A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset DOI Creative Commons
Cankun Wang, Anjun Ma, Megan E. McNutt

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: May 24, 2023

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance further investigating these complex relationships. However, implications significance tumor-related microbes remain largely unknown. Studies have demonstrated critical roles host cancer prevention treatment responses. Understanding between can drive diagnosis microbial therapeutics (bugs as drugs). Computational identification cancer-specific their associations is still challenging due to high dimensionality sparsity intratumoral microbiome data, which requires large datasets containing sufficient event observations identify relationships, within communities, heterogeneity composition, other confounding effects that lead spurious associations. To solve issues, we present a bioinformatics tool, MEGA, most strongly associated with 12 types. We demonstrate its utility on dataset from consortium 9 centers Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented heterogeneous graph learned by attention network; it incorporates metabolic phylogenetic information reflect intricate relationships communities; provides multiple functionalities for association interpretations visualizations. analyzed 2704 RNA-seq samples MEGA interpreted tissue-resident signatures each effectively cancer-associated refine tumors.

Language: Английский

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset DOI Creative Commons
Cankun Wang, Anjun Ma, Megan E. McNutt

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: May 24, 2023

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance further investigating these complex relationships. However, implications significance tumor-related microbes remain largely unknown. Studies have demonstrated critical roles host cancer prevention treatment responses. Understanding between can drive diagnosis microbial therapeutics (bugs as drugs). Computational identification cancer-specific their associations is still challenging due to high dimensionality sparsity intratumoral microbiome data, which requires large datasets containing sufficient event observations identify relationships, within communities, heterogeneity composition, other confounding effects that lead spurious associations. To solve issues, we present a bioinformatics tool, MEGA, most strongly associated with 12 types. We demonstrate its utility on dataset from consortium 9 centers Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented heterogeneous graph learned by attention network; it incorporates metabolic phylogenetic information reflect intricate relationships communities; provides multiple functionalities for association interpretations visualizations. analyzed 2704 RNA-seq samples MEGA interpreted tissue-resident signatures each effectively cancer-associated refine tumors.

Language: Английский

Citations

3