Unraveling the efficacy network: A network meta-analysis of adjuvant external beam radiation therapy methods after hepatectomy.

BACKGROUND: Primary liver cancer is a malignant tumor with a high recurrence rate that significantly affects patient prognosis. Postoperative adjuvant external radiation therapy (RT) has been shown to effectively prevent recurrence after liver cancer resection. However, there are multiple RT techniques available, and the differential effects of these techniques in preventing postoperative liver cancer recurrence require further investigation. AIM: To assess the advantages and disadvantages of various adjuvant external RT methods after liver resection based on overall survival (OS) and disease-free survival (DFS) and to determine the optimal strategy. METHODS: This study involved network meta-analyses and followed the PRISMA guidelines. The data of qualified studies published before July 10, 2023, were collected from PubMed, Embase, the Web of Science, and the Cochrane Library. We included relevant studies on postoperative external beam RT after liver resection that had OS and DFS as the primary endpoints. The magnitudes of the effects were determined using risk ratios with 95% confidential intervals. The results were analyzed using R software and STATA software. RESULTS: A total of 12 studies, including 1265 patients with hepatocellular carcinoma (HCC) after liver resection, were included in this study. There was no significant heterogeneity in the direct paired comparisons, and there were no significant differences in the inclusion or exclusion criteria, intervention measures, or outcome indicators, meeting the assumptions of heterogeneity and transitivity. OS analysis revealed that patients who underwent stereotactic body radiotherapy (SBRT) after resection had longer OS than those who underwent intensity modulated radiotherapy (IMRT) or 3-dimensional conformal RT (3D-CRT). DFS analysis revealed that patients who underwent 3D-CRT after resection had the longest DFS. Patients who underwent IMRT after resection had longer OS than those who underwent 3D-CRT and longer DFS than those who underwent SBRT. CONCLUSION: HCC patients who undergo liver cancer resection must consider distinct advantages and disadvantages when choosing between SBRT and 3D-CRT. IMRT, a RT technique that is associated with longer OS than 3D-CRT and longer DFS than SBRT, may be a preferred option.

RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction.

BACKGROUND: Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs. METHODS: In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations. RESULTS: To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.

Expressions of glia maturation factor-ß by tumor cells and endothelia correlate with neovascularization and poor prognosis in human glioma.

Glia maturation factor-ß (GMF-ß) has been reported to promote glial differentiation, and act as a negative prognostic indicator in certain cancers. However, its roles in glioma progression remain unclear. Since neurogenesis and vasculogenesis were proved to share some common regulators during gliomagenesis, we aim to explore the potential impact of GMF-ß on tumor neovascularization and patient survival in glioma. In this study, we first detected GMF-ß expression not only in tumor cells but also in microvascular endothelia by double immunohistochemical staining. Both tumoral and endothelial GMF-ß expression levels were positively correlated with tumor grade and microvessel density (MVD), while negatively associated with poor prognoses of the patients. Interestingly, multivariate analysis demonstrated that endothelial GMF-ß expression level was the only independent predictor of progression-free and overall survival of glioma patients. The results of in vitro angiogenesis assay showed that GMF-ß knockdown significantly inhibited tubulogenesis of human U87 glioblastoma cells. Furthermore, GMF-ß knockdown suppressed tumor growth and the formation of human-CD31 positive (glioma cell-derived) microvessels in a mouse orthotopic U87 glioma model. Our results demonstrated that GMF-ß is an important player in glioma progression via promoting neovascularization. GMF-ß may therefore be a novel prognostic marker as well as a potential therapeutic target for glioma.