Gastric Cancer Lesions Research Articles

Pepsinogen I, pepsinogen II, gastrin-17, and Helicobacter pylori serological biomarkers in the diagnosis of precursor lesions of gastric cancer.

Atrophic gastritis and intestinal metaplasia are precursor lesions of gastric cancer. The aim of this study was to determine the usefulness of the biomarkers pepsinogen I(PgI), pepsinogen II (PgII), gastrin-17, and H. pylori antibodies in the identification of precursor lesions. We studied 129 patients with gastric symptoms. The biomarker status was determined using GastroPanel by means of the ELISA-technique. Biomarkers detected atrophy in 14% of the subjects, and 49.6% had positive antibodies for H. pylori. A PgI/PgII ratio < 3 was an important risk biomarker for precursor lesions in our population (OR = 9.171, 95% CI: 1.723-48.799, p = 0.009); however, biomarkers showed low accuracy with histopathological study. In the Western Mexican population, precursor lesions (AG, IM) are common in adults (45%) with dyspepsia but infrequent in children (8%). H. pylori infection was detected in 41.3% of adults and 16.0% of children. Of the studied biomarkers, a PgI/PgII ratio < 3 was an important risk factor for precursor lesions such as AG or IM in our population, with an OR of 9.171 (95% CI: 1.723-48.799, p = 0.009).

Machine learning-driven SERS analysis platform for rapid and accurate detection of precancerous lesions of gastric cancer.

A novel approach is proposed leveraging surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques, principal component analysis (PCA)-centroid displacement-based nearest neighbor (CDNN). This label-free approach can identify slight abnormalities between SERS spectra of gastric lesions at different stages, offering a promising avenue for detection and prevention of precancerous lesion of gastric cancer (PLGC). The agaric-shaped nanoarray substrate was prepared using gas-liquid interface self-assembly and reactive ion etching (RIE) technology to measure SERS spectra of serum from mice model with gastric lesions at different stages, and then a SERS spectral recognition model was trained and constructed using the PCA-CDNN algorithm. The results showed that the agaric-shaped nanoarray substrate has good uniformity, stability, cleanliness, and SERS enhancement effect. The trained PCA-CDNN model not only found the most important features of PLGC, but also achieved satisfactory classification results with accuracy, area under curve (AUC), sensitivity, and specificity up to 100%. This demonstrated the enormous potential of this analysis platform in the diagnosis of PLGC.

OLFM4 promotes the progression of intestinal metaplasia through activation of the MYH9/GSK3β/β-catenin pathway

BackgroundIntestinal metaplasia (IM) is classified into complete intestinal metaplasia (CIM) and incomplete intestinal metaplasia (IIM). Patients diagnosed with IIM face an elevated susceptibility to the development of gastric cancer, underscoring the critical need for early screening measures. In addition to the complexities associated with diagnosis, the exact mechanisms driving the progression of gastric cancer in IIM patients remain poorly understood. OLFM4 is overexpressed in several types of tumors, including colorectal, gastric, pancreatic, and ovarian cancers, and its expression has been associated with tumor progression.MethodsIn this study, we used pathological sections from two clinical centers, biopsies of IM tissues, precancerous lesions of gastric cancer (PLGC) cell models, animal models, and organoids to explore the role of OLFM4 in IIM.ResultsOur results show that OLFM4 expression is highly increased in IIM, with superior diagnostic accuracy of IIM when compared to CDX2 and MUC2. OLFM4, along with MYH9, was overexpressed in IM organoids and PLGC animal models. Furthermore, OLFM4, in combination with Myosin heavy chain 9 (MYH9), accelerated the ubiquitination of GSK3β and resulted in increased β-catenin levels through the Wnt signaling pathway, promoting the proliferation and invasion abilities of PLGC cells.ConclusionsOLFM4 represents a novel biomarker for IIM and could be utilized as an important auxiliary means to delimit the key population for early gastric cancer screening. Finally, our study identifies cell signaling pathways involved in the progression of IM.

