Bing Wang’s research while affiliated with Qingdao University and other places

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Publications (3)


The flow chart displaying the entire research.
Gene screening and functional enrichment analysis. (A) Volcanic plot of DEGs in TCGA-STAD. (B) Venn diagram showing 692 differentially expressed MRGs and IRGs. (C, D) Bar chart revealing the outcomes of KEGG pathways enrichment (C) and GO functional enrichment (D) of 692 differentially expressed MRGs and IRGs.
Molecular subtypes based on differentially expressed MRGs and IRGs. (A) Heatmap of consensus clustering matrix (k=4) showing four clusters (C1 = 157; C2 = 66; C3 = 81; C4 = 44) for MIRGs. (B) tSNE plot depicting distribution of four clusters. (C) The Kaplan–Meier curves displaying significant differentiation in overall survival time of patients between different phenotypes (P < 0.001). (D–G) Violin plots showing the ESTIMATE score (D), tumor purity (E), immune score (F) and stromal score (G) across different phenotypes. (H) The box diagrams displaying the difference of checkpoints’ expression in four clusters. (I) Heatmap of GSVA demonstrating biological functions and signaling pathways in four subgroups. (*, **, *** represent P < 0.05, P < 0.01, P < 0.001, respectively.).
Construction and verification of MIRGs. (A) Forest plot of univariate cox regression analysis screening 22 DEGs linked to survival. (B, C) LASSO regression generating 13 genes for MIRGs. (D) The Kaplan-Meier curves illustrating significant differentiation in survival of patients between risk subgroups in the TCGA cohort. (E) Distribution of survival status in the TCGA cohort. (F) Distribution of risk score in the TCGA cohort. (G) Heatmaps of 13 genes from MIRGs in the TCGA cohort. (H) ROC curves evaluating the predictive accuracy of MIRGs in the TCGA cohort. (I) The Kaplan-Meier curves illustrating significant differentiation in survival of patients between risk subgroups in the GEO cohort. (J) Distribution of survival status in the GEO cohort. (K) Distribution of risk score in the GEO cohort. (L) Heatmaps of 13 genes from MIRGs in the GEO cohort. (M) ROC curves evaluating the predictive accuracy of MIRGs in the GEO cohort.
Development of nomograms based on clinical features and risk scores. (A, D) The nomograms predicting the 1-, 2- and 3-year survival rate of patients with GC in the TCGA cohort (A) and in the GEO cohort (D). (B, E) Calibration curves for nomograms in the TCGA cohort (B) and in the GEO cohort (E). (C, F) ROC curves assessing the prognostic accuracy of nomogram and other clinical features in the TCGA cohort (C) and in the GEO cohort (F).

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Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer
  • Article
  • Full-text available

May 2024

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17 Reads

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2 Citations

Xiaoxi Han

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Chuanyu Leng

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Shufen Zhao

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[...]

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Weiwei Qi

Background Gastric cancer (GC) poses a global health challenge due to its widespread prevalence and unfavorable prognosis. Although immunotherapy has shown promise in clinical settings, its efficacy remains limited to a minority of GC patients. Manganese, recognized for its role in the body’s anti-tumor immune response, has the potential to enhance the effectiveness of tumor treatment when combined with immune checkpoint inhibitors. Methods Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases was utilized to obtain transcriptome information and clinical data for GC. Unsupervised clustering was employed to stratify samples into distinct subtypes. Manganese metabolism- and immune-related genes (MIRGs) were identified in GC by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. We conducted gene set variation analysis, and assessed the immune landscape, drug sensitivity, immunotherapy efficacy, and somatic mutations. The underlying role of NPR3 in GC was further analyzed in the single-cell RNA sequencing data and cellular experiments. Results GC patients were classified into four subtypes characterized by significantly different prognoses and tumor microenvironments. Thirteen genes were identified and established as MIRGs, demonstrating exceptional predictive effectiveness in GC patients. Distinct enrichment patterns of molecular functions and pathways were observed among various risk subgroups. Immune infiltration analysis revealed a significantly greater abundance of macrophages and monocytes in the high-risk group. Drug sensitivity analysis identified effective drugs for patients, while patients in the low-risk group could potentially benefit from immunotherapy. NPR3 expression was significantly downregulated in GC tissues. Single-cell RNA sequencing analysis indicated that the expression of NPR3 was distributed in endothelial cells. Cellular experiments demonstrated that NPR3 facilitated the proliferation of GC cells. Conclusion This is the first study to utilize manganese metabolism- and immune-related genes to identify the prognostic MIRGs for GC. The MIRGs not only reliably predicted the clinical outcome of GC patients but also hold the potential to guide future immunotherapy interventions for these patients.

