The Fast Track for Microbiome Research

The mycobiome in human cancer: analytical challenges, molecular mechanisms, and therapeutic implications

The polymorphic microbiome is considered a new hallmark of cancer. Advances in High-Throughput Sequencing have fostered rapid developments in microbiome research. The interaction between cancer cells, immune cells, and microbiota is defined as the immuno-oncology microbiome (IOM) axis. Fungal microbes (the mycobiome), although representing only ∼ 0.1-1% of the microbiome, are a critical immunologically active component of the tumor microbiome. Accumulating evidence suggests a possible involvement of commensal and pathogenic fungi in cancer initiation, progression, and treatment responsiveness. The tumor-associated mycobiome mainly consists of the gut mycobiome, the oral mycobiome, and the intratumoral mycobiome. However, the role of fungi in cancer remains poorly understood, and the diversity and complexity of analytical methods make it challenging to access this field. This review aims to elucidate the causal and complicit roles of mycobiome in cancer development and progression while highlighting the issues that need to be addressed in executing such research. We systematically summarize the advantages and limitations of current fungal detection and analysis methods. We enumerate and integrate these recent findings into our current understanding of the tumor mycobiome, accompanied by the prospect of novel and exhilarating clinical implications.

A practical guide to amplicon and metagenomic analysis of microbiome data

Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.