Lung cancer is one of the most aggressive cancers and the leading cause of cancer mortality in the US, mainly due to the lack of early detection. Meanwhile, gene expression profiling can identify molecular responses to carcinogen exposure and tumorigenesis. We have previously identified lung cancer-associated gene expression alterations in the normal bronchial airway epithelium of ever smokers with and without lung cancer. These alterations are the basis of a diagnostic test that is useful in clinical decision-making in patients with suspect lung cancer. Despite this success, further improvements in early lung cancer diagnosis are needed, along with a better understanding of airway biology during the initiation and development of lung cancer.
Towards these goals, for the first aim of my thesis, I explored whether normal-appearing bronchial airway gene expression reflects lung cancer histologic subtypes. Genes differentially expressed in the bronchial airway between patients with lung squamous cell carcinoma and lung adenocarcinoma were identified and confirmed in independent data. Using a method developed based on independent component analysis (ICA), cell type-specific gene modules were derived from airway single-cell RNA-sequencing data and shown to be altered between lung cancer subtypes.
Secondly, I sought to investigate whether integrating the bronchial airway molecular biomarker with radiomic features (i.e., quantitative features derived from radiographic images) could yield a better diagnosis for lung cancer screening. Using clinical variables, molecular signatures, and radiomic imaging features, I built and tested an integrated biomarker to improve discrimination between malignant and benign Indeterminate Pulmonary Nodules (IPNs).
Finally, as COVID-19 became a pandemic during my thesis work, I sought to utilize large-scale genomic data from multiple cohorts to investigate possible clinical risk factors related to SARS-CoV-2 entry and disease severity. My analysis showed that smoking affects the expression of host genes involved in SARS-CoV-2 entry differently in the nasal and bronchial airways. The work has implications about how smoking might modulate SARS-CoV-2 infection and COVID-19 disease severity.
Collectively, this work leverages computational approaches to identify airway biology associated with lung cancer subtypes, improve the diagnosis of lung cancer in patients with IPNs, and reveal relationship between smoking and SARS-CoV-2 infection. / 2024-02-18T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/43909 |
| Date | 18 February 2022 |
| Creators | Shi, Xingyi |
| Contributors | Lenburg, Marc, Beane, Jennifer |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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