Ph.D. Thesis “Applying meta-analysis to develop novel anti-tumor strategies“
- Data Collection & Cleaning
- Downloaded the pre-processed and RNAseq with Expectation Maximization (RSEM) normalized gene expression profiles from TCGA pan-cancer atlas database
- Utilized qualitative and quantitative data and performed data wrangling and cleaning to ensure data quality for robust analysis.
- Independent Component Analysis (ICA)
- Conducted consensus independent component analysis (c-ICA) on the datasets to identify independent components of gene expression.
- Identified statistically independent components (IC) in which AdipoQ or its receptors were outliers, and selected these components for downstream analysis.
- Gene Expression Analysis
- Based on the identified outlier components, further analysis was performed to understand the gene expression profiles of AdipoQ and its receptors in the context of cancer progression.
- Pathway Enrichment & Correlation Analysis
- Conducted pathway enrichment analysis using the Molecular Signature Database to explore metabolic impacts.
- Performed Spearman correlation analysis to link gene expression with clinical features of cancer patients.
- Statistical Testing & Survival Analysis
- Applied Gene Set Enrichment Analysis (GSEA) to identify significant differences between high and low expression groups.
- Cox proportional hazards regression to assess survival outcomes based on gene expression.
- Used random forest modeling to predict patient survival based on expression levels.
- Meta-Analysis & Final Predictions
- Conducted meta-analysis to combine findings and predict the role of AdipoQ and receptors in cancer.
- Identified key immune pathways activated by higher expression of AdipoQ and receptors, promoting tumor progression.
- Key Findings
- Higher activity of IC in which AdipoQ and its receptors are outliers correlates with worse survival and advanced tumor staging.
- Patients with higher IC activity were diagnosed at later cancer stages, reinforcing their role in tumor progression.
Applying My Knoweldge to Public Health Solutions
Systematic Data Utilization for Health Interventions
During my PhD, I focused on data reuse for generating insights to improve healthcare strategies. This work highlighted how publicly available data, when analyzed effectively, can provide significant cost reductions in disease management. My research reinforced the need for scalable interventions that can be adjusted in real-time based on up-to-date clinical data.
Contributing to Global Health Initiatives
In 2022, I worked as a grant consusltant for m4h consultancy on World Bank grant proposals. My role involved collaborating with infectious disease experts, a One Health policy advisor, and project managers to perform a systematic literature review using PubMed and Medline. This allowed me to share evidence-based recommendations on pandemic prevention and control, emphasizing cost-effective solutions. I actively integrated stakeholder feedback into the grant-writing process, ensuring that the recommendations were not only scientifically sound but also aligned with practical, on-the-ground needs.
I I also wrote multiple articles stressing the urgent need to consider environmental exposures and exosomes and their impact on our health. These articles explored the role of exosomes in women’s reproductive health and discussed the challenges faced by global organizations like the WHO and the EU in formulating effective policies on exosomes.
Why data driven approach?
I chose to focus on data-driven public health strategies because of the immense potential to scale effective interventions. Data, when used properly, can unlock insights that lead to better program implementation and outcomes. While alternative paths such as purely academic research were available, I found that applying my knowledge in real-world settings—where evidence translates directly to impact—offered the greater potential to improve population health.