Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments.

BACKGROUND: Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients. METHODS: Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients. RESULTS: The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker. CONCLUSION: The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

A Wnt-related gene expression signature to improve the prediction of prognosis and tumor microenvironment in gastric cancer.

Background: Most gastric cancer (GC) patients were diagnosed in the advanced stages without obvious symptoms, which resulted in the increased risk of death. Although the combination therapies have showed survival benefit of patients, there is still urgent need to explore the underlying mechanisms of GC development and potential novel targets for clinical applications. Numerous studies have reported the upregulation of Wnt signaling pathway in human GC, which play important role during GC development and progression. However, the current understanding of Wnt signaling pathway is still limited due to its complexity and contradictory effect on different stages of GC tumor microenvironment. Method: We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen Wnt signaling pathway-associated genes by ssGSEA and correlation analysis. Three molecular subtypes were constructed based on a consistent clustering analysis. The key Wnt-related genes were screened through univariate cox analysis, lasso, and stepwise regression. In addition, the Gene Set Enrichment Analysis (GSEA) were performed to explore potential molecular pathways regulated by the Wnt-related gene signatures. ESTIMATE was utilized for evaluating the immune cell populations in GC tumor microenvironment. Results: Three molecular subtypes associated to Wnt were identified, and 7 key Wnt-related genes were screened to establish a predictive RiskScore model. These three molecular subtypes showed significant prognostic differences and distinct functional signaling pathways. We also found the downregulated immune checkpoint expression in the clust1 with good prognosis. The RiskScore model was successfully validated in GSE26942 dataset. Nomogram based on RiskScore and Gender had better prognostic predictive ability. Conclusion: In summary, our study showed that the Wnt-related genes could be used to predict prognosis of GC patients. The risk model we established showed high accuracy and survival prediction capability.

Everolimus ameliorates Helicobacter pylori infection-induced inflammation in gastric epithelial cells.

Helicobacter pylori (H.pylori) infection caused by gastric mucosal inflammation plays a pivotal role in the progression of gastric diseases. The recruitment and attachment of monocytes to the gastric mucosal epithelium are a major event in the early stages of H. pylori-associated gastric diseases. Everolimus is a mechanistic/mammalian target of rapamycin (mTOR) inhibitor used to prevent tumor growth by inhibiting the PI3K signaling pathway. Here, we examined the pharmacological role of Everolimus against H.pylori-induced damage in gastric epithelial cells. Firstly, we found that Everolimus ameliorated H.pylori-induced oxidative stress by reducing reactive oxygen species (ROS) and malondialdehyde (MDA). Secondly, Everolimus significantly reduced the expressions of the pro-inflammatory cytokines interleukin (IL)-6, tumor necrosis factor-α (TNF-α), and IL-8. Moreover, it decreased the production of the pro-inflammatory chemokines C-X-C motif ligand 1 (CXCL1) and macrophage chemoattractant protein-1 (MCP-1). Importantly, Everolimus suppressed the induction of the adhesion molecule intracellular adhesion molecule-1 (ICAM-1) and the attachment of THP-1 monocytes to gastric epithelial AGS cells. Our data also shows that Everolimus inhibited the activation of the NF-κB signaling pathway. Therefore, we conclude that Everolimus could protect gastric epithelial cells by mitigating H.pylori-induced inflammatory response and the attachment of monocytes to epithelial cells.