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Genome-scale analysis identifies SERPINE1 and SPARC as diagnostic and prognostic biomarkers in gastric cancer.

ABSTRACT: Background:Gastric cancer (GC) is one of the most common types of malignancy and is associated with high morbidity and mortality rates around the world. With poor clinical outcomes, potential biomarkers for diagnosis and prognosis are important to investigate. Objective:The aim of this study is to investigate the gene expression module of GC and to identify potential diagnostic and prognostic biomarkers. Method:Microarray data (GSE13911, GSE29272, GSE54129, and GSE79973), including 293 stomach tumor tissues and 196 normal tissues, were analyzed to identify differentially expressed genes (DEGs). DEGs were identified in four profiles by intersecting four overlapping subsets, including 90 downregulated and 45 upregulated DEGs in common. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses have been showed that extracellular matrix was the most enriched signal pathway. Furthermore, hub genes were analyzed by protein-protein interaction network and clinical outcomes were assessed by Kaplan-Meier survival analysis. Two independent datasets were used to validate the differential expression of two hub genes: Serpin Family E Member 1 (SERPINE1) and Secreted Protein Acidic and Cysteine Rich (SPARC). Results:Validation of independent datasets indicated that SERPINE1 and SPARC expression were drastically increased in gastric tumor tissues and associated with poor outcomes in GC patients. The expression of SERPINE1 was related to race (Asian and White) (P< 0.05). Conclusion:SERPINE1 and SPARC were significantly upregulated in gastric tissues and associated with poor outcomes. The investigations of SERPINE1 and SPARC may promote their predictive and prognostic value in GC.

SUBMITTER: Liao P

PROVIDER: S-EPMC6199229 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Genome-scale analysis identifies SERPINE1 and SPARC as diagnostic and prognostic biomarkers in gastric cancer.

Liao Ping P Li Wei W Liu Ruizheng R Teer Jamie K JK Xu Biaobo B Zhang Wei W Li Xi X Mcleod Howard L HL He Yijing Y

OncoTargets and therapy 20181015

<h4>Background</h4>Gastric cancer (GC) is one of the most common types of malignancy and is associated with high morbidity and mortality rates around the world. With poor clinical outcomes, potential biomarkers for diagnosis and prognosis are important to investigate.<h4>Objective</h4>The aim of this study is to investigate the gene expression module of GC and to identify potential diagnostic and prognostic biomarkers.<h4>Method</h4>Microarray data (GSE13911, GSE29272, GSE54129, and GSE79973), i ...[more]

PMID: 30410354

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Project description:Gastric cancer (GC) is one of the leading causes of malignancy‑associated mortality worldwide. However, the underlying molecular mechanisms of GC are unclear and the prognosis of GC is poor. Therefore, it is important and urgent to explore the underlying mechanisms and screen for novel diagnostic and prognostic biomarkers, as well as therapeutic targets. In the current study, scale‑free gene co‑expression networks were constructed using weighted gene co‑expression network analysis, the potential associations between gene sets and clinical features were investigated, and the hub genes were identified. The gene expression profiles of GSE38749 were downloaded from the Gene Expression Omnibus database. RNA‑seq and clinical data for GC from The Cancer Genome Atlas were utilized for verification. Furthermore, the expression of candidate biomarkers in gastric tissues was investigated. Survival analysis was performed using Kaplan‑Meier and log‑rank test. The predictive role of candidate biomarkers in GC was evaluated using a receiver operator characteristic (ROC) curve. Gene Ontology, gene set enrichment analysis and gene set variation analysis methods were used to interpret the function of candidate biomarkers in GC. A total of 29 modules were identified via the average linkage hierarchical clustering. A significant module consisting of 48 genes associated with clinical traits was found; three genes with high connectivity in the clinical significant module were identified as hub genes. Among them, SLC5A6 and microfibril‑associated protein 2 (MFAP2) were negatively associated with the overall survival, and their expression was elevated in GC compared with non‑tumor tissues. Additionally, ROC curves indicated that SLC5A6 and MFAP2 showed a good diagnostic power in discriminating cancerous from normal tissues. SLC5A6 and MFAP2 were identified as novel diagnostic and prognostic biomarkers in GC patients; both of these genes were first reported here in connection with GC and deserved further research.

