Enolase-phosphatase 1 (ENOPH1) is a newly identified enzyme associated with stress responses and neurodevelopmental disorders. Our previous study found that ENOPH1 mediates cerebral cell apoptosis and blood-brain barrier (BBB) dysfunction during early cerebral ischemia. Ubiquitination has been identified in neuronal damage and the neuroinflammatory response in ischemic stroke. However, whether ENOPH1 regulates ischemia-induced protein ubiquitination alteration is yet unclear. Hence, the present study explored changes in the ubiquitinomic in early ischemic brain tissues between wildtype and ENOPH1 knockout mice using a comprehensive quantitative analysis. Our results showed that 4000 ubiquitination-modified sites in 1613 proteins were quantified, with 772 ubiquitinated sites in 464 proteins significantly decreasing or increasing after ENOPH1 knockout (fold change >1.5 or <1/1.5, p < 0.05). When compared to our previous parallel proteome profiles, common differential proteins FKBP5 and Claudin-11 were observed and further validated. ENOPH1 regulates the degradation of FKBP5 and the promotion of Claudin-11 by ubiquitination mediation, leading to the activation or inhibition of nuclear-initiated steroid signaling and transendothelial migration pathways. These findings, for the first time, identified ubiquitinomic features of early ischemic brain tissues after ENOPH1 knockout, suggesting that ENOPH1 may regulate neuroinflammatory stress and barrier function by modifying FKBP5 and Claudin-11 protein ubiquitination.

ENOPH1 (Enolase-phosphatase 1), a member of the HAD-like hydrolase superfamily, has been linked to a range of physiological conditions, including neurological disorders. However, its involvement in tumorigenesis remains underexplored. This study is the first to conduct a pan-cancer analysis of ENOPH1, aiming to elucidate its role in multiple cancers through various bioinformatics platforms.

We conducted a thorough analysis using data from UCSC databases. ENOPH1 expression in tumor and normal tissues was evaluated using R language software. Survival analyses, genetic alterations, and RNA modifications were assessed through the GEPIA2 and cBioPortal platforms. The relationships between ENOPH1 and immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and homologous recombination deficiency (HRD) were examined using TIMER2 and R software. ENOPH1-related gene enrichment analysis was performed using the STRING and GEPIA2 databases, followed by Gene Ontology (GO) and KEGG pathway enrichment analyses.

ENOPH1 expression was significantly upregulated in various cancers, including ACC, BLCA, BRCA, and COAD. High ENOPH1 expression was associated with poor overall survival (OS) in cancers such as KICH, LIHC, BRCA and LUAD. High ENOPH1 expression was associated with poor disease specific survival (DSS) in cancers such as KICH, LIHC, BRCA and MESO. Genetic alterations of ENOPH1, primarily mutations and deep deletions, were identified in UCEC, BLCA, and OV. ENOPH1 showed significant correlations with RNA modifications (m1A, m5C, m6A), immune checkpoints, and immune modulators across multiple cancer types. ENOPH1 was positively correlated with TMB, MSI, and HRD in cancers like BLCA, BRCA, and STAD. Furthermore, enrichment analysis revealed that ENOPH1 interacts with proteins involved in critical pathways such as AMPK, Hippo, and PI3K-AKT, suggesting its role in cancer progression.

This pan-cancer analysis reveals ENOPH1's potential as a prognostic biomarker and its involvement in key signaling pathways across multiple cancers. Our findings provide new insights into the role of ENOPH1 in tumorigenesis and highlight its potential as a therapeutic target in cancer treatment.

Previous research has highlighted the critical role of amino acid metabolism (AAM) in the pathophysiology of sepsis. The present study aimed to explore the potential diagnostic and prognostic value of AAM-related genes (AAMGs) in sepsis, as well as their underlying molecular mechanisms. Gene expression profiles from the Gene Expression Omnibus (GSE65682, GSE185263 and GSE154918 datasets) were analyzed. Based on weighted gene co-expression network analysis and machine learning algorithms, hub AAMGs were identified in the GSE65682 database. Subsequently, hub AAMGs were evaluated for their expression levels and diagnostic and prognostic significance in sepsis, as well as their interactions with regulatory pathways and role in immune cell infiltration. Additionally, trends in AAMG expression were validated using clinical samples, and their functions in sepsis were confirmed through an in vitro model. In total, four AAMGs were identified, two of which, methionine synthase (MTR) and methionine-R-isomerase 1 (MRI1), demonstrated significant differential expression in the GSE65682, GSE185263 and GSE154918 datasets, which was further validated using clinical samples. A diagnostic nomogram based on MTR and MRI1 expression demonstrated strong diagnostic effectiveness across the three aforementioned databases. Moreover, the expression of both genes were negatively correlated with sepsis prognosis and showed stratified prognostic capabilities. Newly identified pathways included KRAS and IL-2/STAT5 signaling. MTR and MRI1 negatively correlated with the infiltration of inflammatory cells, such as M1 macrophages and neutrophils, and positively correlated with anti-inflammatory cells, such as CD8+ T and dendritic cells. In vitro experiments further demonstrated that overexpression of MTR could mitigate the inhibition of cloning and proliferation induced by LPS and ATP in RAW 264.7 cells. These findings highlighted the potential of MTR and MRI1 as biomarkers for diagnosing and prognosticating sepsis, potentially acting through the regulation of methionine in the pathophysiology of this disease. The present study provided new insights into the role of AAM in the mechanisms underlying sepsis and in the potential development of future targeted therapies.

