Revealing the Mechanisms of Tumorigenesis Through Multi-Omics Analyses
type: CreativeWork
description: Over the past two decades, our understanding of cancer has undergone a paradigm shift. What was once thought of as an abnormal population of proliferating cells, is now recognized as a complex system shaped by dynamic interactions across genomic and environmental scales. Advances in research have revealed a multifaceted etiology of tumorigenesis, extending from just hallmark oncogenic mutations to include large-scale genomic alterations, transcriptomic dysregulation, epigenetic reprogramming, and external influences such as environmental carcinogens and oncogenic pathogens. This heterogeneity, coupled with genomic evolution and multilayered crosstalk within tumor ecosystems, highlighted the complexity of cancer. The advancement of high-throughput sequencing technologies has revolutionized cancer research, enabling comprehensive profiling of tumors through integrative multi-omics approaches. Large-scale cohort studies leveraging these tools have identified diverse cancer phenotypes, offering insights into the molecular diversity of malignancies. Yet, a critical challenge remains: the inability to systematically trace tumor evolution from the very beginning to progression in humans, which hinders the development of targeted therapeutic strategies. To address this challenge, my research employs controlled in vitro models to dissect early tumorigenic mechanisms of two understudied oncogenic drivers which also lack clinically actionable therapies: viral infection induced Merkel cell carcinoma (MCC) and transient centrosome loss induced chromosomal instability (CIN) in prostate cancer (PCa). By integrating multi-omics profiling with network biology and machine learning algorithms, this work systematically maps the potential molecular mechanism driving malignancy in these cancers. For MCC, I identified that viral infection rewires key signaling circuits that force normal cells transform into neuroendocrine state tumor cells; in PCa, I defined stratification signatures link…
abstract: Over the past two decades, our understanding of cancer has undergone a paradigm shift. What was once thought of as an abnormal population of proliferating cells, is now recognized as a complex system shaped by dynamic interactions across genomic and environmental scales. Advances in research have revealed a multifaceted etiology of tumorigenesis, extending from just hallmark oncogenic mutations to include large-scale genomic alterations, transcriptomic dysregulation, epigenetic reprogramming, and external influences such as environmental carcinogens and oncogenic pathogens. This heterogeneity, coupled with genomic evolution and multilayered crosstalk within tumor ecosystems, highlighted the complexity of cancer. The advancement of high-throughput sequencing technologies has revolutionized cancer research, enabling comprehensive profiling of tumors through integrative multi-omics approaches. Large-scale cohort studies leveraging these tools have identified diverse cancer phenotypes, offering insights into the molecular diversity of malignancies. Yet, a critical challenge remains: the inability to systematically trace tumor evolution from the very beginning to progression in humans, which hinders the development of targeted therapeutic strategies. To address this challenge, my research employs controlled in vitro models to dissect early tumorigenic mechanisms of two understudied oncogenic drivers which also lack clinically actionable therapies: viral infection induced Merkel cell carcinoma (MCC) and transient centrosome loss induced chromosomal instability (CIN) in prostate cancer (PCa). By integrating multi-omics profiling with network biology and machine learning algorithms, this work systematically maps the potential molecular mechanism driving malignancy in these cancers. For MCC, I identified that viral infection rewires key signaling circuits that force normal cells transform into neuroendocrine state tumor cells; in PCa, I defined stratification signatures link…
author: Yang, Jiawen
language: en
keywords: cancer biology, chromosomal instability, computational biology, regulatory network, virology
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2025
date modified: 2026-07-24T00:56:11Z
provider: University of Arizona
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Arizona.

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10150/677511