Bioinformatic analysis of the gene expression profile in muscle atrophy after spinal cord injury

Huang, Haohai; Xue, Jinju; Zheng, Jiaxuan; Tian, Haiquan; Fang, Yehan; Wang, Wei; Wang, Guangji; Hou, Dan; Lin, Jianping

doi:10.1038/s41598-021-01302-6

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…Muscle atrophy is an important manifestation of sarcopenia. Previous studies have shown that pathway changes related to protein ubiquitination and energy production are common features during muscle atrophy (20). In addition, recent studies have also shown that mitochondrial dysfunction (21), inflammatory pathways (22), and drug toxicity are also closely related to the occurrence of muscle atrophy.…”

Section: Discussionmentioning

confidence: 99%

Colorectal Cancer Chemotherapy Drug Bevacizumab May Induce Muscle Atrophy Through CDKN1A and TIMP4

et al. 2022

Front. Oncol.

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The muscle in the organism has the function of regulating metabolism. Long-term muscle inactivity or the occurrence of chronic inflammatory diseases are easy to induce muscle atrophy. Bevacizumab is an antiangiogenic drug that prevents the formation of neovascularization by inhibiting the activation of VEGF signaling pathway. It is used in the first-line treatment of many cancers in clinic. Studies have shown that the use of bevacizumab in the treatment of tumors can cause muscle mass loss and may induce muscle atrophy. Based on bioinformatics analysis, this study sought the relationship and influence mechanism between bevacizumab and muscle atrophy. The differences of gene and sample expression between bevacizumab treated group and control group were studied by RNA sequencing. WGCNA is used to find gene modules related to bevacizumab administration and explore biological functions through metascape. Differential analysis was used to analyze the difference of gene expression between the administration group and the control group in different muscle tissues. The key genes timp4 and CDKN1A were obtained through Venn diagram, and then GSEA was used to explore their biological functions in RNA sequencing data and geo chip data. This study studied the role of bevacizumab in muscle through the above methods, preliminarily determined that timp4 and CDKN1A may be related to muscle atrophy, and further explored their functional mechanism in bevacizumab myotoxicity.

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Section: Discussionmentioning

confidence: 99%

Colorectal Cancer Chemotherapy Drug Bevacizumab May Induce Muscle Atrophy Through CDKN1A and TIMP4

et al. 2022

Front. Oncol.

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“…KEGG enriched 30 signaling pathways, among which the important signaling pathways included the citrate cycle (TCA cycle), ascorbate and aldarate metabolism, drug metabolism, TGF-beta signaling pathway, tyrosine metabolism, sphingolipid metabolism, fatty acid degradation, pyruvate metabolism, TNF signaling pathway, relaxin signaling pathway, and fluid shear stress and atherosclerosis. The citrate cycle (TCA cycle), pyruvate metabolism, and fatty acid degradation have also been predicted to be important signaling pathways in muscle atrophy in spinal cord injury ( Huang et al, 2021 ). The TGF-beta signaling pathway has been previously reported to play an important regulatory role in muscle atrophy ( Yoshihara et al, 2022 ) and is an important signaling pathway in the regulation of muscle atrophy ( Liu et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

2022

Front. Genet.

