Objective : To investigate the effect of chromosome instability(CIN) associated gene polypeptide N-acetylgalactosaminyltransferase 7(GALNT7) on proliferation and apoptosis of HCT116 colon cancer cells. Methods : The HCT116 cell line withGALNT7knockdown was constructed by lentiviral infection. The correlation betweenGALNT7and CIN was verified by chromosome spread assay. The effect ofGALNT7on cell proliferation was detected by live cell counting, and the effect ofGALNT7on cell cycle distribution was detected by flow cytometry and Western blot. Caspase-3 activity and Western blot assays were used to detect the effect ofGALNT7on apoptosis. Results : HCT116 cells showed a slower proliferation rate upon knocking down ofGALNT7, and exhibited a more scattered karyotype distribution and a phenotype of increased degree of CIN. Inhibition ofGALNT7in HCT116 cells resulted in cell cycle arrest, upregulation of P21 and downregulation of CDK6 protein levels, as well as increased levels of Caspase-3 activity, cleaved PARP1 and PUMA protein expression, and decreased levels of BCL-2 protein expression. Conclusion TheGALNT7gene may promote proliferation and inhibit apoptosis of HCT116 colon cancer cells through the suppression of CIN generation.

The morphological characteristics of hepatocellular carcinoma(HCC)tumor margins are pivotal in influencing patient's prognosis and the selection of therapeutic strategies.This paper re-viewed the classification methods of HCC tumor margins,ranging from traditional macroscopic classifi-cations to refined classification systems based on multi-omics analysis,and analyzed the role of these classification methods in guiding the formulation of personalized treatment plans.Additionally,this paper emphasized the crucial role of three-dimensional imaging techniques in assessing tumor margin morphology and outlined future research directions,including validating the effectiveness of multi-omics classification systems and developing new imaging and molecular biomarkers to achieve more precise treatment plans and prolong patient survival.

Objective: To investigate differentially expressed genes associated with liver cancer using bioinformatics methods, and to screen out molecular markers for early diagnosis of liver cancer and potential molecular targets for immunotherapy. Methods: The microarray data associated with liver cancer were downloaded from Gene Expression Omnibus. JMP software was used for correlation analysis of GSE datasets, Limma program in R language was used to screen out differentially expressed genes, and the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis were performed for differentially expressed genes. A protein-protein interaction (PPI) network was also established for analysis. An analysis of specific expression associated with liver cancer was performed with reference to RNA-seq transcriptome data for other tumors obtained from TCGA to further identify specific differentially expressed genes in liver cancer, and a survival curve analysis was performed for patients with liver cancer. Results: A total of 92 differentially expressed genes were identified, with 21 upregulated genes and 71 downregulated genes. Through the GO, KEGG, and PPI analyses, RNA-seq data verified that only glypican 3 (GPC3) was upregulated in liver cancer, and MBL2, SDS, SLCO1B3, TDO2, SAA4, and SPP2 were downregulated. Conclusions GPC3 might act as a target for immunotherapy, and other molecular markers may become molecular markers for early detection of liver cancer and potential targets for immunotherapy.