Integrated bioinformatics, network pharmacology, and artificial intelligence to predict the mechanism of celastrol against muscle atrophy caused by colorectal cancer

Wu, Ming; Zhang, Yan

doi:10.3389/fgene.2022.1012932

Cited by 3 publications

(1 citation statement)

References 64 publications

(65 reference statements)

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“…Ten, using the starBase database (https://starbase.sysu.edu.cn), precursor DE-miRNAs were labeled 3p or 5p, and candidate DE-miRNAs-targeted mRNAs were sourced from the miRDB (https://www.mirdb.org/), miRTarBase (https:// mirtarbase.mbc.nctu.edu.tw/), and TargetScan databases (https://www.targetscan.org/) [17,18]. We categorized the mRNAs as targeted mRNAs of PCa if the mRNAs were predicted in all three databases to be DE-miRNAs-targeted mRNAs.…”

Section: Determination Of the Intended De-mrnasmentioning

confidence: 99%

Integrated Analysis of a ceRNA (lncRNA-miRNA-mRNA) Regulatory Network for Prostate Cancer

Wu,

Wang,

Chen

et al. 2024

European Journal of Cancer Care

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Objective. This study was to construct a ceRNA (lncRNA-miRNA-mRNA) regulatory network for prostate cancer (PCa) and to explore the prognostic correlation, key biological functions, and pathways of core RNAs. Methods. Three subgene expression matrices (miRNA, lncRNA, and mRNA expression matrix) were taken from the TCGA database and used in this investigation. Differential expression analysis and differential expression miRNAs were carried out. Next, the ceRNA (lncRNA-miRNA-mRNA) regulatory complex was used to visualize our data and show how they interacted. Ultimately, the primary molecular roles and biological pathways of PCa were identified using enrichment analysis by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). An effect of AC016773.1 on prostate cancer cell proliferation was investigated by knocking out AC016773.1 in an animal model. The interrelationship between AC016773.1 and hsa-mir-25 was validated using RNA immunoprecipitation technology. Results. The lncRNA, miRNA, and mRNA expression matrices obtained from the TCGA database contain 16901, 1881, and 19962 transcripts, respectively. Through differential expression analysis, we obtained 2010 de lncRNA comparative information, 75 lncRNA and miRNA interaction pairs, and 31 targeted de mRNAs. Through the differential expression analysis of RNA nodes in the ceRNA regulatory network, we found that compared with the NP group, in the PCa group, there were 14 lncRNAs upregulated and 25 lncRNAs downregulated, 1 miRNA upregulated and 3 miRNA downregulated, and 10 mRNA upregulated and 21 mRNA downregulated. In KEGG enrichment analysis, the pathways identified by targeted DE-mRNAs were mainly related to calcium signaling pathway, EGFR tyrosine kinase inhibitor resistance, melanoma, PI3K−Akt signaling pathway, and proteoglycans in cancer. In animal models, it was found that knocking down AC016773.1 significantly reduced tumor volume and weight, indicating that AC016773.1 may promote the proliferation of PCa cells. The use of RNA immunoprecipitation technology indicates a direct binding between AC016773.1 and hsa-mir-25. Conclusion. We elucidated the network regulatory relationship of lncRNA-miRNA-mRNA in PCa and further explored the key molecular functions, biological pathways, and prognostic value of targeted DE-mRNAs.

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Section: Determination Of the Intended De-mrnasmentioning

confidence: 99%

Integrated Analysis of a ceRNA (lncRNA-miRNA-mRNA) Regulatory Network for Prostate Cancer

Wu,

Wang,

Chen

et al. 2024

European Journal of Cancer Care

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AlphaFold2 and its applications in the fields of biology and medicine

Yang

Zeng

Zhao

et al. 2023

Sig Transduct Target Ther

151

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AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

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Potential Targets and Signaling Mechanisms of Cinnamaldehyde Enhancing Intestinal Function and Nutritional Regulation in Fat Greenling (Hexagrammos otakii)

Gu,

Zhan,

Guo

et al. 2024

Aquaculture Nutrition

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Cinnamaldehyde is an ideal feed additive with good immune enhancement and anti-inflammatory regulation effects. However, the anti-inflammatory regulation mechanism in fat greenling (Hexagrammos otakii, H. otakii) remains unclear. The nine targets of cinnamaldehyde were gathered in identified by the Traditional Chinese Medicine Systems Pharmacology database and Uniprot database, and 1,320 intestinal inflammation disease (IIF)-related proteins were screened from DrugBank, Online Mendelian Inheritance in Man (OMIM), Genecards, and Pharmacogenetics and Pharmacogenomics Knowledge Base (PHARMGKB) Databases. According to the Gene Ontology enrichment results and Kyoto Encyclopedia of Genes and Genomes pathway results, cinnamaldehyde may regulated the responses to bacteria, lipopolysaccharide, an inflammatory cytokine, and external stimuli via the nuclear factor kappa-B (NFκB) signaling pathway within on inflammatory network. In addition, the protein–protein interaction analysis assisted in obtaining the closely related inflammatory regulatory proteins, including the C5a anaphylatoxin chemotactic receptor 1 (C5aR1), transcription factor p65 (RELA), prostaglandin G/H synthase 2 (PTGS2), and toll-like receptor 4 (TLR4), which were confirmed as the bottleneck nodes of the network to be more deeply verified via the molecular docking. Moreover, a cinnamaldehyde feeding model was established for evaluating the anti-inflammatory effect of cinnamaldehyde in vivo. According to the current findings implied that cinnamaldehyde may play a protective role against IIF H. otakii by reducing inflammation through the C5 complement (C5)/C5aR1/interleukin-6 (IL-6) and TLR4/NFκB/PTGS2 pathway. The study focused on investigating the action mechanism of cinnamaldehyde on IIF through combining pharmacology and experimental verification in vivo, which provided a fresh perspective on the promoting effect of cinnamaldehyde on IIF in fish.

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Integrated bioinformatics, network pharmacology, and artificial intelligence to predict the mechanism of celastrol against muscle atrophy caused by colorectal cancer

Cited by 3 publications

References 64 publications

Integrated Analysis of a ceRNA (lncRNA-miRNA-mRNA) Regulatory Network for Prostate Cancer

Integrated Analysis of a ceRNA (lncRNA-miRNA-mRNA) Regulatory Network for Prostate Cancer

AlphaFold2 and its applications in the fields of biology and medicine

Potential Targets and Signaling Mechanisms of Cinnamaldehyde Enhancing Intestinal Function and Nutritional Regulation in Fat Greenling (Hexagrammos otakii)

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