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Identification of the hub genes and prognostic indicators of gastric cancer and correlation of indicators with tumor-infiltrating immune cell levels.

ABSTRACT: Aims: To identify the hub genes and prognostic indicators of gastric cancer (GC) and determine the correlation between prognostic indicators and the tumor-infiltrating immune cell levels so as to provide useful information for future GC diagnosis and treatment. Methods: The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset and two microarray datasets were used to screen the overlapping differentially expressed genes (DEGs) between normal gastric and GC tissue samples. Hub genes were screened via protein-protein interaction networks and module analysis of the overlapping DEGs. Their expression was validated at the cell level and tissue level using the ONCOMINE database. The prognostic indicators of overall survival (OS) and disease-free survival was identified by Cox proportional hazards regression analysis based on tumor grade and cancer stage. The expression of hub genes was validated at the cell level. The correlation of prognostic indicators with the tumor-infiltrating immune cell levels was analyzed using Tumor IMmune Estimation Resource. Results: Ten hub genes, namely CDC6, CDC20, BUB1B, TOP2A, CDK1, AURKA, CCNA2, CCNB1, MAD2L1, and KIF11, were screened and their upregulation in the GC tissue was verified. Three prognostic factors, namely LUM, VCAN, and EFNA4, were identified; their expression was higher in GC cells than in normal cells. LUM, VCAN, and EFNA4 were correlated with tumor-infiltrating immune cell levels in GC. Significance: The identified hub genes and prognostic indicators of GC could be useful indicators for future GC diagnosis and treatment.

SUBMITTER: Ji Y

PROVIDER: S-EPMC8176244 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGastric cancer (GC) is the fourth most frequently diagnosed malignancy and the second leading cause of cancer-associated mortality worldwide. The tumor microenvironment, especially tumor-infiltrating immune cells (TIICs), exhibits crucial roles both in promoting and inhibiting cancer growth. The aim of the present study was to evaluate the landscape of TIICs and develop a prognostic nomogram in GC.Materials and methodsA gene expression profile obtained from a dataset from The Cancer Genome Atlas (TCGA) was used to quantify the proportion of 22 TIICs in GC by the CIBERSORT algorithm. LASSO regression analysis and multivariate Cox regression were applied to select the best survival-related TIICs and develop an immunoscore formula. Based on the immunoscore and clinical information, a prognostic nomogram was built, and the predictive accuracy of it was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and the calibration plot. Furthermore, the nomogram was validated by data from the International Cancer Genome Consortium (ICGC) dataset.ResultsIn the GC samples, macrophages (25.3%), resting memory CD4 T cells (16.2%) and CD8 T cells (9.7%) were the most abundant among 22 TIICs. Seven TIICs were filtered out and used to develop an immunoscore formula. The AUC of the prognostic nomogram in the TCGA set was 0.772, similar to that in the ICGC set (0.730) and whole set (0.748), and significantly superior to that of TNM staging alone (0.591). The calibration plot demonstrated an outstanding consistency between the prediction and actual observation. Survival analysis revealed that patients with GC in the high-immunoscore group exhibited a poor clinical outcome. The result of multivariate analysis revealed that the immunoscore was an independent prognostic factor.DiscussionThe immunoscore could be used to reinforce the clinical outcome prediction ability of the TNM staging system and provide a convenient tool for risk assessment and treatment selection for patients with GC.

Project description:Background Metabolic syndrome (MetS) refers to a cluster of metabolic disorders that are mainly caused by obesity. Skeletal muscle is a central component of systemic metabolism. However, the mechanism of skeletal muscle metabolic impairment in obesity remains unclear. This study aimed to identify key early biomarkers in skeletal muscle for the prevention and treatment of MetS in obesity. Methods The GSE85439 dataset was downloaded from the Gene Expression Omnibus database. Gene set enrichment and immune cell infiltration analyses were performed for genome-wide genes. Differentially expressed genes (DEGs) between obese and control mice were screened and subjected to functional enrichment analysis, and a protein–protein interaction network was constructed. The results of the bioinformatics analysis were confirmed by immunofluorescence and real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Results Enrichment analysis indicated that the genes expressed in obese mice were mainly associated with acute inflammatory response. Immune cell infiltration analysis of 190 DEGs with consistent trends showed that the numbers of mast cells (MCs) and active dendritic cells were significantly higher in obese mice than in control mice. Immunofluorescence analysis confirmed that the number of MCs present in the skeletal muscle was higher in obese mice than in control mice, although no difference was observed in the active dendritic cell count. Functional enrichment analysis showed that the DEGs were mainly associated with transcriptional regulation. In the clusters of the protein-protein interaction network, four acute-phase-response genes (SAA1, SAA2, ORM1, and HP) were significantly correlated with transcription-regulating genes (SHH, IGF2, and CELA1); these seven genes were identified as hub genes. The qRT-PCR results showed that the expression levels of SAA1, SAA2, IGF2, and CELA1 were significantly higher in obese mice than in control mice; however, those of HP, ORM1, and SHH did not differ significantly between the two groups. Conclusions The skeletal muscle of obese mice exhibits elevated MC infiltration and increased SAA1, SAA2, CELA1, and IGF2 expression. The identification of these biomarkers has increased our understanding of the potential functional mechanisms of skeletal muscle in obesity. These potential biomarkers may serve as targets for the prevention and treatment of MetS.

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Identification of the hub genes and prognostic indicators of gastric cancer and correlation of indicators with tumor-infiltrating immune cell levels.

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