Mendelian Randomization Analyses of Chronic Immune-Mediated Diseases, Circulating Inflammatory Biomarkers, and Cytokines in Relation to Liver Cancer.

Liver cancer is closely linked to chronic inflammation. While observational studies have reported positive associations between extrahepatic immune-mediated diseases and systemic inflammatory biomarkers and liver cancer, the genetic association between these inflammatory traits and liver cancer remains elusive and merits further investigation. We conducted a two-sample Mendelian randomization (MR) analysis, using inflammatory traits as exposures and liver cancer as the outcome. The genetic summary data of both exposures and outcome were retrieved from previous genome-wide association studies (GWAS). Four MR methods, including inverse-variance-weighted (IVW), MR-Egger regression, weighted-median, and weighted-mode methods, were employed to examine the genetic association between inflammatory traits and liver cancer. Nine extrahepatic immune-mediated diseases, seven circulating inflammatory biomarkers, and 187 inflammatory cytokines were analyzed in this study. The IVW method suggested that none of the nine immune-mediated diseases were associated with the risk of liver cancer, with odds ratios of 1.08 (95% CI 0.87-1.35) for asthma, 0.98 (95% CI 0.91-1.06) for rheumatoid arthritis, 1.01 (95% CI 0.96-1.07) for type 1 diabetes, 1.01 (95% CI 0.98-1.03) for psoriasis, 0.98 (95% CI 0.89-1.08) for Crohn's disease, 1.02 (95% CI 0.91-1.13) for ulcerative colitis, 0.91 (95% CI 0.74-1.11) for celiac disease, 0.93 (95% CI 0.84-1.05) for multiple sclerosis, and 1.05 (95% CI 0.97-1.13) for systemic lupus erythematosus. Similarly, no significant association was found between circulating inflammatory biomarkers and cytokines and liver cancer after correcting for multiple testing. The findings were consistent across all four MR methods used in this study. Our findings do not support a genetic association between extrahepatic inflammatory traits and liver cancer. However, larger-scale GWAS summary data and more genetic instruments are needed to confirm these findings.

LncRNA model predicts liver cancer drug resistance and validate in vitro experiments.

Introduction: Hepatocellular carcinoma (HCC) patients may benefit from chemotherapy, but drug resistance is an important obstacle to favorable prognoses. Overcoming drug resistance is an urgent problem to be solved. Methods: Differential expression analysis was used to identify long non-coding RNAs (LncRNAs) that differed in chemotherapy-sensitive and chemotherapy-resistant patients. Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. A back propagation (BP) network was then used to validate the predictive capacity of important LncRNAs. The molecular functions of hub LncRNAs were investigated via qRT-PCR and cell proliferation assay. Molecular-docking technique was used to explore candidate drug of targets of hub LncRNA in the model. Results: A total of 125 differentially expressed LncRNAs between sensitive and resistant patients. Seventeen important LncRNAs were identified via RF, and seven factors were identified via LR. With respect to SVM, the top 15 LncRNAs of AvgRank were selected. Five merge chemotherapy-related LncRNAs were used to predict chemotherapy resistance with high accuracy. CAHM was a hub LncRNA of model and expression high in sorafenib resistance cell lines. In addition, the results of CCK8 showed that the sensitivity of HepG2-sorafenib cells to sorafenib was significantly lower than that of HepG2; and the sensitivity of HepG2-sorafenib cells transfected with sh-CAHM was significantly higher than that of Sorafenib. In the non-transfection group, the results of clone formation experiments showed that the number of clones formed by HepG2-sorafenib cells treated with sorafenib was significantly more than that of HepG2; after HepG2-sorafenib cells were transfected with sh-CAHM, the number of clones formed by Sorafenib treatment was significantly higher than that of HepG2 cells. The number was significantly less than that of HepG2-s + sh-NC group. Molecular Docking results indicate that Moschus was candidate drug for target protein of CAHM. Conclusion: Five chemotherapy-related LncRNAs could predict drug resistance in HCC with high accuracy, and the hub LncRNA CAHM has potential as a new biomarker for HCC chemotherapy resistance.

