Identification of microbial markers across populations in early detection of colorectal cancer

Abstract

Several studies have investigated the association between microbial and colorectal cancer (CRC). However, the replicable markers for early stage adenoma diagnosis across multiple populations remain elusive. Here, a meta-analysis of six studies, comprising a total of 1057 fecal samples, was performed to identify candidate markers. By adjusting the potential confounders, 11 and 26 markers (P<0.05) were identified and separately applied into constructing Random Forest classifier models to discriminate adenoma from control, and adenoma from CRC, achieving robust diagnostic accuracy with AUC = 0.80 and 0.89, respectively. Moreover, these markers demonstrated high diagnostic accuracy in independent validation cohorts. Pooled functional analysis and targeted qRT-PCR based genetic profiles reveal that the altered microbiome triggers different pathways of ADP-heptose and menaquinone biosynthesis (P<0.05) in adenoma vs. control and adenoma vs. CRC sequences respectively. The combined analysis of heterogeneous studies confirm adenoma-specific but universal markers across multi-populations, which improves early diagnosis and prompt treatment of CRC.

Alterations in Co-abundant Bacteriome in Colorectal Cancer and Persistent Microbial Dysbiosis After Surgery

“…Bacteria correlated with CRC prognosis, ASV 769 Odoribacter splanchnicus and bacteria correlated with a NC gut, ASV 694 Barnesiella intestinihominis, ASV 194 Anaerostipes hadrus, ASV 766 Blautia spp. and ASV 583 Eubacterium hallii group have more than 2 Log 2 fold increase in abundance in pre-op CRC (Figure 3B) (21,22,23,24,25,26). However, these differences were no longer found in postop CRC compared to NC.…”

Faecal microbial biomarkers in early diagnosis of colorectal cancer

“…These markers also showed high diagnostic accuracy in independent validation cohorts and were also ascertained to be adenoma‐specific. 79 Table 1 below summarizes some major findings reported in studies on the use of faecal microbial biomarkers as non‐invasive tools in the early detection of CRC.…”

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data

“…One of the classical paradigms is identifying statistically significant taxa and functions that can differentiate two or more groups as biomarkers [10][11][12]. Additionally, given machine learning's success under multiple scenarios, there are several attempts to adopt them in microbial marker identification, such as random forest and deep feedforward networks [13,14]. However, these approaches face several issues.…”

“…16S rRNA data suffer from a low taxonomic resolution and an absence of functional information [15]. The taxonomic analysis alone may induce spurious biomarkers since diverse microbial communities from different patients can perform remarkably similar functional capabilities [14]. While MG data can provide information at all taxonomic levels as well as potential functional information, researchers have found that: (i) a great proportion of reads cannot be successfully mapped to existing reference genomes during taxonomic identification, which leads to the potential loss of valuable information [16].…”