Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2024-10-18 DOI:10.1016/j.compbiolchem.2024.108245

Sabkat Mahmud , Alvira Ajadee , Md. Bayazid Hossen , Md. Saiful Islam , Reaz Ahmmed , Md. Ahad Ali , Md. Manir Hossain Mollah , Md. Selim Reza , Md. Nurul Haque Mollah

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引用次数: 0

Abstract

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA.

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通过基因表达谱分析发现甲状腺癌的诊断和治疗生物标记物。

头颈部最常见的内分泌癌症是甲状腺癌（THCA）。虽然越来越多的证据表明 THCA 与基因改变有关，但研究人员尚未完全了解这种关系背后的确切分子机制。关于THCA的分子根源和遗传生物标志物，还有很多东西需要了解。虽然药物疗法是癌症转移后的最佳选择，但不幸的是，大多数患者在接受药物治疗数年后会逐渐产生抗药性。因此，多靶点不同变体的治疗药物可能是有效治疗 THCA 的关键。为了了解 THCA 发病和进展的分子机制并探索多靶点不同变体的治疗药物，我们使用 LIMMA 统计方法从六个微阵列基因表达数据集中检测了 80 个 THCA 和非 THCA 样本之间常见的差异表达基因（cDEGs）。通过蛋白-蛋白相互作用（PPI）网络分析，我们确定了排名前8位的差异表达基因（TIMP1、FN1、THBS1、RUNX2、SHANK2、TOP2A、LRP2和ACTN1）为导致THCA的关键基因（KGs），其中6个KGs（TIMP1、TOP2A、FN1、ACTN1、RUNX2、THBS1）上调，2个KGs（LRP2、SHANK2）下调。通过方框图（Box plots）与独立的TCGA数据库进行的KGs表达模式分析也证实了它们的上调和下调模式。对THCA不同发展阶段KGs的表达分析表明，这些KGs可作为早期诊断和预后的生物标志物。KGs的泛癌症分析表明，KGs与包括THCA在内的多种癌症有很大的相关性。通过基因调控网络（GRN）分析，发现一些转录因子（TFs）和微RNAs是KGs的关键转录和转录后调控因子。cDEGs 的富集分析揭示了与 THCA 显著相关的几种关键分子功能、生物过程、细胞成分和通路。这些发现突出了受已识别的关键基因（KGs）影响的关键机制，为深入了解它们在 THCA 发展中的作用提供了依据。然后，我们通过与KGs介导的受体蛋白的分子对接、ADME/T分析以及与独立受体的交叉验证，发现了6种可再利用的药物分子（恩替瑞尼、伊马替尼、泊纳替尼、索拉非尼、瑞替莫和帕佐帕尼）。因此，这些发现可能成为湿法实验室研究人员和临床医生考虑有效治疗 THCA 的有用资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.