Expression of tripartite motif-containing 44 and its prognostic and clinicopathological value in human malignancies:a meta-analysis.

BACKGROUND: Previous researches have reported that tripartite motif-containing 44 (TRIM44) is related to the prognosis of multiple human tumors. This study was designed to systematically assess the prognostic value of TRIM44 in human malignancies and summarize its possible tumor-related mechanisms. METHODS: The available databases were searched for eligible studies that evaluated the clinicopathological and prognostic roles of TRIM44 in patients with malignancies. The hazard ratios (HR) and odds ratios (OR) were combined to assess the predictive role of TRIM44 using Stata/SE 14.1 software. RESULTS: A total of 1740 patients from thirteen original studies were finally included in this study. The results of the combined analysis showed that over-expression of TRIM44 protein was significantly correlated with shorter overall survival (OS) (HR = 1.94, 95% CI: 1.60-2.35) and worse disease-free survival (DFS) (HR = 2.13, 95% CI: 1.24-3.65) in cancer patients. Additionally, the combined ORs indicated that elevated expression level of TRIM44 protein was significantly associated with lymph node metastasis (OR = 2.69, 95% CI: 1.71-4.24), distant metastasis (OR = 10.35, 95% CI: 1.01-106.24), poor tumor differentiation (OR = 1.78, 95% CI: 1.03-3.09), increased depth of tumor invasion (OR = 2.72, 95% CI: 1.73-4.30), advanced clinical stage (OR = 2.75, 95% CI: 2.04-3.71), and recurrence (OR = 2.30, 95% CI: 1.34-3.95). Furthermore, analysis results using Gene Expression Profiling Interactive Analysis (GEPIA) showed that the expression level of TRIM44 mRNA was higher in most tumor tissues than in the corresponding normal tissues, and the relationship between TRIM44 mRNA level and prognosis in various malignant tumors also explored in GEPIA and OS analysis webservers. CONCLUSIONS: TRIM44 may serve as a valuable prognostic biomarker and a potential therapeutic target for patients with malignancies.

Prognostic value of lncRNA FEZF1 antisense RNA 1 over-expression in oncologic outcomes of patients with solid tumors.

BACKGROUND: FEZ family zinc finger 1 antisense RNA 1 (FEZF1-AS1), as a novel lncRNA, was reported to be up-regulated in various cancers and involved in tumor progression. This study systematically assessed the prognostic value of FEZF1-AS1 in solid tumors. METHODS: Web of Science, PubMed, EMBASE, Chinese National Knowledge Infrastructure, and Wanfang databases were searched for eligible studies that evaluated the prognostic role of FEZF1-AS1 expression in cancer patients. Pooled hazard ratios (HRs) and combined odds ratios (ORs) with their 95% confidence intervals (CIs) were calculated. The meta-analysis was conducted using Stata/SE 14.1. RESULTS: Fifteen original studies involving 1378 patients were enrolled. Pooled results showed that increased expression of FEZF1-AS1 significantly correlated with shorter overall survival (OS) in cancer patients (HR 2.04, 95% CI 1.60-2.47), and also shorter disease-free survival (DFS) (HR 2.08, 95% CI 1.27-2.89). Additionally, the combined ORs indicated that increased FEZF1-AS1 expression was significantly associated with lymph node metastasis (OR 3.35, 95% CI 1.98-5.67), distant metastasis (OR 3.10, 95% CI 1.86-5.15), poor tumor differentiation (OR 2.90, 95% CI 1.45-5.80), high depth of tumor invasion (OR 2.72, 95% CI 1.36-5.43), and advanced clinical stage (OR 2.76, 95% CI 1.75-4.35). Expression analysis using the Gene Expression Profiling Interactive Analysis database indicated that the expression of FEZF1-AS1 was higher in tumor tissues than that in the corresponding normal tissues. The results of survival analysis revealed that increased FEZF1-AS1 expression was correlated with poor OS and DFS in cancer patients. CONCLUSIONS: LncRNA FEZF1-AS1 may serve as a valuable prognostic biomarker for clinical outcomes in various solid tumors.