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Comprehensive analysis of prognostic genes in gastric cancer

ABSTRACT: Background: Gastric cancer is associated with high mortality, and effective methods for predicting prognosis are lacking. We aimed to identify potential prognostic markers associated with the development of gastric cancer through bioinformatic analyses. Methods: Gastric cancer-associated gene expression profiles were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The key genes involved in the development of gastric cancer were obtained by differential expression analysis, coexpression analysis, and short time-series expression miner (STEM) analysis. The potential prognostic value of differentially expressed genes was further evaluated using a Cox regression model and risk scores. Hierarchical clustering was applied to validate the impact of key genes on the overall survival of gastric cancer patients. Results: A total of 1381 genes were consistently dysregulated in the development of gastric cancer. Among them, 186 genes affected the overall survival of gastric cancer patients. The following genes had areas under the receiver operating characteristic curve greater than 0.9 in both datasets and were therefore considered key genes: ADAM12, CEP55, LRFN4, INHBA, ADH1B, DPT, FAM107A, and LOC100506388. LRFN4, DPT, and LOC100506388 were identified as potential prognostic genes for gastric cancer through a nomogram. Overexpression of LRFN4 and LOC100506388 was associated with a higher risk of gastric cancer. Finally, we found that tumors were infiltrated with high levels of Th2 cells and mast cells, and the infiltration levels were associated with overall survival in gastric cancer patients. Conclusions: We found that key dysregulated genes may have a prognostic value for the development of gastric cancer.

SUBMITTER: Huang S

PROVIDER: S-EPMC8580339 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe transcription factor Spi-1 proto-oncogene (SPI1, also known as PU.1) is a key regulator of signal communication in the immune system and is essential for the development of myeloid cells and lymphocytes. However, the potential role of SPI1 in gastric cancer (GC) and the correlations between SPI1 and immune infiltration remain unclear.MethodsIn the present study, multiple databases including ONCOMINE, TIMER, Kaplan-Meier Plotter, and The Cancer Genome Atlas were used to explore the expression levels and prognostic value of SPI1 in GC. cBioPortal was used to explore the possible reasons for the increased expression of SPI1 in GC. The correlations between SPI1 expression and tumor-infiltrating immune cells (TICs) were analyzed using CIBERSORT and TIMER. Gene set enrichment analysis was used to determine the biological function of SPI1 in the development of GC. In addition, a risk signature based on SPI1-related immunomodulators was constructed to accurately evaluate the prognosis of patients with GC. The upregulation of SPI1 expression in GC was further confirmed through immunohistochemistry, western blotting, and real-time quantitative PCR (RT-qPCR) assay.ResultsThe expression of SPI1 was increased significantly in GC according to multiple databases, and high expression of SPI1 was related to poor prognosis and progression of GC. The main factor influencing the high expression of SPI1 mRNA in GC may be diploidy, not DNA methylation. Moreover, immunohistochemistry, western blotting, and RT-qPCR assays also confirmed the upregulated expression of SPI1 in GC. CIBERSORT analysis revealed that SPI1 expression was correlated with seven types of TICs (naive B cells, resting memory CD4 T cells, activated memory CD4 T cells, activated natural killer cells, resting natural killer cells, M2 macrophages, and resting dendritic cells). Gene set enrichment analysis indicated that SPI1 might be related to immune activation in GC and participate in cell cycle regulation. In addition, based on SPI1-related immunomodulators, we developed multiple-gene risk prediction signatures and constructed a nomogram that can independently predict the clinical outcome of GC.ConclusionThe results of the present study suggest that SPI1 has a critical role in determining the prognosis of GC patients and may be a potential immunotherapeutic target.

Project description:BackgroundCervical cancer (CC) is the leading cause of cancer-related death in women. A limited number of studies have investigated whether immune-prognostic features can be used to predict the prognosis of CC. This study aimed to develop an improved prognostic risk scoring model (PRSM) for CC based on immune-related genes (IRGs) to predict survival and determine the key prognostic IRGs.MethodsWe downloaded the gene expression profiles and clinical data of CC patients from the TCGA and GEO databases. The ESTIMATE algorithm was used to calculate the score for both immune and stromal cells. Differentially expressed genes (DEGs) in different subpopulations were analyzed by "Limma". A weighted gene co-expression network analysis (WGCNA) was used to establish a DEG co-expression module related to the immune score. Immune-related gene pairs (IRGPs) were constructed, and univariate- and Lasso-Cox regression analyses were used to analyze prognosis and establish a PRSM. A log-rank test was used to verify the accuracy and consistency of the scoring model. Identification of the predicted key IRG was ensured by the application of functional enrichment, DisNor, protein-protein interactions (PPIs) and heatmap. Finally, we extracted the key prognostic immune-related genes from the gene expression data, validated the key genes by immunohistochemistry and analyzed the correlation between their expression and drug sensitivity.ResultsA new PRSM was developed based on 22 IRGPs. The prognosis of the low-risk group in the model group (P < 0.001) and validation group (P = 0.039) was significantly better than that in the high-risk group. Furthermore, M1 and M2 macrophages were highly expressed in the low-risk group. Retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) and the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway were significantly enriched in the low-risk group. Three representative genes (CD80, CD28, and LCP2) were markers of CC prognosis. CD80 and CD28 may more prominent represent important indicators to improve patient prognosis. These key genes was positively correlated with drug sensitivity. Finally, we found that differences in the sensitivity to JNK inhibitors could be distinguished based on the use and risk grouping of this PRSM.ConclusionsThe prognostic model based on the IRGs and key genes have potential clinical significance for predicting the prognosis of CC patients, providing a foundation for clinical prognosis judgment and individualized treatment.

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Comprehensive analysis of prognostic genes in gastric cancer

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