Mining cancer biology through bioinformatic analysis of proteomic data

Manfredi, Marcello; Brandi, Jessica; Carlo, Claudia Di; Vanella, Virginia Vita; Barberis, Elettra; Marengo, Emílio; Patrone, Mauro; Cecconi, Daniela

doi:10.1080/14789450.2019.1654862

Cited by 27 publications

(24 citation statements)

References 87 publications

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“…Despite the recent advances in diagnostics and therapeutics, the survival of patients with GC shows no significant improvement. GC is a multifaceted disorder and involves multiple factors, complicated biological processes and unpredictable outcomes 2 . It is unreasonable that a single molecular marker could be accurately used for the prediction, prevention and personalized medicine (PPPM) practice in GC.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, numerous molecular alterations at various levels, including DNA (genome), RNA (transcriptome), proteins (proteome) and metabolites (metabolome), have been demonstrated to participate in the progression of GC and are intertwined with diverse pathway networks. Recent studies have revealed that high‐throughput proteomics renders a promising approach to understand the progression of complex tumorigenesis and to identify potent cancer biomarkers 2 . Proteomics is considered as a state‐of‐the‐art, large‐scale and systematic analytic approach, enabling the identification and quantification of proteins in specific biological specimens 3 .…”

Section: Introductionmentioning

confidence: 99%

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Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer

Zhou

Zhang

et al. 2020

J Cellular Molecular Medi

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The aim of the present study was to explore the underlying mechanisms involved in gastric cancer (GC) formation using data‐independent acquisition (DIA) quantitative proteomics analysis. We identified the differences in protein expression and related functions involved in biological metabolic processes in GC. Totally, 745 differentially expressed proteins (DEPs) were found in GC tissues vs. gastric normal tissues. Despite enormous complexity in the details of the underlying regulatory network, we find that clusters of proteins from the DEPs were mainly involved in 38 pathways. All of the identified DEPs involved in oxidative phosphorylation were down‐regulated. Moreover, GC possesses significantly altered biological metabolic processes, such as NADH dehydrogenase complex assembly and tricarboxylic acid cycle, which is mostly consistent with that in KEGG analysis. Furthermore the higher expression of UQCRQ, NDUFB7 and UQCRC2 were positively correlated with a better prognosis, implicating these proteins may as novel candidate diagnostic and prognostic biomarkers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer

Zhou

Zhang

et al. 2020

J Cellular Molecular Medi

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“…Statistical programming language R (version 4.0.2) was used for log2 transformation of the data, and the two datasets were merged [ 15 ]. “SVA” package was used for batch correction.…”

Section: Methodsmentioning

confidence: 99%

Identification of hub genes in rheumatoid arthritis through an integrated bioinformatics approach

Long

Zhou

et al. 2021

J Orthop Surg Res

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Background Rheumatoid arthritis (RA) is a common chronic autoimmune disease characterized by inflammation of the synovial membrane. However, the etiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify the key genes involved in RA. Methods GSE77298 was downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen the differentially expressed genes (DEGs). Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway were analyzed by using the DAVID online tool. The STRING database was used to analyze the interaction of differentially encoded proteins. PPI interaction network was divided into subnetworks using MCODE algorithm and was analyzed using Cytoscape. Gene set enrichment analysis (GSEA) was performed to identify relevant biological functions. qRT-PCR analysis was also performed to verify the expression of identified hub DEGs. Results A total of 4062 differentially expressed genes were selected, including 1847 upregulated genes and 2215 downregulated genes. In the biological process, DEGs were mainly concentrated in the fields of muscle filament sliding, muscle contraction, intracellular signal transduction, cardiac muscle contraction, signal transduction, and skeletal muscle tissue development. In the cellular components, DEGs were mainly concentrated in the parts of cytosol, Z disk, membrane, extracellular exosome, mitochondrion, and M band. In molecular functions, DEGs were mainly concentrated in protein binding, structural constituent of muscle, actin binding, and actin filament binding. KEGG pathway analysis shows that DEGs mainly focuses on pathways about lysosome, Wnt/β-catenin signaling pathway, and NF-κB signaling pathway. CXCR3, GNB4, and CXCL16 were identified as the core genes that involved in the progression of RA. By qRT-PCR analysis, we found that CXCR3, GNB4, and CXCL16 were significantly upregulated in RA tissue as compared to healthy controls. Conclusion In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the progression of RA, and provide candidate targets for diagnosis and treatment of RA.

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“…During the last decades, bioinformatics analysis was widely used to screen genetic alterations at the genome level, 14 , 15 so as to identify the differentially expressed genes (DEGs). Besides, network pharmacology has been successfully introduced to reveal the mechanisms of TCM, which coincides with the connotation of multi-component, multi-targets, and multi-pathways holistic strategy.…”

Section: Introductionmentioning

confidence: 99%

Identification of Active Compounds and Mechanism of Huangtu Decoction for the Treatment of Ulcerative Colitis by Network Pharmacology Combined with Experimental Verification

Chen¹,

He²,

Liu³

et al. 2021

DDDT

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Introduction Huangtu decoction (HTD) has been widely used in the treatment of gastrointestinal bleeding, ulcerative colitis (UC) and gastrointestinal tumors in China, but its active compounds and mechanism are still not clear yet. The present research aimed to identify the active compounds and mechanism of HTD for the treatment of UC. Methods Firstly, the chemical compounds of HTD were qualitatively identified based on Q Exactive Orbitrap LC-MS/MS, and their potential targets were predicted through SwissTargetPrediction. Secondly, the differential expressed genes (DEGs) in colon tissues of UC patients and normal controls were retrieved from the GEO database. Thirdly, the overlapping targets of DEGs and the predicted targets were obtained and subjected to GO and KEGG analysis. Finally, the key targets in the most significantly enriched pathway were verified by in vivo experiment, and the protein and mRNA expressions of matrix metalloproteinase-1 (MMP1), MMP3, MMP7, MMP9 and MMP12 were determined by immunohistochemistry (IHC), Western blotting (WB) and quantitative real-time-PCR (qRT-PCR). Results A total of 47 compounds were identified and 29 overlapping targets were obtained from HTD extract. The most significantly enriched pathway of overlapping targets involved was MMP. HTD improved the pathological damage in colon tissues of DSS-induced UC model and significantly decreased the serum levels of IL-1β and IL-6. The protein and mRNA expressions of MMP1, MMP3 and MMP9 in colon tissues were significantly decreased after HTD treatment. Conclusion HTD treatment can alleviate the colonic inflammation via inhibiting MMPs including MMP1, MMP3 and MMP9.

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Mining cancer biology through bioinformatic analysis of proteomic data

Cited by 27 publications

References 87 publications

Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer

Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer

Identification of hub genes in rheumatoid arthritis through an integrated bioinformatics approach

Identification of Active Compounds and Mechanism of Huangtu Decoction for the Treatment of Ulcerative Colitis by Network Pharmacology Combined with Experimental Verification

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