Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis

Identification of the γ-glutamyl cycle as a novel therapeutic target and 5-oxoproline as a new biomarker for diagnosing pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignant solid tumours, and abnormal metabolic reprogramming in the tumour microenvironment is regarded as an important contributor to its pathogenesis.

OBJECTIVES

As there is an urgency to identify new targets based on the metabolic features that are highly refractory to PDAC treatment, this study aimed to identify suitable therapeutic targets for PDAC.

METHODS

In this study, gene set enrichment and Kyoto Encyclopedia of Genes and Genomes analyses were performed on 163 PDAC tissue samples and 165 normal pancreatic tissue samples from The Cancer Genome Atlas and Genotype-Tissue Expression databases to identify alterations in critical metabolites that may contribute to PDAC pathogenesis. Furthermore, ultra-performance liquid chromatography-tandem mass spectrometry was performed to identify significant metabolic pathways between 24 pairs of tumour and adjacent non-tumour tissues and between serum samples from PDAC patients and healthy donors.

RESULTS

Fifty-one tissue metabolites and 26 serum metabolites were altered in PDAC. Among them, those in the γ-glutamyl cycle were the most substantially changed, and 5-oxoproline was the biomarker of PDAC with the most significantly decreased levels.

CONCLUSIONS

The γ-glutamyl cycle and 5-oxoproline might be potential biomarkers and therapeutic targets to improve the diagnosis, therapy, and prognosis of PDAC.

Identification of a 5-gene-based signature to predict prognosis and correlate immunomodulators for rectal cancer

BACKGROUND

Specific tumor markers have yet to be identified in rectal cancer. This study aims to identify a novel genetic signature in rectal cancer to provide clues for survival and immunotherapy.

METHODS

DEGs were obtained from two GEO datasets of rectal cancer. By using data from TCGA and GSE133057, two cohorts of rectal cancer were applied to establish and evaluate the signature. A nomogram was constructed for training and validation. We integrated the risk-score with clinicopathological features and assessed its interplay with immune cells and molecules. Finally, our study performed functional annotations, gene-targeted miRNAs, and single-cell analysis.

RESULTS

A total of 468 DEGs were identified, and a signature consisting of 5 genes (CLIC5, ENTPD8, PACSIN3, HGD, and GNG7) was selected to calculate the risk-score. The model exhibited high performance in time-dependent ROC and a nomogram. Further results showed that overall survival was significantly worse in the high-risk group. As an independent prognostic factor, the risk-score was associated with vascular invasion. There was a dramatic difference in nonregulatory CD4+ and CD8+ T cells between the high and low-risk groups, and the 5 genes were correlated with immune inhibitors. There was a considerable difference in autophagy, immune, cell cycle, infection, and apoptosis-associated terms and pathways in GO and KEGG. The functional states of differentiation, apoptosis, and quiescence were closely related to the 5-gene signature in single-cell analysis.

CONCLUSION

Our results suggest that the signature could serve as a novel prognostic biomarker in rectal cancer, which might benefit decision-making regarding immunotherapy.

Effects of Lactobacillus acidophilus KLDS1.0901 on Proliferation and Apoptosis of Colon Cancer Cells

