RESUMO
Background: Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms. Materials and methods: We downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs. Results: A total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine. Conclusion: The novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.
RESUMO
Background: Epidemiologic studies have produced conflicting results on the effects of metformin on pancreatic cancer. This study aimed to observe and analyze whether metformin use is associated with better prognosis in pancreatic cancer. Materials and Methods: In this retrospective cohort study, all baseline data were retrieved from The Chinese Medicine Information Retrieval System (https://dc.wzhospital.cn/vpn/index.html) of The First Affiliated Hospital of Wenzhou Medical University. Survival data were collected by follow-up visits and medical records. Overall survival was the primary endpoint, while progression-free survival and disease-free survival were secondary endpoints. Progression or recurrence was assessed with radiologic images. Results: Seventy-six metformin users and 92 metformin nonusers diagnosed with pancreatic cancer from 2012 to 2020 in this hospital were enrolled. The adjusted hazard ratio for overall survival for metformin users was 0.50 (95% confidence interval = 0.33-0.76), where median overall survival was 16.0 months for metformin users versus 11.5 months for metformin nonusers. The protective effect was also found by analyzing progression-free survival (adjusted hazard ratio = 0.39, 95% confidence interval = 0.18-0.86) and disease-free survival (adjusted hazard ratio = 0.30, 95% confidence interval = 0.14-0.68). In the subgroup analysis, metformin use had a statistically significant association with prolongation of survival in stage I to II pancreatic cancer patients (hazard ratio = 0.47, 95% confidence interval = 0.25-0.91), but not for advanced tumor stage (hazard ratio for IV stage = 0.62, 95% confidence interval = 0.33-1.19), after adjustment for other risk factors. Conclusion: Metformin use is related to favorable survival outcomes of pancreatic cancer, especially in early tumor stage.