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Updated: Apr 21, 2026
Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
Published on: October 10, 2025
Integrated DNA Copy Number and Gene Expression Regulatory Network Analysis of Non-small Cell Lung Cancer Metastasis
Seyed M Iranmanesh1, Nancy L Guo2
1Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA.
Abstract:
Integrative analysis of multi-level molecular profiles can distinguish interactions that cannot be revealed based on one kind of data in the analysis of cancer susceptibility and metastasis. DNA copy number variations (CNVs) are common in cancer cells, and their role in cell behaviors and relationship to gene expression (GE) is poorly understood. An integrative analysis of CNV and genome-wide mRNA expression can discover copy number alterations and their possible regulatory effects on GE. This study presents a novel framework to identify important genes and construct potential regulatory networks based on these genes. Using this approach, DNA copy number aberrations and their effects on GE in lung cancer progression were revealed. Specifically, this approach contains the following steps: (1) select a pool of candidate driver genes, which have significant CNV in lung cancer patient tumors or have a significant association with the clinical outcome at the transcriptional level; (2) rank important driver genes in lung cancer patients with good prognosis and poor prognosis, respectively, and use top-ranked driver genes to construct regulatory networks with the COpy Number and EXpression In Cancer (CONEXIC) method; (3) identify experimentally confirmed molecular interactions in the constructed regulatory networks using Ingenuity Pathway Analysis (IPA); and (4) visualize the refined regulatory networks with the software package Genatomy. The constructed CNV/mRNA regulatory networks provide important insights into potential CNV-regulated transcriptional mechanisms in lung cancer metastasis.
Insights
This study reveals how DNA copy number variations (CNVs) impact gene expression in lung cancer progression. The novel framework identifies key genes and regulatory networks driving cancer metastasis.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- DNA copy number variations (CNVs) are prevalent in cancer but their influence on gene expression and cancer progression is not fully understood.
- Integrative analysis of multi-level molecular data offers deeper insights into cancer susceptibility and metastasis than single-data approaches.
Purpose of the Study:
- To develop a novel framework for integrative analysis of CNV and genome-wide mRNA expression data.
- To identify key driver genes and construct regulatory networks associated with lung cancer progression and metastasis.
- To reveal CNV-regulated transcriptional mechanisms in lung cancer.
Main Methods:
- Candidate driver genes were selected based on significant CNVs or association with clinical outcomes.
- Driver genes were ranked for good and poor prognosis, and regulatory networks were constructed using the COpy Number and EXpression In Cancer (CONEXIC) method.
- Experimentally validated interactions were identified using Ingenuity Pathway Analysis (IPA), and networks were visualized with Genatomy.
Main Results:
- The study identified significant DNA copy number aberrations and their regulatory effects on gene expression in lung cancer.
- Novel regulatory networks were constructed, highlighting potential mechanisms in lung cancer metastasis.
- Key driver genes influencing lung cancer prognosis were ranked.
Conclusions:
- The integrative framework effectively reveals CNV-driven gene expression changes in lung cancer.
- The constructed networks provide valuable insights into molecular mechanisms underlying lung cancer metastasis.
- This approach enhances understanding of cancer biology by integrating multi-level molecular data.