Clinicopathological Features of Early Gastric Cancer Complicated by Autoimmune Gastritis

Introduction: In the post-Helicobacter pylori era, autoimmune gastritis (AIG) is attracting increasing attention as an origin of gastric cancer. Here, we performed clinicopathological examination of gastric cancer complicating AIG treated in our hospital. Methods: Eighty-six early gastric cancer lesions complicating AIG in 50 patients were treated by endoscopic submucosal dissection (ESD) at our hospital in 2008–2022. Their clinicopathological characteristics were compared with those of a control group comprising 2,978 early gastric cancer lesions (excluding lesions in the remnant stomach after surgery) in 2,278 patients treated by ESD during the same period. Results: Mean age was significantly higher in the AIG group than in the control group (74.7 years vs. 70.9 years; p < 0.01). In the respective groups, the occurrence rate of synchronous/metachronous lesions was 38.0% and 20.4% (p < 0.01), the ratio of longitudinal cancer locations (upper/middle/lower third [U/M/L]) was 27/32/27 and 518/993/1,467 (p < 0.01), the ratio of circumferential cancer locations (lesser curvature/greater curvature/anterior wall/posterior wall) was 25/31/12/18 and 1,259/587/475/657 (p < 0.01), the ratio of major macroscopic types (I/IIa/IIb/IIc) was 13/38/5/30 and 65/881/220/1,812 (p < 0.01). The rates of multiple gastric cancer and cancers in the U region, at the greater curvature, and of protruding types were significantly higher in the AIG group. Conclusion: The occurrence rate of multiple gastric cancer was significantly higher in gastric cancer complicating AIG (approximately 40%), and compared with the control group, the proportions of cancers at the U region, at the greater curvature, and of protruding types were significantly higher.

Detection of Early Gastric Cancer and Lesion Segmentation Based on Deep Learning

Background: Early detection and precise identification of gastric cancer tumors significantly enhance patient outcomes. Conventional methods often rely on manual interpretation of endoscopic images, which can be time-consuming and subjective. Recent advancements in deep learning offer promising alternatives for automating and improving these diagnostic processes. Objective: The primary objective of this study is to explore the effectiveness of a deep learning model in detecting early gastric cancer and segmenting lesions from endoscopic images. Methods: This retrospective study was conducted at Khalifa Gul Nawaz Teaching Hospital /BMC Bannu, from December 2021 to November 2022. Medical records and endoscopic images from 180 patients with suspected or confirmed gastric lesions were analyzed. Images were diversified in terms of lesion types and characteristics. Preprocessing steps including standardization and enhancement techniques were applied to improve image quality for analysis. Results: The deep learning model achieved an accuracy of 90% in identifying gastric cancer lesions, with a sensitivity of 85% and specificity of 92%. The area under the curve (AUC-ROC) was calculated to be 0.61, indicating a good discriminative performance of the model. Conclusion: The deep learning model demonstrated significant potential for enhancing the detection and segmentation of early gastric cancer from endoscopic images, providing a valuable tool for gastroenterologists in the early diagnosis and treatment planning.

Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market

Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.

Comparison of 68 Ga-FAPI-04 PET/CT with 18 F-FDG PET/CT for diagnosis and staging of gastric and colorectal cancer.

The objective of this study is to evaluate the effectiveness of 68 Ga-FAPI-04 PET/computed tomography (CT) for the diagnosis of primary and metastatic gastric cancer and colorectal cancer lesions as compared with 18 F-FDG PET/CT. Fifty-nine patients who underwent both 18 F-FDG and 68 Ga-FAPI-04 for initial staging or restaging were enrolled. Histopathological findings and clinical imaging follow-up were used as the reference standard. The diagnostic performance and TNM staging of the two tracers were calculated and compared. The maximum standardized uptake value (SUV max ), tumour-to-mediastinal blood pool ratio (TBR) (lesions SUV max /ascending aorta SUV mean ), and tumour-to-normal liver parenchyma ratio (TLR) (lesions SUV max /liver SUV mean ) of primary and metastatic lesions between two imaging modalities were measured and compared using the Wilcoxon signed-rank test and paired t -test. The two imaging agents are comparable for the detection of primary tumors. The sensitivity of 68 Ga-FAPI-04 PET/CT was higher than that of 18 F-FDG PET/CT for detecting lymph node metastases, peritoneal metastases, liver metastases, and bone metastases. In the patient-based analysis, the TLR for all lesions was significantly higher with 68 Ga-FAPI-04 PET/CT than with 18 F-FDG PET/CT (all P < 0.05). The accuracy (92.2 vs. 70.3%, P = 0.002) and sensitivity of 68 Ga-FAPI-04 were significantly higher than that of 18 F-FDG (78.6 vs. 71.4%, P = 0.011) in determining the lymph node status. 68 Ga-FAPI-04 has a higher accuracy in staging ( P = 0.041), which is mainly due to the ability of distant metastases detection. 68 Ga-FAPI-04 PET/CT may be superior in evaluating the diagnostic efficiency and staging accuracy of gastric and colorectal cancer.

