Objective To screen the core genes related to colon cancer in order to provide a new research direction for molecular di-agnosis,targeted therapy and prognosis evaluation of colon cancer.Methods Colon cancer differentially expressed genes(DEGs)were screened out from the gene expression database(GEO),and functional annotation and pathway enrichment analysis of DEGs were per-formed by gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)analysis in Metascape database.The network of protein interactions(PPI)was built utilizing the String database and Cytoscape software to identify the core genes.Survival analysis of se-lected core genes was performed using Kaplan-Meier plotter database,and expression level of core genes was verified using GEPIA2database.Metascape data was used to conduct KEGG pathway enrichment analysis on the selected core genes,and the signal pathway of core gene enrichment was identified.COL1A1 in the core gene was selected for immunohistochemical verification.Results A total of 468 DEGs were identified,including 162 UDEGs and 306 DDEGs.In the two datasets,there were 194 overlapping genes,of which 54 were up-regulated and 140 were down-regulated.With the help of CytoHubba plugin,the top 10gene nodes in the score were selected and identified as the possible core genes of colon cancer(COL1A1,MMP3,COL1A2,COL11A1,THBS2,BGN,MMP1,COL10A1,SPP1,INHBA).The prognosis and expression levels of these 10genes were analyzed,and the expression levels of the 10 core genes were different in colon cancer tissues and normal colon tissues.All of the 10 core genes were correlated with colon cancer prognosis except MMP1,among which MMP3 was positively correlated and the other 8genes were negatively correlated.KEGG analysis showed that core genes were mainly concentrated in three signaling pathways:cytoskeleton,extracellular matrix(ECM)receptor interaction and pro-tein digestion and absorption in muscle cells.Immunohistochemistry analysis revealed a marked increase in the expression level of COL1A1 within the tumor tissue compared to that in paracancer tissue.Conclusion The core genes COL1A1,MMP3,COL1A2,COL11A1,THBS2,BGN,MMP1,COL10A1,SPP1 and INHBA selected by bioinformation analysis may be involved in the occurrence and development of colon cancer,and may be related to the prognosis of patients with colon cancer.It is anticipated that it will serve as a clinically significant biomarker and a therapeutic target for colon cancer.

Objective To screen the core genes related to colon cancer in order to provide a new research direction for molecular di-agnosis,targeted therapy and prognosis evaluation of colon cancer.Methods Colon cancer differentially expressed genes(DEGs)were screened out from the gene expression database(GEO),and functional annotation and pathway enrichment analysis of DEGs were per-formed by gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)analysis in Metascape database.The network of protein interactions(PPI)was built utilizing the String database and Cytoscape software to identify the core genes.Survival analysis of se-lected core genes was performed using Kaplan-Meier plotter database,and expression level of core genes was verified using GEPIA2database.Metascape data was used to conduct KEGG pathway enrichment analysis on the selected core genes,and the signal pathway of core gene enrichment was identified.COL1A1 in the core gene was selected for immunohistochemical verification.Results A total of 468 DEGs were identified,including 162 UDEGs and 306 DDEGs.In the two datasets,there were 194 overlapping genes,of which 54 were up-regulated and 140 were down-regulated.With the help of CytoHubba plugin,the top 10gene nodes in the score were selected and identified as the possible core genes of colon cancer(COL1A1,MMP3,COL1A2,COL11A1,THBS2,BGN,MMP1,COL10A1,SPP1,INHBA).The prognosis and expression levels of these 10genes were analyzed,and the expression levels of the 10 core genes were different in colon cancer tissues and normal colon tissues.All of the 10 core genes were correlated with colon cancer prognosis except MMP1,among which MMP3 was positively correlated and the other 8genes were negatively correlated.KEGG analysis showed that core genes were mainly concentrated in three signaling pathways:cytoskeleton,extracellular matrix(ECM)receptor interaction and pro-tein digestion and absorption in muscle cells.Immunohistochemistry analysis revealed a marked increase in the expression level of COL1A1 within the tumor tissue compared to that in paracancer tissue.Conclusion The core genes COL1A1,MMP3,COL1A2,COL11A1,THBS2,BGN,MMP1,COL10A1,SPP1 and INHBA selected by bioinformation analysis may be involved in the occurrence and development of colon cancer,and may be related to the prognosis of patients with colon cancer.It is anticipated that it will serve as a clinically significant biomarker and a therapeutic target for colon cancer.

