A systematic review of microbial markers for risk prediction of colorectal neoplasia

BACKGROUND: Substantial evidence indicates that dysbiosis of the gut microbial community is associated with colorectal neoplasia. This review aims to systematically summarise the microbial markers associated with colorectal neoplasia and to assess their predictive performance. METHODS: A comprehensive literature search of MEDLINE and EMBASE databases was performed to identify eligible studies. Observational studies exploring the associations between microbial biomarkers and colorectal neoplasia were included. We also included prediction studies that constructed models using microbial markers to predict CRC and adenomas. Risk of bias for included observational and prediction studies was assessed. RESULTS: Forty-five studies were included to assess the associations between microbial markers and colorectal neoplasia. Nine faecal microbiotas (i.e., Fusobacterium, Enterococcus, Porphyromonas, Salmonella, Pseudomonas, Peptostreptococcus, Actinomyces, Bifidobacterium and Roseburia), two oral pathogens (i.e., Treponema denticola and Prevotella intermedia) and serum antibody levels response to Streptococcus gallolyticus subspecies gallolyticus were found to be consistently associated with colorectal neoplasia. Thirty studies reported prediction models using microbial markers, and 83.3% of these models had acceptable-to-good discrimination (AUROC > 0.75). The results of predictive performance were promising, but most of the studies were limited to small number of cases (range: 9–485 cases) and lack of independent external validation (76.7%). CONCLUSIONS: This review provides insight into the evidence supporting the association between different types of microbial species and their predictive value for colorectal neoplasia. Prediction models developed from case-control studies require further external validation in high-quality prospective studies. Further studies should assess the feasibility and impact of incorporating microbial biomarkers in CRC screening programme

Association between antibiotic use during early life and early-onset colorectal cancer risk overall and according to polygenic risk and FUT2 genotypes

Early-onset colorectal cancer (EOCRC) has been increasing worldwide. Potential risk factors may have occurred in childhood or adolescence. We investigated the associations between early-life factors and EOCRC risk, with a particular focus on long-term or recurrent antibiotic use (LRAU) and its interaction with genetic factors. Data on the UK Biobank participants recruited between 2006 and 2010 and followed up to February 2022 were used. We used logistic regression to estimate adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) of the associations between LRAU during early life and EOCRC risk overall and by polygenic risk score (constructed by 127 CRC-related genetic variants) and Fucosyltransferase 2 (FUT2), a gut microbiota regulatory gene. We also assessed the associations for early-onset colorectal adenomas, as precursor lesion of CRC, to examine the effect of LRAU during early-life and genetic factors on colorectal carcinogenesis. A total of 113 256 participants were included in the analysis, with 165 EOCRC cases and 719 EOCRA cases. LRAU was nominally associated with increased risk of early-onset CRC (OR = 1.48, 95% CI = 1.01-2.17, P = .046) and adenomas (OR = 1.40, 95% CI = 1.17-1.68, P < .001). When stratified by genetic polymorphisms of FUT2, LRAU appeared to confer a comparatively greater risk for early-onset adenomas among participants with rs281377 TT genotype (OR = 1.10, 95% CI = 0.79-1.52, P = .587, for CC genotype; OR = 1.75, 95% CI = 1.16-2.64, P = .008, for TT genotype; Pinteraction  = .089). Our study suggested that LRAU during early life is associated with increased risk of early-onset CRC and adenomas, and the association for adenomas is predominant among individuals with rs281377 TT/CT genotype. Further studies investigating how LRAU contributes together with genetic factors to modify EOCRC risk, particularly concerning the microbiome-related pathway underlying colorectal carcinogenesis, are warranted

Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms

Background. Stage II colorectal cancer patients had heterogeneous prognosis, and patients with recurrent events had poor survival. In this study, we aimed to identify stage II colorectal cancer recurrence associated genes by microarray meta-analysis and build predictive models to stratify patients’ recurrence-free survival. Methods. We searched the GEO database to retrieve eligible microarray datasets. The microarray meta-analysis was used to identify universal recurrence associated genes. Total samples were randomly divided into the training set and the test set. Two survival models (lasso Cox model and random survival forest model) were trained in the training set, and AUC values of the time-dependent receiver operating characteristic (ROC) curves were calculated. Survival analysis was performed to determine whether there was significant difference between the predicted high and low risk groups in the test set. Results. Six datasets containing 651 stage II colorectal cancer patients were included in this study. The microarray meta-analysis identified 479 recurrence associated genes. KEGG and GO enrichment analysis showed that G protein-coupled glutamate receptor binding and Hedgehog signaling were significantly enriched. AUC values of the lasso Cox model and the random survival forest model were 0.815 and 0.993 at 60 months, respectively. In addition, the random survival forest model demonstrated that the effects of gene expression on the recurrence-free survival probability were nonlinear. According to the risk scores computed by the random survival forest model, the high risk group had significantly higher recurrence risk than the low risk group (HR = 1.824, 95% CI: 1.079–3.084, p = 0.025). Conclusions. We identified 479 stage II colorectal cancer recurrence associated genes by microarray meta-analysis. The random survival forest model which was based on the recurrence associated gene signature could strongly predict the recurrence risk of stage II colorectal cancer patients

FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms

Abstract Early identification of metastatic or recurrent colorectal cancer (CRC) patients who will be sensitive to FOLFOX (5‐FU, leucovorin and oxaliplatin) therapy is very important. We performed microarray meta‐analysis to identify differentially expressed genes (DEGs) between FOLFOX responders and nonresponders in metastatic or recurrent CRC patients, and found that the expression levels of WASHC4, HELZ, ERN1, RPS6KB1, and APPBP2 were downregulated, while the expression levels of IRF7, EML3, LYPLA2, DRAP1, RNH1, PKP3, TSPAN17, LSS, MLKL, PPP1R7, GCDH, C19ORF24, and CCDC124 were upregulated in FOLFOX responders compared with nonresponders. Subsequent functional annotation showed that DEGs were significantly enriched in autophagy, ErbB signaling pathway, mitophagy, endocytosis, FoxO signaling pathway, apoptosis, and antifolate resistance pathways. Based on those candidate genes, several machine learning algorithms were applied to the training set, then performances of models were assessed via the cross validation method. Candidate models with the best tuning parameters were applied to the test set and the final model showed satisfactory performance. In addition, we also reported that MLKL and CCDC124 gene expression were independent prognostic factors for metastatic CRC patients undergoing FOLFOX therapy

Novel Chlorinated Polyfluorinated Ether Sulfonates and Legacy Per-/Polyfluoroalkyl Substances: Placental Transfer and Relationship with Serum Albumin and Glomerular Filtration Rate

Per- and polyfluoroalkyl substances (PFASs) may cross the placental barrier and lead to fetal exposure. However, little is known about the factors that influence maternal-fetal transfer of these chemicals. PFAS concentrations were analyzed in 100 paired samples of human maternal sera collected in each trimester and cord sera at delivery; these samples were collected in Wuhan, China, 2014. Linear regression was used to estimate associations of transfer efficiencies with factors. Chlorinated polyfluorinated ether sulfonates (Cl-PFAESs, 6:2 and 8:2) were frequently detected (>99%) in maternal and cord sera. A significant decline in PFAS levels during the three trimesters was observed. A U-shape trend for transfer efficiency with increasing chain length was observed for both carboxylates and sulfonates. Higher transfer efficiencies of PFASs were associated with advancing maternal age, higher education, and lower glomerular filtration rate (GFR). Cord serum albumin was a positive factors for higher transfer efficiency (increased 1.1–4.1% per 1g/L albumin), whereas maternal serum albumin tended to reduce transfer efficiency (decreased 2.4–4.3% per 1g/L albumin). Our results suggest that exposure to Cl-PFAESs may be widespread in China. The transfer efficiencies among different PFASs were structure-dependent. Physiological factors (e.g., GFR and serum albumin) were observed for the first time to play critical roles in PFAS placental transfer