High risk of multiple gastric cancers in Japanese individuals with Lynch syndrome.

Lynch syndrome (LS) is a dominantly inherited syndrome characterized by an increased risk for LS associated tumors such as colorectal cancer (CRC) and gastric cancer (GC). However, the clinical benefit of surveillance for GC remains unclear while it has already been recommended for CRC. This study aimed to elucidate the clinical features of GC in Japanese individuals with LS, and the risk of developing multiple GCs to build regional-tailored surveillance programs in LS patients with GC. Data on Japanese individuals with LS were retrospectively collected from a single institution. The clinical features of GC, including the cumulative risk of multiple GCs, were analyzed. Among 96 individuals with LS (MLH1/MSH2/MSH6, 75:20:1), 32 GC lesions were detected in 15 individuals with LS (male/female, 11:4). The median age at initial GC diagnosis was 52.7 y (range: 28-71). Histological examination revealed a predominance of intestinal type (19/24: 87.5%). Moreover, the majority of the GC lesions (82%) were determined to have high-frequency of microsatellite instability. The cumulative risk of individuals with LS developing GC at 70 y was 31.3% (MLH1 36.1%, MSH2 18.0%). Notably, the cumulative risk of individuals with LS developing metachronous and/or synchronous GCs at 0, 10 and 20 y after initial diagnosis of GC was 26.7%, 40.7%, and 59.4%, respectively. Due to a higher risk of developing multiple GCs, intensive surveillance might be especially recommended for Japanese individuals with LS associated initial GC.

Detection of chromosomal instability using ultrasensitive chromosomal aneuploidy detection in the diagnosis of precancerous lesions of gastric cancer.

The diagnosis of Precancerous Lesions of Gastric Cancer (PLGC) is challenging in clinical practice. We conducted a clinical study by analyzing the information of relevant chromosome copy number variations (CNV) in the TCGA database followed by the UCAD technique to evaluate the value of Chromosomal Instability (CIN) assay in the diagnosis of PLGC. Based on the screening of gastric cancer related data in TCGA database, CNV analysis was performed to explore the information of chromosome CNV related to gastric cancer. Based on the gastroscopic pathology results, 12 specimens of patients with severe atrophy were screened to analyze the paraffin specimens of gastric mucosa by UCAD technology, and to explore the influence of related factors on them. The results of CNV in TCGA database suggested that chromosome 7, 8, and 17 amplification was obvious in patients with gastric cancer. UCAD results confirmed that in 12 patients with pathologic diagnosis of severe atrophy, five of them had positive results of CIN, with a positive detection rate of 41.7%, which was mainly manifested in chromosome seven and chromosome eight segments amplification. We also found that intestinalization and HP infection were less associated with CIN. And the sensitivity of CIN measurement results was significantly better than that of tumor indicators. The findings suggest that the diagnosis of PLGC can be aided by UCAD detection of CIN, of which Chr7 and 8 may be closely related to PLGC.

Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images

Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by medical practitioners to screen for gastric cancer. However, the symptoms of gastric cancer at different stages of advancement vary significantly, particularly in the case of early gastric cancer (EGC). The manifestations of EGC are often indistinct, leading to a detection rate of less than 10%. In recent years, researchers have focused on leveraging deep learning algorithms to assist medical professionals in detecting EGC and thereby improve detection rates. To enhance the ability of deep learning to detect EGC and segment lesions in gastroscopic images, an Improved Mask R-CNN (IMR-CNN) model was proposed. This model incorporates a “Bi-directional feature extraction and fusion module” and a “Purification module for feature channel and space” based on the Mask R-CNN (MR-CNN). Our study includes a dataset of 1120 images of EGC for training and validation of the models. The experimental results indicate that the IMR-CNN model outperforms the original MR-CNN model, with Precision, Recall, Accuracy, Specificity and F1-Score values of 92.9%, 95.3%, 93.9%, 92.5% and 94.1%, respectively. Therefore, our proposed IMR-CNN model has superior detection and lesion segmentation capabilities and can effectively aid doctors in diagnosing EGC from gastroscopic images.

