ArticleFrontiers in immunology2022
Joint Analysis of Microbial and Immune Cell Abundance in Liver Cancer Tissue Using a Gene Expression Profile Deconvolution Algorithm Combined With Foreign Read Remapping.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Metatranscriptome analysis to unveil the molecular signatures of transcriptionally active pathogens associated with bovine mastitis.Frontiers in veterinary science · 2025Article
- Subgingival host-microbiome metatranscriptomic changes following scaling and root planing in grade II/III periodontitis.Journal of clinical periodontology · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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