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A predictive model was developed by integrating clinicopathological features with molecular biomarkers, using GEO gene-expression analysis, differential expression and WGCNA, and machine-learning classifiers (Random Forest, SVM). LASSO regression selected CAG risk factors and built a nomogram. Immunohistochemistry validated IGFBP7, whose IHC score improved prediction.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-novel-risk-stratification-strategy-for-precision-prevention-of-gastric-cancer-based-on-clinicopathological-features-and-igfbp7-abstract/363659/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-novel-risk-stratification-strategy-for-precision-prevention-of-gastric-cancer-based-on-clinicopathological-features-and-igfbp7-abstract/363659.png","ImageObject",300,407,{"name":92,"@type":93},"Ben ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the study in patients with chronic atrophic gastritis?","Question",{"text":113,"@type":114},"To develop and validate a cost-effective, high-accuracy risk prediction model that identifies CAG patients at high risk for progression to gastric cancer, enabling precision surveillance and early intervention.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were biomarkers selected and validated?",{"text":118,"@type":114},"Gene-expression profiles from GEO were analyzed to find differentially expressed genes and coexpression modules, then machine-learning models evaluated biomarkers. LASSO regression identified risk factors, and immunohistochemistry was used to measure the key biomarker protein expression, integrating IGFBP7 into the clinical model.",{"name":120,"@type":111,"acceptedAnswer":121},"Which factors were identified as independent risks for gastric cancer progression?",{"text":122,"@type":114},"Age, smoking history, and histological severity were identified as independent risk factors for GC progression.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},363659,1790187262,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},2336478951081,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nA novel risk stratification strategy for precision prevention of gastric cancer based on clinicopathological features and IGFBP7  \nXiaohui Guo1,2, XiaoliXie1, Yujian He1, Yuqi Liu3, Xiaoxu Jin1, Yanyan Wang4, Jianhua Wang2 & Zhijie Feng1􀀍  \nChronic atrophic gastritis (CAG) is a critical precursor lesion of gastric cancer (GC); however, precise tools for identifying high-risk individuals are currently lacking. This study aimed to develop a predictive model integrating clinicopathological characteristics and molecular biomarkers to  \nenable individualized risk assessment for GC progression in CAG patients. Gene expression profiles from GEO were analyzed using differential expression genes(DEGs) analysis, weighted gene coexpression network analysis (WGCNA) . Random Forest and Support Vector Machine models evaluated biomarkers. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to identify risk factors for CAG, thus constructing a nomogram. The protein expression level of the key biomarker was evaluated through immunohistochemistry, which was subsequently integrated into the clinical model to create a novel model. Age, smoking history, and histological severity were identified as independent risk factors for GC progression. The clinical nomogram based on these factors demonstrated good predictive capability. Multi-omics analysis revealed that IGFBP7 was significantly upregulated in tissues from patients who progressed to GC. Integrating the IHC score of IGFBP7  \ninto the clinical prediction model significantly enhanced its predictive performance. We successfully developed and validated a nomogram model combining clinical risk factors and the biomarker  \nIGFBP7. This model effectively identifies CAG patients at high risk for GC, providing a practical tool for implementing precision surveillance and early intervention.  \nKeywords Chronic atrophic gastritis, Gastric cancer, Risk prediction, Nomogram, Insulin-like growth factor-binding protein 7 (IGFBP7), Machine learning  \nGastric cancer (GC) ranks as the fifth most common malignancy and the third leading cause of cancer-related mortality globally1,2, posing a persistent and severe challenge to public health systems. Gastric carcinogenesis often follows a multi-step precancerous cascade, with chronic atrophic gastritis (CAG) recognized as the initial stage3,4. From CAG, lesions can progressively evolve through intestinal metaplasia (IM), dysplasia, and ultimately to invasive GC5,6. This “inflammation-cancer transformation” is typically driven by synergistic factors including Helicobacter pylori infection7, gut microbiota dysbiosis8, genetic variations9, oxidative stress10, and immune dysregulation11, leading to persistent mucosal damage and pathological changes such as chief cell transdifferentiation, spasmolytic polypeptide-expressing metaplasia (SpEM), incomplete IM, and dysplasia12, 13. Studies indicate that the annual incidence of GC is approximately 0.1% in CAG patients, rising to 0.25% with IM, and escalating sharply to 6% in patients with moderate-to-severe dysplasia14, underscoring the urgency for early risk intervention at the precancerous stage.  \nAlthough histopathological staging systems (e.g., OLGA/OLGIM) are clinically used to assess the severity of atrophic gastritis15, 16, significant limitations remain in accurately predicting individual risk of progression to GC, due to the inherent heterogeneity of gastric mucosal lesions and the insidious nature of early clinical  \n1Department of Gastroenterology, Hebei Key Laboratory of Gastroenterology, Hebei Clinical Research Center for Digestive Diseases, The Second Hospital of Hebei Medical University, Hebei Institute of Gastroenterology, Shijiazhuang 050000, Hebei, China. 2Department of Gastroenterology, Affiliated Hospital of Hebei Engineering University, Handan 056000, Hebei, China. 3Hebei Engineering Unive","cbCaiszYZAJgBAh8","https://ap.wps.com/l/cbCaiszYZAJgBAh8","pdf",2608589,13,"English","# Methods\n## Data Collection and processing","[{\"question\":\"What is the main goal of the study in patients with chronic atrophic gastritis?\",\"answer\":\"To develop and validate a cost-effective, high-accuracy risk prediction model that identifies CAG patients at high risk for progression to gastric cancer, enabling precision surveillance and early intervention.\"},{\"question\":\"How were biomarkers selected and validated?\",\"answer\":\"Gene-expression profiles from GEO were analyzed to find differentially expressed genes and coexpression modules, then machine-learning models evaluated biomarkers. LASSO regression identified risk factors, and immunohistochemistry was used to measure the key biomarker protein expression, integrating IGFBP7 into the clinical model.\"},{\"question\":\"Which factors were identified as independent risks for gastric cancer progression?\",\"answer\":\"Age, smoking history, and histological severity were identified as independent risk factors for GC progression.\"}]","A novel risk stratification strategy for precision prevention of gastric cancer based on clinicopathological features and IGFBP7 - Abstract | PDF",1790152530,33]