Dataset Information

Identification and validation of molecular subtypes and prognostic signature for stage I and stage II gastric cancer based on neutrophil extracellular traps.

ABSTRACT:

Purpose

This study identified subtypes and prognostic signature of stage I and stage II gastric cancer based on neutrophil extracellular trap (NET)-related genes.

Methods

The gene expression data associated with stage I and stage II gastric cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. NET-related genes were obtained from previous reference. Differentially expressed NET-related genes were selected by consensus cluster analysis. The differences in immune infiltration between two subtypes were analyzed. Prognosis-related genes were further screened by univariate Cox regression analysis. Gene Set Enrichment Analysis (GSEA) of prognostic signatures was conducted with clusterprofiler. Finally, a miRNA-mRNA-transcription factor (TF) network was constructed.

Results

Total 43 differential NET-related genes were obtained and two subtypes were obtained based on these genes. Patients of cluster 2 had a better prognosis compared to cluster 1. Eight types of immune cells were differential in infiltration level between two subtypes. Following univariate Cox regression analysis, two genes of CXC chemokine receptor 4 (CXCR4) and nuclear factor, erythroid 2-like 2 (NFE2L2) significantly related to patient survival were selected. GSEA of single gene revealed that CXCR4 was associated with allograft rejection and NFE2L2 was associated with drug metabolism-cytochrome P450. A network with 421 miRNA-mRNA-TF regulatory pairs was constructed.

Conclusion

The present study identified two subtypes and a prognostic signature for stage I and stage II gastric cancer based on NET-related genes.

SUBMITTER: Mu L

PROVIDER: S-EPMC10787308 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Publications

Identification and validation of molecular subtypes and prognostic signature for stage I and stage II gastric cancer based on neutrophil extracellular traps.

Mu Lei L Qiu Gang G

Open medicine (Warsaw, Poland) 20240112 1

<h4>Purpose</h4>This study identified subtypes and prognostic signature of stage I and stage II gastric cancer based on neutrophil extracellular trap (NET)-related genes.<h4>Methods</h4>The gene expression data associated with stage I and stage II gastric cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. NET-related genes were obtained from previous reference. Differentially expressed NET-related genes were selected by consensus cluster analy ...[more]

PMID: 38221932

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Project description:BackgroundCancer cells induce neutrophil extracellular traps (NETs) to promote tumor progression and metastasis. However, only a few studies have focused on the role of NETs in Neuroblastoma (NB).MethodsFirst, based on the expression of NET-related genes, consensus clustering analysis was conducted to cluster NB samples into different subtypes. Differential analysis was performed to identify DEGs between subtypes. Functional items and related pathways of DEGs were identified using enrichment analysis. Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. Furthermore, independent prognostic analysis was performed. Immune infiltration analysis was performed to identify differential immune cells. Finally, the verification of prognostic model genes were taken by the immunohistochemical staining and quantitative real-time PCR.ResultsConsensus clustering analysis demonstrated that NB samples were clustered into two subtypes. There were 125 DEGs between the two subtypes of NB. Moreover, the enrichment analysis results showed that the DEGs were mainly associated with 'external side of plasma membrane,' 'immune receptor activity' 'regulation of leukocyte migration' GO items. There were also several GO items related to neutrophils, such as regulation of neutrophil migration and differentiation. KEGG pathways revealed that the DEGs were correlated with in immunity-related activities, including 'Complement and coagulation cascades,' 'Neutrophil extracellular trap formation, 'T cell receptor signaling pathway,' 'PD-L1 expression and PD-1 checkpoint pathway in cancer' and so on. A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. Furthermore, a nomogram for forecasting the survival rates of patients with NB was established based on the risk score, age at diagnosis, and MYCN status. Eight differential immune cells (CD8 + T cells, resting mast cells, etc.) were acquired between the two risk subgroups. The expression levels of five prognostic model genes at the protein and mRNA were verified and all results were consistent with the results of our bioinformatics analysis.ConclusionWe initially found that five NET-related genes were significantly differentially expressed in NETs-associated molecular isoforms and two Netrg molecular isoforms were found to be associated with poorer prognosis. This stratification might provide insight into the prediction of prognosis and ideal immunotherapy strategies for patients with NB. However, we also noted that the formation of NETs is a complex biological process involving the regulation of multiple cytokines and cellular interactions. Therefore, the exact roles of these genes and their specific mechanisms in the formation of NETs and the development of NB still need to be further investigated.

Project description:BackgroundNeutrophil extracellular traps (NETs) released by neutrophils are crucial for cancer development, metastasis, and can indicate gastric cancer (GC) patients' prognosis. This study reveals the relevance of NETs-related genes to GC through transcriptome analysis.MethodsWe obtained transcriptome sequencing data of GC from UCSC Xena and screened prognostic NETs-related genes by GEPIA2 database. The signature for NETs was subsequently created using the LASSO-Cox regression. The clinical value of model was further explored using the nomogram and was externally validated by the GEO database. After that, we employed GO, KEGG, and GSEA enrichment analyses to evaluate the bio-functional enrichment and related pathways. Additionally, ESTIMATE, MCP counter, and ssGSEA scores were used to investigate the immunological microenvironment of GC patients. Finally, in the external cohort, neutrophil elastase (NE)-DNA complexes were measured by ELISA, and the prognostic value of NE-DNA in GC was investigated using Cox analysis.ResultsSeven NETs-associated genes (PDE4B, CD93, CTSG, IL6, ELANE, KCNJ15, and CRISPLD2) were filtered to establish the signature and participated in building the nomogram. In comparison to the high-risk group, the overall survival (OS) was much longer in the low-risk group (P=0.005). The validation cohort demonstrated the acceptable predictive ability of the nomogram. The signature was enriched in biological features such as extracellular matrix organization, epithelial-mesenchymal transition and inflammatory response. Moreover, there were substantial differences in immune cell infiltration across the different risk groups (p<0.001), especially the high-risk group having more immune cells that are engaged in the antigen presentation process and associated functions. Finally, in the external cohort, NE-DNA levels were shown to be an independent factor affecting OS prognosis (p=0.006).ConclusionOverall, this research identified a novel signature based on seven NETs-associated genes to predict prognosis and identify tumor microenvironment of GC. And high NE-DNA level may be a critical factor in the poor OS associated with NETs.

Project description:BackgroundSoft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS.MethodsWe applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR).ResultsUsing the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS.ConclusionIn conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

Dataset Information

Identification and validation of molecular subtypes and prognostic signature for stage I and stage II gastric cancer based on neutrophil extracellular traps.

Purpose

Methods

Results

Conclusion

Publications

Identification and validation of molecular subtypes and prognostic signature for stage I and stage II gastric cancer based on neutrophil extracellular traps.

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