Colon cancer is the most common type of malignant tumor. The cytotoxicity effect of lactic acid bacteria may be active by inhibiting cancer cell proliferation, producing anticancer compounds, and inducing apoptosis in cancer cells, but the mechanism is unclear. Our previous study revealed that Lactobacillus acidophilus KLDS1.0901 has good probiotic properties. In this study, We screened out the highest inhibition rate of L. acidophilus KLDS1.0901 and assessed the effects on the proliferation of HT-29, Caco-2, and IEC-6 cells. Then, the apoptosis mechanism of HT-29 cells was studied when treated with L. acidophilus KLDS1.0901. Results showed that L. acidophilus KLDS1.0901 inhibited the proliferation of HT-29 and Caco-2 cells in a dose-dependent manner and reached the maximum under the condition of multiplicity of infection (MOI) = 100 (rate of Lactobacillus to cells) at 48 h. With the increase in time and MOI, reactive oxygen species in HT-29 cells, the apoptosis rates of HT-29 cells were increased, and the amount of blue fluorescence of the cells was also increased after Hoechst 33258 staining. Furthermore, L. acidophilus KLDS1.0901 reduced the mitochondrial membrane potential of HT-29 cells. Notably, 1,133 differentially expressed genes were screened by transcriptomics research, including 531 up-regulated genes and 602 down-regulated genes. These genes were involved in the nuclear factor κB and PI3K-AKT signaling pathways related to the apoptosis of HT-29 cells. These findings suggested that L. acidophilus KLDS1.0901 has the potential to be used in the development of a new type of functional foods for adjuvant treatment of colon cancer.

Quantifying RNA modifications by mass spectrometry: a novel source of biomarkers in oncology

Despite significant progress in targeted therapies, cancer recurrence remains a major cause of mortality worldwide. Identification of accurate biomarkers, through molecular profiling in healthy and cancer patient samples, will improve diagnosis and promote personalized medicine. While genetic and epigenetic alterations of DNA are currently exploited as cancer biomarkers, their robustness is limited by tumor heterogeneity. Recently, cancer-associated changes in RNA marks have emerged as a promising source of diagnostic and prognostic biomarkers. RNA epigenetics (also known as epitranscriptomics) is an emerging field in which at least 150 chemical modifications in all types of RNA (mRNA, tRNA, lncRNA, rRNA, and microRNA) have been detected. These modifications fine-tune gene expression in both physiological and pathological processes. A growing number of studies have established links between specific modified nucleoside levels in solid/liquid biopsies, and cancer onset and progression. In this review, we highlight the potential role of epitranscriptomic markers in refining cancer diagnosis and/or prognosis. RNA modification patterns may contain important information for establishing an initial diagnosis, monitoring disease evolution, and predicting response to treatment. Furthermore, recent developments in mass spectrometry allow reliable quantification of RNA marks in solid biopsies and biological fluids. We discuss the great potential of mass spectrometry for identifying epitranscriptomic biomarker signatures in cancer diagnosis. While there are various methods to quantify modified nucleosides, most are unable to detect and quantify more than one type of RNA modification at a time. Mass spectrometry analyses, especially GC-MS/MS and LC-MS/MS, overcome this limitation and simultaneously detect modified nucleosides by multiple reaction monitoring. Indeed, several groups are currently validating mass spectrometry methods that quantify several nucleosides at one time in liquid biopsies. The challenge now is to exploit these powerful analytical tools to establish epitranscriptomic signatures that should open new perspectives in personalized medicine. This review summarizes the growing clinical field of analysis of RNA modifications and discusses pre-analytical and analytical approaches, focusing in particular on the development of new mass spectrometry tools and their clinical applications.

A Prognostic Model Based on Six Metabolism-Related Genes in Colorectal Cancer

An increasing number of studies have shown that abnormal metabolism processes are closely correlated with the genesis and progression of colorectal cancer (CRC). In this study, we systematically explored the prognostic value of metabolism-related genes (MRGs) for CRC patients. A total of 289 differentially expressed MRGs were screened based on The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB), and 72 differentially expressed transcription factors (TFs) were obtained from TCGA and the Cistrome Project database. The clinical samples obtained from TCGA were randomly divided at a ratio of 7 : 3 to obtain the training group (n = 306) and the test group (n = 128). After univariate and multivariate Cox regression analyses, we constructed a prognostic model based on 6 MRGs (AOC2, ENPP2, ADA, GPD1L, ACADL, and CPT2). Kaplan–Meier survival analysis of the training group, validation group, and overall samples proved that the model had statistical significance in predicting the outcomes of patients. Independent prognosis analysis suggested that this risk score might serve as an independent prognosis factor for CRC patients. Moreover, we combined the prognostic model and the clinical characteristics in a nomogram to predict the overall survival of CRC patients. Furthermore, gene set enrichment analysis (GSEA) was conducted to identify the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in the high- and low-risk groups, which might provide novel therapeutic targets for CRC patients. We discovered through the protein-protein interaction (PPI) network and TF-MRG regulatory network that 7 hub genes were retrieved from the PPI network and 4 kinds of differentially expressed TFs (NR3C1, MYH11, MAF, and CBX7) positively regulated 4 prognosis-associated MRGs (GSTM5, PTGIS, ENPP2, and P4HA3).

