Estrogen-related receptor-gamma influences Helicobacter pylori infection by regulating TFF1 in gastric cancer

Exploring the impact of estrogen-related receptor gamma on metabolism and disease

Estrogen-related receptor gamma (ERRγ) is a member of the ERR orphan nuclear receptor family which possesses three subtypes, α, β, and γ. ERRγ is reportedly predominantly expressed in metabolically active tissues and cells, which promotes positive and negative effects in different tissues. ERRγ overexpression in the liver, pancreas, and thyroid cells is related to liver cancer, oxidative stress, reactive oxygen species (ROS) regulation, and carcinoma. Reduced ERRγ expression in the brain, immune cells, tumor cells, and energy metabolism causes neurological dysfunction, gastric cancer, and obesity. ERRγ is a constitutive receptor; however, its transcriptional activity also depends on co-regulators, agonists, and antagonists, which, when after forming a complex, can play a role in targeting and treating diseases. Moreover, ERRγ has proven crucial in regulating cellular and metabolic activity. However, many functions mediated via ERRγ remain unknown and require further exploration. Hence, considering the importance of ERRγ, this review focuses on the critical findings and interactions between ERRγ and co-regulators, agonists, and antagonists alongside its relationship with downstream and upstream signaling pathways and diseases. This review highlights new findings and provides a path to understanding the current ideas and future studies on ERRγ-mediated cellular activity.

Metabolic regulator ERRγ governs gastric stem cell differentiation into acid-secreting parietal cells

Parietal cells (PCs) produce gastric acid to kill pathogens and aid digestion. Dysregulated PC census is common in disease, yet how PCs differentiate is unclear. Here, we identify the PC progenitors arising from isthmal stem cells, using mouse models and human gastric cells, and show that they preferentially express cell-metabolism regulator and orphan nuclear receptor Estrogen-related receptor gamma (Esrrg, encoding ERRγ). Esrrg expression facilitated the tracking of stepwise molecular, cellular, and ultrastructural stages of PC differentiation. EsrrgP2ACreERT2 lineage tracing revealed that Esrrg expression commits progenitors to differentiate into mature PCs. scRNA-seq indicated the earliest Esrrg+ PC progenitors preferentially express SMAD4 and SP1 transcriptional targets and the GTPases regulating acid-secretion signal transduction. As progenitors matured, ERRγ-dependent metabolic transcripts predominated. Organoid and mouse studies validated the requirement of ERRγ for PC differentiation. Our work chronicles stem cell differentiation along a single lineage in vivo and suggests ERRγ as a therapeutic target for PC-related disorders.

The mechanism of dehydroandrographolide inhibiting metastasis in gastric cancer based on network pharmacology and bioinformatics

Gastric cancer (GC) is the most aggressive malignant tumor of the digestive tract. However, there is still a lack of effective treatment methods in clinical practice. Studies have shown that dehydroandrographolide (DA) has been shown to have anti-cancer activity in a variety of cancers, but it has not been reported in GC. Firstly, we obtained data on DA target genes, GC-related genes, and differentially expressed genes (DEGs) from the PharmMapper, GeneCards, and GEO databases, respectively. Then, the STRING database was used to construct the protein-protein interaction network of intersection genes, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of intersection genes were performed. Finally, 8 hub target genes were identified by analyzing their expression and prognostic survival, and molecular docking between the hub genes and DA was performed. In this study, 293 DA drug target genes, 11,366 GC-related genes, and 3184 DEGs were identified. Gene Ontology and KEGG analysis showed that the intersection genes of DA targets and GC-related genes were mainly related to cancer pathways involving apoptosis and cell adhesion. The intersection genes of DEGs, DA targets, and GC-related genes were also mainly related to cancer pathways involving chemical carcinogenesis, and drug metabolism. The molecular docking results showed that the 8 hub target genes had an apparent affinity for DA, which could be used as potential targets for DA treatment of GC. The results of this study show that the molecular mechanism by which DA inhibits GC metastasis involves multiple target genes. It may play an essential role in inhibiting the invasion and metastasis of GC by regulating the expression and polymorphism of hub target genes, such as MMP9, MMP12, CTSB, ESRRG, GSTA1, ADHIC, CA2, and AKR1C2.

Melatonin inhibited the progression of gastric cancer induced by Bisphenol S via regulating the estrogen receptor 1

In recent years, Bisphenol S (BPS) has increasingly been used as an alternative to Bisphenol A (BPA) in food, paper, and personal care products. It is imperative to clarify the relationship between BPS and tumors in order to treat and prevent diseases. This study discovered a new method for predicting tumor correlations between BPS interactive genes. According to analyses conducted by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, interactive genes were primarily found in gastric cancer. Based on gene-targeted prediction and molecular docking, BPS appears to exert potential gastric cancer-causing effects through estrogen receptor 1 (ESR1). In addition, gastric cancer patients' prognosis could be accurately predicted by a bisphenol-based prognostic prediction model. Subsequently, the proliferation and migration abilities of gastric cancer cells were further demonstrated to be significantly enhanced by BPS. Similarly, molecular docking analysis revealed that melatonin is also highly correlated with gastric cancer and BPS. In cell proliferation and migration assays, melatonin and BPS exposure inhibited the invasion abilities of gastric cancer cells compared to BPS-exposure. Our research provided a new direction for the exploration the correlation between cancer and environmental toxicity.

Causal associations of circulating Helicobacter pylori antibodies with stroke and the mediating role of inflammation

BACKGROUND

Observational studies have shown that Helicobacter pylori (H. pylori) infection and H. pylori antibodies are associated with an increased risk of stroke. However, which and how H. pylori antibodies serve as the causal determinant of the development of stroke remains largely unknown.