Deep Learning Based Gastric Cancer and Disease Detection System

Abstract: Gastric cancer, commonly known as stomach cancer, is a leading cause of cancer-related deaths worldwide. Early detection of gastric cancer is crucial for improving patient outcomes and reducing mortality rates. In recent years, advances in medical imaging and machine learning techniques have shown great promise in aiding the early diagnosis of gastric cancer. This abstract presents an innovative approach for gastric cancer detection using machine learning algorithms. Our proposed system leverages state-of-the-art convolutional neural networks (CNNs) for the automatic analysis of endoscopic images, to identify potential malignancies. Preprocessing techniques are employed to enhance the quality of input images and reduce noise. Feature extraction methods are then applied to extract relevant information from the images, and these features are used to train a machine learning model. The machine learning model is trained on a large dataset of gastric cancer cases, including various subtypes, to ensure robust performance. We employ cross-validation techniques to assess the model's accuracy, sensitivity, specificity, and overall performance. The proposed system demonstrates high accuracy in identifying gastric cancer lesions, even in cases where subtle or early-stage abnormalities may be difficult to detect by human experts. In conclusion, our advanced machine learning approach for gastric cancer detection shows promise in revolutionizing early diagnosis, improving patient outcomes, and reducing the burden on healthcare systems. Further research and clinical validation are necessary to fully assess the system's efficacy in a real-world clinical setting.

Analysis of endoscopic and pathological features of 6961 cases of gastric cancer

Gastric cancer (GC) stage and tissue differentiation affect treatment efficacy and prognosis, highlighting the importance of understanding the risk factors that affect these parameters. Therefore, this study analyzed risk factors affecting the GC stage and differentiation and the relationships between the cancer site and the sex and age of the patient. We collected clinical data from 6961 patients with GC, including sex, age, endoscopic lesion location, and pathological differentiation. Patients were grouped based on GC stage (early or advanced), differentiation (well or poorly differentiated), and lesion site (upper stomach [cardia and fundus], middle stomach [gastric body], and lower stomach [gastric antrum]). Differences in sex, age, location, stage, and degree of differentiation were assessed based on these groupings. Univariate analysis revealed that the disease location and differentiation significantly differed based on the GC stage (P < 0.05), whereas sex, age, site, and stage significantly differed based on GC differentiation (P < 0.05). A multivariate analysis confirmed these factors as independent risk factors affecting GC. Moreover, lesion sites significantly differed between sexes (P < 0.05) and among age groups (P < 0.05). Although the effects of family history, lifestyle, and Helicobacter pylori infection status of the patients were not considered, this single-center retrospective study established independent risk factors for GC.Trial registration ChiCTR2200061989.

Effects of Helicobacter pylori and Moluodan on the Wnt/β-catenin signaling pathway in mice with precancerous gastric cancer lesions.

Helicobacter pylori (H. pylori) is the primary risk factor for gastric cancer (GC), the Wnt/β-Catenin signaling pathway is closely linked to tumourigenesis. GC has a high mortality rate and treatment cost, and there are no drugs to prevent the progression of gastric precancerous lesions to GC. Therefore, it is necessary to find a novel drug that is inexpensive and preventive to against GC. To explore the effects of H. pylori and Moluodan on the Wnt/β-Catenin signaling pathway and precancerous lesions of GC (PLGC). Mice were divided into the control, N-methyl-N-nitrosourea (MNU), H. pylori + MNU, and Moluodan groups. We first created an H. pylori infection model in the H. pylori + MNU and Moluodan groups. A PLGC model was created in the remaining three groups except for the control group. Moluodan was fed to mice in the Moloudan group ad libitum. The general condition of mice were observed during the whole experiment period. Gastric tissues of mice were grossly and microscopically examined. Through quantitative real-time PCR (qRT-PCR) and Western blotting analysis, the expression of relevant genes were detected. Mice in the H. pylori + MNU group showed the worst performance in general condition, gastric tissue visual and microscopic observation, followed by the MNU group, Moluodan group and the control group. QRT-PCR and Western blotting analysis were used to detect the expression of relevant genes, the results showed that the H. pylori + MNU group had the highest expression, followed by the MNU group, Moluodan group and the control group. H. pylori can activate the Wnt/β-catenin signaling pathway, thereby facilitating the development and progression of PLGC. Moluodan suppressed the activation of the Wnt/β-catenin signaling pathway, thereby decreasing the progression of PLGC.

