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Page 14 of 18 Fabbrini et al. Microbiome Res Rep 2023;2:25 https://dx.doi.org/10.20517/mrr.2023.25

for HCs and yellow for CRCs in the plot). Node size is proportional to the overabundance value. Nodes colored in red represent the
detected hubs by means of topological analyses of the global network, considering degree, betweenness, closeness and eigenvector
centralities. The network layout was driven by the module detection with a mechanical spin-glass algorithm from the igraph R package,
considering both positive and negative interactions. Such interactions between nodes are displayed as edges and colored according to
the type of association (blue for positive interactions, red for negative ones), and line thickness is proportional to the adjacency values
inferred during the network computation step. The figure highlights the relevance of networking as the CRC group clearly shows a
different way of populating modules, with possibly harmful microorganisms particularly encompassed in the same module (i.e., module
3), which appears to be characteristic of this group. Network images were created using Cytoscape. CRC: Patients with colorectal
cancer; HC: healthy control.

help explain the biological mechanisms underlying CRC pathogenesis and progression. The global
networking approach allowed for retrieving a comprehensive overview of the network structures, detecting
the modules of closely interacting taxa in the ecosystem, and observing how the two groups populated such
structures. In particular, the HC group showed an even population of modules, while CRC showed a strong
dominance of one module over the others. This module was particularly overabundant with species
previously reported as potential opportunistic pathogens or CRC-associated species (e.g., Escherichia coli,
Akkermansia muciniphila, Alistipes spp., Bacteroides spp.) [73,74] . The interpretation of the overabundance
network, particularly when there is robust segregation of nodes into modules, aids in identifying potential
sub-assemblies of microorganisms within the microbial community. Their identification may contribute to
the understanding of the underlying factors that explain the observed conditions. In our case, the canonical
statistical analysis of the relative abundance did not allow for detecting differences in the species populating
module 3 in the global network; nevertheless, the interpretation is in agreement with the previous results in
the literature, which makes the implementation of this analysis potentially very informative.

CONCLUSIONS
In recent years, networking approaches are gaining more and more attention in the microbiome field, and
tailored tools are continuously being developed to address issues related to the nature of microbiome data.
Aside from standard statistical methods and discriminant analyses, networking allows inferring deeper
relationships between the microorganisms of the microbial community, possibly providing additional
insights into the forces that shape the ecosystem. Far from being the gold standard technique for microbial
analyses, networking analysis is indeed a great tool for microbiome studies, especially for describing a novel
ecosystem as well as for deriving further (compositional and/or functional) insights from well-characterized
ecosystems such as human feces. The strength of networking is that the analyses can be conducted at
multiple levels, for example, on a local or global scale, and considering topological (modularity, centrality,
hub) or ecological (cohesion, keystone) parameters in order to produce an all-round assessment of the
community structure.

On the other hand, the limitations of networking analysis applied to microbiome data are yet considerable,
given the need for large sample sizes, computational power, and the struggle to implement statistical tests
on the final outputs, as most of the time, they consist of single values. Nonetheless, the added boost to the
ever-growing microbiome field provided by networking techniques should be acknowledged and
implemented. With the development of increasingly tailored tools and - perhaps in the future - machine
learning methods to help identify patterns and meaningful relationships between nodes, networking might
actually become the gold standard for microbiome analysis.

Box 1. Important terms related to networking analysis.
Node - A node is a fundamental unit within the network representing an individual entity; in a
microbiomea network analysis, a node is represented by a bacterial taxon at a specific level (e.g., genus,
species, etc.).