Identification of volatile active components in Acori Tatarinowii Rhizome essential oil from different regions in China by C6 glioma cells

Background

Acori Tatarinowii Rhizome (ATR) is a well-recognized Chinese herbal medicine prescribed to treat neurological disorders. The essential oil (ATEO) is considered as the active fraction of ATR and the content of ATEO is used as the only indicator for ATR content determination. The quality of ATEO varies widely due to region difference; however, little is known about how to study ATEO quality chemically and biologically in response to region difference. Thus, it is of great importance to identify volatile active components in ATEO to conduct quality study. In this study, we analyzed ATEO from different regions in China using chemical component analysis combined with biological activity evaluation.

Methods

GC-MS was used to obtain different volatile component profiles of ATEO and significantly changed volatile components were screened out. The neuroprotective activities of ATEO, including anti-oxidation, anti-inflammation and neurotrophic functions, were revealed in C6 glioma cells. The correlation study between the bioactivities and the components was performed.

Results

57 volatile components, including terpenoids, phenylpropanoids, aromatic compounds, and other aliphatic compounds, were identified. 8 volatile components (β-asarone, cis-methyl isoeugenol, γ-asarone, methyleugenol, calarene, longifolene, β-caryophyllene and caryophyllene oxide) from ATEO were significantly changed due to region difference and 2 of them (β-asarone and γ-asarone) showed strong correlation with neuroprotective activities.

Conclusions

Our results reveal that ATEO from different regions in China show great changes in chemical composition and biological activity. Moreover, phenylpropanoids (β-asarone and γ-asarone) present strong correlation with the bioactivities, which are considered as volatile active components in ATEO. The findings will be useful for the development of quality study of ATEO.

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes

Background/Aims: The incidence of gastric cancer (GC) ranks fifth among common tumors and GC is the third leading cause of cancer-related death worldwide. The aim of this study was to develop and validate a nomogram for predicting the overall survival (OS) of patients with GC. Methods: DNA methylation (DNAm)-driven genes were identified by integrating DNAm and gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. Another cohort (GSE62254) was used for external validation. Results: Thirteen differentially expressed DNAm-driven genes were narrowed down to a six-gene signature (PODN, NPY, MICU3, TUBB6 and RHOJ were hypermethylated, and MYO1A was hypomethylated), which was associated with OS (P < 0.05) after survival and LASSO regression analyses. These differentially expressed genes (DEGs) with altered DNAm statuses were included in the prognostic risk score model. The univariate Cox regression analysis indicated that risk score, age, and number of positive lymph nodes were significantly associated with survival time in GC patients. The multivariate Cox regression analysis also indicated that these variables were significant prognostic factors for GC. A nomogram including these variables was constructed, and its performance in predicting the 1-, 3- and 5-year survival outcomes of GC patients was estimated through time-dependent receiver operating characteristic (ROC) curves. In addition, the clinical benefit of this model was revealed by decision curve analysis (DCA). Pathway enrichment analysis suggested that these DNAm-driven genes might impact tumor progression by affecting signaling pathways such as the "ECM RECEPTOR INTERACTION" and "DNA REPLICATION" pathways. Conclusions: The altered status of the DNAm-driven gene signature (PODN, MYO1A, NPY, MICU3, TUBB6 and RHOJ) was significantly associated with the OS of GC patients. A nomogram incorporating risk score, age and number of positive lymph nodes can be conveniently used to facilitate the individualized prediction of OS in patients with GC.

