Metabolomic Characterization Reveals ILF2 and ILF3 Affected Metabolic Adaptions in Esophageal Squamous Cell Carcinoma

Altered purine and pentose phosphate pathway metabolism in uteroplacental insufficiency-induced intrauterine growth restriction offspring rats impairs intestinal function

BACKGROUND

This study aimed to identify metabolic alterations in the small intestine of newborn rats with intrauterine growth restriction (IUGR), a condition linked to intestinal dysfunction.

METHODS

Pregnant Sprague Dawley rats underwent bilateral uterine artery ligation on gestational day 17 to induce intrauterine growth restriction or sham surgery. Rat pups were delivered spontaneously on gestational day 22. Small intestine tissues were collected on postnatal days 0 and 7 from offspring. Liquid chromatography-mass spectrometry analysis was performed to investigate untargeted metabolomic profiles. Western blot analysis assessed protein expression of key regulators.

RESULTS

Newborn rats with intrauterine growth restriction exhibited distinct small intestine metabolic profiles compared to controls on postnatal day 0. Notably, significant alterations were observed in purine metabolism, the pentose phosphate pathway, and related pathways. Western blot analysis revealed a decrease expression in transketolase, a key enzyme of the pentose phosphate pathway, suggesting impaired activity of the pentose phosphate pathway. Additionally, decreased expression of tight junction proteins ZO-1 and occludin indicated compromised intestinal barrier function in rats with intrauterine growth restriction. Similar metabolic disruptions persisted on postnatal day 7, with further reductions in tricarboxylic acid cycle intermediates and folate biosynthesis precursors. Interestingly, lysyl-glycine, a protein synthesis marker, was elevated in rats with intrauterine growth restriction.

CONCLUSIONS

Our findings reveal a distinct metabolic signature in the small intestine of neonatal rats with intrauterine growth restriction, characterized by disruptions in the pentose phosphate pathway, purine metabolism, and energy production pathways. These novel insights suggest potential mechanisms underlying IUGR-associated intestinal dysfunction and impaired growth.

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.

Serum Metabolite Biomarkers for Pancreatic Tumors: Neuroendocrine and Pancreatic Ductal Adenocarcinomas-A Preliminary Study

BACKGROUND

Pancreatic cancer is the most common pancreatic solid malignancy with an aggressive clinical course and low survival rate. There are a limited number of reliable prognostic biomarkers and a need to understand the pathogenesis of pancreatic tumors; neuroendocrine (PNET) and pancreatic ductal adenocarcinomas (PDAC) encouraged us to analyze the serum metabolome of pancreatic tumors and disturbances in the metabolism of PDAC and PNET.

METHODS

Using the AbsoluteIDQ^® p180 kit (Biocrates Life Sciences AG, Innsbruck, Austria) with liquid chromatography-mass spectrometry (LC-MS), we identified changes in metabolite profiles and disrupted metabolic pathways serum of NET and PDAC patients.

RESULTS

The concentration of six metabolites showed statistically significant differences between the control group and PDAC patients (p.adj < 0.05). Glutamine (Gln), acetylcarnitine (C2), and citrulline (Cit) presented a lower concentration in the serum of PDAC patients, while phosphatidylcholine aa C32:0 (PC aa C32:0), sphingomyelin C26:1 (SM C26:1), and glutamic acid (Glu) achieved higher concentrations compared to serum samples from healthy individuals. Five of the tested metabolites: C2 (FC = 8.67), and serotonin (FC = 2.68) reached higher concentration values in the PNET serum samples compared to PDAC, while phosphatidylcholine aa C34:1 (PC aa C34:1) (FC = -1.46 (0.68)) had a higher concentration in the PDAC samples. The area under the curves (AUC) of the receiver operating characteristic (ROC) curves presented diagnostic power to discriminate pancreatic tumor patients, which were highest for acylcarnitines: C2 with AUC = 0.93, serotonin with AUC = 0.85, and PC aa C34:1 with AUC = 0.86.

