Systematic Characterization of Small Nucleolar RNAs in Cancer

癌症中小核仁 RNA 的系统表征

• 批准号: 10914508
• 负责人: Leng Han
• 金额: $ 41.47万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10914508
• 关键词: Antisense Oligonucleotides; Attenuated; Biological; Biological Markers; Biological Models; Biomedical Research; Breast Cancer Cell; Breast Cancer cell line; Cancer Patient; Cell Proliferation; Clinical; Communities; Computing Methodologies; Data; Development; Diagnostic; Drug resistance; Exhibits; FDA approved; Fatty acid glycerol esters; Foundations; Future; Genetic; Genetic Transcription; Goals; Growth; In Vitro; Individual; Invaded; Malignant Neoplasms; Mammary Tumorigenesis; Mammary gland; Molecular; Multiomic Data; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Play; Process; Prognosis; Proliferating; Quantitative Trait Loci; RNA; RNA-targeting therapy; Research; Research Personnel; Role; Sampling; Signal Pathway; Signal Transduction; Small Nucleolar RNA; Solid Neoplasm; System; The Cancer Genome Atlas; Therapeutic; Transcription Alteration; Work; Xenograft procedure; cancer drug resistance; cancer initiation; cancer subtypes; cancer type; clinically relevant; clinically significant; data portal; data resource; deep learning; diagnostic value; differential expression; disease diagnostic; drug response prediction; effective therapy; genetic variant; improved; in vivo; individual response; insight; large scale data; malignant breast neoplasm; mammary; mouse model; novel; prognostic; prognostic value; refractory cancer; response; success; survival outcome; targeted cancer therapy; targeted treatment; therapeutic RNA; therapeutic target; treatment strategy; triple-negative invasive breast carcinoma; tumor; tumorigenesis; user-friendly; Antisense Oligonucleotides; Attenuated; Biological; Biological Markers; Biological Models; Biomedical Research; Breast Cancer Cell; Breast Cancer cell line; Cancer Patient; Cell Proliferation; Clinical; Communities; Computing Methodologies; Data; Development; Diagnostic; Drug resistance; Exhibits; FDA approved; Fatty acid glycerol esters; Foundations; Future; Genetic; Genetic Transcription; Goals; Growth; In Vitro; Individual; Invaded; Malignant Neoplasms; Mammary Tumorigenesis; Mammary gland; Molecular; Multiomic Data; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Play; Process; Prognosis; Proliferating; Quantitative Trait Loci; RNA; RNA-targeting therapy; Research; Research Personnel; Role; Sampling; Signal Pathway; Signal Transduction; Small Nucleolar RNA; Solid Neoplasm; System; The Cancer Genome Atlas; Therapeutic; Transcription Alteration; Work; Xenograft procedure; cancer drug resistance; cancer initiation; cancer subtypes; cancer type; clinically relevant; clinically significant; data portal; data resource; deep learning; diagnostic value; differential expression; disease diagnostic; drug response prediction; effective therapy; genetic variant; improved; in vivo; individual response; insight; large scale data; malignant breast neoplasm; mammary; mouse model; novel; prognostic; prognostic value; refractory cancer; response; success; survival outcome; targeted cancer therapy; targeted treatment; therapeutic RNA; therapeutic target; treatment strategy; triple-negative invasive breast carcinoma; tumor; tumorigenesis; user-friendly

