Results
Literature screening and characteristics of eligible studies. A
total of 233 articles were identified using the search strategies,
including 113 articles from PubMed, 5 articles from Cochrane library,
115 articles from Web of Science. Totally, 184 articles were excluded
after careful filtration. Among them, 148 articles were duplicates and
36 had inappropriate abstracts and titles. Then the left 49 full-text
articles were assessed for their eligibility, which contains 8 articles
without diagnostic method, 13 articles without exact data on recurrence
rates, 11 articles without using F. nucleatum , B. fragilisor E. coli as the single biomarker and 6 articles with other
chemotherapeutics included. Finally, 11 articles were included in this
meta‑analysis, which involved 443 participants (200 CRC patients with
high F. nucleatum abundance and 243 CRC patients with lowF. nucleatum abundance, 144 CRC patients with high B.
fragilis abundance and 121 CRC patients with low B. fragilisabundance, 400 CRC patients with high E. coli abundance and 26
CRC patients with low E. coli abundance). The selection process
is illustrated in Figure 1.
Basic characteristics and diagnostic performance of F. nucleatum ,B. fragilis or E. coli in these 11 studies are shown in
Table 1. Fecal gut microbiota was evaluated by RNA in situhybridization (RNA-ISH), quantitative real-time PCR (RT-PCR), droplet
digital PCR or multiplex PCR. All studies included in the meta-analysis
used the histopathological examination as the gold standard. Figure 2
shows an overview of the methodological quality results. In general, the
overall quality of the eligible studies was high.
Heterogeneity. It is
known that the threshold effect, one source of heterogeneity, can be
determined by calculating the Spearman correlation coefficient between
sensitivity and specificity for all included studies. However, in our
meta-analysis, Spearman’s rank correlation coefficient was -0.94,
calculated by an equation using logarithm of sensitivity and
1-specificity. SROC distributed advisably (AUC: 0.79, 95% CI:
0.74-0.81; Figure 3), which indicated no statistical significance
(P = 0.83). Then we performed the meta-regression based on the
variables, including ethnicity, median age, detection methods, CRC
patients’ number, sample size of 5-FU resistance to explain this
heterogeneity (Table 1). Among the five factors, sample size of 5-FU
resistance was identified as statistically significant (P =
0.012), indicating that sample size of CRC patients with 5-FU resistance
was responsible for the relatively high heterogeneity.
Diagnostic performance in meta-regression analysis. As forest
plot revealed significant heterogeneity between studies, single‑factor
meta‑regression analysis was applied to screen the potential variables
impacting on the pooled data. The performance of F. nucleatum for
5-FU resistance of CRC is shown in forest plot (Figure 4): pooled
sensitivity: 0.65 (95% CI:0.60-0.69), specificity: 0.70 (95%
CI:0.59-0.87), PLR: 2.57 (95% CI:1.47-3.21), NLR: 0.52 (95%
CI:0.43-0.63) and DOR: 4.92 (95% CI:2.23-7.33). In contrast, the
performance of B. fragilis for 5-FU resistance of CRC is lower
than F. nucleatum (Figure 4): pooled sensitivity: 0.51 (95%
CI:0.42-0.54), specificity: 0.36 (95% CI:0.21-0.53), PLR: 0.82 (95%
CI:0.79-0.95), NLR: 1.55 (95% CI:1.01-1.62) and DOR: 0.53 (95%
CI:0.31-0.82). The performance of E. coli for 5-FU resistance of
CRC manifested remarkably higher sensitivity but poor specificity
(Figure 4): pooled sensitivity: 0.93 (95% CI:0.90-0.95), specificity:
0.06 (95% CI:0.04-0.92), PLR: 0.99 (95% CI:0.94-1.05), NLR: 1.57 (95%
CI:0.87-1.76) and DOR: 0.63 (95% CI:0.57-0.76).