Predicting sulforaphane-induced adverse effects in colon cancer patients via in silico investigation

Božić, Dragica; Baralić, Katarina; Živančević, Katarina; Miljaković, Evica Antonijević; Ćurčić, Marijana; Antonijević, Biljana; Djordjević, Aleksandra Buha; Bulat, Zorica; Zhang, Yi; Li, Yang; Đukić-Ćosić, Danijela

doi:10.1016/j.biopha.2021.112598

Cited by 16 publications

(13 citation statements)

References 44 publications

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“…Centrosomal protein, 55 kD (CEP55) has been reported to be a www.nature.com/scientificreports/ potential biomarker and therapeutic target for PDAC and lung cancer 47 . Several studies have shown that CEP55 is carcinogenic in the colon and esophageal cancers 48,49 . Targeting protein for xenopus kinesin-like protein 2 (TPX2) is a cell cycle-associated gene that plays a pro-oncogenic role in hepatocellular carcinoma cells and acts synergistically with anti-cancer drugs 50 .…”

Section: Discussionmentioning

confidence: 99%

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Wang

2022

Sci Rep

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Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.

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Section: Discussionmentioning

confidence: 99%

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Wang

2022

Sci Rep

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“…Moreover, there could be false-positive results and missing interactions due to potential bias toward well-studied interactions 40 , 61 . Finally, further studies are needed to identify all possible molecular changes by formaldehyde exposure, considering other types of omics data and other factors, such as detailed dose–response relationships, duration, and sensitivity of the exposed individuals 62 – 64 .…”

Section: Discussionmentioning

confidence: 99%

Network-based integrated analysis for toxic effects of high-concentration formaldehyde inhalation exposure through the toxicogenomic approach

Kang

Lee

Shin

et al. 2022

Sci Rep

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Formaldehyde is a colorless, pungent, highly reactive, and toxic environmental pollutant used in various industries and products. Inhaled formaldehyde is a human and animal carcinogen that causes genotoxicity, such as reactive oxygen species formation and DNA damage. This study aimed to identify the toxic effects of inhaled formaldehyde through an integrated toxicogenomic approach utilizing database information. Microarray datasets (GSE7002 and GSE23179) were collected from the Gene Expression Omnibus database, and differentially expressed genes were identified. The network analyses led to the construction of the respiratory system-related biological network associated with formaldehyde exposure, and six upregulated hub genes (AREG, CXCL2, HMOX1, PLAUR, PTGS2, and TIMP1) were identified. The expression levels of these genes were verified via qRT-PCR in 3D reconstructed human airway tissues exposed to aerosolized formaldehyde. Furthermore, NRARP was newly found as a potential gene associated with the respiratory and carcinogenic effects of formaldehyde by comparison with human in vivo and in vitro formaldehyde-exposure data. This study improves the understanding of the toxic mechanism of formaldehyde and suggests a more applicable analytic pipeline for predicting the toxic effects of inhaled toxicants.

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“…In addition, the research method in the paper did not determine the quantitative aspects of the reaction between PCBs and specific targets (dose-response relationship), so future research will focus more on the quantitative aspects of the reaction between PCBs and specific targets [ 64 ]. Since data mining mainly depends on online sources (such as the TCMSP database) and the quality of existing interactions, without considering the individual sensitivity of exposed subjects, route of administration, duration, and individual drug metabolic rate, it should mainly be regarded as an insight into further in vivo and in vitro laboratory testing [ 65 , 66 ]. The research methods also did not involve the differences in the toxicokinetics of different PCBs and did not investigate the addition/interaction effect of PCBs on specific targets.…”

Section: Discussionmentioning

confidence: 99%

Network Toxicology Guided Mechanism Study on the Association between Thyroid Function and Exposures to Polychlorinated Biphenyls Mixture

Xing

et al. 2022

BioMed Research International

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Polychlorinated biphenyls (PCBs) are persistent and highly toxic pollutants, which can accumulate in organisms and produce toxic effects, especially damaging the function of thyroid hormones. So far, the molecular mechanism of PCBs mixture and their metabolites interfering with thyroid hormones has not been studied thoroughly except for individual compounds. In this study, PubMed, Web of Science, and STITCH databases were used to search PCBs and their corresponding target proteins. The intersection of PCBs and thyroid hormone dysfunction target proteins was obtained from GeneCards. The “compounds-targets-pathways” network was constructed by Cytoscape software. And KEGG and Go analyses were performed for key targets. Finally, molecular docking was used to verify the binding effect. Four major active components, five key targets, and 10 kernel pathways were successfully screened by constructing the network. Functional enrichment analysis showed that the interference was mediated by cancer, proteoglycans, PI3K-Akt, thyroid hormone, and FoxO signaling pathways. The molecular docking results showed that the binding energies were less than -5 kcal·mol-1. PCBs and their metabolites may act on the key targets of MAPK3, MAPK1, RXRA, PIK3R1, and TP53. The toxic effect of sulfated and methyl sulfone PCBs is greater. The method of screening targets based on the simultaneous action of multiple PCBs can provide a reference for other research. The targets were not found in previous metabolite toxicity studies. It also provides a bridge for the toxic effects and experimental research of PCBs and their metabolites in the future.

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Predicting sulforaphane-induced adverse effects in colon cancer patients via in silico investigation

Cited by 16 publications

References 44 publications

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Network-based integrated analysis for toxic effects of high-concentration formaldehyde inhalation exposure through the toxicogenomic approach

Network Toxicology Guided Mechanism Study on the Association between Thyroid Function and Exposures to Polychlorinated Biphenyls Mixture

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