Colon cancer ranked third among the most frequently diagnosed cancers worldwide. Amino acid metabolic reprogramming was related to the occurrence and development of colon cancer. We looked for the amino acid metabolism genes (AMGs) associated with amino acid metabolism from molecular signatures database as prognostic markers and constructed amino acid metabolism scoring model (AMS). According to AMS, the patients were divided into high AMS and low AMS groups, and the prognostic characteristics, molecular phenotypes, somatic cell mutation characteristics, immune cell infiltration characteristics, and immunotherapy effect of the two groups were systematically analyzed. Finally, the compounds targeting AMGs were also screened. We screen out 6 prognostic AMGs (P < 0.05) and construct an AMS model based on them. K-M curve indicated that OS in low AMS group was significantly higher than that in high group (P < 0.05), which were validated in multiple datasets. And different AMS groups had different molecular phenotypes, somatic cell mutation characteristics and immune cell infiltration characteristics. Low AMS group had a better effect for immunotherapy. In addition, we predicted potential therapeutic compounds that could bind to AMGs target proteins. AMS model can be used as a hierarchical tool to evaluate the prognosis, immune infiltration characteristics and immunotherapy response ability of colon cancer. And the compounds screened based on AMGs may become new anti-tumor drugs.

This review investigates the role of amino acid metabolism in the tumor microenvironment of colorectal cancer (CRC) and explores potential targeted therapeutic strategies. The paper synthesized current research on amino acid metabolism in the colorectal cancer tumor microenvironment, focusing on amino acids such as tryptophan, methionine, glutamine, and arginine. It examined their impact on tumor growth, immune evasion, and patient prognosis, as well as the metabolic reprogramming of tumor cells and complex tumor microenvironment interactions. Aberrant amino acid metabolism was a hallmark of colorectal cancer, influencing tumor proliferation, survival, and invasiveness. Key findings included: Tryptophan metabolism via the kynurenine and serotonin pathways significantly affected immune response and tumor progression in CRC. Methionine influenced T cell function and DNA methylation, playing a critical role in tumor development. Glutamine was extensively used by tumor cells for energy metabolism and supported immune cell function. Arginine metabolism impacted CD8+ T cell functionality and tumor growth. The review also discussed the dual roles of immune cells in the tumor microenvironment and the potential of targeting amino acid metabolic pathways for CRC treatment. In conclusion, amino acid metabolism significantly impacts the colorectal cancer tumor microenvironment and immunity. Understanding these metabolic pathways provides valuable insights into CRC pathogenesis and identifies potential therapeutic targets. Future research should focus on developing treatments that disrupt these metabolic processes to improve patient outcomes in CRC.

Recurrent ovarian cancer patients, especially those resistant to platinum, lack effective curative treatments. To address this, we conducted a phase 2 clinical trial (NCT02853318) combining pembrolizumab with bevacizumab, to increase T cell infiltration into the tumor, and oral cyclophosphamide, to reduce the number of regulatory T cells. The trial accrued 40 heavily pretreated recurrent ovarian cancer patients. The primary endpoint, progression free survival, was extended to a median of 10.2 months. The secondary endpoints demonstrated an objective response rate of 47.5%, and disease control in 30% of patients for over a year while maintaining a good quality of life. We performed comprehensive molecular, immune, microbiome, and metabolic profiling on samples of trial patients. Here, we show increased T and B cell clusters and distinct microbial patterns with amino acid and lipid metabolism are linked to exceptional clinical responses. This study suggests the immune milieu and host-microbiome can be leveraged to improve antitumor response in future immunotherapy trials.