Cuproptosis is a new type of cell death that depends on intracellular copper accumulation to trigger the aggregation of mitochondrial lipoacylated protein and the degradation of iron-sulfur cluster protein,with a different mechanism of action from autophagy,ferroptosis,pyroptosis,and necroptosis.Cuproptosis is closely association with the development of liver cancer and resistance to antitumor drugs,as well as the progression of various liver diseases such as hereditary liver diseases,nonalcoholic fatty liver disease,viral hepatitis,and liver cirrhosis.This article summarizes the mechanism of cuproptosis and its role in liver diseases,in order to provide a reference for further research and treatment of liver diseases.

forkhead box protein C1(FOXC1)is a cancer-related transcription factor that plays an important role in various signaling pathways related to cancer development.FOXC1 is involved in the regulation of cancer cells from many aspects,including proliferation,metastasis and recurrence.FOXC1 can promote cell cycle progression and cell proliferation by up-regulating the expression of oncogenes,such as cyclin D1.FOXC1 promotes epithelial-mes-enchymal transition(EMT),in which cells change from polarized epithelial cells to cells with stromal characteris-tics,which contributes to tumor cell infiltration and migration.FOXC1 may play a role in tumor angiogenesis by reg-ulating genes associated with angiogenesis.FOXCI can enhance cell migration and invasion by regulating reactive ox-ygen species and activating β-catenin expression.In summary,FOXC1 functions through a variety of mechanisms.

Objective To investigate the risk factors of infection after hepatectomy for liver cancer, and to establish and validate a risk prediction model. Methods The clinical data of 167 patients with primary liver cancer who underwent hepatectomy in People's Hospital of Wuhan University from January 2020 to March 2022 were retrospectively collected. All patients were divided into postoperative infection group ( n =28) and non-infection group ( n =139) according to whether postoperative infection complications occurred. The t -test or Mann-Whitney U test was used for comparison of continuous data between two groups and the chi-square test was used for comparison of categorical data between two groups. Univariate analysis and logistic regression analysis were used to screen the risk factors of infection after hepatectomy for hepatocellular carcinoma, and a nomogram risk prediction model for postoperative infection was established. All patients were randomly divided into training cohort ( n =119) and the validation cohort ( n =48) according to the ratio of 7∶ 3, the Bootstrap method was used for internal validation of the model, and the model calibration curve and ROC curve were used to evaluate the calibration and discrimination of the nomogram model. Results Postoperative infection occurred in 28 of 167 patients (16.8%). Logistic regression analysis showed that diabetes, CONUT score ≥4 points, preoperative NLR, operation time, intraoperative blood loss, and drainage tube placement time > 7 d were independent risk factors for infection after hepatectomy for liver cancer (all P < 0.05). Based on the nomogram constructed from the above six risk factors, the area under the ROC curve of the training cohort and the validation cohort was 0.848, and 0.853, respectively. The calibration curve of the nomogram model shows that the predicted value is basically consistent with the actual observed value, indicating that the accuracy of the nomogram model prediction is better. Conclusion The individualized nomogram risk prediction model based on diabetes, CONUT score ≥4 points, preoperative NLR, operation time, intraoperative blood loss, and drainage tube placement time > 7 d has good predictive performance and has high predictive value for high-risk patients.

Objective To analyze the serological markers and surgical indicators associated with biliary complications after orthotopic liver transplantation, explore their influencing factors and predictive indicators. Methods A retrospective analysis was performed for the clinical data of 101 patients who underwent orthotopic liver transplantation in Renmin Hospital of Wuhan University from January 2016 to June 2022, according to the presence or absence of biliary complication (BC) at 6 months after surgery, they were divided into BC group with 21 patients and non-BC group with 80 patients.The t -test or the Mann-Whitney U test was used for comparison of continuous data between groups, and the chi-square test was used for comparison of categorical data between groups.Univariate and multivariate Logistic regression analyses were performed, and the receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of combined indicators. Results Among the 101 patients, 21(20.8%) experienced BC.The multivariate Logistic regression analysis showed that MELD score (odds ratio[ OR ]=0.134, 95% confidence interval[ CI ]: 0.031-0.590, P =0.008), SⅡ/Alb ( OR =1.415, 95% CI : 1.181-1.696, P =0.001), and plasma transfusion volume ( OR =1.001, 95% CI : 1.000-1.002, P =0.032) were independent risk factors for the development of BC in patients after liver transplantation.MELD score, SⅡ/Alb, plasma transfusion volume, MELD+SⅡ/Alb, and MELD+SⅡ/Alb+plasma transfusion volume had an area under the ROC curve of 0.712, 0.870, 0.712, 0.900, and 0.918, respectively, in predicting BC after liver transplantation. Conclusion SⅡ/Alb, plasma transfusion volume and MELD score are independent risk fators for BC after liver transplantation.The combination of three indicators has good predictive value and clinical guiding significance for BC after liver transplantation.

