Helicobacter pylori is one of the most common pathogenic bacterium worldwide, infecting about 50% of the world’s population. It is a major cause of several upper gastrointestinal diseases, including peptic ulcers and gastric cancer. The emergence of H. pylori resistance to antibiotics has been a major clinical challenge in the field of gastroenterology. In the course of H. pylori infection, some bacteria invade the gastric epithelium and are encapsulated into a self-produced matrix to form biofilms that protect the bacteria from external threats. Bacteria with biofilm structures can be up to 1000 times more resistant to antibiotics than planktonic bacteria. This implies that targeting biofilms might be an effective strategy to alleviate H. pylori drug resistance. Therefore, it is important to develop drugs that can eliminate or disperse biofilms. In recent years, anti-biofilm agents have been investigated as alternative or complementary therapies to antibiotics to reduce the rate of drug resistance. This article discusses the formation of H. pylori biofilms, the relationship between biofilms and drug resistance in H. pylori , and the recent developments in the research of anti-biofilm agents targeting H. pylori drug resistance.

BackgroundInvasive micropapillary breast carcinoma (IMPC) is a relatively rare pathological type of invasive breast cancer. Little is currently known on the efficacy and safety of breast-conserving treatment (BCT, lumpectomy plus postsurgical radiation) compared with mastectomy in women diagnosed with early-stage IMPC. Accordingly, we sought to investigate the long-term prognostic differences between BCT and mastectomy in patients with T1-3N0-3M0 invasive micropapillary breast carcinoma using data from the Surveillance, Epidemiology, and End Results (SEER) database.Materials and MethodsWe retrospectively analyzed 1,203 female patients diagnosed with early-stage IMPC between 2004 and 2015 from the SEER database. The impact of different surgical approaches on patient prognosis was assessed by the Kaplan-Meier method and Cox proportional risk models.ResultsA total of 609 and 594 patients underwent mastectomy and BCT, respectively. Compared with patients who underwent a mastectomy, patients in the BCT group were older and had lower tumor diameters, lower rates of lymph nodes metastasis, and higher rates of ER receptor positivity and PR receptor positivity (p < 0.05). Kaplan-Meier plots showed that the overall survival (OS) and breast cancer-specific survival (BCSS) were higher in the BCT group than in the mastectomy group. In subgroup analysis, patients with T2 stage in the BCT group had better OS than the mastectomy group. Multivariate analysis showed no statistical difference in OS and BCSS for patients in the mastectomy group compared with the BCT group (hazard ratio (HR) = 0.727; 95% confidence interval (95% CI) 0.369–1.432, p = 0.357; HR = 0.762; 95% CI 0.302–1.923, p = 0.565; respectively). During the multivariate analysis and stratifying for the T stage, a better OS was found for patients with T2 stage in the BCT group than the mastectomy group (HR = 0.333, 95% CI: 0.149–0.741, p = 0.007). There was no significant difference in OS for patients with T1 and T3 stages between the BCT and mastectomy groups (p > 0.05).ConclusionIn women with early-stage IMPC, BCT was at least equivalent to mastectomy in terms of survival outcomes. When both procedures are feasible, BCT should be recommended as the standard surgical treatment, especially for patients with T2 disease.

BackgroundFor patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.MethodsA retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsThe LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.ConclusionTwo prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.

BackgroundTriple-negative breast cancer (TNBC) is a special subtype of breast cancer. Transient Receptor Potential (TRP) channel superfamily has emerged as a novel and interesting target in a variety of tumors. However, the association of TRP channel–related genes with TNBC is still unclear.MethodsThe The Cancer Genome Atlas (TCGA)-TNBC and GSE58812 datasets were downloaded from the public database. The differentially expressed TRP channel–related genes (DETGs) were screened by limma package, and mutations of the above genes were analyzed. Subsequently, new molecular subtypes in TNBC-based DETGs were explored by consensus clustering analysis. In addition, Lasso–Cox regression analysis was used to divide it into two robust risk subtypes: high-risk group and low-risk group. The accuracy and distinguishing ability of above models were verified by a variety of methods, including Kaplan–Meier survival analysis, ROC analysis, calibration curve, and PCA analysis. Meanwhile, CIBERSORT algorithm was used to excavate status of immune-infiltrating cells in TNBC tissues. Last, we explored the therapeutic effect of drugs and underlying mechanisms of risk subgroups by pRRophetic package and GSEA algorithm, respectively.ResultsA total of 19 DETGs were identified in 115 TNBC and 113 normal samples from TCGA database. In addition, missense mutation and SNP were the most common variant classification. According to Lasso–Cox regression analysis, the risky formula performed best when nine genes were used: TRPM5, TRPV2, HTR2B, HRH1, P2RY2, MAP2K6, NTRK1, ADCY6, and PRKACB. Subsequently, Kaplan–Meier survival analysis, ROC analysis, calibration curve, and Principal Components Analysis (PCA) analysis showed an excellent accuracy for predicting OS using risky formula in each cohort (P < 0.05). Specifically, high-risk group had a shorter OS compared with low-risk group. In addition, T-cell CD4 memory activated and macrophages M1 were enriched in normal tissues, whereas Tregs were increased in tumor tissues. Note that the low-risk group was better therapeutic effect to docetaxel, doxorubicin, cisplatin, paclitaxel, and gemcitabine than the high-risk group (P < 0.05). Last, in vitro assays, Quantitative Real-time PCR (qRT-PCR) indicated that TRPM5 was significantly highly expressed in MDA-MB-231 and MDA-MB-468 cells compared with that in MCF-10A cells (P < 0.01).ConclusionWe identified a risky formula based on expression of TRP channel–related genes that can predict prognosis, therapeutic effect, and status of tumor microenvironment for patients with TNBC.