Project description:BackgroundGastric cancer (GC) is one of the most common cancer worldwide. It is essential to identify non-invasive diagnostic and prognostic biomarkers of GC. The aim of the present study was to screen candidate biomarkers associated with the pathogenesis and prognosis of GC by a novel strategy.MethodsThe expression level of gene higher in cancer than in adjacent non-cancer tissue was defined as "positive", and the top 5% genes with "positive rate" were filtered out as candidate diagnostic biomarkers in three Gene Expression Omnibus (GEO) datasets. Further, a prognostic risk model was constructed by multivariate Cox regression analysis in GEO dataset and validated in The Cancer Genome Atlas (TCGA). The expression level of candidate biomarkers was determined in serum and serum-derived exosomes of GC patients. Moreover, the effect of biomarkers in exosomes on migration of GC cells was analyzed by transwell assay.ResultsTen candidate biomarkers (AGT, SERPINH1, WNT2, LIPG, PLAU, COL1A1, MMP7, MXRA5, CXCL1 and COL11A1) were identified with efficient diagnostic value in GC. A prognostic gene signature consisted of AGT, SERPINH1 and MMP7 was constructed and showed a good performance in predicting overall survivals in TCGA. Consistently, serum levels of the three biomarkers also showed high sensitivity and specificity in distinguishing GC patients from controls. In addition, the expression level of the three biomarkers were associated with malignant degree and decreased after surgery in GC patients. Moreover, the expression level of AGT and MMP7 in exosomes correlated positively with serum level. The exosomes derived from serum of GC patients can promote migration of SGC-7901 cells. After neutralized the expression level of three proteins in exosomes with antibodies, the migration of GC cells was obviously suppressed.ConclusionsOur findings provided a novel strategy to identify diagnostic biomarkers based on public datasets, and suggested that the three-gene signature was a candidate diagnostic and prognostic biomarker for patients with GC.

Project description:PurposeThe serine protease inhibitor clade E member 1 (SERPINE1) has been studied as a potential biomarker in a variety of cancers, but poorly studied in gastric cancer (GC). The purpose of this study was to explore the prognostic value of SERPINE1 in GC and primarily analyze its functions.MethodsWe analyzed the the prognostic value of SERPINE1 and studied the relationship with clinicopathologic biomarkers in gastric cancer. The expression of SERPINE1 was analyzed by GEO and TCGA databases. Moreover, we validated the results by immunohistochemistry. Next, the correlation analysis between SERPINE1 and the cuproptosis-related genes was analyzed by the "Spearman" method. CIBERSORT and TIMER algorithms were used to analyze the correlation of SERPINE1 with immune infiltration. Furthermore, GO and KEGG gene enrichment analyses were used to study the functions and pathways that SERPINE1 might be involved in. Then, drug sensitivity analysis was performed using CellMiner database. Finally, a cuproptosis-immune-related prognostic model was constructed using genes related to immune and cuproptosis, and verified against external datasets.ResultsSERPINE1 was up-regulated in gastric cancer tissues, which tends toward poor prognosis. Using immunohistochemistry experiment, the expression and prognostic value of SERPINE1 were verified. Then, we found that SERPINE1 was negatively correlated with cuproptosis-related genes FDX1, LIAS, LIPT1, and PDHA1. On the contrary, SERPINE1 was positively correlated with APOE. This indicates the effect of SERPINE1 on the cuproptosis process. Furthermore, by conducting immune-related analyses, it was revealed that SERPINE1 may promote the inhibitory immune microenvironment. The infiltration level of resting NK cells, neutrophils, activated mast cells, and macrophages M2 was positively correlated with SERPINE1. However, B cell memory and plasma cells were negatively correlated with SERPINE1. Functional analysis showed that SERPINE1 was closely related to angiogenesis, apoptosis, and ECM degradation. The KEGG pathway analysis showed that SERPINE1 may be associated with P53, Pi3k/Akt, TGF-β, and other signaling pathways. Drug sensitivity analysis showed that SERPINE1 could be also seen as a potential treatment target. The risk model based on SERPINE1 co-expression genes could better predict the survival of GC patients than SERPINE1 alone. We also verified the prognostic value of the risk score by GEO external datasets.ConclusionSERPINE1 is highly expressed in gastric cancer and related to poor prognosis. SERPINE1 may regulate cuproptosis and the immune microenvironment by a series of pathways. Therefore, SERPINE1 as a prognostic biomarker and potential therapeutic target deserves further study.

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Genome-scale analysis identifies SERPINE1 and SPARC as diagnostic and prognostic biomarkers in gastric cancer.

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Genome-scale analysis identifies SERPINE1 and SPARC as diagnostic and prognostic biomarkers in gastric cancer.

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