Comparison of Endoscopic Submucosal Dissection With Endoscopic Mucosal Resection After Circumferential Precutting to Treat Gastric Adenomas ≤15 mm

Objectives: Endoscopic submucosal dissection (ESD) is a widely used approach for the resection of superficial gastric neoplastic lesions. Endoscopic mucosal resection (EMR) is acceptable for lesions &lt;10–15 mm in size. Herein, we compared the clinical outcomes of ESD with those of EMR after circumferential precutting (EMR-P) for gastric adenomas ≤15 mm.Methods: We retrospectively analyzed the data of 213 patients with 228 gastric adenomas ≤15 mm in size who were treated endoscopically at a single tertiary hospital in Korea between November 2018 and October 2022. We evaluated the complete endoscopic resection rate, recurrence rate, procedurer-elated complications, and procedure time according to the procedure used.Results: Among the 228 gastric adenomas, 49 were treated with EMR-P and 179 with ESD. The histological complete resection rate was higher in the ESD group than in the EMR-P group (87% vs. 57%, &lt;i&gt;p&lt;/i&gt;&lt;0.001). No significant between-group differences were observed in endoscopic en bloc resection rates (ESD vs. EMR-P, 96% vs. 90%; &lt;i&gt;p&lt;/i&gt;=0.081). The procedure time was significantly shorter in the EMR-P group than in the ESD group (28.9±19.7 min vs. 8.8±5.9 min, &lt;i&gt;p&lt;/i&gt;&lt;0.001). The local recurrence rate in patients with histologically incomplete resection did not differ between the two groups (ESD vs. EMR-P, 8.7% vs. 9.5%; &lt;i&gt;p&lt;/i&gt;=0.924).Conclusions: For gastric adenomas ≤15 mm, EMR-P may be the preferable treatment method considering the en bloc resection rate, procedure time, and local recurrence rate. However, considering the complete resection rate, ESD is recommended as the treatment of choice for high-grade adenomas and early gastric cancer lesions.

Diagnostic performance of blue laser imaging for early detection of gastric cancer: A systematic review and meta-analysis.

Gastric cancer (GC) is associated with a significant global health burden and high mortality rates when diagnosed at later stages. The diagnosis often occurs at advanced stages when treatment options are limited and less effective. Early detection strategies are crucial to improving survival rates and outcomes for patients. Blue laser imaging (BLI) is an image-enhanced endoscopy technique that utilizes white light and narrow-band light to detect pathological changes in the mucosal architecture. This study aims at investigating the diagnostic performance of BLI for the detection of GC. A comprehensive search was conducted across multiple databases from inception until March 2023. Studies assessing the diagnostic efficacy of BLI for GC detection were included. The sensitivity, specificity and accuracy of BLI were calculated using pooled proportions and 95% confidence intervals (CI) with a random-effects model. Heterogeneity among the included studies was assessed using the I2 statistic. Six studies were included in the pooled analysis. There were 708 patients with 380 GC lesions. Most of the lesions involved the lower two-thirds of the stomach. The pooled performance metrics of BLI for GC detection were as follows: sensitivity of 91.9% (95% CI 83.3-96.3%; I2 = 82.3%), specificity of 93.4% (95% CI 82.0-97.8%; I2 = 87.9%) and accuracy of 95.4% (95% CI 72.6-99.8%; I2 = 73.6%). BLI demonstrates high diagnostic efficacy for the detection of GC. BLI can be a valuable tool in clinical practice. However, large-scale, randomized controlled studies are needed to further establish the role of BLI in routine clinical practice for GC detection.

Efficacy and safety of endoscopic submucosal dissection for early gastric cancer and precancerous lesions in elderly patients.

