Background: It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice. Methods: We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15). Results: A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset. Conclusions: We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.

Background: Sigmoid colon cancer with spinal metastases is rare in distant metastasis. In addition, the prognosis of colon cancer patients with spinal metastases is extremely poor. In order to find effective therapeutic agents, we need to know the biological characteristics of such patients from related models.Methods: We collected sigmoid colon cancer tissue from a young female subject who was diagnosed with sigmoid colon cancer with multiple spinal metastases. We successfully established a sigmoid colon cancer organoid using this tissue and investigated drug screening in the patient. HE staining, immunohistochemistry, and DNA sequencing were utilized to compare the biological characteristics between the original tumor and the organoid. Furthermore, we investigated the drug screening of the sigmoid colon cancer organoid in vitro.Results: A colon cancer organoid from sigmoid colon cancer with spinal metastases was successfully established. The organoid culture maintained the morphological features, histological features, and genomic landscape of the corresponding sigmoid colon cancer cells. Moreover, we performed drug screening tests to evaluate the effects of chemotherapeutic drugs and targeted drugs.Conclusion: The sigmoid colon cancer organoid with spinal metastases was a favorable preclinical model to explore the clinicopathologic characteristics of colon cancer patients with spinal metastases.