Stomach and esophageal cancer are two major types of the most prevalent cancers, with both their morbidity and mortality ranking in the top 10 among all cancers [1,2]. Globally, stomach cancer has been reported to rank fifth in incidence and fourth in mortality [3,4]. Esophageal cancer ranks seventh and sixth worldwide, respectively [5,6]. Notably, China […]

Targeting pathogenic proteins with small-molecule inhibitors (SMIs) has become a widely used strategy for treating malignant tumors. However, most intracellular proteins have been proven to be undruggable due to a lack of active sites, leading to a significant challenge in the design and development of SMIs. In recent years, the proteolysis-targeting chimeric technology and related emerging degradation technologies have provided additional approaches for targeting these undruggable proteins. These degradation technologies show a tendency of superiority over SMIs, including the rapid and continuous target consumption as well as the stronger pharmacological effects, being a hot topic in current research. This review mainly focuses on summarizing the development of protein degradation technologies in recent years. Their advantages, potential applications, and limitations are also discussed. We hope this review would shed light on the design, discovery, and clinical application of drugs associated with these degradation technologies.

Early detection through screening is critical for reducing gastric cancer (GC) mortality. However, in most high-prevalence regions, large-scale screening remains challenging due to limited resources, low compliance and suboptimal detection rate of upper endoscopic screening. Therefore, there is an urgent need for more efficient screening protocols. Noncontrast computed tomography (CT), routinely performed for clinical purposes, presents a promising avenue for large-scale designed or opportunistic screening. Here we developed the Gastric Cancer Risk Assessment Procedure with Artificial Intelligence (GRAPE), leveraging noncontrast CT and deep learning to identify GC. Our study comprised three phases. First, we developed GRAPE using a cohort from 2 centers in China (3,470 GC and 3,250 non-GC cases) and validated its performance on an internal validation set (1,298 cases, area under curve = 0.970) and an independent external cohort from 16 centers (18,160 cases, area under curve = 0.927). Subgroup analysis showed that the detection rate of GRAPE increased with advancing T stage but was independent of tumor location. Next, we compared the interpretations of GRAPE with those of radiologists and assessed its potential in assisting diagnostic interpretation. Reader studies demonstrated that GRAPE significantly outperformed radiologists, improving sensitivity by 21.8% and specificity by 14.0%, particularly in early-stage GC. Finally, we evaluated GRAPE in real-world opportunistic screening using 78,593 consecutive noncontrast CT scans from a comprehensive cancer center and 2 independent regional hospitals. GRAPE identified persons at high risk with GC detection rates of 24.5% and 17.7% in 2 regional hospitals, with 23.2% and 26.8% of detected cases in T1/T2 stage. Additionally, GRAPE detected GC cases that radiologists had initially missed, enabling earlier diagnosis of GC during follow-up for other diseases. In conclusion, GRAPE demonstrates strong potential for large-scale GC screening, offering a feasible and effective approach for early detection. ClinicalTrials.gov registration: NCT06614179 .

Background: Our previous study revealed marked differences in tongue images between individuals with gastric cancer and those without gastric cancer. However, the biological mechanism of tongue images as a disease indicator remains unclear. Tongue coating, a major factor in tongue appearance, is the visible layer on the tongue dorsum that provides a vital environment for oral microorganisms. While oral microorganisms are associated with gastric and intestinal diseases, the comprehensive function profiles of oral microbiota remain incompletely understood. Metaproteomics has unique strength in revealing functional profiles of microbiota that aid in comprehending the mechanism behind specific tongue coating formation and its role as an indicator of gastric cancer. Methods: We employed pressure cycling technology and data-independent acquisition (PCT-DIA) mass spectrometry to extract and identify tongue-coating proteins from 180 gastric cancer patients and 185 non-gastric cancer patients across 5 independent research centers in China. Additionally, we investigated the temporal stability of tongue-coating proteins based on a time-series cohort. Finally, we constructed a machine learning model using the stochastic gradient boosting algorithm to identify individuals at high risk of gastric cancer based on tongue-coating microbial proteins. Results: We measured 1432 human-derived proteins and 13,780 microbial proteins from 345 tongue-coating samples. The abundance of tongue-coating proteins exhibited high temporal stability within an individual. Notably, we observed the downregulation of human keratins KRT2 and KRT9 on the tongue surface, as well as the downregulation of ABC transporter COG1136 in microbiota, in gastric cancer patients. This suggests a decline in the defense capacity of the lingual mucosa. Finally, we established a machine learning model that employs 50 microbial proteins of tongue coating to identify individuals at a high risk of gastric cancer, achieving an area under the curve (AUC) of 0.91 in the independent validation cohort. Conclusions: We characterized the alterations in tongue-coating proteins among gastric cancer patients and constructed a gastric cancer screening model based on microbial-derived tongue-coating proteins. Tongue-coating proteins are shown as a promising indicator for identifying high-risk groups for gastric cancer.

Younger premenopausal women are more prone to developing ovarian metastases (OM) of gastric cancer (GC) than metastases of other organs; however, the molecular mechanisms remain unclear. Here we perform single-cell RNA sequencing on 45 tumor samples from 18 GC patients with OM. Interestingly, fibroblasts in OM of GC express high levels of estrogen receptor (ER) and midkine (MDK), interacting with tumor cells through activating ER-MDK-LRP1 (low-density lipoprotein receptor-related protein 1) signaling axis. Functional experiments demonstrate that estrogen stimulation induces MDK secretion by ovarian fibroblasts, and binding of MDK to LRP1 increases GC cell migration and invasion. Furthermore, in vivo, estrogen stimulation remarkably augments ovarian engraftment and metastasis of LRP1⁺ GC cells. Collectively, our findings reveal that ER⁺ ovarian fibroblasts secrete MDK under estrogen influence, driving OM of GC via the MDK-LRP1 axis. Our study holds the potential to catalyze innovative therapeutic strategies aimed at intercepting and managing OM in GC.