Joint Analysis of Microbial and Immune Cell Abundance in Liver Cancer Tissue Using a Gene Expression Profile Deconvolution Algorithm Combined With Foreign Read Remapping

Recent transcriptomics and metagenomics studies showed that tissue-infiltrating immune cells and bacteria interact with cancer cells to shape oncogenesis. This interaction and its effects remain to be elucidated. However, it is technically difficult to co-quantify immune cells and bacteria in their respective microenvironments. To address this challenge, we herein report the development of a complete a bioinformatics pipeline, which accurately estimates the number of infiltrating immune cells using a novel Particle Swarming Optimized Support Vector Regression (PSO-SVR) algorithm, and the number of infiltrating bacterial using foreign read remapping and the GRAMMy algorithm. It also performs systematic differential abundance analyses between tumor-normal pairs. We applied the pipeline to a collection of paired liver cancer tumor and normal samples, and we identified bacteria and immune cell species that were significantly different between tissues in terms of health status. Our analysis showed that this dual model of microbial and immune cell abundance had a better differentiation (84%) between healthy and diseased tissue. sp., sp., sp., sp., as well as regulatory T cells (Tregs), resting mast cells, monocytes, M2 macrophases, neutrophils were identified as significantly different (Mann Whitney Test, FDR< 0.05). Our open-source software is freely available from GitHub at https://github.com/gutmicrobes/PSO-SVR.git.