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Cepharanthine, a regulator of keap1-Nrf2, inhibits gastric cancer growth through oxidative stress and energy metabolism pathway

December 2023

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24 Reads

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20 Citations

Cell Death Discovery

Cepharanthine (CEP), a bioactive compound derived from Stephania Cephalantha Hayata , is cytotoxic to various malignancies. However, the underlying mechanism of gastric cancer is unknown. CEP inhibited the cellular activity of gastric cancer AGS, HGC27 and MFC cell lines in this study. CEP-induced apoptosis reduced Bcl-2 expression and increased cleaved caspase 3, cleaved caspase 9, Bax, and Bad expression. CEP caused a G2 cell cycle arrest and reduced cyclin D1 and cyclin-dependent kinases 2 (CDK2) expression. Meanwhile, it increased oxidative stress, decreased mitochondrial membrane potential, and enhanced reactive oxygen species (ROS) accumulation in gastric cancer cell lines. Mechanistically, CEP inhibited Kelch-like ECH-associated protein (Keap1) expression while activating NF-E2 related factor 2 (Nrf2) nuclear translocations, increasing transcription of Nrf2 target genes quinone oxidoreductase 1 (NQO1), heme oxygenase 1 (HMOX1), and glutamate-cysteine ligase modifier subunit (GCLM). Furthermore, a combined analysis of targeted energy metabolism and RNA sequencing revealed that CEP could alter the levels of metabolic substances such as D (+) - Glucose, D-Fructose 6-phosphate, citric acid, succinic acid, and pyruvic acid, thereby altering energy metabolism in AGS cells. In addition, CEP significantly inhibited tumor growth in MFC BALB/c nude mice in vivo, consistent with the in vitro findings. Overall, CEP can induce oxidative stress by regulating Nrf2/Keap1 and alter energy metabolism, resulting in anti-gastric cancer effects. Our findings suggest a potential application of CEP in gastric cancer treatment.


Cepharanthine, a regulator of keap1-Nrf2, inhibits gastric cancer growth through oxidative stress and energy metabolism pathway

May 2023

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61 Reads

Cephalanthine (CEP), a bioactive compound derived from Stephania Cephalantha Hayata , is cytotoxic to various malignancies. However, the underlying mechanism of gastric cancer is unknown. CEP inhibited the cellular activity of gastric cancer AGS and HGC27 cell lines in this study. CEP induced apoptosis, reduced Bcl-2 expression, and increased cleaved caspase 3, cleaved caspase 9, Bax, and Bad expression. CEP caused a G2 cell cycle arrest and reduced cyclin D1 and cyclin-dependent kinases 2 (CDK2) expression. Meanwhile, it increased oxidative stress, decreased mitochondrial membrane potential, and enhanced reactive oxygen species (ROS) accumulation in AGS and HGC27 cells. Mechanistically, CEP inhibited Kelch-like ECH-associated protein (Keap1) expression while activating NF-E2 related factor 2 (Nrf2) expression, increasing transcription of Nrf2 target genes quinone oxidoreductase 1 (NQO1), heme oxygenase 1 (HMOX1), and glutamate-cysteine ligase modifier subunit (GCLM). Furthermore, a combined analysis of targeted energy metabolism and RNA sequencing revealed that CEP could alter the levels of metabolic substances such as D (+) - Glucose, D-Fructose 6-phosphate, citric acid, succinic acid, and pyruvic acid, thereby altering energy metabolism in AGS cells. In addition, CEP significantly inhibited tumor growth in MFC BALB/c nude mice in vivo , consistent with the in vitro findings. Overall, CEP can induce oxidative stress by regulating Nrf2/Keap1 and alter energy metabolism, resulting in anti-ovarian tumor effects. Our findings suggest a potential application of CEP in gastric cancer treatment.

Citations (2)


... In a univariate analysis, the risk score and TNM stage emerged as significant predictors of survival in gastric cancer patients (P < 0.001) (Fig. 6F); furthermore, multivariate analysis indicated a statistically significant correlation (P < 0.001) between the risk score, age, and survival, even after controlling for other variables (Fig. 6G). We compared our own model to 14 other published GC models to better highlight its predictive capability [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. When compared to other models, ours has a higher C-index (Fig. 6H). ...

Reference:

Single-cell RNA-sequencing and genome-wide Mendelian randomisation along with abundant machine learning methods identify a novel B cells signature in gastric cancer
Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer

... It is a bisbenzylisoquinoline alkaloid isolated from tubers of the climbing vine Stephania cepharantha [46,47]. Cepharanthine is an anti-inflammatory and antineoplastic compound [47][48][49][50][51][52][53] that additionally has antiparasitic effects [54][55][56]. It has been shown to greatly decrease levels of the malaria parasite in infected mice as well as inhibit parasite growth in vitro [57]. ...

Cepharanthine, a regulator of keap1-Nrf2, inhibits gastric cancer growth through oxidative stress and energy metabolism pathway

Cell Death Discovery