Comprehensive review of Bayesian network applications in gastrointestinal cancers

Table 5 Summary of Bayesian network applications in colorectal cancer research

Data type	Bayesian network algorithm	Key findings	Ref.
Observational data on CRC, including risk factors such as alcohol consumption, smoking, diabetes, and hypertension	Structure learning algorithms combined with expert knowledge to construct BN model	The BN model effectively segmented populations into risk subgroups and identified modifiable risk factors with significant predictive influence on CRC risk	[53]
Simulation models of CRC progression and natural history, including parameters for risk factors and disease progression	Bayesian calibration using Hamiltonian Monte Carlo-based algorithms integrated with ANN emulators	The Bayesian framework successfully calibrated CRC simulation models, accurately predicting outcomes within confidence intervals, and reduced computational complexity, enabling efficient uncertainty quantification and improved policy analysis for CRC	[54]
Genetic and expression data from 275 normal colon and 276 CRC samples in the SYSCOL cohort	Bayesian network model	BN revealed tumor-specific (transposable elements) TE-eQTLs that influence the expression of cancer driver genes, demonstrating TEs' role in activating oncogenic pathways and providing insights into tumor-specific regulatory mechanisms	[55]
Clinical data of 1253 CRC patients under 50 years of age from the Yonsei Cancer Center, encompassing 93 clinical features	Bayesian network-based synthesizing model	The BN-based model generated a synthetic population of 5005 individuals with no significant statistical differences from the original data. Training predictive models with synthetic data improved performance, especially for small datasets	[56]
Plasma concentrations of heavy metals (As, Cd, Cr, Hg, Pb) and tumor tissue NGS data from CRC patients	BKMR	BKMR analysis revealed that Pb, As, and Cd were significant contributors to increased mutation rates, particularly indels. Mutational signatures showed strong correlations with heavy metal exposure, and shifts in the mutational landscape were observed between high and low exposure groups	[57]
CRC-associated loci from genome-wide association studies (GWAS) and multi-omics datasets	iRIGS, a Bayesian approach	The iRIGS identified 105 high-confidence risk genes, including CEBPB, which promotes CRC cell proliferation through oncogenic pathways such as MAPK, PI3K-Akt, and Ras signaling	[58]
Epidemiological data related to gut microbiome and CRC risk	Multivariate Mendelian randomization analysis based on Bayesian model	Nine bacteria were identified with a robust causal relationship to CRC development, including Streptococcus thermophilus, Bacteroides ovatus, and others	[59]
Clinicopathologic, immune, microbial, and genomic variables from 815 stage II-III CRC patients	BART	The BART risk model identified seven stable survival predictors and successfully stratified patients into low, intermediate, and high-risk groups with statistically significant survival differences	[52]
CRC patients with poorly differentiated and moderately differentiated tumors, analyzed through fecal microbiota	RDP classifier Bayesian algorithm	The study identified distinct GM associated with poorly differentiated CRC, including high abundance of Bifidobacterium and other bacteria	[60]
Colon cancer (microsatellite stable/instable stage III) samples analyzed through multi-omics data (gene expression, DNA methylation, copy number variation)	IntOMICS, an integrative framework based on Bayesian networks	IntOMICS successfully integrated multi-omics data and biological prior knowledge to uncover regulatory networks, revealing deeper insights into genetic information flow and identifying potential predictive biomarkers for stage III colon cancer	[61]
Rectal cancer clinical data from 705 patients who underwent radical resection	Tree-augmented naïve Bayes algorithm	The BN model, incorporating factors like age, CEA, CA19-9, CA125, differentiation status, T stage, N stage, KRAS mutation, and postoperative chemotherapy, showed higher accuracy (AUC = 80.11%) in predicting 3-year OS compared to a nomogram (AUC = 74.23%)	[62]
Time series transcriptomic data from normal and tumor cells of colorectal tissue	DBNs	The DBN-based classifier achieved high classification accuracy, revealing significant differences in gene regulatory networks between normal and tumor cells in CRC, particularly in the neighborhoods of oncogenes and cancer tissue markers	[63]
Gene expression profiles of COAD tumor samples from TCGA and normal colon tissues from GTEx	Bayesian network model	The BN analysis identified 14 upregulated DEGs significantly correlated with tumor stages, and Cox regression highlighted tumor stage, STMN4, and FAM135B dysregulation as independent prognostic factors for COAD survival outcomes	[64]
Clinical data of colon cancer patients, including 18 prognostic biomarkers and three clinical features	Bayesian binary classifiers, including a Bayesian bimodal neural network and a single modal BNN classifier	The Bayesian bimodal neural network achieved the best results in terms of AUC (0.8083), macro F1-score (0.7300), and concordance index (0.7238), demonstrating superior robustness compared to non-Bayesian models and the Bayesian single modal classifier	[65]
Normal mucosa samples from 100 colon cancer patients and 50 healthy donors, including genetic variants, DNA methylation markers, and gene expression data	Bayesian network model	The BN analysis revealed that most combinations showed the canonical pathway where methylation markers cause gene expression variation (60.1%), with 33.9% showing non-causal relationships, and 6% indicating gene expression causes variation in methylation markers	[66]
