Obesity-Driven Pancreatic Cancer Research: A Streamlit Interactive Analysis Tool Based on Bayesian Models and Interactome Analysis

This article introduces the Cell Analysis Viewer tool developed by arunviswanathan91 (GitHub link: https://github.com/arunviswanathan91/cell-analysis-viewer, released on May 31, 2026). Built on the Streamlit framework, this tool integrates Bayesian machine learning models and protein interactome analysis, aiming to provide interactive data visualization and analysis support for the study of molecular mechanisms underlying obesity-driven pancreatic cancer. Core functions include data import, Bayesian network construction, interactome visualization, differential expression analysis, etc., helping researchers explore the association between obesity and pancreatic cancer.

Obesity is a global public health issue; the global obesity rate has nearly doubled over the past 40 years. In addition to metabolic diseases, obesity is closely linked to various cancers such as pancreatic cancer. Pancreatic cancer has high malignancy and poor prognosis. Obesity promotes its occurrence through mechanisms like chronic inflammation, insulin resistance, abnormal secretion of adipokines, and gut microbiota dysbiosis. Deeply understanding these molecular mechanisms is crucial for the development of prevention and treatment strategies, and this tool is designed precisely to explore these associations.

Cell Analysis Viewer is a web application developed using Python and Streamlit. Its core functions include:

1. Data Import: Supports multiple formats such as gene expression data and clinical data
2. Bayesian Model Analysis: Constructs and analyzes Bayesian network models
3. Interactome Visualization: Interactive display of protein interaction networks
4. Differential Expression Analysis: Identifies key differentially expressed genes
5. Pathway Enrichment Analysis: Explores relevant biological pathways
6. Result Export: Supports export of charts and data

Technically, it relies on Streamlit (concise, data-native, real-time updates), as well as libraries like Pandas/NumPy (data processing), PyMC3/NetworkX (machine learning), Plotly/PyVis (visualization), and BioPython/gseapy (bioinformatics).

The tool adopts several key technologies:

1. Bayesian Network Model: Through steps like structure learning, parameter learning, and inference, it handles uncertainty, integrates prior knowledge, and identifies associations between obesity-related genes and pancreatic cancer biomarkers;
2. Protein Interactome Analysis: Constructs networks from databases like STRING, analyzes metrics such as degree centrality and betweenness centrality, and supports interactive visualization;
3. Differential Expression Analysis: Uses statistical methods like t-tests and ANOVA, and displays results via volcano plots and heatmaps;
4. Pathway Enrichment Analysis: Utilizes databases like KEGG and GO, and explores biological pathways through methods like hypergeometric tests.

The tool's application scenarios include:

• Cancer Mechanism Research: Exploring obesity-related gene expression changes and constructing regulatory networks;
• Biomarker Discovery: Screening diagnostic/prognostic biomarkers and identifying therapeutic targets;
• Teaching and Training: Serving as a bioinformatics teaching tool and demonstrating analysis workflows;
• Data Sharing and Collaboration: Supporting multi-center research and promoting result transparency.

Usage workflow:

1. Data preparation (gene expression matrix, sample grouping, etc.);
2. Import and preprocessing (quality check, standardization);
3. Differential expression analysis;
4. Pathway enrichment analysis;
5. Network analysis;
6. Bayesian model analysis.

The tool has the following limitations:

• Data Quality: Batch effects in public data and sample size limitations affect statistical power;
• Model Assumptions: Conditional independence assumptions of Bayesian networks and prior distributions influence results;
• Computational Resources: Large-scale network inference is computationally intensive;
• Biological Interpretation: Statistical associations require experimental validation.

Future directions:

• Function Expansion: Integrating single-cell and spatial transcriptomics data;
• Performance Optimization: Distributed computing and cloud deployment;
• User Experience: Interactive tutorials and mobile-friendly interfaces;
• Knowledge Integration: Knowledge graphs and multi-omics data integration.

Cell Analysis Viewer demonstrates the potential of Streamlit in bioinformatics visualization, providing an intuitive interactive platform for obesity-driven pancreatic cancer research. Such open-source tools lower the threshold for bioinformatics analysis, promoting research transparency and reproducibility. For cancer researchers and bioinformaticians, this project is worth attention and contribution, and it is expected to become a standard analysis platform for cancer research, helping to combat cancer.