Differentially expressed genes ASPN, COL1A1, FN1, VCAN and MUC5AC are potential prognostic biomarkers for gastric cancer

Jiang, Kaiyuan; Li, Hongmei; Xie, Dongyi; Xiao, Qiang

doi:10.3892/ol.2019.9952

Cited by 56 publications

(66 citation statements)

References 56 publications

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“…A strong correlation of elevated ECM expression and poor prognosis was also found in luminal BCa (Riaz et al, 2012). In pediatric osteosarcoma, a signature of high ECM turnover was found to be highly prognostic for chemoresistance (Mintz et al, 2005) and, in gastric cancer expression of a signature containing Col1a1, Fn1, and Muc5a, was highly predictive of overall and diseasefree survival (Jiang et al, 2019). Besides indicators for immune suppression, high expression of collagens Col3a1, Col4a1, and Col5a2 is correlated with poor prognosis in glioblastoma .…”

Section: Ecm Profiles As Prognostic Markersmentioning

confidence: 77%

Extracellular Matrix in the Tumor Microenvironment and Its Impact on Cancer Therapy

Henke

Nandigama

Ergün

2020

Front. Mol. Biosci.

725

613

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Solid tumors are complex organ-like structures that consist not only of tumor cells but also of vasculature, extracellular matrix (ECM), stromal, and immune cells. Often, this tumor microenvironment (TME) comprises the larger part of the overall tumor mass. Like the other components of the TME, the ECM in solid tumors differs significantly from that in normal organs. Intratumoral signaling, transport mechanisms, metabolisms, oxygenation, and immunogenicity are strongly affected if not controlled by the ECM. Exerting this regulatory control, the ECM does not only influence malignancy and growth of the tumor but also its response toward therapy. Understanding the particularities of the ECM in solid tumor is necessary to develop approaches to interfere with its negative effect. In this review, we will also highlight the current understanding of the physical, cellular, and molecular mechanisms by which the pathological tumor ECM affects the efficiency of radio-, chemo-, and immunotherapy. Finally, we will discuss the various strategies to target and modify the tumor ECM and how they could be utilized to improve response to therapy.

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Section: Ecm Profiles As Prognostic Markersmentioning

confidence: 77%

Extracellular Matrix in the Tumor Microenvironment and Its Impact on Cancer Therapy

Henke

Nandigama

Ergün

2020

Front. Mol. Biosci.

725

613

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“…MUC5AC is a wellknown marker of gastric differentiation, which is considered to be a very important prognostic indicator of GC and is often used in clinical evaluation [27][28][29]. It has been found that the expression of MUC5AC in stomach decreases with the development of intestinal metaplasia, and the expression of MUC5AC is related to tumor stage: the expression level of MUC5AC in advanced GC is lower than that in early GC [30]. This is consistent with our analysis results that the expression of MUC5AC in normal tissues is higher than that in GC tissues.…”

Section: Discussionmentioning

confidence: 99%

Identification of novel biomarkers, MUC5AC, MUC1, KRT7, GAPDH, CD44 for gastric cancer

Yang¹

2020

Med Oncol

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Gastric cancer (GC) is one of the most common malignant tumors in the world, and it is also the third largest cause of cancerrelated death in the world. As far as we know, no biomarker has been widely accepted for early diagnosis and prognosis prediction of gastric cancer. The purpose of this study is to find potential biomarkers to predict the prognosis of GC. The gene expression profiles of GSE2685 were downloaded from GEO database. Morpheus was used to calculate the differentially expressed genes (DEGs) between primary advanced gastric cancer tissues and noncancerous gastric tissues. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein-protein interaction (PPI) network of DEGs was constructed. Kaplan-Meier Plotter was used to determine the overall survival (OS) outcomes of UC5AC, MUC1, KRT7, GAPDH, CD44, and GEPIA was used to determine the Pearson correlation analysis. In total, 710 DEGs were identified in GC, including 396 upregulated genes and 314 downregulated genes. GO enrichment revealed that they were mainly enriched in binding, catalytic activity, cellular process and cell. KEGG pathway revealed that they were mainly enriched in metabolic pathways, pathways in cancer and PI3K-Akt signaling pathway. MUC5AC, MUC1, KRT7, GAPDH, CD44 were identified from the PPI network. MUC5AC, MUC1, KRT7, GAPDH, CD44 were demonstrated to have prognostic value for patients with GC. MUC5AC, MUC1 exhibited low expression levels in GC tissues, KRT7, GAPDH, CD44 presented high expression levels in GC tissues. In particular, KRT7 is hardly expressed in normal gastric tissues. MUC5AC and MUC1 were negatively correlated with GAPDH, CD44, respectively; and GAPDH was positively correlated with CD44 and KRT7, respectively. Moreover. MUC5AC, MUC1, KRT7, GAPDH, and CD44 are not only related to GC but also to apoptosis pathway. Results from the present study suggested that MUC5AC, MUC1, KRT7, GAPDH, CD44 may represent novel prognostic biomarkers for GC.

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“…Previous research has highlighted a number of genes associated with GC. [24][25][26] The specific molecular mechanisms governing this disease, however, are complex, and as such further data mining efforts are needed to identify relevant candidate biomarkers for GC diagnosis.…”

Section: Discussionmentioning

confidence: 99%

<p>ICAM1 Regulates the Development of Gastric Cancer and May Be a Potential Biomarker for the Early Diagnosis and Prognosis of Gastric Cancer</p>

Chen

et al. 2020

CMAR

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Background: Gastric cancer (GC) is among the most common forms of cancer affecting the digestive system. This study sought to identify hub genes regulating early GC (EGC) in order to explore their potential for early diagnosis and prognosis of patients. Methods: We utilized a publically available dataset from the Gene Expression Omnibus database (GSE55696). Differences between EGC and LGIN with respect to gene expression were compared using the limma software. Identified differentially expressed genes (DEGs) were subjected to gene ontology (GO) and pathway enrichment analyses with the DAVID application, and the STRING website and Cytoscape software were used to construct a protein-protein interaction (PPI) network incorporating these DEGs. This network was in turn used to identify hub genes among selected DEGs, which were analyzed with the Kaplan-Meier Plotter database. In addition, Western blotting, qRT-PCR, immunohistochemistry, and UALCAN were all employed to validate the relationship between the expression of these genes and GC patient prognosis. Results: A total of 482 DEGs were identified, with GO analyses indicating an increase in the expression of genes linked with the development of cancer. Pathway analyses also indicated that these genes play a role in certain cancer-related pathways. The PPI network highlighted four potential hub genes, of which only ICAM1 was linked to a poor GC patient prognosis. This link between ICAM1 and GC patient outcomes was confirmed via UALCAN, Western blotting, immunohistochemistry, and qRT-PCR. Conclusion: ICAM1 may therefore modulate tumor progression in GC, thus potentially representing a valuable prognostic and diagnostic biomarker of EGC.

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Differentially expressed genes ASPN, COL1A1, FN1, VCAN and MUC5AC are potential prognostic biomarkers for gastric cancer

Cited by 56 publications

References 56 publications

Extracellular Matrix in the Tumor Microenvironment and Its Impact on Cancer Therapy

Extracellular Matrix in the Tumor Microenvironment and Its Impact on Cancer Therapy

Identification of novel biomarkers, MUC5AC, MUC1, KRT7, GAPDH, CD44 for gastric cancer

<p>ICAM1 Regulates the Development of Gastric Cancer and May Be a Potential Biomarker for the Early Diagnosis and Prognosis of Gastric Cancer</p>

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