cell metabolic reprogramming, paving the way for further investigation into the combination of such approaches with existing anti-cancer therapies.

Analysis of metabolic characteristics of metabolic syndrome in elderly patients with gastric cancer by non-targeted metabolomics

BACKGROUND

The relationship between metabolic syndrome (MetS) and gastric cancer (GC), which is a common metabolic disease, has attracted much attention. However, the specific metabolic characteristics of MetS in elderly patients with GC remain unclear.

AIM

To investigate the differentially abundant metabolites and metabolic pathways between preoperative frailty and MetS in elderly patients with GC based on nontargeted metabolomics techniques.

METHODS

In this study, 125 patients with nonfrail nonmeal GC were selected as the control group, and 50 patients with GC in the frail group were selected as the frail group. Sixty-five patients with GC combined with MetS alone were included in the MetS group, and 50 patients with GC combined with MetS were included in the MetS group. Nontargeted metabolomics techniques were used to measure plasma metabolite levels by ultrahigh-performance liquid chromatography-mass spectrometry. Multivariate statistical analysis was performed by principal component analysis, orthogonal partial least squares, pattern recognition analysis, cluster analysis, and metabolic pathway annotation.

RESULTS

A total of 125 different metabolites, including amino acids, glycerophospholipids, sphingolipids, fatty acids, sugars, nucleosides and nucleotides, and acidic compounds, were identified via nontargeted metabolomics techniques. Compared with those in the control group, there were 41, 32, and 52 different metabolites in the MetS group, the debilitated group, and the combined group, respectively. Lipid metabolites were significantly increased in the MetS group. In the weak group, amino acids and most glycerol phospholipid metabolites decreased significantly, and fatty acids and sphingosine increased significantly. The combined group was characterized by significantly increased levels of nucleotide metabolites and acidic compounds. The alanine, aspartic acid, and glutamate metabolic pathways were obviously enriched in the asthenic group, and the glycerol and phospholipid metabolic pathways were obviously enriched in the combined group.

CONCLUSION

Elderly GC patients with simple frailty, simple combined MetS, and frailty combined with MetS have different metabolic characteristics, among which amino acid and glycerophospholipid metabolite levels are significantly lower in frail elderly GC patients, and comprehensive supplementation of fat and protein should be considered. Many kinds of metabolites, such as amino acids, lipids, nucleotides, and acidic compounds, are abnormally abundant in patients with MetS combined with fthenia, which may be related to tumor-related metabolic disorders.

Analysis of metabolic characteristics of metabolic syndrome in elderly patients with gastric cancer by non-targeted metabolomics

BACKGROUND

The relationship between metabolic syndrome (MetS) and gastric cancer (GC), which is a common metabolic disease, has attracted much attention. However, the specific metabolic characteristics of MetS in elderly patients with GC remain unclear.

AIM

To investigate the differentially abundant metabolites and metabolic pathways between preoperative frailty and MetS in elderly patients with GC based on nontargeted metabolomics techniques.

METHODS

In this study, 125 patients with nonfrail nonmeal GC were selected as the control group, and 50 patients with GC in the frail group were selected as the frail group. Sixty-five patients with GC combined with MetS alone were included in the MetS group, and 50 patients with GC combined with MetS were included in the MetS group. Nontargeted metabolomics techniques were used to measure plasma metabolite levels by ultrahigh-performance liquid chromatography-mass spectrometry. Multivariate statistical analysis was performed by principal component analysis, orthogonal partial least squares, pattern recognition analysis, cluster analysis, and metabolic pathway annotation.

RESULTS

A total of 125 different metabolites, including amino acids, glycerophospholipids, sphingolipids, fatty acids, sugars, nucleosides and nucleotides, and acidic compounds, were identified via nontargeted metabolomics techniques. Compared with those in the control group, there were 41, 32, and 52 different metabolites in the MetS group, the debilitated group, and the combined group, respectively. Lipid metabolites were significantly increased in the MetS group. In the weak group, amino acids and most glycerol phospholipid metabolites decreased significantly, and fatty acids and sphingosine increased significantly. The combined group was characterized by significantly increased levels of nucleotide metabolites and acidic compounds. The alanine, aspartic acid, and glutamate metabolic pathways were obviously enriched in the asthenic group, and the glycerol and phospholipid metabolic pathways were obviously enriched in the combined group.

CONCLUSION

Elderly GC patients with simple frailty, simple combined MetS, and frailty combined with MetS have different metabolic characteristics, among which amino acid and glycerophospholipid metabolite levels are significantly lower in frail elderly GC patients, and comprehensive supplementation of fat and protein should be considered. Many kinds of metabolites, such as amino acids, lipids, nucleotides, and acidic compounds, are abnormally abundant in patients with MetS combined with fthenia, which may be related to tumor-related metabolic disorders.

Multi-omics landscape analysis reveals the pan-cancer association of arginine biosynthesis genes with tumor immune evasion and therapy resistance

Background

The metabolism of arginine, a conditionally essential amino acid, plays a crucial role in cancer progression and prognosis. However, a more detailed understanding of the influence of arginine biosynthesis genes in cancer is currently unavailable.

Methods

We performed an integrative multi-omics analysis using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to determine the characteristics of these genes across multiple cancer types. To measure the overall activity of arginine biosynthesis genes in cancer, we calculated arginine biosynthesis scores based on gene expression.

Results

Our results indicated that the arginine biosynthesis score was negatively correlated with immune-related pathways, immune infiltration, immune checkpoint expression, and patient prognosis, and single-cell data further clarified that patients with high arginine biosynthesis scores showed a reduced proportion of T and B cells in an immune desert tumor microenvironment and were insensitive to immunotherapy. We also identified several potential drugs through the Cancer Therapeutic Response Portal (CTRP) and Genomics of Drug Sensitivity in Cancer (GDSC) databases that could target arginine biosynthesis genes and potentially improve the response rate to immunotherapy in patients with a high arginine biosynthesis fraction.

Conclusion

Overall, our analyses emphasize that arginine biosynthesis genes are associated with immune evasion in several cancers. Targeting these genes may facilitate more effective immunotherapy.

Integrating ferroptosis-related genes (FRGs) and prognostic models to enhance UCEC outcome prediction and therapeutic insights

Ferroptosis is closely associated with uterine corpus endometrial carcinoma (UCEC) development. This project aimed to identify new potential biomarkers to predict the prognosis of UCEC. In this work, UCEC transcriptome data along with clinical information was retrieved from the TCGA database including a total of 382 FRGs. We performed univariate Cox regression analysis to evaluate ferroptosis-related genes (FRGs) for prognostic significance. The genes with prognostic significance were then analyzed using LASSO-Cox to construct a prognosis model. The model genes were further characterized through various proteomic analyses and expression detection in clinical samples. A multivariate Cox regression model was constructed containing four FRGs (CDKN1A, CDKN2A, CEBPG, NOS2). Among four FRGs, higher expressions of CDKN2A, CEBPG, and NOS2 were associated with poorer overall survival probability, while higher expression of CDKN1A was associated with better overall survival probability. The area under the receiver operating characteristic curve of the risk model was 0.617, 0.688, and 0.693 for 1 year, 3 years, and 5 years, respectively. Moreover, proteomic analysis showed that the protein expression of CDKN1A, CDKN2A, and CEBPG was higher in tumor tissues than that in normal tissues. Higher protein expression of CDKN1A and CDKN2A predicted poorer survival probability. Besides, CDKN1A protein had an interaction relationship with CDKN2A protein or NOS2 protein. In clinical samples, all four FRGs were upregulated in UCEC tissues, regardless of gene expression or protein expression. Our four FRGs risk model provides new insights for predicting the prognosis of UCEC patients.

Prognostic Implication of Plasma Metabolites in Gastric Cancer

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients' quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan-Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.

Plasma metabolome identifies potential biomarkers of gastric precancerous lesions and gastric cancer risk

OBJECTIVES

Currently, metabolic biomarkers with great practicability of gastric cancer (GC) and gastric precancerous lesions (GPL) are scarce. Thus, we are devoted to determining the plasma metabolic profiles of patients with GPL or GC and validate candidate biomarkers for disease diagnosis.

