TRAT1 overexpression delays cancer progression and is associated with immune infiltration in lung adenocarcinoma

Xiao, Xiao-Yue; Guo, Qiang; Song, Tie; Wu, Chuangyan; Chen, Jiu-Ling; Ding, Yulong; Wan, Jun-Hao; Chen, Shanshan; Wang, Sihua

doi:10.3389/fonc.2022.960866

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…In humans, defects in the CD3D, CD3E and CD3Z genes are a cause of severe immunode ciency and appear early in life and increase susceptibility to infection [16]. Overexpression of T cell receptor (TCR)-related transmembrane junction 1 (TRA1) inhibits the growth and migration of lung adenocarcinoma cells, thereby delaying cancer progression [17]. We have concluded from bioinformatic analyses that CDs are not a major source of cancer.…”

Section: Discussionmentioning

confidence: 99%

Prognosis of patients with septic shock predicted by bioinformatics analysis based on gene expression profiling

Yang

Chen

Guo

et al. 2023

Preprint

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Objective: The aim of this study was to perform bioinformatic analysis of gene expression profiles in septic shock in order to explore potential characteristic genes in patients with septic shock and thus to predict prognosis of septic shock. Methods: Peripheral blood specimens from patients with septic shock (n=11) and normal human volunteers (n=10) were sequenced for RNA, and the R-language based integrated differential expression and pathway analysis (iDEP) (http://bioinformatics.sdstate.edu/idep/) web tool was used to perform gene high and low expression groups Screening analysis ultimately yielded differentially expressed genes (DEGs, log2FC ≥ 4, q-value ≤ 0.01). DEGs were analysed by gene ontology enrichment and screened for potential core genes using protein-protein interactions (PPI). In addition, survival data in GSE95233 were used to observe the correlation between core genes and prognosis. Results: The analysis resulted in the screening of four differential genes that are indicative of the diagnosis, differential diagnosis and prognosis of septic shock disease, namely: CD6, CD247, LCK and CD3E. Conclusion: This study identified five signalling pathways and four core genes through bioinformatic analysis of gene expression profiles that may represent molecular mechanisms for the onset, progression and risk prediction of septic shock.

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Section: Discussionmentioning

confidence: 99%

Prognosis of patients with septic shock predicted by bioinformatics analysis based on gene expression profiling

Yang

Chen

Guo

et al. 2023

Preprint

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“…IL-4 is capable of triggering cytotoxic responses in IL-17-producing CD8+ T cells (Tc17 cells), with IL-4/AKT signaling inducing TRAT1 upregulation and TCR stabilization in these Tc17 cells, thus enhancing their cytotoxic activity ( 29 ). TRAT1 expression is closely related to immune activity and the degree of infiltration by a range of immune cell populations including CD8+, cytotoxic, Th1, Th17, and Treg cells ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics and Raman spectroscopy-based identification of key pathways and genes enabling differentiation between acute myeloid leukemia and T cell acute lymphoblastic leukemia

et al. 2023

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Acute myeloid leukemia (AML) and T cell acute lymphoblastic leukemia (T-ALL) are two of the most prevalent hematological malignancies diagnosed among adult leukemia patients, with both being difficult to treat and associated with high rates of recurrence and mortality. In the present study, bioinformatics approaches were used to analyze both of these types of leukemia in an effort to identify characteristic gene expression patterns that were subsequently validated via Raman spectroscopy. For these analyses, four Gene Expression Omnibus datasets (GSE13204, GSE51082, GSE89565, and GSE131184) pertaining to acute leukemia were downloaded, and differentially expressed genes (DEGs) were then identified through comparisons of AML and T-ALL patient samples using the R Bioconductor package. Shared DEGs were then subjected to Gene Ontology (GO) enrichment analyses and were used to establish a protein-protein interaction (PPI) network analysis. In total, 43 and 129 upregulated and downregulated DEGs were respectively identified. Enrichment analyses indicated that these DEGs were closely tied to immune function, collagen synthesis and decomposition, inflammation, the synthesis and decomposition of lipopolysaccharide, and antigen presentation. PPI network module clustering analyses further led to the identification of the top 10 significantly upregulated and downregulated genes associated with disease incidence. These key genes were then validated in patient samples via Raman spectroscopy, ultimately confirming the value of these genes as tools that may aid the differential diagnosis and treatment of AML and T-ALL. Overall, these results thus highlight a range of novel pathways and genes that are linked to the incidence and progression of AML and T-ALL, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of these devastating malignancies.

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“…In 6 well plate, the clean tip of pipette is used to create “wound” when cells form confluent monolayer, and cells cultured in RPMI-1640 were supplemented with 0% fetal bovine serum. Transfected cells were used to capture the image of wound edge with optical microscope at 0 h. After 24 h of incubation, the cell images of the same area were captured for measurement, and the wound healing rate was calculated [ 19 ]. Transfected cells were investigated for transwell migration.…”

Section: Methodsmentioning

confidence: 99%

“…According to the manufacturer's agreement of transwell System, fix the transwell chamber with 5% paraformaldehyde for 15 min, and then dye it with 0.1% crystal violet for 10 min. Transmembrane cells were calculated under inverted fluorescence microscope [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

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A novel risk model of three gefitinib-related genes FBP1, SBK1 and AURKA is related to the immune microenvironment and is predicting prognosis of lung adenocarcinoma patients

Guo,

Li,

Jiang

et al. 2023

Aging

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Purpose: Gefitinib, an anticancer drug, has been reported to potentially improve the prognosis of patients with lung adenocarcinoma (LUAD). This study aims to investigate the roles and mechanisms of Gefitinib. Methods: The effects of Gefitinib on the growth and migration of LUAD cells were assessed using various methods, including CCK-8, flow cytometry, wound healing, and Transwell assays. To analyze the function and mechanisms of the differentially expressed Gefitinib target genes (GTGs), data from the TCGA database were utilized. Kaplan-Meier survival and ROC analysis identified prognostic-related GTGs and constructed a prognostic nomogram in LUAD. Consensus clustering, COX analysis and survival analysis evaluated the relationship between GTGs and the prognosis of LUAD patients. The mechanisms of the risk model involved LUAD progression, and the relationship between the risk model and immune microenvironment were investigated. Results: Gefitinib could inhibit proliferation, migration and invasion and promote cell apoptosis. 84 DEGTGs were involved in RAS, MAPK, ERBB pathways. The DEGTGs (FBP1, SBK1, and AURKA) were the independent risk factors for dismal prognosis of LUAD patients and were used to establish risk model and nomogram. Gefitinib could promote the expression of FBP1 and inhibit the expression of SBK1 and AURKA. High-risk LUAD patients had the dismal prognosis, and the high-risk score group was significantly associated with the immune microenvironment. Conclusion: FBP1, SBK1, and AURKA are prognostic risk factors, and the risk model and nomogram of FBP1, SBK1 and AURKA are associated with dismal prognosis and immune cell infiltration, and have huge prospects for application in evaluating the prognosis in LUAD.

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TRAT1 overexpression delays cancer progression and is associated with immune infiltration in lung adenocarcinoma

Cited by 7 publications

References 34 publications

Prognosis of patients with septic shock predicted by bioinformatics analysis based on gene expression profiling

Prognosis of patients with septic shock predicted by bioinformatics analysis based on gene expression profiling

Bioinformatics and Raman spectroscopy-based identification of key pathways and genes enabling differentiation between acute myeloid leukemia and T cell acute lymphoblastic leukemia

A novel risk model of three gefitinib-related genes FBP1, SBK1 and AURKA is related to the immune microenvironment and is predicting prognosis of lung adenocarcinoma patients

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