At present, hepatectomy has become the preferred treatment modality for most benign and malignant hepatobiliary diseases. Liver failure is a common complication after hepatectomy, and for malignant diseases, how to remove the lesion to the maximum extent and reduce the incidence rate of liver failure after hepatectomy is the key problem at present. Accurate and adequate preoperative evaluation of liver reserve function can provide a basis for judging the progression, therapeutic outcome, and prognosis of liver diseases. There are currently various methods for evaluating liver reserve function and surgical feasibility, each with its own advantages and disadvantages, and there is still a lack of a single comprehensive evaluation method. This article reviews the characteristics of commonly used evaluation methods and related research advances.

Objective:To investigate the effect of ring finger protein 187 (RNF187) on cell pro-liferation, migration and invasion of hepatocellular carcinoma (HCC).Methods:Messenger RNA (mRNA) level of RNF187 in HCC was analyzed by bioinformatics. Huh7 cells transfected with small interfering RNA (siRNA) of negative control or target gene respectively were classified as non-transfection (NC) group and RNF187 knockdown group. After 24 hours of transfection, the above two groups dimethyl sulfoxide (DMSO) were used as NC+ DMSO group and RNF187 knockdown + DMSO group. 24 hours after transfection with siRNA of target gene, the cells dealt with bafliomycin A1 (BFA) were set as RNF187 knockdown + BFA group. The regulation of RNF187 on malignant biological behavior and autophagy level of HCC cells were explored by cell counting kit-8 (CCK8) proliferation assay, cell scratch assay, transwell assay and western blot.Results:Compared with normal liver tissue, the mRNA level of RNF187 was higher in HCC tissue ( P<0.05). Compared with NC group, the absorbance at 48 h and 72 h and the scratch healing rate at 12 h and 24 h of RNF187 knockdown group were all lower, the differences were statistically significant (all P<0.001). The number of transmembrane cells in RNF187 knockdown group (39.50±5.57) at 24 h was lower than that in NC group (128.25±17.35), the differences were statistically significant ( t=9.74, P<0.001). Compared with NC group, the relative expression of total LC3 and Beclin-1 in RNF187 knockdown group all increased, while the relative expression of phosphorylated mammalian target of rapamycin decreased, the difference were statistically significant (all P<0.05). Compared with RNF187 knockdown+ DMSO group, the autophagy flow level, the 48 h and 72 h absorbance, the scratch healing rate at 24 h in RNF187 knockdown + BFA group were higher, the differences were statistically significant (all P<0.001). The number of transmembrane cells in RNF187 knockdown + BFA group (119.00±2.65) was more than that in RNF187 knockdown + DMSO group (57.67±2.52), the differences were statistically significant ( t=29.09, P<0.001). Conclusion:RNF187 is highly expressed in HCC tissue and knockdown of RNF187 inhibits the malignant biological behavior of HCC by enhancing the level of autophagy.

Liver cancer has a high degree of malignancy and its prognosis is poor. The biological behavior of liver cancer with its high degree of malignancy and high tumor heterogeneity impact on its therapeutic effect. The rapid development of precision therapy in the field of cancer provides a platform for application of the precision therapy mode in liver cancer treatment. This article reviews the combined impact by using immune checkpoint inhibitors therapy under precision medicine with targeted therapy, radiotherapy, chemotherapy, oncolytic viruses, tumor vaccines, fecal bacteria transplantation and other therapies.

Tumor microenvironment is composed of tumor cells and their internal and external environment, and the components vary slightly between different types of tumor. Liver cancer microenvironment is a tumor-promoting microenvironment constructed by hepatoma cells and immune cells and can lead to the development, invasion, and metastasis of liver cancer by recruiting inflammatory cells, inhibiting antitumor immune response, promoting angiogenesis, and promoting drug resistance. This article discusses the characteristics of liver cancer microenvironment, the composition and role of liver cancer microenvironment, and the new advances in microenvironment-targeting therapy for liver cancer.