High expression of TREM2 promotes EMT via the PI3K/AKT pathway in gastric cancer: bioinformatics analysis and experimental verification

Li, Chunmei; Hou, Xiaoming; Yuan, Shengtao; Zhang, Yigan; Wang, Yuan; Liu, Xiaoguang; Li, Juan; Wang, Yuping; Guan, Qun; Zhou, Yongning

doi:10.7150/jca.55077

Cited by 23 publications

(15 citation statements)

References 59 publications

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“…Domain and EC domain proteins have an effect on the activity of membrane-type MMP (38), and serum MMP-9 levels in patients with lung cancer with lymph node metastasis were significantly higher than in those without lymph node metastasis. SPARC may have a role in the treatment of NSCLC, which is mostly treated with chemotherapy regimens such as paclitaxel, and studies have indicated that high SPARC gene expression has an important impact on the drugs used to treat NSCLC (39)(40)(41). The experimental results of the present study also suggested that the protein expression level of SPARC was higher in NSCLC than in normal lung tissues and that patients in the high SPARC expression group had unfavorable prognosis, which is consistent with the trends indicated by previous studies (24,42).…”

Section: Discussionmentioning

confidence: 99%

Prognostic significance of SPARC expression in non‑small cell lung cancer: A meta‑analysis and bioinformatics analysis

Shi

Zhang

et al. 2022

Oncol Lett

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The aim of the present study was to elucidate the significance of secreted protein acidic and cysteine rich (SPARC) expression in non-small cell lung cancer (NSCLC) in terms of clinicopathology, immune-cell infiltration and survival prognosis. A meta-analysis and bioinformatics analysis were performed using studies retrieved with PubMed, Web of Science, Wanfang Data and the Chinese National Knowledge Infrastructure databases. The meta-analysis suggested that, compared with normal tissues, SPARC expression was elevated in NSCLC tissues. The expression of SPARC was not significantly associated with TNM stage and lymph-node metastasis, and was positively associated with patient gender. Regarding the differential expression of SPARC and the relationship between expression levels and survival, the Oncomine database was consulted and Kaplan-Meier curves were drawn. It was indicated that SPARC mRNA expression levels were higher in NSCLC tissues than in normal tissues. Low expression of SPARC mRNA was negatively associated with overall survival, first progression survival and post-progression survival of patients. Further exploration of the relationship between SPARC expression and survival by univariate analysis indicated that TNM stage, lymph node metastasis, distant metastasis and depth of infiltration of lung cancer were negatively associated with patient prognosis. Cox multifactorial analysis suggested that SPARC expression levels and TNM stage were risk factors significantly affecting the prognosis of patients with NSCLC. Analysis with the GEPIA and UALCAN databases further indicated that the mRNA expression level of SPARC in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was higher than that in normal lung tissue, and the SPARC expression levels were affected by factors such as the TNM stage of lung cancer.A lower the level of SPARC mRNA expression was associated with a better relative survival prognosis of patients. In the Human Protein Atlas database, the expression level of SPARC protein was higher in LUAD and LUSC than in normal lung tissue. In the Timer database, the expression level of SPARC was closely linked to immune cells related to the occurrence of lung cancer, and the degree of immune-cell infiltration and SPARC protein expression were closely related to the prognosis of patients with lung cancer. Immune cells were indicated to exhibit significant inhibition of DNA proliferation mutation mechanisms in lung cancer (P<0.05). In summary, SPARC expression may be used as a valuable indicator of prognosis in patients with NSCLC, which may provide new approaches for preventative treatment.

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Section: Discussionmentioning

confidence: 99%

Prognostic significance of SPARC expression in non‑small cell lung cancer: A meta‑analysis and bioinformatics analysis

Shi

Zhang

et al. 2022

Oncol Lett

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“…The TREM2 as a tumor driver is usually overexpressed in numerous carcinoma including gastric, hepatic, and ovarian carcinoma. The overexpression of TREM2 always promotes the invasion, proliferation, and migration of tumor cells [18][19][20]. Interestingly, experimental models of hepatic carcinoma in the context of liver fibrosis yielded the opposing effect, with TREM2 promoting fibrosis but inhibiting tumorigenesis [19].…”

Section: Introductionmentioning

confidence: 99%

TREM2 as a Potential Immune-Related Biomarker of Prognosis in Patients with Skin Cutaneous Melanoma Microenvironment

et al. 2023

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Background. The skin cutaneous melanoma (SKCM) is a devastating form of skin cancer triggered by genetic and environmental factors, and the incidence of SKCM has rapidly increased in recent years. Immune infiltration of the tumor microenvironment is positively associated with overall survival in many tumors. Triggering receptor expressed on myeloid cells 2 (TREM2) is a transmembrane receptor of the immunoglobulin superfamily and a crucial signaling hub for multiple pathological pathways that mediate immunity. Although numerous evidences suggest a crucial role for TREM2 in tumorigenesis of some tumors, no systematic SKCM analysis of TREM2 is available. Mehods. The relationship between TREM2 expression and diagnostic and prognostic value of SKCM patients via using The Cancer Genome Atlas (TCGA) data. The expression level of TREM2 and clinical characteristic correlation in SKCM patients were assessed by the Wilcoxon rank sum test. The cox regression methods, Kaplan-Meier (KM), and log-rank test were used to assess the impact of TREM2 expression on the overall survival (OS). Furthermore, the Gene Set Enrichment Analysis (GSEA) and TIMER were performed to evaluate the enrichment pathways and potential functions and quantify the immune cell infiltration level for TREM2 expression. Results. The TREM2 in SKCM sample expression levels was significantly higher than in normal tissues. Moreover, this expression level of TREM2 was also associated with the BMI of SKCM patients. KM overall survival analysis and OS curve displayed that a high-level TREM2 expression was significantly correlated with a better SKCM prognosis of patients as compared with a low level of TREM2 expression. The GSEA analysis also revealed that TREM2 was associated with immune functions, such as neutrophil activation. Conclusion. TREM2 played a crucial role in SKCM, which might be a prognostic biomarker and correlated with immune infifiltrates in SKCM patients.

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“…WGCNA is a widely used target therapy analysis tool, which clusters related genes, according to some clinical characteristics of research subjects. There have been many studies on gastric cancer tumor markers in recent years, and most of the WGCNA clusterings are based on differentially expressed genes (DEGs) in the research dataset ( Li et al, 2021 ; Xiang et al, 2021 ; Zhang et al, 2022 ). In contrast, this study performed the WGCNA analysis on all gastric cancer–related genes in one dataset and fused them with selected features using a supervised learning method, i.e., Fisher score algorithm on five combined datasets, preserving the diversity of the gastric cancer hub genes.…”

Section: Discussionmentioning

confidence: 99%

ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning

et al. 2022

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Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.

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High expression of TREM2 promotes EMT via the PI3K/AKT pathway in gastric cancer: bioinformatics analysis and experimental verification

Cited by 23 publications

References 59 publications

Prognostic significance of SPARC expression in non‑small cell lung cancer: A meta‑analysis and bioinformatics analysis

Prognostic significance of SPARC expression in non‑small cell lung cancer: A meta‑analysis and bioinformatics analysis

TREM2 as a Potential Immune-Related Biomarker of Prognosis in Patients with Skin Cutaneous Melanoma Microenvironment

ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning

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