Genetic data from 55105 CRC cases and 65079 controls, along with an independent cohort of 101987 individuals including 1699 CRC cases	LDpred, a Bayesian approach	The LDpred-derived polygenic risk score showed the highest discriminatory accuracy for CRC risk prediction, identifying 30% of individuals without a family history at similar risk to those with a family history, suggesting the potential for earlier screening	[67]
Fecal microbiome samples from 45 rectal cancer patients before preoperative CCRT	Bayesian network model	The BN analysis identified Duodenibacillus massiliensis as linked with an improved complete response rate after preoperative CCRT, suggesting its potential as a predictive biomarker	[68]
Gene expression data from primary colon cancer and CLM samples	Fast and FFBN	FFBN successfully constructed gene regulatory networks for colon cancer and colon to liver metastasis, revealing unique molecular mechanisms for CLM and shared similarities with primary liver and colon cancers	[69]
Gut microbiota data related to CRC	Bayesian networks combined with IDA (Intervention calculus when the DAG is absent)	Four species-Fusobacterium, Citrobacter, Microbacterium, and Slackia-were identified as having non-null lower bounds of causal effects on CRC, supporting the role of specific microbial communities in CRC progression	[70]
CRC metastasis-related transcription factors (RNA and protein levels)	Bayesian network model	The BN analysis identified LMO7 and ARL8A as potential clinical biomarkers for CRC metastasis	[71]
Gene expression data from 153 colon cancer samples and 19 normal control samples (from TCGA project)	BRPCA	The approach identified 7 molecular subtypes of colon cancer with 44 feature genes, offering a finer classification compared to previous studies	[72]
Protein-protein interaction network data for CRC	Dynamic Bayesian network	The study identified biomarkers with high accuracy and F1-scores, with Alpha-2-HS-glycoprotein identified as a dominant hub gene in CRC	[73]
Gene expression data from LS174T cell lines, normal and adenoma samples, and CRC-related samples	Naive Bayesian network	The BN model demonstrated accurate and reproducible prediction results for normal, adenoma, CRC, and related test samples, with high prediction accuracies	[74]
Gene expression data related to Wnt signaling pathway in human CRC	Static Bayesian network	The biologically inspired Bayesian models, which include epigenetic modifications, improved prediction accuracy for CRC, revealing a significant difference in the activation state of the β-catenin transcription complex between tumorous and normal samples	[75]
Registry data of patients with colon cancer from the Department of Defense Automated Central Tumor Registry	ml-BBNs	The ml-BBNs demonstrated high accuracy in predicting recurrence and mortality in colon cancer, with AUCs ranging from 0.85 to 0.90, and positive predictive values for recurrence and mortality between 78% and 84%; the model identified which high-risk patients benefit from adjuvant therapy, with the largest benefit for elderly patients with high T-stage tumors	[50]
Somatic mutation data from 906 stage II/III CRC from the VICTOR clinical trial	Bayesian network model	The BN analysis revealed significant associations between microsatellite instability, chromosomal instability, and specific mutations (TP53, KRAS, BRAF, PIK3CA, NRAS), and proposed a new molecular classification for CRC with improved prognostic capabilities, particularly for disease-free survival in certain groups	[76]
Population-based data from the SEER registry, including 146248 records of colon cancer patients	ml-BBN	The ml-BBN model accurately estimated OS with an AUC of 0.85, identifying significant prognostic factors such as age, race, tumor histology, and AJCC staging, and demonstrating improved survival predictions compared to existing models	[77]
Clinical data from 53 patients with colon carcinomatosis, including 31 clinical-pathological, treatment-related, and outcome variables	Step-wise ml-BBN	The BBN model identified three predictors of OS: Performance status, Peritoneal Cancer Index, and the ability to undergo CRS +/- HIPEC. The model achieved an AUC of 0.71, with positive and negative predictive values of 63.3% and 68.3%, respectively, and demonstrated strong classification for OS predictions	[51]
Clinical data from 278 CRC patients undergoing SLN mapping	A probabilistic Bayesian network model	The BN model predicted FN SLN mapping with an (AUC of 0.84-0.86, achieving positive and negative predictive values of 83% and 97%, respectively. The number of SLN (< 3) and tumor-replaced nodes independently predicted FN SLN	[78]
Gene expression data from cDNA arrays and clinical-pathological data of 494 CRC patients, focused on nodal metastasis prediction	A Bayesian neural network with automatic relevance determination	Tumor matrilysin was identified as a key predictor of nodal metastasis, with the Bayesian model achieving strong predictive performance, suggesting potential causality between matrilysin expression and nodal metastasis	[48]

CRC: Colorectal cancer; BN: Bayesian network; ANN: Artificial neural network; eQTLs: Expression quantitative trait loci; BKMR: Bayesian kernel machine regression; iRIGS: Integrative risk gene selector; BART: Bayesian additive regression trees; AUC: Area under curve; OS: Overall survival; CEA: Carcinoembryonic antigen; CA19-9: Carbohydrate antigen199; DBNs: Dynamic Bayesian networks; COAD: Colorectal adenocarcinoma; CCRT: Concurrent chemoradiation; CLM: Colon to liver metastasis; FFBN: Furious Bayesian Network; BRPCA: Bayesian robust principal component analysis; ml-BBNs: Machine-learned Bayesian Belief Networks; AJCC: American Joint Committee on Cancer; FN: False negative; SLN: Sentinel lymph node.