With advancements in the development of endoscopic technologies, the endoscopic submucosal dissection (ESD) has been one of the gold-standard therapies for early gastric cancer. To investigate the efficacy and safety ESD in the treatment of early gastric cancer and precancerous lesions in the elderly patients. Seventy-eight elderly patients with early gastric cancer and precancerous lesions admitted to the Third Affiliated Hospital of Qiqihar Medical University were selected and classified into two groups according to the different surgical therapies they received between January 2021 and June 2022. Among them, 39 patients treated with ESD were included in an experimental group, and 39 patients treated with endoscopic mucosal resection (EMR) were included in a control group. We compared the basic intraoperative conditions, postoperative short-term recovery, long-term recovery effects and functional status of gastric mucosa between the two groups; the basic intraoperative conditions included lesion resection, intraoperative bleeding and operation time; the postoperative short-term recovery assessment indexes were length of hospital stay and incidence of surgical complications; and the long-term recovery assessment indexes were the recurrence rate at 1 year postoperatively and the survival situation at 1 year and 3 years postoperatively; and we compared the preoperative and predischarge serum pepsinogen I (PG I) and PG II levels and PG I/PG II ratio in the two groups before surgery and discharge. The curative resection rate and the rate of en bloc resection were higher in the experimental group than in the control group. The intraoperative bleeding volume was higher in the experimental group than in the control group. The operation time was longer in the experimental group than that in the control group, and the rate for base residual focus was lower in the experimental group than that of the control group, and the differences were all statistically significant (all P < 0.05). The length of hospital stay was longer in the experimental group than in the control group, and the incidence of surgical complications, 1-year postoperative recurrence rate and 3-year postoperative survival rate were lower in the experimental group than in the control group, and the differences were statistically significant (all P < 0.05). However, the difference in the 1-year postoperative survival rate was not statistically significant between the two groups (P > 0.05). Before discharge, PG I and PG I/PG II ratio were elevated in both groups compared with the preoperative period, and the above indexes were higher in the experimental group than those in the control group, and the differences were statistically significant (both P < 0.05). Moreover, before discharge, PG II level was lower in both groups compared with the preoperative period, and the level was lower in the experimental group than in the control group, and the differences were all statistically significant (all P < 0.05). Compared with EMR, ESD surgery is more thorough. It reduces the rate of base residual focus, recurrence rate, surgical complications, and promotes the recovery of gastric cells and glandular function. It is safe and suitable for clinical application.

Gastric cancer detection rates using GI endoscopy with serologic risk stratification: a randomized controlled trial

Background and aimsEfforts have been made to develop an endoscopic screening system incorporating serological gastric cancer (GC) risk stratification (ABC classification) alongside annual population-based GC screening using barium. We conducted a randomized controlled trial (RCT) to compare GC detection rates between the Ba-Endo group, which underwent annual barium tests for primary screening followed by detailed endoscopic examinations, and the ABC-Endo group, where endoscopy intervals were determined based on individual gastric cancer risk in the ABC classification. MethodsIn total, 1,206 individuals from Yurihonjo and Nikaho city, Akita Prefecture, were randomized through the minimization method using sex and age as allocation factors. The intervention study was conducted for both groups over 5 years. The Ba-Endo group received annual barium tests, the ABC-Endo group underwent esophagogastroduodenoscopy (EGD) at different intervals: group A (EGD only at entry), B (EGD once every 3 years), C (EGD once every 2 years), and D (EGD every year) respectively. ResultsThere were 24 detected GC lesions, with a GC detection rate of 1.9%. GC detection rates in the Ba-Endo and ABC-Endo groups were 2.0% and 1.8%, respectively, with no significant differences between groups (P=1.0). However, the rate of GC cured by endoscopic resection alone was 41.6% in the Ba-Endo group and significantly higher at 90.9% in the ABC-Endo group (P=0.02). ConclusionThere were no differences between Ba-Endo and ABC-Endo groups in GC detection rates. However, the rate of detected GCs that could be cured by endoscopic resection alone was significantly higher in the ABC-Endo group.

Autophagy-Related Gene ATG7 Polymorphism Could Potentially Serve as a Biomarker of the Progression of Atrophic Gastritis.

Cytotoxin-associated gene A (CagA) is an oncoprotein that H. pylori injects into the host's gastric epithelial cells and that induces proinflammatory cytokines, such as interleukin (IL)-18 and IL-1β. As a result, it leads to atrophic gastritis (AG), a precancerous lesion of gastric cancer. On the other hand, host cells degrade CagA using autophagy systems. However, few studies exist about the single nucleotide polymorphisms (SNPs) in MAP1LC3A, MAP1LC3B, ATG4A, ATG4B, ATG4C, ATG7, and ATG13, which belong to the autophagy-related genes concerning AG. This study aimed to detect biomarkers associated with AG. Herein, H. pylori-positive subjects (n = 200) were divided into the AG (n = 94) and non-AG (n = 106) groups. Thirty tag SNPs were selected from the above seven candidate genes. The SNP frequency between the two groups was analyzed. The frequency of the C/T or T/T genotype at rs4683787 of ATG7 was significantly lower in the AG group than in the non-AG group (p = 0.034, odds ratio = 0.535). Based on multivariate analysis, the C/C genotype of rs4684787 and age were independently associated with gastric mucosal atrophy. This finding helps stratify the patients needing timely endoscopic screening or early eradication of H. pylori.

Celastrus orbiculatus extract reverses precancerous lesions of gastric cancer by inhibiting autophagy via regulating the PDCD4-ATG5 signaling pathway.

Celastrus orbiculatus ethyl acetate extract (COE) is the main extract of the stem of the Chinese herbal C. orbiculatus, which has anti-tumor and anti-inflammatory biological effects. Our previous study showed that COE had a certain reversal effect on the precancerous lesions of gastric cancer (PLGC) in rats, but the exact mechanism of action remains elusive. We aimed to explore the therapeutic effects of COE on PLGC and the potential mechanisms. The PLGC rat model was successfully constructed by N-methyl-N´-nitro-N-nitrosoguanidine (MNNG) multifactorial induction method. Then, COE was prepared to treat the PLGC rat model. Hematoxylin & eosin staining was used to observe gastric mucosal lesions in rats, AB-PAS and HID-AB staining were used to observe intestinal metaplasia. PDCD4-ATG5 signaling pathway was detected by immunohistochemistry (IHC) and reverse transcription polymerase chain reaction (RT-PCR) in vivo, and autophagy level was detected by IHC, transmission electron microscopy, and RT-PCR in vivo. Besides, the PLGC (MC) cell model was successfully constructed by treating GES-1 cells with MNNG. Then, the morphology, proliferation, and apoptosis of MC cells, and the role of the PDCD4-ATG5 signaling pathway and autophagy in MC cells were evaluated by COE and after the overexpression of PDCD4 treatment. COE significantly improved gastric mucosal injury and cellular heteromorphism and retarded the progression of PLGC in rats. Further studies indicated COE not only inhibited the level of autophagy but also interfered with the PDCD4-ATG5 signaling pathway in vivo. On the other hand, COE treatment could effectively reverse MC cell damage, inhibit MC cell proliferation, and promote MC cell apoptosis. Furthermore, COE also promoted PDCD4 and inhibited ATG5 expression in vitro, and the inhibitory effect of COE on ATG5-mediated autophagy was further enhanced after the overexpression of PDCD4. The study revealed that COE could regulate the PDCD4-ATG5 signaling pathway to inhibit autophagy in gastric epithelial cells, which contributes to reversing the progression of PLGC.

CrossU-Net: Dual-modality cross-attention U-Net for segmentation of precancerous lesions in gastric cancer

Gastric precancerous lesions (GPL) significantly elevate the risk of gastric cancer, and precise diagnosis and timely intervention are critical for patient survival. Due to the elusive pathological features of precancerous lesions, the early detection rate is less than 10%, which hinders lesion localization and diagnosis. In this paper, we provide a GPL pathological dataset and propose a novel method for improving the segmentation accuracy on a limited-scale dataset, namely RGB and Hyperspectral dual-modal pathological image Cross-attention U-Net (CrossU-Net). Specifically, we present a self-supervised pre-training model for hyperspectral images to serve downstream segmentation tasks. Secondly, we design a dual-stream U-Net-based network to extract features from different modal images. To promote information exchange between spatial information in RGB images and spectral information in hyperspectral images, we customize the cross-attention mechanism between the two networks. Furthermore, we use an intermediate agent in this mechanism to improve computational efficiency. Finally, we add a distillation loss to align predicted results for both branches, improving network generalization. Experimental results show that our CrossU-Net achieves accuracy and Dice of 96.53% and 91.62%, respectively, for GPL lesion segmentation, providing a promising spectral research approach for the localization and subsequent quantitative analysis of pathological features in early diagnosis.