Helicobacter pylori CagA protein induces gastric cancer stem cell-like properties through the Akt/FOXO3a axis

The presence of Helicobacter pylori (H. pylori) infection poses a substantial risk for the development of gastric adenocarcinoma. The primary mechanism through which H. pylori exerts its bacterial virulence is the cytotoxin CagA. This cytotoxin has the potential to induce inter-epithelial mesenchymal transition, proliferation, metastasis, and the acquisition of stem cell-like properties in gastric cancer (GC) cells infected with CagA-positive H. pylori. Cancer stem cells (CSCs) represent a distinct population of cells capable of self-renewal and generating heterogeneous tumor cells. Despite evidence showing that CagA can induce CSCs-like characteristics in GC cells, the precise mechanism through which CagA triggers the development of GC stem cells (GCSCs) remains uncertain. This study reveals that CagA-positive GC cells infected with H. pylori exhibit CSCs-like properties, such as heightened expression of CD44, a specific surface marker for CSCs, and increased ability to form tumor spheroids. Furthermore, we have observed that H. pylori activates the PI3K/Akt signaling pathway in a CagA-dependent manner, and our findings suggest that this activation is associated with the CSCs-like characteristics induced by H. pylori. The cytotoxin CagA, which is released during H. pylori infection, triggers the activation of the PI3K/Akt signaling pathway in a CagA-dependent manner. Additionally, CagA inhibits the transcription of FOXO3a and relocates it from the nucleus to the cytoplasm by activating the PI3K/Akt pathway. Furthermore, the regulatory function of the Akt/FOXO3a axis in the transformation of GC cells into a stemness state was successfully demonstrated.

LncRNA expression signature identified using genome-wide transcriptomic profiling to predict lymph node metastasis in patients with stage T1 and T2 gastric cancer

BACKGROUND

Lymph node (LN) status is vital to evaluate the curative potential of relatively early gastric cancer (GC; T1-T2) treatment (endoscopic or surgery). Currently, there is a lack of robust and convenient methods to identify LN metastasis before therapeutic decision-making.

METHODS

Genome-wide expression profiles of long noncoding RNA (lncRNA) in primary T1 gastric cancer data from The Cancer Genome Atlas (TCGA) was used to identify lncRNA expression signature capable of detecting LN metastasis of GC and establish a 10-lncRNA risk-prediction model based on deep learning. The performance of the lncRNA panel in diagnosing LN metastasis was evaluated both in silico and clinical validation methods. In silico validation was conducted using TCGA and Asian Cancer Research Group (ACRG) datasets. Clinical validation was performed on T1 and T2 patients, and the panel's efficacy was compared with that of traditional tumor markers and computed tomography (CT) scans.

RESULTS

Profiling of genome-wide RNA expression identified a panel of lncRNA to predict LN metastasis in T1 stage gastric cancer (AUC = 0.961). A 10-lncRNA risk-prediction model was then constructed, which was validated successfully in T1 and T2 datasets (TCGA, AUC = 0.852; ACRG, AUC = 0.834). Thereafter, the clinical performance of the lncRNA panel was validated in clinical cohorts (T1, AUC = 0.812; T2, AUC = 0.805; T1 + T2, AUC = 0.764). Notably, the panel demonstrated significantly better performance compared with CT and traditional tumor markers.

CONCLUSIONS

The novel 10-lncRNA could diagnose LN metastasis robustly in relatively early gastric cancer (T1-T2), with promising clinical potential.

Hypothesis of potential active components in Angelica sinensis by using biomembrane extraction and high performance liquid chromatography

The screening and analysis of bioactive components in traditional Chinese medicines (TCMs) is very important not only for the quality control of crude drugs but also for elucidating the therapeutic principle. In this study, a method for screening potential active components from TCMs was developed by using biomembrane extraction and high performance liquid chromatography. Based on the methodology, aqueous extract of Angelica sinensis (WEAS) was used, and four compounds were detected by HPLC in the desorption eluate of red cell membrane extraction for WEAS. The compounds were identified as ferulic acid, ligustilide, senkyunolide H and senkyunolide I based on their UV, MS and NMR spectra. Actually, ferulic acid and ligustilide are considered as major active components in Angelica sinensis. Therefore, this method may be applied to predict the potential bioactivities of multiple compounds in TCMs simultaneously.