METHODS

In this hospital-based case-control study, 68 plasma samples from 27 non-atrophic gastritis (NAG, control), 31 GPL, and 10 GC patients were collected for targeted metabolomics analysis. Univariate and multivariate analyses were used for selecting the differential metabolites. A receiver operating characteristic curve combined with binary logistic regression analysis was performed to test the diagnostic performance of the differential metabolites. Dietary data were obtained using a semiquantitative food frequency questionnaire.

RESULTS

Distinct metabolomic profiles were noted for NAG, GPL, and GC. Compared to the NAG patients, the levels of 5 metabolites in the GPL group and 4 metabolites in the GC group were found to significantly elevate. Compared with the model involving 9 traditional risk factors (AUC: 0.89, 95%CI: 0.78-1.00), Trimethylamine N-oxide, the most significant metabolite (P = 2.00 × 10-5, FDR = 0.003, FC > 2, VIP > 2), showed a good diagnostic performance for the patients with GC (AUC: 0.90, 95%CI: 0.78-1.00), and its diagnostic performance has been further improved with the integration of Rhamnose (AUC: 0.96, 95%CI: 0.89-1.00).

CONCLUSION

In our study, 9 defined metabolites might serve as meaningful biomarkers for identifying the high-risk population of GPL and GC, possibly enhancing the prevention and control of GPL and GC.

Identification of a novel plasma metabolite panel as diagnostic biomarker for hepatocellular carcinoma

BACKGROUND AND AIMS

Metabolic reprogramming is one of the hallmarks of cancer. Hepatocellular carcinoma (HCC) is one of the most lethal malignancy camcer, but the early diagnosis of HCC remains difficult. In this study, we searched for potential plasma metabolite biomarkers of HCC.

METHODS

A total of plasma samples of 104 HCC, 76 cirrhosis and 10 healthy subjects were assessed and validated through Gas chromatography-Mass spectrometry. Receiver-operating characteristic curves (ROC) combined with multivariate statistical analyses were used to assess the diagnostic performance of metabolites and combinations.

RESULTS

10 metabolites in screening cohort were significantly changed in the plasma of HCC patients. Multivariate logistic regression analysis of candidate metabolites in validation cohort showed that N-formylglycine, oxoglutaric acid, citrulline and heptaethylene glycol could distinguish HCC from cirrhosis. The combination of these four metabolites showed a better performance than AFP with the Area Under the Curve (AUC), sensitivity, specificity as 0.940, 84.00%, 97.56%, respectively. In further, the panel of N-formylglycine, heptaethylene glycol and citrulline can more effectively discriminate early stage HCC from cirrhosis than AFP (AUC: 0.835 vs. 0.634). Finally, heptaethylene glycol could significantly inhibit the proliferation, migration and invasion of HCC cells in vitro.

CONCLUSION

The combination of plasma N-formylglycine, oxoglutaric acid, citrulline, and heptaethylene glycol can be an efficient novel diagnostic biomarker for HCC.

Crosstalk between fatty acid metabolism and tumour-associated macrophages in cancer progression

Over the last few decades, cancer has been regarded as an independent and self sustaining progression. The earliest hallmarks of cancer comprise of sustaining proliferative signalling, avoiding growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Nonetheless, two emerging hallmarks are being described: aberrant metabolic pathways and evasion of immune destruction. Changes in tumour cell metabolism are not restricted to tumour cells alone; the products of the altered metabolism have a direct impact on the activity of immune cells inside the tumour microenvironment, particularly tumour-associated macrophages (TAMs). The complicated process of cancer growth is orchestrated by metabolic changes dictating the tight mutual connection between these cells. Here, we discuss approaches to exploit the interaction of cancer cells' abnormal metabolic activity and TAMs. We also describe ways to exploit it by reprogramming fatty acid metabolism via TAMs.

Microbiota and metabolites alterations in proximal and distal gastric cancer patients

Background

Globally, gastric cancer is the third most common cancer and the third leading cause of cancer death. Proximal and distal gastric cancers have distinct clinical and biological behaviors. The microbial composition and metabolic differences in proximal and distal gastric cancers have not been fully studied and discussed.

Methods

In this study, the gastric microbiome of 13 proximal gastric cancer tissues, 16 distal gastric cancer tissues, and their matched non-tumor tissues were characterized using 16S rRNA amplicon sequencing. Additionally, 10 proximal gastric cancer tissues, 11 distal gastric cancer tissues, and their matched non-tumor tissues were assessed by untargeted metabolomics.

