Dataset Information

Integrated profiling identifies SLC5A6 and MFAP2 as novel diagnostic and prognostic biomarkers in gastric cancer patients.

ABSTRACT: 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.

SUBMITTER: Sun T

PROVIDER: S-EPMC6959404 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrated profiling identifies SLC5A6 and MFAP2 as novel diagnostic and prognostic biomarkers in gastric cancer patients.

Sun Tao T Wang Danhua D Ping Ying Y Sang Yiwen Y Dai Yibei Y Wang Yiyun Y Liu Zhenping Z Duan Xiuzhi X Tao Zhihua Z Liu Weiwei W

International journal of oncology 20191216 2

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 ...[more]

PMID: 31894266

Similar Datasets

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:Neuroblastoma (NB) is the most common extracranial solid tumor during infancy, causing up to 10% of mortality in children. NB shows notable heterogeneity with regard to histology and clinical behavior, ranging from low-risk localized tumors (LR-NB) to high-risk disease (HR-NB), characterized by aggressive metastatic phenotype, resistance to treatment, and fatal relapse occurrence. Risk stratification demands high accuracy, as it will determine the therapeutic treatment. However, the current therapeutic stratification, based on clinical and molecular risk factors, does not allow to discriminate among patients with similar clinical-pathological parameters who receive the same treatment despite showing markedly different clinical courses. The need of novel diagnostic molecular tools in oncology has led to increasing interest in liquid biopsies as a source of biomarkers, as they provide a minimally invasive method. Body fluids are a source of exosomes, nanosized extracellular vesicles that can drive tumor growth and chemoresistance. We previously identified exosomal microRNAs (exo-miRNA) indicative of HR-NB patient sensitivity/resistance to chemotherapy. As exosomes from cancer patients carry proteins that reflect the surface and cytoplasm content of parental cancer cells, including immunosuppressive molecules known to be associated with cancer progression and/or response to therapy, we extended our study to the exosomal proteins (Exo-prot).The purpose of the study is to identify Exo-prot that (i) are specifically expressed in NB patients and (ii) are associated with tumor phenotype and disease stage, in order to improve risk stratification and refine diagnosis.We isolated exosomes from plasma specimens of 24 HR-NB patients and 24 LR-NB patients at diagnosis and of 24 age-matched healthy controls (CTRL). Exo-prot expression was measured by liquid chromatography-mass spectrometry. Missing value distribution and differential expression analysis (performed with two different imputation methods) were applied to identify the most relevant Exo-prot. ROC analysis assessed the diagnostic value of the identified Exo-prot. We demonstrated that NB patients have a different Exo-prot expression profile compared to CTRL. The deregulated Exo-prot in NB specimens act mainly in tumor-associated pathways and build a strictly connected network. Furthermore, HR-NB patients show a different Exo-miR expression profile compared to LR-NB subjects, with the modulation of molecules involved in cell migration, proliferation and metastasis. Importantly, we show that NCAM, NCL, LUM and VASP have a diagnostic value in discriminating NB patients from CTRL; while MYH9, FN1, CALR, AKAP12 and, with a lower performance, LTBP1 can differentiate HR-NB and LR-NB patients with high accuracy. We demonstrated that Exo-prot deregulated in NB vs CTRL subjects and in HR-NB vs LR-NB patients contribute to NB tumor development and to the aggressive metastatic NB phenotype, respectively. We identified Exo-prot with significant diagnostic value for NB patients and able to efficiently identify the HR-NB subset of patients, which can be employed to improve risk stratification. Our results highlight the applicability of Exo-prot evaluation for integrating NB diagnosis and risk assessment.

Project description:BackgroundMolecular characteristics are essential for the classification and grading of gliomas. However, diagnostic classification of midline glioma is still debatable and substantial molecular and clinical heterogeneity within each subgroup suggested that they should be further stratified. Here, we studied the mutation landscape of Chinese midline glioma patients in hope to provide new insights for glioma prognosis and treatment.MethodsTissue samples from 112 midline glioma patients underwent next-generation sequencing targeting 425 cancer-relevant genes. Gene mutations and copy number variations were investigated for their somatic interactions and prognostic effect using overall survival data. Pathway-based survival analysis was performed for ten canonical oncogenic pathways.ResultsWe identified several currently established diagnostic and prognostic biomarkers of glioma, including TP53 (33%), EGFR (26%), TERT (24%), PTEN (21%), PIK3CA (14%), ATRX (14%), BRAF (13%), and IDH1/2 (6%). Among all genetic aberrations with more than 5% occurrence rate, six mutations and three copy number gains were greatly associated with poor overall survival (univariate, P < 0.1). Of these, TERT mutations (hazard ratio [HR], 3.00; 95% confidence interval [CI], 1.37-6.61; P = 0.01) and PIK3CA mutations (HR, 2.04; 95% CI, 1.08-3.84; P = 0.02) remained significant in multivariate analyses. Additionally, we have also identified a novel MCL1 amplification (found in 31% patients) as a potential independent biomarker for glioma (multivariate HR, 2.78; 95% CI, 1.53-5.08; P < 0.001), which was seldom reported in public databases. Pathway analyses revealed significantly worse prognosis with abnormal PI3K (HR, 1.81; 95% CI, 1.12-2.95; P = 0.01) and cell cycle pathways (HR, 1.97; 95% CI, 1.15-3.37; P = 0.01), both of which stayed meaningful after multivariate adjustment.ConclusionsIn this study, we discovered shorter survival in midline glioma patients with PIK3CA and TERT mutations and with abnormal PI3K and cell cycle pathways. We also revealed a novel prognostic marker, MCL1 amplification that collectively provided new insights and opportunities in understanding and treating midline gliomas.

Dataset Information

Integrated profiling identifies SLC5A6 and MFAP2 as novel diagnostic and prognostic biomarkers in gastric cancer patients.

Publications

Integrated profiling identifies SLC5A6 and MFAP2 as novel diagnostic and prognostic biomarkers in gastric cancer patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets