Glutamine metabolism genes prognostic signature for stomach adenocarcinoma and immune infiltration: potential biomarkers for predicting overall survival

Characterization of tumor suppressors and oncogenes evaluated from TCGA cancers

Mutations in oncogenes and tumor suppressor genes can significantly impact cellular function during cancer development. A comprehensive analysis of their mutation patterns and significant gene ontology terms can provide insights into cancer emergence and suggest potential targets for drug development. This study analyzes twelve cancer subtypes by focusing on significant genetic and molecular factors. Two common genetic mutations associated with cancer are single nucleotide variants (SNVs) and copy number alterations (CNAs). Oncogenes, derived from mutated proto-oncogenes, disrupt normal cell functions and promote cancer, while tumor suppressor genes, often inactivated by mutations, regulate cell processes like proliferation and DNA damage response. This study analyzed datasets from The Cancer Genome Atlas (TCGA), which provides extensive genomic data across various cancers. In our analysis results, many genes with significant p-values based on Kaplan Meier gene expression data were identified in eight cancers (BRCA, BLCA, HNSC, KIRC, LUAD, KIRP, LUSC, STAD). Moreover, STAD is the only cancer for genes with both significant p-values and functional terms reported. Interestingly, we found that LIHC was the cancer reported with only one CNA mutated gene and its survival plot p-value being significant. Additionally, KICH has no reported significant genes at all. Our study proposed the relationship between tumor suppressor and oncogenes and shed light on cancer tumorigenesis due to genetic mutations.

Deciphering glutamine metabolism patterns for malignancy and tumor microenvironment in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer characterized by metabolic reprogramming. Glutamine metabolism is pivotal in metabolic reprogramming, contributing to the significant heterogeneity observed in ccRCC. Consequently, developing prognostic markers associated with glutamine metabolism could enhance personalized treatment strategies for ccRCC patients. This study obtained RNA sequencing and clinical data from 763 ccRCC cases sourced from multiple databases. Consensus clustering of 74 glutamine metabolism related genes (GMRGs)- profiles stratified the patients into three clusters, each of which exhibited distinct prognosis, tumor microenvironment, and biological characteristics. Then, six genes (SMTNL2, MIOX, TMEM27, SLC16A12, HRH2, and SAA1) were identified by machine-learning algorithms to develop a predictive signature related to glutamine metabolism, termed as GMRScore. The GMRScore showed significant differences in clinical prognosis, expression profile of immune checkpoints, abundance of immune cells, and immunotherapy response of ccRCC patients. Besides, the nomogram incorporating the GMRScore and clinical features showed strong predictive performance in prognosis of ccRCC patients. ALDH18A1, one of the GRMGs, exhibited elevated expression level in ccRCC and was related to markedly poorer prognosis in the integrated cohort, validated by proteomic profiling of 232 ccRCC samples from Fudan University Shanghai Cancer Center (FUSCC). Conducting western blotting, CCK-8, transwell, and flow cytometry assays, we found the knockdown of ALDH18A1 in ccRCC significantly promoted apoptosis and inhibited proliferation, invasion, and epithelial-mesenchymal transition (EMT) in two human ccRCC cell lines (786-O and 769-P). In conclusion, we developed a glutamine metabolism-related prognostic signature in ccRCC, which is tightly linked to the tumor immune microenvironment and immunotherapy response, potentially facilitating precision therapy for ccRCC patients. Additionally, this study revealed the key role of ALDH18A1 in promoting ccRCC progression for the first time.

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration

BACKGROUND

Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis.

METHODS

We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset.

RESULTS

In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929).

CONCLUSION

This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.