Laboratory of Immunobiology and Control of Parasites, Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Laboratory of Pulmonary Immunology and Mechanics, Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Sci Rep. 2024 Nov 2;14(1):26476. doi: 10.1038/s41598-024-77126-x.
The 4T1 model is extensively employed in murine studies to elucidate the mechanisms underlying the carcinogenesis of triple-negative breast cancer. Molecular biology serves as a cornerstone in these investigations. However, accurate gene expression analyses necessitate data normalization employing housekeeping genes (HKGs) to avert spurious results. Here, we initially delve into the characteristics of the tumor evolution induced by 4T1 in mice, underscoring the imperative for additional tools for tumor monitoring and assessment methods for tracking the animals, thereby facilitating prospective studies employing this methodology. Subsequently, leveraging various software platforms, we assessed ten distinct HKGs (GAPDH, 18 S, ACTB, HPRT1, B2M, GUSB, PGK1, CCSER2, SYMPK, ANKRD17) not hitherto evaluated in the 4T1 breast cancer model, across tumors and diverse tissues afflicted by metastasis. Our principal findings underscore GAPDH as the optimal HKG for gene expression analyses in tumors, while HPRT1 emerged as the most stable in the liver and CCSER2 in the lung. These genes demonstrated consistent expression and minimal variation among experimental groups. Furthermore, employing these HKGs for normalization, we assessed TNF-α and VEGF expression in tissues and discerned significant disparities among groups. We posit that this constitutes the inaugural delineation of an ideal HKG for experiments utilizing the 4T1 model, particularly in vivo settings.
4T1 模型在小鼠研究中被广泛用于阐明三阴性乳腺癌发生的机制。分子生物学是这些研究的基础。然而，为了避免虚假结果，需要使用管家基因 (HKGs) 对基因表达进行准确的分析。在这里，我们首先研究了 4T1 在小鼠中诱导的肿瘤进化特征，强调需要额外的肿瘤监测工具和跟踪动物的评估方法，从而促进采用这种方法的前瞻性研究。随后，我们利用各种软件平台，评估了在 4T1 乳腺癌模型中尚未评估的十个不同的 HKG（GAPDH、18S、ACTB、HPRT1、B2M、GUSB、PGK1、CCSER2、SYMPK、ANKRD17），跨越肿瘤和受转移影响的不同组织。我们的主要发现强调了 GAPDH 作为肿瘤基因表达分析的最佳 HKG，而 HPRT1 在肝脏中表现最为稳定，CCSER2 在肺部中表现最为稳定。这些基因在实验组之间表现出一致的表达和最小的变化。此外，我们使用这些 HKG 进行归一化，评估了组织中的 TNF-α 和 VEGF 表达，并发现组间存在显著差异。我们认为，这首次确定了 4T1 模型实验中理想的 HKG，特别是在体内环境中。
