School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India.
Department of Electrical Engineering, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India.
Med Oncol. 2023 Dec 28;41(1):36. doi: 10.1007/s12032-023-02246-9.
The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.
乳腺癌药物反应异质性的精确分子机制仍有待充分理解。迫切需要鉴定与标准抗癌药物临床反应密切相关的关键基因，这些药物临床上用于治疗乳腺癌患者。在本研究中，评估了使用基于机器学习的方法从乳腺癌患者的转录组数据中识别临床药物反应的效用。这里开发了一个计算框架，可用于识别与临床药物反应和癌症进展相关的关键基因，为预测潜在的预后生物标志物和治疗靶点提供了巨大机会。该框架利用 DeSeq2、基因本体论 (GO)、京都基因与基因组百科全书 (KEGG)、 Cytoscape 和机器学习技术来寻找这些关键基因。通过提取总 RNA 并进行 qRT-PCR 来量化从网络中选择的几个关键基因的相对表达。在我们的研究中，我们通过实验检查了一些关键基因（如 APOA2、DLX5、APOC3、CAMK2B 和 PAK6）的表达，这些基因被预测在乳腺癌肿瘤发生和对紫杉醇等抗癌药物的反应中发挥重要作用。然而，需要进一步的实验验证来获得这些基因在调节药物反应和癌症进展中的机制见解，这可能在癌症治疗和精准肿瘤学中发挥关键作用。