Integrative analysis of the inverse expression patterns in pancreas development and cancer progression

BACKGROUND

As the malignant tumor, pancreatic cancer with a meager 5-years survival rate has been widely concerning. However, the molecular mechanisms that result in malignant transformation of pancreatic cells remain elusive.

AIM

To investigate the gene expression profiles in normal or malignant transformed pancreas development.

METHODS

MaSigPro and ANOVA were performed on two pancreas development datasets downloaded from the Gene Expression Omnibus database. Six pancreatic cancer datasets collected from TCGA database were used to establish differentially expressed genes related to pancreas development and pancreatic cancer. Moreover, gene clusters with highly similar interpretation patterns between pancreas development and pancreatic cancer progression were established by self-organizing map and singular value decomposition. Additionally, the hypergeometric test was performed to compare the corresponding interpretation patterns. Abnormal regions of metabolic pathway were analyzed using the Sub-pathway-GM method.

RESULTS

This study established the continuously upregulated and downregulated genes at different stages in pancreas development and progression of pancreatic cancer. Through analysis of the differentially expressed genes, we established the inverse and consistent direction development-cancer pattern associations. Based on the application of the Subpathway-GM analysis, we established 17 significant metabolic sub-pathways that were closely associated with pancreatic cancer. Of note, the most significant metabolites sub-pathway was related to glycerophospholipid metabolism.

CONCLUSION

The inverse and consistent direction development-cancer pattern associations were established. There was a significant correlation in the inverse patterns, but not consistent direction patterns.

Metabolomics biotechnology, applications, and future trends: a systematic review

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed. Mass spectrometry-based metabolomics has been used in a variety of disease research areas. However, the deep research of metabolites remains a difficult and lengthy process. Fortunately, mass spectrometry is considered to be a universal tool with high specificity and sensitivity and is widely used around the world. Mass spectrometry technology has been applied to various basic disciplines, providing technical support for the discovery and identification of endogenous substances in living organisms. The combination of metabolomics and mass spectrometry is of great significance for the discovery and identification of metabolite biomarkers. The mass spectrometry tool could further improve and develop the exploratory research of the life sciences. This mini review discusses metabolomics biotechnology with a focus on recent applications of metabolomics as a powerful tool to elucidate metabolic disturbances and the related mechanisms of diseases.

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed.

Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis

To identify metabolic pathways that were perturbed in pancreatic cancer (PC), we investigated gene‐metabolite networks by integration of metabolomic and transcriptomic. In this research, we undertook the metabolomic study of 43 paired human PC samples, aiming to identify key metabolic alterations in PC. We also carried out in vitro experiments to validate that the key metabolite cytidine and its related gene ENTPD8 played an important role in PC cell proliferation. We screened out 13 metabolites differentially expressed in PC tissue (PCT) by liquid chromatography/mass spectrometry analysis on 34 metabolites, and the partial least square discrimination analysis results revealed that 9 metabolites among them were remarkably altered in PCT compared to adjacent noncancerous tissue (variable importance in projection >1, P < .05). Among the 9 metabolites, 7 might be potential biomarkers. The most significantly enriched metabolic pathway was pyrimidine metabolism. We analyzed 351 differentially expressed genes from The Cancer Genome Atlas and intersected them with Kyoto Encyclopedia of Genes and Genomes metabolic pathways. We found that ENTPD8 had a gene‐metabolite association with cytidine in the CTP dephosphorylation pathway. We verified by in vitro experiments that the CTP dephosphorylation pathway was changed in PCT compared with adjacent noncancerous tissue. ENTPD8 was downregulated in PCT, causing a reduction in cytidine formation and hence weakened CTP dephosphorylation in pyrimidine metabolism.