A2M is a potential core gene in intrahepatic cholangiocarcinoma

Zhang, Guanran; Liu, Xuyue; Sun, Zhenjie; Feng, Xiaoning; Wang, Haiyan; Hao, Jing; Zhang, Xiaoli

doi:10.1186/s12885-021-09070-2

Cited by 6 publications

(9 citation statements)

References 48 publications

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“…Alpha-2-macroglobulin (A2M) is a plasma protein that modulates the activity of proteases by inhibiting them [25]. A2M protein has the molecular structure of tetramers, and its identical subunits have a molecular weight of approximately 179,000 Da, including the carbohydrate groups [26,27].…”

Section: Discussionmentioning

confidence: 99%

Proteomic Analyses Reveal the Role of Alpha-2-Macroglobulin in Canine Osteosarcoma Cell Migration

Wilk,

Michalak,

Owczarek

et al. 2024

IJMS

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Canine osteosarcoma (OSA) is an aggressive bone neoplasia with high metastatic potential. Metastasis is the main cause of death associated with OSA, and there is no current treatment available for metastatic disease. Proteomic analyses, including matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI TOF/TOF MS), are widely used to select molecular targets and identify proteins that may play a key role in primary tumours and at various steps of the metastatic cascade. The main aim of this study was to identify proteins differently expressed in canine OSA cell lines with different malignancy phenotypes (OSCA-8 and OSCA-32) compared to canine osteoblasts (CnOb). The intermediate aim of the study was to compare canine OSA cell migration capacity and assess its correlation with the malignancy phenotypes of each cell line. Using MALDI-TOF/TOF MS analyses, we identified eight proteins that were significantly differentially expressed (p ≤ 0.05) in canine OSA cell lines compared to CnOb: cilia- and flagella-associated protein 298 (CFAP298), general transcription factor II-I (GTF2I), mirror-image polydactyly gene 1 protein (MIPOL1), alpha-2 macroglobulin (A2M), phosphoglycerate mutase 1 (PGAM1), ubiquitin (UB2L6), ectodysplasin-A receptor-associated adapter protein (EDARADD), and leucine-rich-repeat-containing protein 72 (LRRC72). Using the Simple Western technique, we confirmed high A2M expression in CnOb compared to OSCA-8 and OSCA-32 cell lines (with intermediate and low A2M expression, respectively). Then, we confirmed the role of A2M in cancer cell migration by demonstrating significantly inhibited OSA cell migration by treatment with A2M (both at 10 and 30 mM concentrations after 12 and 24 h) in a wound-healing assay. This study may be the first report indicating A2M’s role in OSA cell metastasis; however, further in vitro and in vivo studies are needed to confirm its possible role as an anti-metastatic agent in this malignancy.

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Section: Discussionmentioning

confidence: 99%

Proteomic Analyses Reveal the Role of Alpha-2-Macroglobulin in Canine Osteosarcoma Cell Migration

Wilk,

Michalak,

Owczarek

et al. 2024

IJMS

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“…The STRING is a database devoted to PPI networks across the entire organism and always used to analyze PPI of target genes [ 48 ], reveal the enrichment of PPI in gut microbiota [ 10 ] and identify the hub genes of purine metabolism [ 49 ]. We hence used The TRING database to explore the PPI of purine metabolism for bacterial species, the result that bacteria enriching purine salvage related proteins was less abundance in HUA mice and easy to be killed by antibiotic was a novel finding, although we could not completely rule out the possibility of inter-species PPI in our study, which is one of its shortcomings.…”

Section: Discussionmentioning

confidence: 99%

Antibiotic-induced gut microbiota dysbiosis has a functional impact on purine metabolism

Liu

et al. 2023

BMC Microbiol

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Background Dysbiosis of the gut microbiota is closely linked to hyperuricemia. However, the effect of the microbiome on uric acid (UA) metabolism remains unclear. This study aimed to explore the mechanisms through which microbiomes affect UA metabolism with the hypothesis that modifying the intestinal microbiota influences the development of hyperuricemia. Results We proposed combining an antibiotic strategy with protein-protein interaction analysis to test this hypothesis. The data demonstrated that antibiotics altered the composition of gut microbiota as UA increased, and that the spectrum of the antibiotic was connected to the purine salvage pathway. The antibiotic-elevated UA concentration was dependent on the increase in microbiomes that code for the proteins involved in purine metabolism, and was paralleled by the depletion of bacteria-coding enzymes required for the purine salvage pathway. On the contrary, the microbiota with abundant purine salvage proteins decreased hyperuricemia. We also found that the antibiotic-increased microbiota coincided with a higher relative abundance of bacteria in hyperuricemia mice. Conclusions An antibiotic strategy combined with the prediction of microbiome bacterial function presents a feasible method for defining the key bacteria involved in hyperuricemia. Our investigations discovered that the core microbiomes of hyperuricemia may be related to the gut microbiota that enriches purine metabolism related-proteins. However, the bacteria that enrich the purine salvage-proteins may be a probiotic for decreasing urate, and are more likely to be killed by antibiotics. Therefore, the purine salvage pathway may be a potential target for the treatment of both hyperuricemia and antibiotic resistance.

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“…WGCNA is a widely used target therapy analysis tool, which clusters related genes, according to some clinical characteristics of research subjects. There have been many studies on gastric cancer tumor markers in recent years, and most of the WGCNA clusterings are based on differentially expressed genes (DEGs) in the research dataset ( Li et al, 2021 ; Xiang et al, 2021 ; Zhang et al, 2022 ). In contrast, this study performed the WGCNA analysis on all gastric cancer–related genes in one dataset and fused them with selected features using a supervised learning method, i.e., Fisher score algorithm on five combined datasets, preserving the diversity of the gastric cancer hub genes.…”

Section: Discussionmentioning

confidence: 99%

ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning

et al. 2022

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Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.

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A2M is a potential core gene in intrahepatic cholangiocarcinoma

Cited by 6 publications

References 48 publications

Proteomic Analyses Reveal the Role of Alpha-2-Macroglobulin in Canine Osteosarcoma Cell Migration

Proteomic Analyses Reveal the Role of Alpha-2-Macroglobulin in Canine Osteosarcoma Cell Migration

Antibiotic-induced gut microbiota dysbiosis has a functional impact on purine metabolism

ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning

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