Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Abstract

Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development; therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Road of no return — loss of TP53 paves a defined evolution path from gastric preneoplasia-to-cancer

“…After confirming the genotypic evolution trajectory, Curtis et al further applied single cell sequencing (sc-RNA) to decipher the reprogramming of transcriptional signatures initiated by TP53 deficiency. As expected, when compared with wild-type HGOs and a normal mucosal gene expression signature, such as MUC5AC , TFF1 , and TFF2 , those TP53 −/− HGOs stepwise reduce expression of these markers but gradually acquire GC-associated signatures, including increased expression of claudins ( CLDN3/4/7 ) and carcinoembryonic antigen ( CEACAM5 / 6 ), which phenocopy the molecular events as detected in gastric tumorigenesis models in vivo 17 , 18 . Moreover, the transcriptional comparison between early, middle, and late phases of the same TP53 −/− HGOs consistently identified 13 consistently upregulated genes, such as CLDN4 , TM4SF1 , and ZFAS1 , suggesting these factors are required for the establishment of neoplasia status ( Figure 1B ).…”

Pre-endoscopic screening of precancerous lesions in gastric cancer using deep learning

“…The selection of baseline characteristics is based on individuals aged ≥ 45 who meet any of the following criteria, which are indicative of a high-risk profile for gastric cancer: 1) Long-term residence in high-incidence areas of gastric cancer; 2) Hp infection; 3) History of chronic atrophic gastritis, gastric ulcer, gastric polyp, residual stomach after surgery, hypertrophic gastritis, pernicious anemia, or other precancerous diseases of the stomach; 4) First-degree relatives with a history of gastric cancer; 5) Presence of other high-risk factors for gastric cancer such as high salt intake, pickled diet, smoking, and heavy alcohol consumption [25][26][27][28]. Since our screening is conducted in high-risk areas, we ignore the first criterion.…”