Abstract Despite recent advancements in treatment options for cancer, a majority of cancer types continue to lack fully characterized and effective targeted therapies. This insufficiency has resulted in the demand for alternative, previously unconsidered treatment approaches to improve disease diagnostics, prognoses, and patient survival outcomes. Recently, we performed integrative analysis for small nucleolar RNA (snoRNAs) in a large number of patient samples and identified 46 snoRNAs that exhibit broad-spectrum clinical significance with 12 or more types of cancer. We developed a data portal, snoRNA in cancers (SNORic), which allows researchers to explore the significance of individual snoRNAs in cancer. SNORic has been accessed >100,000 times since its release in 2017, suggesting its broad impact in the biomedical research community. We provided initial genetic evidence indicating that elevated expression of snoRNAs facilitated the tumorigenesis of mammary gland malignancies. Mechanistically we demonstrated that SNORD46 plays important roles in promoting the initiation, growth, invasion and progression of TNBC. Therefore, elucidation of the roles of snoRNAs in promoting tumorigenesis serves as the first step in the development of a novel class of snoRNA-based biomarkers and therapeutic targets. Our central hypothesis is that snoRNAs serve as essential RNA targets in promoting cancer initiation, progression, and drug resistance, which could be attenuated in vivo by an antisense oligonucleotide-based targeted therapy. In specific aim 1, we will delineate the diagnostic and prognostic values of these snoRNAs in triple-negative breast cancer (Aim 1.1). We will demonstrate the molecular mechanism that these snoRNAs promote TNBC cell proliferation, mobility and invasion (Aim 1.2). We will demonstrate that antisense oligonucleotide-based snoRNA targeted therapy effectively inhibits TNBC growth in vivo (Aim 1.3). We will evaluate and interpret causal effects through molQTL analysis (Aim 1.4). In specific aim 2, we will determine the role of three snoRNAs in breast cancer drug resistance (Aim 2.1). We will understand the molecular mechanisms for drug resistance through multi-omics data (Aim 2.2). To expand our perspective on drug resistance, we will predict the drug response from individual snoRNA expression with the augmentation of deep learning (Aim 2.3). We will study the drug responses effects among snoRNA-based subtypes (Aim 2.4). We will build a user-friendly data portal for releasing the date generated through integrative analysis (Aim 2.5). This study will significantly advance the prognostic, diagnostic, and therapeutic potential of snoRNAs; the absence of this research work will greatly hinder the realization of snoRNA-based therapeutic considerations for cancer patients.

抽象的 尽管最近在癌症治疗方案方面取得了进步，但大多数癌症类型仍然缺乏完全 表征有效的靶向疗法。这种不足导致了对替代方案的需求， 以前未经考虑的治疗方法可以改善疾病诊断，预后和患者 生存结果。最近，我们对大核仁RNA（SNORNA）进行了综合分析 患者样本的数量，并鉴定出46个snornas，具有宽光谱临床意义，有12个 或更多类型的癌症。我们开发了一个数据门户，癌症中的SNORNA（SNORIC），这使研究人员允许研究人员 探索单个snornas在癌症中的重要性。自从Snoric被访问> 100,000次以来 它于2017年发布，这表明其在生物医学研究界的广泛影响。我们提供了最初 遗传证据表明snoRNA的表达升高促进了乳腺的肿瘤发生 腺恶性肿瘤。从机械上讲，我们证明了SNORD46在促进 TNBC的起始，生长，侵袭和进展。因此，阐明了snornas的角色 促进肿瘤发生是开发新型SNORNA类的第一步 生物标志物和治疗靶标。 我们的核心假设是，snoRAS是促进癌症开始的必不可少的RNA靶标， 进展和耐药性，可以通过反义寡核苷酸在体内减弱的耐药性 靶向疗法。在特定目标1中，我们将描述这些snornas的诊断和预后值 三阴性乳腺癌（AIM 1.1）。我们将证明这些snornas的分子机制 促进TNBC细胞增殖，迁移率和侵袭（AIM 1.2）。我们将证明这是反义的 基于寡核苷酸的SNORNA靶向治疗有效地抑制了体内TNBC的生长（AIM 1.3）。我们将 通过MOLQTL分析评估和解释因果效应（AIM 1.4）。在特定目标2中，我们将确定 三个snornas在乳腺癌耐药性中的作用（AIM 2.1）。我们将了解分子 通过多词数据数据具有耐药性的机制（AIM 2.2）。扩大我们对毒品的看法 抗性，我们将通过增强来预测单个snorna表达的药物反应 深度学习（目标2.3）。我们将研究基于SNORNA的亚型的药物反应影响（AIM 2.4）。 我们将构建一个用户友好的数据门户，以释放通过集成分析生成的日期（AIM 2.5）。 这项研究将大大提高snornas的预后，诊断和治疗潜力。这 缺乏这项研究工作将极大地阻碍基于SNORNA的治疗考虑因素的实现 适用于癌症患者。