A b s t r a c tIntroduction: There is an academic debate over surgical treatments of liver hydatid cyst disease. In this study, a systematic review and meta-analysis were carried out in order to evaluate the pros and cons of both PAIR (Puncture, Aspiration, Injection, Respiration) and laparoscopic techniques by considering the outcomes of liver hydatid cysts. Material and methods: We designed descriptive Boolean queries to search two databases, PubMed and Scopus, to derive the articles published in the period of January 2000 to December 2016 in order to evaluate the outcomes of these research articles. The outcomes of laparoscopic and PAIR procedures include the rates of cure, postoperative complications, recurrences, and mortality, which were extracted, assessed, and used as their corresponding effect sizes. Results: Fifty-seven studies including a total of 2832 patients (PAIR group n = 1650 and laparoscopic group = 1182) were analyzed. In this meta-analysis study, a random effect model of correlations of outcomes (postoperative complications, mortalities, recurrences, and cure rates) of PAIR and laparoscopy procedures was used. The meta-analysis and the forest plots of the two procedures show that the PAIR approach is superior in terms of cure, complication, and mortality rates compared with the laparoscopy technique. However, the recurrence rate is low in laparoscopic approaches. Moreover, Egger's tests for determining publication bias and heterogeneity tests were also performed. Conclusions: This study shows promising trends toward an advantage of PAIR procedures in treatment of liver hydatid cyst in comparison with laparoscopic procedures. The PAIR procedure is superior to laparoscopy due to having a higher cure rate and lower complication and mortality rates; however, the latter has a lower recurrence rate.

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Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essential to diagnose the disease at early stages. The present study aims to take advantage of the systems biology approach on genome wide gene expression profiling datasets to identify the potential biomarkers involved at stage I, stage II, stage III, and stage IV as well as in the integrated group. Three HER2-negative breast cancer microarray datasets were retrieved from the GEO database, including normal, stage I, stage II, stage III, and stage IV samples. Additionally, one dataset was also extracted to test the developed predictive models trained on the three datasets. The analysis of gene expression profiles to identify differentially expressed genes (DEGs) was performed after preprocessing and normalization of data. Then, statistically significant prioritized DEGs were used to construct protein–protein interaction networks for the stages for module analysis and biomarker identification. Furthermore, the prioritized DEGs were used to determine the involved GO enrichment and KEGG signaling pathways at various stages of the breast cancer. The recurrence survival rate analysis of the identified gene biomarkers was conducted based on Kaplan–Meier methodology. Furthermore, the identified genes were validated not only by using several classification models but also through screening the experimental literature reports on the target genes. Fourteen (21 genes), nine (17 genes), eight (10 genes), four (7 genes), and six (8 genes) gene modules (total of 53 unique genes out of 63 genes with involving those with the same connectivity degree) were identified for stage I, stage II, stage III, stage IV, and the integrated group. Moreover, SMC4, FN1, FOS, JUN, and KIF11 and RACGAP1 genes with the highest connectivity degrees were in module 1 for abovementioned stages, respectively. The biological processes, cellular components, and molecular functions were demonstrated for outcomes of GO analysis and KEGG pathway assessment. Additionally, the Kaplan–Meier analysis revealed that 33 genes were found to be significant while considering the recurrence-free survival rate as an alternative to overall survival rate. Furthermore, the machine learning calcification models show good performance on the determined biomarkers. Moreover, the literature reports have confirmed all of the identified gene biomarkers for breast cancer. According to the literature evidence, the identified hub genes are highly correlated with HER2-negative breast cancer. The 53-mRNA signature might be a potential gene set for TNM based stages as well as possible therapeutics with potentially good performance in predicting and managing recurrence-free survival rates at stages I, II, III, and IV as well as in the integrated group. Moreover, the identified genes for the TNM-based stages can also be used as mRNA profile signatures to determine the current stage of the breast cancer.