CONCLUSIONS

The observations presented provide better insight into the metabolism of pancreatic tumors, and improve the diagnosis and classification of tumors. Serum-circulating metabolites can be easily monitored without invasive procedures and show the present clinical patients' condition, helping with pharmacological treatment or dietary strategies.

High expression of HNRNPR in ESCA combined with 18F-FDG PET/CT metabolic parameters are novel biomarkers for preoperative diagnosis of ESCA

Background

The aim of this study was to determine the expression and function of heterogeneous nuclear ribonucleoprotein R (HNRNPR) in esophageal carcinoma (ESCA), the correlation between its expression and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computerized tomography scan (PET/CT)-related parameters. We also investigated whether ¹⁸F-FDG PET/CT can be used to predict the expression of HNRNPR in ESCA.

Methods

We analyzed patients with ESCA who underwent ¹⁸F-FDG PET/CT before surgery, and their tissues were stained with HNRNPR IHC. The associated parameters were derived using the ¹⁸F-FDG PET imaging data, and the correlation with the IHC score was evaluated. The Oncomine, TCGA, and GEO datasets were used to investigate HNRNPR expression in the pan- and esophageal cancers, as well as its relationship with N6-methyladenosine (m6A) modification and glycolysis. The R software, LinkedOmics, GeneMANIA, and StringOnline tools were used to perform GO/KEGG, GGI, and PPI analyses on the HNRNPR.

Results

HNRNPR is highly expressed in the majority of pan-cancers, including ESCA, and is associated with BMI, weight, and history of reflux in patients with ESCA. HNRNPR is somewhat accurate in predicting the clinical prognosis of ESCA. HNRNPR expression was positively correlated with SUV_max, SUV_mean, and TLG in ESCA (p < 0.05). The combination of these three variables provides a strong predictive value for HNRNPR expression in ESCA. GO/KEGG analysis showed that HNRNPR played a role in the regulation of cell cycle, DNA replication, and the Fannie anemia pathway. The analysis of the TCGA and GEO data sets revealed a significant correlation between HNRNPR expression and m6A and glycolysis-related genes. GSEA analysis revealed that HNRNPR was involved in various m6A and glycolysis related-pathways.

Conclusion

HNRNPR overexpression correlates with ¹⁸F-FDG uptake in ESCA and may be involved in the regulation of the cell cycle, m6A modification, and cell glycolysis. ¹⁸F-FDG PET/CT-related parameters can predict the diagnostic accuracy of HNRNPR expression in ESCA.

Identification and Validation of a Prognostic Immune-Related Gene Signature in Esophageal Squamous Cell Carcinoma

Esophageal carcinoma (EC) is a common malignant cancer worldwide. Esophageal squamous cell carcinoma (ESCC), the main type of EC, is difficult to treat because of the widespread morbidity, high fatality rates, and low quality of life caused by postoperative complications and no specific molecular target. In this study, we screened genes to establish a prognostic model for ESCC. The transcriptome expression profiles of 81 ESCC tissues and 340 normal esophageal mucosal epithelium tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts. The transcriptome expression datasets of 19 esophageal squamous carcinoma cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE). The R software Limma package was used to identify 6,231 differentially expressed genes and 647 differentially expressed immune-related genes between normal and ESCC tissues. Gene functional analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was used to screen out 18 immune-related prognostic genes. We then established the prognostic and risk signature using these genes, and the patients were divided into low-risk and high-risk groups. Compared with high-risk group patients, the low-risk group patients had longer overall survival. M1 macrophages and resting dendritic cells were differentially distributed between the low-risk and high-risk groups and were related to patient survival. We also examined the functional immune cell and immune molecule levels in low-risk and high-risk group patients, with significant differences in the tumor microenvironment between the two groups. To further verify the accuracy of the prognostic risk model, we performed area under the ROC curve (AUC) analysis. The AUC value was 0.931 for the prognostic risk, which was better than the microsatellite instability (MSI) and Tumor Immune Dysfunction and Exclusion (TIDE) scores. In conclusion, we found 18 immune-related prognostic genes related to the occurrence of ESCC and established a prognostic model for predicting disease severity.