Helicobacter pylori (H. pylori) infection is the strongest risk factor for the initiation and progression of gastric cancer. However, the mechanism of H. pylori-induced pathogenesis remains unclear. In this study, we investigate the role of H. pylori infection in JMJD2B upregulation and the mechanism underlying gastric carcinogenesis. We find that JMJD2B can be induced by H. pylori infection via β-catenin pathway. β-catenin directly binds to JMJD2B promoter and stimulates JMJD2B expression following H. pylori infection. Increased JMJD2B, together with NF-κB, binds to COX-2 promoter to enhance its transcription by demethylating H3K9me3 locally. JMJD2B and COX-2 expression is upregulated in H. pylori infected mice in vivo. Furthermore, JMJD2B and COX-2 expression is gradually increased in human gastric tissues from gastritis to gastric cancer. The level of JMJD2B and COX-2 in H. pylori-positive gastritis tissues is significantly higher than that in H. pylori-negative tissues. Moreover, a positive correlation between JMJD2B and COX-2 expression is found in both gastritis and gastric cancer tissues. Therefore, JMJD2B is a crucial factor in triggering H. pylori-induced chronic inflammation and progression of gastric carcinogenesis and it may serve as a novel target for the intervention of gastric cancer.

Aim This study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer. Methods Based on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve. Results We screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable. Conclusions This study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

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