Results

There was no significant difference in microbial diversity and richness between the proximal and distal gastric cancer tissues. At the genus level, the abundance of Rikenellaceae_RC9_gut_group, Porphyromonas, Catonella, Proteus, Oribacterium, and Moraxella were significantly increased in Proximal T, whereas that of Methylobacterium_Methylorubrum was significantly increased in Distal T. The untargeted metabolomics analysis revealed 30 discriminative metabolites between Distal T and Distal N. In contrast, there were only 4 discriminative metabolites between Proximal T and Proximal N. In distal gastric cancer, different metabolites were scattered through multiple pathway, including the sphingolipid signaling pathway, arginine biosynthesis, protein digestion and absorption, alanine, aspartate and, glutamate metabolism, etc.In proximal gastric cancer, differential microbial metabolites were mainly related to hormone metabolism.

Conclusion

Methylobacterium-Methylorubrum was significantly increased in Distal T, positively correlated with cancer-promoting metabolites, and negatively correlated with cancer-inhibiting metabolites. Rikenellaceae_RC_gut_group was significantly increased in Proximal T and positively correlated with cancer-promoting metabolites. Further studies regarding the functions of the above-mentioned microorganisms and metabolites were warranted as the results may reveal the different mechanisms underlying the occurrence and development of proximal and distal gastric cancers and provide a basis for future treatments.

Importance

First, the differences in microbial composition and metabolites between the proximal and distal gastric cancers were described; then, the correlation between microbiota and metabolites was preliminarily discussed. These microbes and metabolites deserve further investigations as they may reveal the different mechanisms involved in the occurrence and development of proximal and distal gastric cancers and provide a basis for future treatments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03650-x.

Comprehensive analysis of tissue proteomics in patients with papillary thyroid microcarcinoma uncovers the underlying mechanism of lymph node metastasis and its significant sex disparities

Background

Lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC) is associated with an increased risk of recurrence and poor prognosis. Sex has been regarded as a critical risk factor for LNM. The present study aimed to investigate the molecular mechanisms underlying LNM and its significant sex disparities in PTMC development.

Methods

A direct data-independent acquisition (DIA) proteomics approach was used to identify differentially expressed proteins (DEPs) in PTMC tumorous tissues with or without LNM and from male and female patients with LNM. The functional annotation of DEPs was performed using bioinformatics methods. Furthermore, The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) dataset and immunohistochemistry (IHC) were used to validate selected DEPs.

Results

The proteomics profile in PTMC with LNM differed from that of PTMC without LNM. The metastasis-related DEPs were primarily enriched in categories associated with mitochondrial dysfunction and may promote tumor progression by activating oxidative phosphorylation and PI3K/AKT signaling pathways. Comparative analyses of these DEPs revealed downregulated expression of specific proteins with well-established links to tumor metastasis, such as SLC25A15, DIRAS2, PLA2R1, and MTARC1. Additionally, the proteomics profiles of male and female PTMC patients with LNM were dramatically distinguishable. An elevated level of ECM-associated proteins might be related to more LNM in male PTMC than in female PTMC patients. The upregulated expression levels of MMRN2 and NID2 correlated with sex disparities and showed a positive relationship with unfavorable variables, such as LNMs and poor prognosis.

Conclusions

The proteomics profiles of PTMC show significant differences associated with LNM and its sex disparities, which further expands our understanding of the functional networks and signaling pathways related to PTMC with LNM.

Exosome Metabolic Patterns on Aptamer-Coupled Polymorphic Carbon for Precise Detection of Early Gastric Cancer

Gastric cancer (GC) presents high mortality worldwide because of delayed diagnosis. Currently, exosome-based liquid biopsy has been applied in diagnosis and monitoring of diseases including cancers, whereas disease detection based on exosomes at the metabolic level is rarely reported. Herein, the specific aptamer-coupled Au-decorated polymorphic carbon (CoMPC@Au-Apt) is constructed for the capture of urinary exosomes from early GC patients and healthy controls (HCs) and the subsequent exosome metabolic pattern profiling without extra elution process. Combining with machine learning algorithm on all exosome metabolic patterns, the early GC patients are excellently discriminated from HCs, with an accuracy of 100% for both the discovery set and blind test. Ulteriorly, three key metabolic features with clear identities are determined as a biomarker panel, obtaining a more than 90% diagnostic accuracy for early GC in the discovery set and validation set. Moreover, the change law of the key metabolic features along with GC development is revealed through making a comparison among HCs and GC at early stage and advanced stage, manifesting their monitoring ability toward GC. This work illustrates the high specificity of exosomes and the great prospective of exosome metabolic analysis in disease diagnosis and monitoring, which will promote exosome-driven precision medicine toward practical clinical application.

Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plasma for BC diagnosis.

METHODS

This study enrolled 216 participants, including BC patients, benign patients, and healthy controls (HC) and formed two cohorts, one training cohort and one testing cohort. Plasma samples were collected from each participant and subjected to perform nontargeted metabolomics and proteomics. The metabolic signatures for BC diagnosis were identified through machine learning.

RESULTS

Metabolomics analysis revealed that BC patients showed a significant change of metabolic profiles compared to HC individuals. The alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways were the critical biological metabolic pathways in BC. Proteomics identified 29 upregulated and 2 downregulated proteins in BC. Our integrative analysis found that aspartate aminotransferase (GOT1), L-lactate dehydrogenase B chain (LDHB), glutathione synthetase (GSS), and glutathione peroxidase 3 (GPX3) were closely involved in these metabolic pathways. Support vector machine (SVM) demonstrated a predictive model with 47 metabolites, and this model achieved a high accuracy in BC prediction (AUC = 1). Besides, this panel of metabolites also showed a fairly high predictive power in the testing cohort between BC vs HC (AUC = 0.794), and benign vs HC (AUC = 0.879).

CONCLUSIONS

This study uncovered specific changes in the metabolic and proteomic profiling of breast cancer patients and identified a panel of 47 plasma metabolites, including sphingomyelins, glutamate, and cysteine could be potential diagnostic biomarkers for breast cancer.

Acupuncture Synergized With Bortezomib Improves Survival of Multiple Myeloma Mice via Decreasing Metabolic Ornithine

Multiple myeloma (MM) is a hematological malignancy worldwide in urgent need for novel therapeutic strategies. Since Velcade (bortezomib) was approved for the treatment of relapsed/refractory MM in 2003, we have seen considerable improvement in extending MM patient survival. However, most patients are fraught with high recurrence rate and incurability. Acupuncture is known for alleviating patient symptoms and improving the quality of life, but it is not well investigated in MM, especially in combination with bortezomib. In this study, we employed LC-MS and UHPLC-MS together with bioinformatics methods to test serum samples from 5TMM3VT MM murine model mice with four different treatments [control (C) group, bortezomib (V) treatment group, acupuncture (A) group, and combined (VA) group]. MM mice in group VA had longer survival time than mice in group A or group V. Joint pathway analysis indicated the underlying arginine and proline metabolism pathway among the 32 significantly decreased metabolites in group VA. CCK-8 assay and in vivo experiments validated that ornithine, the metabolite of arginine, promoted MM cell proliferation. In addition, gene expression omnibus (GEO) database analysis suggested that MM patients with higher ornithine decarboxylase 1 (ODC1) expression were evidently associated with poor overall survival. In summary, this study demonstrates the synergistic effects of acupuncture and bortezomib on extending the survival of MM model mice and provides potential therapeutic targets in the treatment of MM.

Structure, Activity and Function of the PRMT2 Protein Arginine Methyltransferase

PRMT2 belongs to the protein arginine methyltransferase (PRMT) family, which catalyzes the arginine methylation of target proteins. As a type I enzyme, PRMT2 produces asymmetric dimethyl arginine and has been shown to have weak methyltransferase activity on histone substrates in vitro, suggesting that its authentic substrates have not yet been found. PRMT2 contains the canonical PRMT methylation core and a unique Src homology 3 domain. Studies have demonstrated its clear implication in many different cellular processes. PRMT2 acts as a coactivator of several nuclear hormone receptors and is known to interact with a multitude of splicing-related proteins. Furthermore, PRMT2 is aberrantly expressed in several cancer types, including breast cancer and glioblastoma. These reports highlight the crucial role played by PRMT2 and the need for a better characterization of its activity and cellular functions.