Small bowel adenocarcinoma (SBA) is a gastrointestinal malignancy with low incidence but poor prognosis, and its pathogenesis is still unclear. This study aimed to explore potential disease-causing biomarkers of SBA. The gene expression datasets of SBA and normal samples were downloaded from the Gene Expression Omnibus database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein‒protein interaction network was established to screen hub genes, and target genes were obtained by Lasso regression analysis of hub genes. An SBA risk prediction model was established based on target genes. The prediction accuracy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The levels of immune cell infiltration and activation of immune pathways were compared between SBA and normal samples using the "ggpubr" and "reshape2" packages. A total of 1058 DEGs were identified. WGCNA showed that the signature gene in the brown module was significantly associated with SBA (p = 7E−17), and 469 CGs were obtained. Four target genes (APOA4, APOB, COL1A2, FN1) were identified and showed excellent prediction of SBA risk (AUC = 0.965). In addition, active dendritic cells and macrophages showed higher infiltration levels in SBA. Meanwhile, the APC_co_stimulation pathway and parainflammation pathway were strongly active in SBA. Four target genes (APOA4, APOB, COL1A2, FN1) may be involved in the pathogenesis of small bowel adenocarcinoma.

Background: Colon cancer is a common gastrointestinal malignancy. This study aimed to explore the relationship between p53 pathway-related genes and prognosis of colon cancer. Methods: The mRNA datasets of colon cancer and adjacent tissues were downloaded from The Cancer Genome Atlas (TCGA) database, and the differential expression of genes in two groups was analyzed. Then, P53 pathway-related genes were intersected with differentially expressed genes (DEGs) to obtain P53 pathway-related differentially expressed genes. Then, overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) in clusters were compared by consistent cluster analysis. Univariate and multivariate Cox regression analysis of DEGs was performed to obtain survival-related DEGs. Risk scores were calculated for each sample based on survival-related DEGs, and patients were divided into high/low risk scores. Prognostic differences, tumor immune cell infiltration levels, and immune pathway activation status were compared between the two groups. Results: We identified 28 DEGs and two clusters. There are significant differences in PFS between the two clusters (P=0.011), and no significant difference between OS and DSS. We obtained 3 DEGs (CDKN2A, BAK1, BTG1) that were significantly related to PFS, and CDKN2A was considered an independent prognostic factor. PFS showed statistically significant difference between high/low risk score groups (P=0.015). There were significant differences in immune cell infiltration level and immune pathway activity between two groups. Conclusion: The p53 pathway-related genes are significantly related to PFS in colon cancer patients and play an important role in regulating the tumor immune microenvironment.

Background: Small bowel adenocarcinoma (SBA) is a gastrointestinal malignancy with low incidence but poor prognosis, and its pathogenesis is still unclear. This study aimed to explore potential disease-causing biomarkers of SBA.Methods: The gene expression datasets of SBA and normal samples were downloaded from the Gene Expression Omnibus database. First, differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein-protein interaction network was established to screen hub genes, and target genes were obtained by Lasso regression analysis of hub genes. An SBA risk prediction model was established based on target genes. The prediction accuracy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The levels of immune cell infiltration and activation of immune pathways were compared between SBA and normal samples using the "ggpubr" and "reshape2" packages.Results: A total of 1058 DEGs were identified. WGCNA showed that the signature gene in the brown module was significantly associated with SBA (p = 7E-17), and 469 CGs were obtained. Four target genes (APOA4, APOB, COL1A2, FN1) were identified and showed excellent prediction of SBA risk (AUC=0.965). In addition, active dendritic cells and macrophages showed higher infiltration levels in SBA. Meanwhile, the APC_co_stimulation pathway and parainflammation pathway were strongly active in SBA.Conclusions: Four target genes (APOA4, APOB, COL1A2, FN1) may be involved in the pathogenesis of small bowel adenocarcinoma.

Background: Crohn's disease is a chronic nonspecific intestinal inflammatory disease with unknown etiology. This study aimed to predict potential novel biomarkers of Crohn's disease.Methods: Gene expression datasets of Crohn's disease and normal samples were downloaded from the Gene Expression Omnibus (GEO) database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by the intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein-protein interaction (PPI) network was established to screen hub genes and then establish a Crohn's disease risk prediction model based on hub genes. A receiver operating characteristic (ROC) curve, and area under the curve (AUC) were used to evaluate the prediction ability of the model. Finally, the mirTarBase database, starBase database, and TargetScan database were used to predict microRNAs (miRNAs) and transcription factors (TFs) that cause Crohn's disease.Results: A total of 74 DEGs were identified. WGCNA showed that the signature gene in the blue module was significantly associated with Crohn's disease (p=4e-6) and obtained 32 CGs. Five hub genes (CDH17, CSF1R, CXCL10, CXCL9, COL3A1) were identified and well predicted Crohn's disease risk (AUC=0.853). Meanwhile, in the miRNA-mRNA regulatory network and TF-mRNA regulatory network, we found that 3 miRNAs (hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p) and 2 TFs (TCF4, HINFP) regulate multiple CGs.Conclusions: Five genes (CDH17, CSF1R, CXCL10, CXCL9 and COL3A1), three miRNAs (hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p) and two TFs (TCF4, HINFP) may be involved in the pathogenesis of Crohn's disease.

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