MedComm (2020). 2026 Mar 2;7(3):e70656. doi: 10.1002/mco2.70656. eCollection 2026 Mar.

ABSTRACT

Chemotherapy is the mainstay in the treatment of advanced gastric cancer (GC); yet, GC showed diverse responses to first-line chemotherapy regimens and the underlying molecular basis is still not clear. Here, we established a system that combined organoid-based chemotherapy regimen screening and transcriptome-based evaluation to identify underlying molecular signatures of different responses to chemotherapy. We generated 19 GC patient-derived organoids (PDOs) from surgically resected specimens with corresponding histological characteristics of parent tumors and tested all of the five most commonly used first-line chemotherapy regimens. Based on the treatment responses, PDOs were classified into double-sensitive, single-sensitive, and not-sensitive groups. PDOs that responded well to chemotherapy presented high expression levels of the P53 pathway genes and low expression levels of cell proliferative activity genes. Furthermore, the chemotherapy-based tumor classification of GC was established. The GC tumor classification was verified by multi-omics features from the TCGA dataset and public drug response datasets. In conclusion, this study systematically evaluated clinical chemotherapy regimens for GC and identified chemotherapy response-associated molecular signatures based on human GC organoids, which are beneficial to the precise treatments of GC.

PMID:41782964 | PMC:PMC12954136 | DOI:10.1002/mco2.70656

J Cancer. 2025 Jan 13;16(4):1243-1263. doi: 10.7150/jca.104424. eCollection 2025.

ABSTRACT

Background: This study aimed to investigate glycogen metabolism in gastric cancer (GC) and develop a glycogen-based riskScore model for predicting GC prognosis. Methods: Patients' expression profiles for 33 tumor types were retrieved from TCGA. Four GC bulk and one single-cell sequencing datasets were obtained from GEO database. This study also enrolled a bladder urothelial carcinoma immunotherapeutic IMvigor210 cohort. The ssGSEA method was conducted to assess glycogen biosynthesis and degradation level. Consensus clustering analysis was conducted to identify different clusters. A glycogen riskScore signature was developed to evaluate prognostic value across different cohorts. Besides, in vitro experiments were conducted to further evaluate the role of glycogen metabolism related genes in GC. Results: Both glycogen biosynthesis and degradation were significantly associated with worse overall survival and were also related with malignant phenotype in GC at both bulk and single-cell levels. Differential outcomes and immune functions were verified in the three identified clusters. The constructed glycogen riskScore model accurately classified GC patients with different outcomes, genomic and immune landscape, and performed well in predicting prognosis through external validation, immunotherapy and pan-cancer cohorts. Furthermore, the riskScore could predict response to chemotherapy and immunotherapy. Functional analyses revealed the signature's connection to pro-tumor and immunosuppression related pathways across pan-cancer. Additionally, glycogen metabolism related genes were found to regulate the malignant phenotypes of GC cells. Conclusion: This study revealed important roles of glycogen metabolism in promoting progression of GC and presented a glycogen riskScore model as a novel tool for predicting prognosis and treatment response.

PMID:39895799 | PMC:PMC11786036 | DOI:10.7150/jca.104424