Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer

Identification of novel nuclear pore complex associated proteins in esophageal carcinoma by an integrated bioinformatics analysis

Nucleoporins (NUPs) are components of the nuclear pore complex (NPC) that participate in the nucleocytoplasmic transport of macromolecules as well as in many essential processes that may be led to carcinogenesis. We selected three expression profile microarray datasets from GEO and as well as TCGA data to identify differentially expressed NUPs genes in esophageal carcinoma. Our findings indicated that NUP133, NUP37, NUP43, NUP50, GLE1 and NDC1 are overexpressed in esophageal carcinoma, among which NUP50 and GLE1genes are reported for the first time in esophageal carcinoma. All identified NUPs were also associated with distant metastasis.Communicated by Ramaswamy H. Sarma.

A Novel Signature Based on m6A Regulator-Mediated Genes Along Glycolytic Pathway Predicts Prognosis and Immunotherapy Responses of Gastric Cancer Patients

N6-methyladenosine (m6A) modification is involved in many aspects of gastric cancer (GC). Moreover, m6A and glycolysis-related genes (GRGs) play important roles in immunotherapeutic and prognostic implication of GC. However, GRGs involved in m6A regulation have never been analyzed comprehensively in GC. Herein, the study aims to identify and validate a novel signature based on m6A-related GRGs in GC patients. Therefore, a m6A-related GRGs signature is established, which can predict the survival of patients with GC and remain an independent prognostic factor in multivariate analyses. Clinical significance of the model is well validated in internal cohort and independent validation cohort. In addition, the expression levels of risk model-related GRGs in clinical samples are validated. Consistent with the database results, all model genes are up-regulated in expression except DCN. After regrouping the patients based on this risk model, the study can effectively distinguish between them in respect to immune-cell infiltration microenvironment and immunotherapeutic response. Additionally, candidate drugs targeting risk model-related GRGs are confirmed. Finally, a nomogram combining risk scores and clinical parameters is created, and calibration plots show that the nomogram can accurately predict survival. This risk model can serve as a reliable assessment tool for predicting prognosis and immunotherapeutic responses in GC patients.

Development of a Comprehensive Gene Signature Linking Hypoxia, Glycolysis, Lactylation, and Metabolomic Insights in Gastric Cancer through the Integration of Bulk and Single-Cell RNA-Seq Data

BACKGROUND

Hypoxia and anaerobic glycolysis are cancer hallmarks and sources of the metabolite lactate. Intriguingly, lactate-induced protein lactylation is considered a novel epigenetic mechanism that predisposes cells toward a malignant state. However, the significance of comprehensive hypoxia-glycolysis-lactylation-related genes (HGLRGs) in cancer is unclear. We aimed to construct a model centered around HGLRGs for predicting survival, metabolic features, drug responsiveness, and immune response in gastric cancer.

METHODS

The integration of bulk and single-cell RNA-Seq data was achieved using data obtained from the TCGA and GEO databases to analyze HGLRG expression patterns. A HGLRG risk-score model was developed based on univariate Cox regression and a LASSO-Cox regression model and subsequently validated. Additionally, the relationships between the identified HGLRG signature and multiple metabolites, drug sensitivity and various cell clusters were explored.

RESULTS

Thirteen genes were identified as constituting the HGLRG signature. Using this signature, we established predictive models, including HGLRG risk scores and nomogram and Cox regression models. The stratification of patients into high- and low-risk groups based on HGLRG risk scores showed a better prognosis in the latter. The high-risk group displayed increased sensitivity to cytotoxic drugs and targeted inhibitors. The expression of the HGLRG BGN displayed a strong correlation with amino acids and lipid metabolites. Notably, a significant difference in immune infiltration, such as that of M1 macrophages and CD8 T cells, was correlated with the HGLRG signature. The abundant DUSP1 within the mesenchymal components was highlighted by single-cell transcriptomics.

CONCLUSION

The innovative HGLRG signature demonstrates efficacy in predicting survival and providing a practical clinical model for gastric cancer. The HGLRG signature reflects the internal metabolism, drug responsiveness, and immune microenvironment components of gastric cancer and is expected to boost patients' response to targeted therapy and immunotherapy.

NF-κB-Related Metabolic Gene Signature Predicts the Prognosis and Immunotherapy Response in Gastric Cancer

Background

Sufficient evidence indicated the crucial role of NF-κB family played in gastric cancer (GC). The novel discovery that NF-κB could regulate cancer metabolism and immune evasion greatly increased its attraction in cancer research. However, the correlation among NF-κB, metabolism, and cancer immunity in GC still requires further improvement.

Methods

TCGA, hTFtarget, and MSigDB databases were employed to identify NF-κB-related metabolic genes (NFMGs). Based on NFMGs, we used consensus clustering to divide GC patients into two subtypes. GSVA was employed to analyze the enriched pathway. ESTIMATE, CIBERSORT, ssGSEA, and MCPcounter algorithms were applied to evaluate immune infiltration in GC. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict patients' response to immunotherapy. We also established a NFMG-related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets.

Results

We used 27 NFMGs to conduct an unsupervised clustering on GC samples and classified them into two clusters. Cluster 1 was characterized by high active metabolism, tumor mutant burden, and microsatellite instability, while cluster 2 was featured with high immune infiltration. Compared to cluster 2, cluster 1 had a better prognosis and higher response to immunotherapy. In addition, we constructed a 12-NFMG (ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM) risk score. Followed analysis indicated that this risk score acted as an effectively prognostic factor in GC.

Conclusion

Our data suggested that GC subtypes classified by NFMGs may effectively guide prognosis and immunotherapy. Further study of these NFMGs will deepen our understanding of NF-κB-mediated cancer metabolism and immunity.

Transcriptomic and Metabolomic Profiling in Helicobacter pylori-Induced Gastric Cancer Identified Prognosis- and Immunotherapy-Relevant Gene Signatures

Background: Chronic Helicobacter pylori (HP) infection is considered the major cause of non-cardia gastric cancer (GC). However, how HP infection influences the metabolism and further regulates the progression of GC remains unknown. Methods: We comprehensively evaluated the metabolic pattern of HP-positive (HP+) GC samples using transcriptomic data and correlated these patterns with tumor microenvironment (TME)-infiltrating characteristics. The metabolic score was constructed to quantify metabolic patterns of individual tumors using principal component analysis (PCA) algorithms. The expression alterations of key metabolism-related genes (MRGs) and downstream metabolites were validated by PCR and untargeted metabolomics analysis. Results: Two distinct metabolic patterns and differential metabolic scores were identified in HP+ GC, which had various biological pathways in common and were associated with clinical outcomes. TME-infiltrating profiles under both patterns were highly consistent with the immunophenotype. Furthermore, the analysis indicated that a low metabolic score was correlated with an increased EMT subtype, immunosuppression status, and worse survival. Importantly, we identified that the expression of five MRGs, GSS, GMPPA, OGDH, SGPP2, and PIK3CA, was remarkably correlated with HP infection, patient survival, and therapy response. Furthermore, the carbohydrate metabolism and citric acid may be downstream regulators of the function of metabolic genes in HP-induced GC. Conclusion: Our findings suggest that there is cross talk between metabolism and immune promotion during HP infection. MRG-specific transcriptional alterations may serve as predictive biomarkers of survival outcomes and potential targets for treatment of patients with HP-induced GC.