A type of colorectal cancer (CRC)，Colitis-associated colorectal cancer (CAC)， is closely associated with chronic inflammation and gut microbiota dysbiosis. Berberine (BBR) has a long history in the treatment of intestinal diseases, which has been reported to inhibit colitis and CRC. However, the mechanism of its action is still unclear. Here, this study aimed to explore the potential protective effects of BBR on azoxymethane (AOM)/dextransulfate sodium (DSS)-induced colitis and tumor mice, and to elucidate its potential molecular mechanisms by microbiota, genes and metabolic alterations. The results showed that BBR inhibited the gut inflammation and improved the function of mucosal barrier to ameliorate AOM/DSS-induced colitis. And BBR treatment significantly reduced intestinal tumor development and ki-67 expression of intestinal tissue along with promoted apoptosis. Through microbiota analysis based on the 16 S rRNA gene, we found that BBR treatment improved intestinal microbiota imbalance in AOM/DSS-induced colitis and tumor mice, which were characterized by an increase of beneficial bacteria, for instance Akkermanisa, Lactobacillus, Bacteroides uniformis and Bacteroides acidifaciens. In addition, transcriptome analysis showed that BBR regulated colonic epithelial signaling pathway in CAC mice particularly by tryptophan metabolism and Wnt signaling pathway. Notably, BBR treatment resulted in the enrichment of amino acids metabolism and microbiota-derived SCFA metabolites. In summary, our research findings suggest that the gut microbiota-amino acid metabolism-Wnt signaling pathway axis plays critical role in maintaining intestinal homeostasis, which may provide new insights into the inhibitory effects of BBR on colitis and colon cancer.

An early evaluating system for autism spectrum disorder (ASD) severity is crucial. Questionnaire survey is challenging for accurately assessing the severity levels for ASD in children.

Offspring with ASD-like phenotypes were induced by treating pregnant mice with Poly (I:C) at GD12.5 and the placentae corresponding to the offspring were obtained by caesarean. The autism severity composite score (ASCS) for offspring was calculated through behavioral tests. HE staining and immunohistochemistry were used to observe the morphology of placenta. Candidate biomarkers were identified by weighted protein co-expression network analysis (WPCNA) combined with machine learning and further validated by ELISA. Sperman's was used to analyze the correlation between biomarkers and metabolome.

The placental weight and mean vascular area of male offspring with ASD-like phenotypes were significantly decreased compared with typical mice. According to the WPCNA, four modules were identified and significantly correlated with ASCS of offspring. Two biomarkers (ASPG and DAD1) with high correlation with ASCS in offspring were identified.

VEGF pathway may contribute to sexual dimorphism in placental morphology within mice with ASD-like phenotypes in term. The placental ASPG and DAD1 levels could reflect ASD-like symptom severity levels in male/female mice offspring.

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

The causality between circulating proteins and thyroid cancer (TC) remains unclear. We employed five large-scale circulating proteomic genome-wide association studies (GWASs) with up to 100,000 participants and a TC meta-GWAS (nCase = 3,418, nControl = 292,703) to conduct proteome-wide Mendelian randomization (MR) and Bayesian colocalization analysis. Protein and gene expressions were validated in thyroid tissue. Through MR analysis, we identified 26 circulating proteins with a putative causal relationship with TCs, among which NANS protein passed multiple corrections (P BH = 3.28e-5, 0.05/1,525). These proteins were involved in amino acids and organic acid synthesis pathways. Colocalization analysis further identified six proteins associated with TCs (VCAM1, LGMN, NPTX1, PLEKHA7, TNFAIP3, and BMP1). Tissue validation confirmed BMP1, LGMN, and PLEKHA7's differential expression between normal and TC tissues. We found limited evidence for linking circulating proteins and the risk of TCs. Our study highlighted the contribution of proteins, particularly those involved in amino acid metabolism, to TCs.

An increasing number of studies have focused on the role of cellular metabolism in the development of colorectal cancer (CRC). However, no work is currently available to synthesize the field through bibliometrics.

To analyze the development in the field of "glucose metabolism" (GM), "amino acid metabolism" (AM), "lipid metabolism" (LM), and "nucleotide metabolism" (NM) in CRC by visualization.

Articles within the abovementioned areas of GM, AM, LM and NM in CRC, which were published from January 1, 1991, to December 31, 2022, are retrieved from the Web of Science Core Collection and analyzed by CiteSpace 6.2.R4 and VOSviewer 1.6.19.

The field of LM in CRC presented the largest number of annual publications and the fastest increase in the last decade compared with the other three fields. Meanwhile, China and the United States were two of the most prominent contributors in these four areas. In addition, Gang Wang, Wei Jia, Maria Notarnicola, and Cornelia Ulrich ranked first in publication numbers, while Jing-Yuan Fang, Senji Hirasawa, Wei Jia, and Charles Fuchs were the most cited authors on average in these four fields, respectively. "Gut microbiota" and "epithelial-mesenchymal transition" emerged as the newest burst words in GM, "gut microbiota" was the latest outburst word in AM, "metastasis", "tumor microenvironment", "fatty acid metabolism", and "metabolic reprogramming" were the up-to-date outbreaking words in LM, while "epithelial-mesenchymal transition" and "apoptosis" were the most recently occurring words in NM.

Research in "cellular metabolism in CRC" is all the rage at the moment, and researchers are particularly interested in exploring the mechanism to explain the metabolic alterations in CRC. Targeting metabolic vulnerability appears to be a promising direction in CRC therapy.

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Early diagnosis of the disease can greatly improve the clinical prognosis for patients with CRC. Unfortunately, there are no current simple and effective early diagnostic markers available. The transfer RNA (tRNA)-derived RNA fragments (tRFs) are a class of small non-coding RNAs (sncRNAs), which have been shown to play an important role in the development and prognosis of CRC. However, only a few studies on tRFs as early diagnostic markers in CRC have been conducted. In this study, previously ignored tRFs expression data were extracted from six paired small RNA sequencing data in the Sequence Read Archive (SRA) database using MINTmap. Three i-tRFs, derived from the tRNA that transports glutamate (i-tRF-Glu), were identified and used to construct a random forest diagnostic model. The model performance was evaluated using the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. The area under the curves (AUC) for the ROC and PR was 0.941 and 0.944, respectively. We further verified the differences in expression of the these i-tRF-Glu in the tissue and plasma of both CRC patients and healthy subjects using quantitative real-time PCR (qRT-PCR). We found that the ROC-AUC of the three was greater than traditional plasma tumor markers such as CEA and CA199. Our bioinformatics analysis suggested that the these i-tRF-Glu are associated with cancer development and glutamate (Glu)-glutamine (Gln) metabolism. Overall, our study uncovered these i-tRF-Glu that have early diagnostic significance and therapeutic potential for CRC, this warrants further investigation into the diagnostic and therapeutic potential of these i-tRF-Glu in CRC.

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Oncogenesis and the development of tumors affect metabolism throughout the body. Metabolic reprogramming (also known as metabolic remodeling) is a feature of malignant tumors that is driven by oncogenic changes in the cancer cells themselves as well as by cytokines in the tumor microenvironment. These include endothelial cells, matrix fibroblasts, immune cells, and malignant tumor cells. The heterogeneity of mutant clones is affected by the actions of other cells in the tumor and by metabolites and cytokines in the microenvironment. Metabolism can also influence immune cell phenotype and function. Metabolic reprogramming of cancer cells is the result of a convergence of both internal and external signals. The basal metabolic state is maintained by internal signaling, while external signaling fine-tunes the metabolic process based on metabolite availability and cellular needs. This paper reviews the metabolic characteristics of gastric cancer, focusing on the intrinsic and extrinsic mechanisms that drive cancer metabolism in the tumor microenvironment, and interactions between tumor cell metabolic changes and microenvironment metabolic changes. This information will be helpful for the individualized metabolic treatment of gastric cancers.

Prostate cancer is among the leading cancers according to both incidence and mortality. Due to the high molecular, morphological and clinical heterogeneity, the course of prostate cancer ranges from slow growth that usually does not require immediate therapeutic intervention to aggressive and fatal disease that spreads quickly. However, currently available biomarkers cannot precisely predict the course of a disease, and novel strategies are needed to guide prostate cancer management. Amino acids serve numerous roles in cancers, among which are energy production, building block reservoirs, maintenance of redox homeostasis, epigenetic regulation, immune system modulation and resistance to therapy. In this article, by using The Cancer Genome Atlas (TCGA) data, we found that the expression of amino acid metabolism-related genes is highly aberrant in prostate cancer, which holds potential to be exploited in biomarker design or in treatment strategies. This change in expression is especially evident for catabolism genes and transporters from the solute carrier family. Furthermore, by using recursive partitioning, we confirmed that the Gleason score is strongly prognostic for progression-free survival. However, the expression of the genes SERINC3 (phosphatidylserine and sphingolipids generation) and CSAD (hypotaurine generation) can refine prognosis for high and low Gleason scores, respectively. Therefore, our results hold potential for novel prostate cancer progression biomarkers.