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Muscle atrophy due to colorectal cancer severely reduces the quality of life and survival time of patients. However, the underlying causative mechanisms and therapeutic agents are not well understood. The aim of this study was to screen and identify the microRNA (miRNA)–mRNA regulatory network and therapeutic targets of celastrol in colorectal cancer causing muscle atrophy via blood exosomes. Datasets were downloaded from the Gene Expression Omnibus online database. Differential expression analysis was first performed using the blood exosome dataset GSE39833 from colorectal cancer and normal humans to identify differentially expressed (DE) miRNAs, and then, transcriptional enrichment analysis was performed to identify important enriched genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by FunRich software. Using the muscle atrophy sample GSE34111, the DE mRNAs in the muscle atrophy sample were analyzed, a regulatory network map was established based on miRNA‒mRNA regulatory mechanisms, further GO and KEGG enrichment analyses were performed for the DE genes in muscle atrophy via Cytoscape’s ClueGO plug-in, and the network pharmacology pharmacophore analysis method was used to analyze the celastrol therapeutic targets, taking intersections to find the therapeutic targets of celastrol, using the artificial intelligence AlphaFold2 to predict the protein structures of the key targets, and finally using molecular docking to verify whether celastrol and the target proteins can be successfully docked. A total of 82 DE miRNAs were obtained, and the top 10 enriched target genes were identified. The enrichment of the 82 miRNAs showed a close correlation with muscle atrophy, and 332 DE mRNAs were found by differential expression analysis in muscle atrophy samples, among which 44 mRNA genes were involved in miRNA‒mRNA networks. The DE genes in muscle atrophy were enriched for 30 signaling pathways, and 228 target genes were annotated after pharmacophore target analysis. The NR1D2 gene, the target of treatment, was found by taking intersections, the protein structure of this target was predicted by AlphaFold2, and the structure was successfully docked and validated using molecular docking. In our present study, colorectal cancer likely enters the muscle from blood exosomes and regulates skeletal muscle atrophy through miRNA‒mRNA regulatory network mechanisms, and celastrol treats muscle through NR1D2 in the miRNA‒mRNA regulatory network.

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“…For the identi cation of differentially expressed genes (DEGs), we performed signi cance analysis of microarrays (SAM) to screen for genes in the previously designated groups showing signi cantly differential expression [7]. Differences with a q value < 0.05 and a fold change > 2.0 or < − 2.0 were considered statistically signi cant.…”

Section: Differentially Expressed Gene Analysismentioning

confidence: 99%

Identification of genes involved in the effects of hypoxia-inducible factor-2 on articular chondrocytes using bioinformatic analysis and validation in osteoarthritis

Zheng

Guo²,

Zhou

et al. 2022

Preprint

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Although over-expression of hypoxia-inducible factor-2 alpha (HIF-2α) can result in cartilage destruction and osteoarthritis (OA) development, the underlying mechanisms remain poorly understood. Here, we investigated the molecular mechanisms in chondrocytes over-expressing HIF-2α. The GeneCloud of Biotechnology Information platform was used to identify differentially expressed genes (DEGs). Using the GEO GSE104794 dataset of control (empty adenovirus, n = 4) and experimental (recombinant adenovirus expressing HIF-2α, n = 4) groups, we performed DEG, Gene Ontology, pathway, pathway network, and gene signal network analyses. Similarly, DEG analysis was performed for the GEO GSE51588 dataset of control (non-OA, n = 4) and experimental (OA, n = 20) groups. Thereafter, intersection of GSE104794 gene signal network analysis and GSE51588 DEG analysis was performed for the key genes, validated by quantitative reverse transcription-polymerase chain reaction. A total of 542 DEGs were identified, among which, the 10 most significant genes in the gene signal network were Nfkb1, Tlr2, Nt5e, Enpp1, Entpd3, Vegfa, Ptgs2, Socs3, Fos, and Epas1. The key genes in OA were LUM, ENTPD3, SMPD3, FGFR3, GPX3, IRAK3, EREG, HTR2A, TLR2, and CDA. Taken together, we screened key genes that are potentially involved in osteoarthritis, thereby providing a basis for identifying valuable markers for this disease.

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Bioinformatic analysis of the gene expression profile in muscle atrophy after spinal cord injury

Cited by 7 publications

References 37 publications

Colorectal Cancer Chemotherapy Drug Bevacizumab May Induce Muscle Atrophy Through CDKN1A and TIMP4

Colorectal Cancer Chemotherapy Drug Bevacizumab May Induce Muscle Atrophy Through CDKN1A and TIMP4

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

Identification of genes involved in the effects of hypoxia-inducible factor-2 on articular chondrocytes using bioinformatic analysis and validation in osteoarthritis

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