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对两种常见皮肤癌的计算网络分析为分子机制提供了见解,并揭示了共同的治疗靶点。

Computational network analysis of two popular skin cancers provides insights into the molecular mechanisms and reveals common therapeutic targets.

作者信息

Mahmud Md Sujan, Paul Bikash Kumar, Hasan Md Rakibul, Islam K M Tanjida, Mahmud Imran, Mahmud Shahin

机构信息

Department of Software Engineering, Daffodil International University, Bangladesh.

Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh.

出版信息

Heliyon. 2025 Jan 3;11(1):e41688. doi: 10.1016/j.heliyon.2025.e41688. eCollection 2025 Jan 15.

DOI:10.1016/j.heliyon.2025.e41688
PMID:39866430
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11761328/
Abstract

Basal Cell Carcinoma (BCC) and Actinic Keratosis (AK) are prevalent skin conditions with significant health complications. The molecular mechanisms underlying these conditions and their potential shared pathways remain ambiguous despite their prevalence. Therefore, this study aims to elucidate the common molecular pathways and potential therapeutic targets for BCC and AK through comprehensive computational network analysis. Linkage analysis was performed to identify common liable genes between BCC and AK. Protein-protein interactions (PPIs), Topological properties, GO enrichment, pathway enrichment, and gene regulatory network analyses were also performed to reveal potential molecular mechanisms and pathways. Furthermore, we evaluated protein-drug interactions (PDIs) to identify potential therapeutic targets. Our analysis revealed 22 common genes between BCC and AK: , , , , , , , , , , , , , , , , , , , , , and . PPI network analysis highlighted TP53 and EGFR as central hubs, validated using RNA-seq data. Co-expression and physical interaction analysis revealed a strong interplay between the common genes at the transcriptional and functional levels. GO analysis identified skin cancer-relevant terms: "skin development", "immune system development", and "response to radiation" as significantly enriched biological processes, while pathway enrichment analysis highlighted several cancer-related pathways enrichment. Gene regulatory network analysis revealed complex interactions between genes, miRNAs, and transcription factors, with , , and playing central roles. PDI network analysis identified ibuprofen as a potential therapeutic agent targeting PTGS2 and BCL2, while other proteins VDR, MMP2, MMP9, and TYR showed interactions with multiple drugs. This computational analysis provides valuable insights into the shared molecular mechanisms of BCC and AK, revealing common pathways and potential therapeutic targets for developing novel treatment strategies and repurposing existing drugs for these prevalent skin cancers. Therefore, these findings may guide future research in understanding and developing targeted therapies for both conditions.

摘要

基底细胞癌(BCC)和光化性角化病(AK)是常见的皮肤疾病,会引发严重的健康并发症。尽管这些疾病很常见,但其潜在的分子机制以及可能的共同途径仍不明确。因此,本研究旨在通过全面的计算网络分析,阐明BCC和AK的共同分子途径及潜在治疗靶点。进行连锁分析以确定BCC和AK之间的共同易感基因。还进行了蛋白质-蛋白质相互作用(PPI)、拓扑性质、基因本体(GO)富集、通路富集和基因调控网络分析,以揭示潜在的分子机制和途径。此外,我们评估了蛋白质-药物相互作用(PDI)以确定潜在的治疗靶点。我们的分析揭示了BCC和AK之间的22个共同基因: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/968bac4681ce/gr10.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/968bac4681ce/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/4e585e50089d/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/f22f2e793d5f/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/7affee21f9d8/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/c8d1750c6bdb/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/d6d1975b1f0c/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/ff5732f42a84/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/15df71300778/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/f5c0c2fe4c2b/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/178e8e0c8a01/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e4b/11761328/968bac4681ce/gr10.jpg

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本文引用的文献

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J Clin Med. 2024 Sep 26;13(19):5730. doi: 10.3390/jcm13195730.
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Therapeutic advances of targeting receptor tyrosine kinases in cancer.靶向治疗癌症受体酪氨酸激酶的治疗进展。
Signal Transduct Target Ther. 2024 Aug 14;9(1):201. doi: 10.1038/s41392-024-01899-w.
3
Advancements in elucidating the pathogenesis of actinic keratosis: present state and future prospects.光化性角化病发病机制研究进展:现状与未来展望
Front Med (Lausanne). 2024 Mar 19;11:1330491. doi: 10.3389/fmed.2024.1330491. eCollection 2024.
4
Risk Factors and Innovations in Risk Assessment for Melanoma, Basal Cell Carcinoma, and Squamous Cell Carcinoma.黑色素瘤、基底细胞癌和鳞状细胞癌的风险因素及风险评估创新
Cancers (Basel). 2024 Feb 29;16(5):1016. doi: 10.3390/cancers16051016.
5
Targeting the RAS/RAF/MAPK pathway for cancer therapy: from mechanism to clinical studies.靶向 RAS/RAF/MAPK 通路治疗癌症:从机制到临床研究。
Signal Transduct Target Ther. 2023 Dec 18;8(1):455. doi: 10.1038/s41392-023-01705-z.
6
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Nucleic Acids Res. 2024 Jan 5;52(D1):D304-D310. doi: 10.1093/nar/gkad1071.
7
DrugBank 6.0: the DrugBank Knowledgebase for 2024.DrugBank 6.0:2024 年版 DrugBank 知识库。
Nucleic Acids Res. 2024 Jan 5;52(D1):D1265-D1275. doi: 10.1093/nar/gkad976.
8
NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update.NCBI GEO:基因表达和表观基因组数据集的归档:23 年的更新。
Nucleic Acids Res. 2024 Jan 5;52(D1):D138-D144. doi: 10.1093/nar/gkad965.
9
High-throughput screening and clinical importance of autophagy-associated genes in basal cell carcinoma.基底细胞癌中自噬相关基因的高通量筛选及其临床意义
Pathol Res Pract. 2023 Oct;250:154786. doi: 10.1016/j.prp.2023.154786. Epub 2023 Aug 29.
10
Topical Treatments for Basal Cell Carcinoma and Actinic Keratosis in the United States.美国基底细胞癌和光化性角化病的局部治疗方法
Cancers (Basel). 2023 Aug 2;15(15):3927. doi: 10.3390/cancers15153927.