Shuming Chen’s research while affiliated with First Affiliated Hospital of China Medical University and other places

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Publications (4)


Study workflow.
Identification of core prognostic genes and enrichment analysis. (A) Venn diagram illustrating the intersection of 685 genes associated with autophagy, senescence, and STAD. (B) Volcano plot of DEASRGs based on intersected genes. (C) GO functional annotation of DEASRGs. (D) KEGG enrichment analysis of DEASRGs. (E) Univariate Cox regression analysis identifying 29 genes. (F) Frequencies of CNV gain and loss among 29 prognostic genes. (G) Circular plots visualizing chromosome distributions of core prognostic genes.
Development and verification the ASRGs signature. (A) LASSO regression model selection curve with log(λ) on the x-axis and partial likelihood deviance on the y-axis. (B) Coefficients of the LASSO regression model. (C, D) KM survival curves of OS. (E, G) Survival curves of patients with GC. (F, H) Distribution of survival status based on risk score. (I, J) Heatmaps of gene expression for the prognostic model genes. (K, M) Comparison of ROC curves. (L, N) ROC curve using temporal information (time-dependent ROC curves).
Association of the prognostic signature with gene clusters and immunological features. (A) The heat map display of consensus clustering is categorized into three cluster (C1 = 277; C2 = 63; C3 = 26). (B) PCA showing the perfect separation of C1, C2 and C3. (C) KM survival curves with three distinct clusters. (D) A Sankey diagram illustrating the link between gene clusters, risk group, and survival status. (E) Variations in risk score among the three gene subtypes. (F-H) ESTIMATE algorithm results for three gene clusters. (I) Expression of immune checkpoints related genes. (J) The heat map depicting variations in immune cell infiltration as determined using TIMER, CIERSORT, quanTIseq, MCPcounter, xCell, and EPIC algorithms.
Establishment and validation of the nomogram. (A, E) A nomogram was established to forecast the 1-year, 3-year, and 5-year OS. (B, F) Calibration plots illustrating the agreement of predicted survival rates compared to the actual observed survival rates. (C, G) A DCA was carried out to compare the net benefits of the nomogram incorporating the prognostic signature, the nomogram excluding the prognostic signature, and other factors. (D, H) The AUC was employed to compare the predictive accuracy of the nomogram with other prognostic markers.

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A prognostic model based on autophagy-and senescence-related genes for gastric cancer: implications for immunotherapy and personalized treatment
  • Article
  • Full-text available

March 2025

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15 Reads

Shuming Chen

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Xiaoxi Han

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Yangyang Lu

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[...]

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Weiwei Qi

Background The process of human aging is accompanied by an increased susceptibility to various cancers, including gastric cancer. This heightened susceptibility is linked to the shared molecular characteristics between aging and tumorigenesis. Autophagy is considered a critical mediator connecting aging and cancer, exerting a dynamic regulatory effect in conjunction with cellular senescence during tumor progression. In this study, a combined analysis of autophagy- and senescence-related genes was employed to comprehensively capture tumor heterogeneity. Methods The gene expression profiles and clinical data for GC samples were acquired from TCGA and GEO databases. Differentially expressed autophagy- and senescence-related genes (DEASRGs) were identified between tumor and normal tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out to provide insights into biological significance. A prognostic signature was established using univariate Cox and LASSO regression analyses. Furthermore, consensus clustering analyses and nomograms were employed for survival prediction. TME and drug sensitivity analyses were conducted to compare differences between the groups. To predict immunotherapy efficacy, the correlations between risk score and immune checkpoints, MSI, TMB, and TIDE scores were investigated. Results A fourteen-gene prognostic signature with superior accuracy was constructed. GC patients were stratified into three distinct clusters, each exhibiting significant variations in their prognosis and immune microenvironments. Drug sensitivity analysis revealed that the low-risk group demonstrated greater responsiveness to several commonly used chemotherapeutic agents for gastric cancer, including oxaliplatin. TME analysis further indicated that the high-risk group exhibited increased immune cell infiltration, upregulated expression of ICs, and a higher stromal score, suggesting a greater capacity for immune evasion. In contrast, the low-risk group was characterized by a higher proportion of microsatellite instability-high (MSI-H) cases, an elevated TIDE score, and a greater TMB, indicating a higher likelihood of benefiting from immunotherapy. In addition, Single-cell sequencing demonstrated that TXNIP was expressed in epithelial cells. Cellular experiments preliminarily verified that TXNIP could promote the proliferation and migration of gastric cancer cells. Conclusion This study presents a robust predictive model for GC prognosis using autophagy- and senescence-related genes, demonstrating its ability to predict immune infiltration, immunotherapy effectiveness, and guide personalized treatment.

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An investigation of the molecular characterization of the tripartite motif (TRIM) family and primary validation of TRIM31 in gastric cancer

July 2024

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10 Reads

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2 Citations

Human Genomics

Most TRIM family members characterized by the E3-ubiquitin ligases, participate in ubiquitination and tumorigenesis. While there is a dearth of a comprehensive investigation for the entire family in gastric cancer (GC). By combining the TCGA and GEO databases, common TRIM family members (TRIMs) were obtained to investigate gene expression, gene mutations, and clinical prognosis. On the basis of TRIMs, a consensus clustering analysis was conducted, and a risk assessment system and prognostic model were developed. Particularly, TRIM31 with clinical prognostic and diagnostic value was chosen for single-gene bioinformatics analysis, in vitro experimental validation, and immunohistochemical analysis of clinical tissue microarrays. The combined dataset consisted of 66 TRIMs, of which 52 were differentially expressed and 43 were differentially prognostic. Significant survival differences existed between the gene clusters obtained by consensus clustering analysis. Using 4 differentially expressed genes identified by multivariate Cox regression and LASSO regression, a risk scoring system was developed. Higher risk scores were associated with a poorer prognosis, suppressive immune cell infiltration, and drug resistance. Transcriptomic data and clinical sample tissue microarrays confirmed that TRIM31 was highly expressed in GC and associated with a poor prognosis. Pathway enrichment analysis, cell migration and colony formation assay, EdU assay, reactive oxygen species (ROS) assay, and mitochondrial membrane potential assay revealed that TRIM31 may be implicated in cell cycle regulation and oxidative stress-related pathways, contribute to gastric carcinogenesis. This study investigated the whole functional and expression profile and a risk score system based on the TRIM family in GC. Further investigation centered around TRIM31 offers insight into the underlying mechanisms of action exhibited by other members of its family in the context of GC. Supplementary Information The online version contains supplementary material available at 10.1186/s40246-024-00631-7.


Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer

May 2024

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16 Reads

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2 Citations

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.


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)


... Additionally, several TRIM proteins, including TRIM15, TRIM47, and TRIM55, have been implicated in EMT. These proteins facilitate EMT by modulating key molecular markers such as E-cadherin, N-cadherin, and Vimentin, contributing to increased cancer cell invasiveness and metastatic potential [74][75][76]145,[151][152][153][154][155][156][157][158][159]. ...

Reference:

The Role and Mechanism of TRIM Proteins in Gastric Cancer
An investigation of the molecular characterization of the tripartite motif (TRIM) family and primary validation of TRIM31 in gastric cancer

Human Genomics

... 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). ...

Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer