Dataset Information

Screening of Potential Biomarkers with Diagnostic Value in Gastric Cancer Using Label-free Global Proteome Analysis

ABSTRACT: Gastric cancer (GC) is one of the most common malignant tumors worldwide. Many patients diagnosed with GC are already at a late stage. Despite the decrease in mortality rates recently, the prognosis of this cancer is poor. Therefore, it is necessary to explore novel biomarkers with early diagnostic value with GC. In this study, we present a large-scale proteomic analysis of 30 GC tissues and 30 matched normal tissues using label-free global proteome profiling. The results allowed identifying 537 differentially expressed proteins, including 280 upregulated and 257 downregulated proteins. The ingenuity pathway analysis (IPA) results indicated that the sirtuin signaling pathway was the most activated whereas the oxidative phosphorylation was the most inhibited pathway. Moreover, the most activated molecular function was cell movement, including tissue invasion by tumor cell lines. Based on IPA results, 15 hub proteins were screened. Using the receiver operating characteristic curve, most of these hub proteins had a high diagnostic power in distinguishing tumors and normal controls. A four-protein (ATP5B-ATP5O-NDUFB4-NDUFB8) diagnostic signature was built using a random forest model. The area under curve (AUC) of this model was 0.864 in the validation set, suggesting a high diagnostic power. In the testing set of plasma enzyme-linked immune sorbent assay, the results achieved an AUC of 0.778 and accuracy of 71.8% to distinguish GC tissues from healthy controls. In conclusion, this study identified potential biomarkers and help to understand the pathogenesis of GC and provide novel and specific therapeutic targets for this cancer.

ORGANISM(S): Homo Sapiens

SUBMITTER: Zhenning Wang

PROVIDER: PXD014404 | iProX | Wed Jun 26 00:00:00 BST 2019

REPOSITORIES: iProX

ACCESS DATA

Publications

Screening of Potential Biomarkers for Gastric Cancer with Diagnostic Value Using Label-free Global Proteome Analysis.

Song Yongxi Y Wang Jun J Sun Jingxu J Chen Xiaowan X Shi Jinxin J Wu Zhonghua Z Yu Dehao D Zhang Fei F Wang Zhenning Z

Genomics, proteomics & bioinformatics 20201201 6

Gastric cancer (GC) is known as a top malignant type of tumors worldwide. Despite the recent decrease in mortality rates, the prognosis remains poor. Therefore, it is necessary to find novel biomarkers with early diagnostic value for GC. In this study, we present a large-scale proteomic analysis of 30 GC tissues and 30 matched healthy tissues using label-free global proteome profiling. Our results identified 537 differentially expressed proteins, including 280 upregulated and 257 downregulated p ...[more]

PMID: 33607292

Similar Datasets

Project description:Background: The therapeutic regimen of immune-mediated necrotizing myopathy (IMNM), a category of immune-related myopathies featuring progressive proximal muscle weakness and extremely high level of serum creatine kinase, remains a formidable barrier for clinicians. The proposed study intended to investigate new diagnostic biomarkers based on high-throughput sequencing data of IMNM and generate novel IMNM management strategies by bioinformatics technology. Methods: Eighteen IMNM patients were synthesized in a local sequencing cohort (high-throughput sequencing data) with Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis to pinpoint robust IMNM-related differentially expressed genes (DEGs). The definitive feature genes were categorized by employing the protein-protein interaction (PPI) network, Least Absolute Shrinkage Selection Operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE), and their sample discriminatory utility was appraised by obtaining the area under the curve (AUC) of the receiver operating curve (ROC) curve. The expression difference of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR). Enrichment analysis of predefined gene sets disclosed the hidden functions of diagnostic DEGs. The 28 immune cell abundance patterns in the IMNM cohort were measured using single-sample gene set enrichment analysis (ssGSEA). Results: We recognized 193 genes that were aberrantly upregulated in the IMNM population and were firmly affiliated with immune-inflammatory responses, regulation of skeletal and cardiac muscle contraction and lipidprotein metabolism. LTK, MYBPH, and MYL4 were authenticated as diagnostic markers for IMNM (all AUC = 1.00), and functional enrichment analysis highlighted that these genes were notably integrated with immune-inflammatory response, autophagy-lysosome pathway and myofiber differentiation and regeneration. Ultimately, ssGSEA revealed a pronounced immune cell infiltrative microenvironment in the IMNM population. The results revealed the most significant relationship among CD4 T cells, CD8 T cells, macrophages, natural killer cells, T helper cells, Th17 cells and dendritic cells. Conclusion: LTK, MYBPH, and MYL4 might be served as diagnostic molecules for IMNM and possibly yield innovative insights into the mechanisms of progression and therapeutic options for IMNM in the future.

Project description:Background: Gastric cancer (GC) remains the third leading cause of cancer death worldwide due to the absence of sensitive and specific biomarkers for its early detection. 5-hydroxymethylcytosine (5hmC)-enriched gene profiles and regions show tissue-specific and tumor specific, and cell-free DNA (cfDNA) 5hmC modification feature sequencing provides a robust tool for gastric cancer detection.Methods: A matched case‒control study design with 50 gastric cancer patients and 50 controls was performed to sequence the 5hmC modification of cfDNA. Significantly differential 5hmC modification genes were identified to construct a gastric cancer diagnostic model. The data set from Gene Expression Omnibus consisted of 61 gastric cancer patients, 90 healthy individuals and 22 benign gastric disease controls was used as an external testing set to test the robustness of the diagnostic model. Results: The vast majority of 5hmC peaks were distributed in the gene body, accounting for more than 90% of the total 5hmC peaks in both the GC and control groups. The diagnostic model was developed based on five different 5hmC modification genes, FBXL7, PDE3A, TPO, SNTG2 and SXBP5. The model could clearly distinguish gastric cancer patients from controls in the training (AUC=0.95, sensitivity=88.6%, specificity=94.3%), validation (AUC=0.87, sensitivity=73.3%, specificity=93.3%) and testing (AUC=0.90, sensitivity=81.9%, specificity=90.2%) sets. The predicted risk scores of the controls were significantly lower than those of GC patients in our own data (P<0.001) or GEO external testing data (P<0.001), but no significant difference was observed between different TNM stage groups (P=0.09 and 0.66). Furthermore, the risk scores of healthy and benign gastric disease controls in the testing set were also not different (P=0.10). Conclusions: The characteristics of 5hmC in cfDNA are specific to GC patients, and the diagnostic model constructed by the 5hmC features of five genes could effectively identify GC patients.

Dataset Information

Screening of Potential Biomarkers with Diagnostic Value in Gastric Cancer Using Label-free Global Proteome Analysis

Publications

Screening of Potential Biomarkers for Gastric Cancer with Diagnostic Value Using Label-free Global Proteome Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets