Bakheet Tala, Al-Mutairi Nada, Doubi Mosaab, Al-Ahmadi Wijdan, Alhosaini Khaled, Al-Zoghaibi Fahad
Molecular BioMedicine Program, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia.
Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia.
Int J Mol Sci. 2025 Jan 25;26(3):1017. doi: 10.3390/ijms26031017.
Breast, colon, and lung carcinomas are classified as aggressive tumors with poor relapse-free survival (RFS), progression-free survival (PF), and poor hazard ratios (HRs) despite extensive therapy. Therefore, it is essential to identify a gene expression signature that correlates with RFS/PF and HR status in order to predict treatment efficiency. RNA-binding proteins (RBPs) play critical roles in RNA metabolism, including RNA transcription, maturation, and post-translational regulation. However, their involvement in cancer is not yet fully understood. In this study, we used computational bioinformatics to classify the functions and correlations of RBPs in solid cancers. We aimed to identify molecular biomarkers that could help predict disease prognosis and improve the therapeutic efficiency in treated patients. Intersection analysis summarized more than 1659 RBPs across three recently updated RNA databases. Bioinformatics analysis showed that 58 RBPs were common in breast, colon, and lung cancers, with HR values < 1 and >1 and a significant Q-value < 0.0001. RBP gene clusters were identified based on RFS/PF, HR, -value, and fold induction. To define union RBPs, common genes were subjected to hierarchical clustering and were classified into two groups. Poor survival was associated with high genes expression, including , and in breast and colon cancer but not with lung cancer; and poor survival was associated with low genes expression, including and in breast, colon, and lung cancer. This study highlights the significant contribution of , , and out of 11 RBP genes as prognostic predictors in patients with breast, colon, and lung cancers and their potential application in personalized therapy.
乳腺癌、结肠癌和肺癌被归类为侵袭性肿瘤，尽管进行了广泛治疗，但无复发生存期（RFS）、无进展生存期（PF）较差，风险比（HRs）也不佳。因此，识别与RFS/PF及HR状态相关的基因表达特征对于预测治疗效果至关重要。RNA结合蛋白（RBPs）在RNA代谢中发挥关键作用，包括RNA转录、成熟和翻译后调控。然而，它们在癌症中的作用尚未完全明确。在本研究中，我们使用计算生物信息学对实体癌中RBPs的功能和相关性进行分类。我们旨在识别有助于预测疾病预后并提高治疗患者治疗效果的分子生物标志物。交叉分析总结了来自三个最近更新的RNA数据库中的1659种以上RBPs。生物信息学分析表明，58种RBPs在乳腺癌、结肠癌和肺癌中常见，HR值<1和>1，且显著Q值<0.0001。基于RFS/PF、HR、-值和诱导倍数确定了RBP基因簇。为了定义联合RBPs，对共同基因进行层次聚类并分为两组。生存率低与高基因表达相关，包括乳腺癌和结肠癌中的 、 和 ，但与肺癌无关；生存率低与低基因表达相关，包括乳腺癌、结肠癌和肺癌中的 和 。本研究强调了11种RBP基因中的 、 和 作为乳腺癌、结肠癌和肺癌患者预后预测指标的重要贡献及其在个性化治疗中的潜在应用。