METHODS

Genome-wide association studies (GWAS) on seven different antibodies of H. pylori-specific proteins, stroke, and stroke subtypes were included in this study. Mendelian randomization (MR) and multivariable MR (MVMR) analysis were performed to assess the causal associations between H. pylori antibodies and the development of stroke and to determine the potential mechanisms underlying the associations.

RESULTS

Genetically predicted serum H. pylori vacuolating cytotoxin-A (VacA) antibody level was associated with an increased risk of all-cause stroke (odds ratio [OR] = 1.04, 95% CI 1.01-1.07, P = 0.017) and cardioembolic stroke (CES, OR = 1.11, 95% CI 1.04-1.18, P = 0.001). The results of multivariable MR (MVMR) showed that C-reactive protein (CRP), but not monocyte chemoattractant protein-1 and peptic ulcer, mediated the causal effects of VacA-positive H. pylori infection on all-cause stroke and CES. No strong causal associations were found between other H. pylori antibodies and stroke and its subtypes.

CONCLUSIONS

Our results demonstrate that H. pylori VacA antibody is the only causal determinant associated with the risk of stroke in the spectrum of H. pylori-related antibodies, in which CRP may mediate the association. This study suggests that inhibition of the CRP signaling pathway may reduce the risk of stroke in patients with VacA-positive H. pylori infection.

Identification and validation of eight estrogen-related genes for predicting prognosis of papillary thyroid cancer

Papillary thyroid cancer (PTC) is one of the most common malignant tumors in female, and estrogen can affect its progression. However, the targets and mechanisms of estrogen action in PTC remain unclear. Therefore, this study focuses on the relationship between estrogen-related genes (ERGs) expression and prognosis in PTC, particularly neuropeptide U (NMU), and its important role in tumor progression. Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differentially expressed genes (DEGs) predominantly enriched in ERGs were identified between PTC and normal tissue. Then, we identified ERGs that contributed most to PTC prognosis, including Transducer of ERBB2 1 (TOB1), trefoil factor 1 (TFF1), phospholipase A and acyltransferase 3 (PLAAT3), NMU, kinesin family member 20A (KIF20A), DNA topoisomerase II alpha (TOP2A), tetraspanin 13 (TSPAN13), and carboxypeptidase E (CPE). In addition, we confirmed that NMU was highly expressed in PTC and explored the effect of NMU on PTC cells proliferation in vitro and in vivo. The results showed that the proliferative capacity of PTC cells was significantly reduced with NMU knockdown. Moreover, the phosphorylation levels of the Kirsten rat sarcoma virus (KRAS) signaling pathway were significantly lower with NMU knockdown. These results suggest that ERGs, especially NMU, may be novel prognostic indicators in PTC.

Coptis, Pinellia, and Scutellaria as a promising new drug combination for treatment of Helicobacter pylori infection

BACKGROUND

Helicobacter pylori (H. pylori) is the most important infectious agent and plays an important role in the progression of chronic gastritis and the development of gastric cancer.

AIM

To identify efficient therapeutic agents or strategies that can treat H. pylori infection.

METHODS

We performed literature analysis, experimental validation, and network pharmacology. First, traditional Chinese medicine (TCM) prescriptions for the treatment of H. pylori infection were obtained from the China National Knowledge Infrastructure, China Biology Medicine, China Science and Technology Journal Database, and WanFang databases. In addition, we conducted a relevant search by Reference Citation Analysis (RCA) (https://www.referencecitationanalysis.com). Next, we used TCM Inheritance Support System V2.5 to identify core drug combinations in the TCM prescriptions. Then, an H. pylori-associated chronic mouse model of gastritis was established. The antibacterial properties and anti-inflammatory potential of the core drug combination were evaluated by the rapid urease test, modified Warthin-Starry silver staining, histopathological analysis, and enzyme linked immunosorbent assay. Finally, the active compounds, hub targets, and potential signaling pathways associated with the core drug combination were analyzed by network pharmacology.

RESULTS

The TCM treatment of H. pylori was mainly based on reinforcing the healthy Qi and eliminating pathogenic factors by simultaneously applying pungent dispersing, bitter descending, cold and warm drugs. The combination of Coptis, Pinellia, and Scutellaria (CPS) was identified as the core drug combination from 207 prescriptions and 168 herbs. This drug combination eradicated H. pylori, alleviated the gastric pathology induced by H. pylori infection, and reduced the expression levels of tumor necrosis factor-α (P = 0.024) and interleukin-1β (P = 0.001). Moreover, a total of 35 compounds and 2807 targets of CPS were identified using online databases. Nine key compounds (tenaxin I, neobaicalein, norwogonin, skullcapflavone II, baicalein, 5,8,2'-trihydroxy-7-methoxyflavone, acacetin, panicolin, and wogonin) and nine hub target proteins (EGFR, PTGS2, STAT3, MAPK3, MAPK8, HSP90AA1, MAPK1, MMP9, and MTOR) were further explored. Seventy-seven signaling pathways were correlated with H. pylori-induced inflammation and carcinogenesis.

CONCLUSION

In summary, we showed that CPS is the core drug combination for treating H. pylori infection. Animal experiments demonstrated that CPS has bacteriostatic properties and can reduce the release of inflammatory cytokines in the gastric mucosa. Network pharmacology predictions further revealed that CPS showed complex chemical compositions with multi-target and multi-pathway regulatory mechanisms. Although the results derived from network pharmacology are not necessarily comprehensive, they still expand our understanding of CPS for treating H. pylori infection.

ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning

Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.

Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.

METHODS

Transcriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.

RESULTS

In the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.

CONCLUSIONS

The present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.