Failure of enhanced recovery after surgery in liver surgery: a systematic review and meta analysis.

Purpose: This study aimed to conduct a systematic review of the literature to identify and summarize the existing evidence regarding ERAS failure and related risk factors after hepatic surgery. The objective was to provide physicians with a better understanding of these factors so that they can take appropriate action to minimize ERAS failure and improve patient outcomes. Method: A literature search of the PubMed MEDLINE, OVID, EMBASE, Cochrane Library, and Web of Science was performed. The search strategy involved terms related to ERAS, failure, and hepatectomy. Result: A meta-analysis was conducted on four studies encompassing a total of 1,535 patients, resulting in the identification of 20 risk factors associated with ERAS failure after hepatic surgery. Four of these risk factors were selected for pooling, including major resection, ASA classification of ≥3, advanced age, and male gender. Major resection and ASA ≥ 3 were identified as statistically significant factors of ERAS failure. Conclusion: The comprehensive literature review results indicated that the frequently identified risk factors for ERAS failure after hepatic surgery are linked to operative and anesthesia factors, including substantial resection and an American Society of Anesthesiologists score of 3 or higher. These insights will assist healthcare practitioners in taking prompt remedial measures. Nevertheless, there is a requirement for future high-quality randomized controlled trials with standardized evaluation frameworks for ERAS programs.

Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma.

BACKGROUND: Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. METHODS: Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan-Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. RESULTS: 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma (p < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. CONCLUSIONS: A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

Identification of differentially expressed genes and signaling pathways involved in endometriosis by integrated bioinformatics analysis.

Endometriosis is a common gynecological disease characterized by the presence and growth of endometrial tissue outside the uterus, including the pelvis and abdominal cavity. This condition causes various clinical symptoms, such as non-menstrual pelvic pain, dysmenorrhea and infertility, seriously affecting the health and quality of life of women. To date, the specific mechanism and the key molecules of endometriosis remain uncertain. The purpose of the present study was to elucidate the mechanisms involved in the development and persistence of the disease. A number of mRNA expression profile datasets (namely GSE11691, GSE23339, GSE25628 and GSE78851) were downloaded from the Gene Expression Omnibus (GEO) database. These gene expression profiles were normalized, and the differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis. A total of 103 DEGs were screened upon excluding the genes that exhibited inconsistency of expression (P<0.05). Furthermore, the Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and construction of protein-protein interaction networks of DEGs were performed using online software. The results revealed that the DEGs were closely associated with cell migration, adherens junction and hypoxia-inducible factor signaling. In addition, immunohistochemical assay results were found to be consistent with the bioinformatics results. The present study may help us understand underlying molecular mechanisms and the development of endometriosis, which has a great clinical significance for early diagnosis of the disease.

Monocytes affect bone mineral density in pre- and postmenopausal women through ribonucleoprotein complex biogenesis by integrative bioinformatics analysis.

Osteoporosis is one of the most common metabolic bone disease among pre- and postmenopausal women. As the precursors of osteoclast cells, circulating monocytes play important role in bone destruction and remodeling. The aim of study is to identify potential key genes and pathways correlated with the pathogenesis of osteoporosis. Then we construct novel estimation model closely linked to the bone mineral density (BMD) with key genes. Weighted gene co-expression network analysis (WGCNA) were conducted by collecting gene data set with 80 samples from gene expression omnibus (GEO) database. Besides, hub genes were identified by series of bioinformatics and machine learning algorithms containing protein-protein interaction (PPI) network, receiver operating characteristic curve and Pearson correlation. The direction of correlation coefficient were performed to screen for gene signatures with high BMD and low BMD. A novel BMD score system was put forward based on gene set variation analysis and logistic regression, which was validated by independent data sets. We identified six modules correlated with BMD. Finally 100 genes were identified as the high bone mineral density signatures while 130 genes were identified as low BMD signatures. Besides, we identified the significant pathway in monocytes: ribonucleoprotein complex biogenesis. What's more, our score validated it successfully.

Long-Term Efficacy of Maintenance Therapy for Multiple Myeloma: A Quantitative Synthesis of 22 Randomized Controlled Trials.

We aimed to quantitatively synthesize data from randomized controlled trials (RCTs) concerning maintenance for multiple myeloma (MM). We searched electronic literature databases and conference proceedings to identify relevant RCTs. We selected eligible RCTs using predefined selection criteria. We conducted meta-analysis comparing maintenance containing new agents and conventional maintenance, and subgroup analysis by transplantation status and mainstay agent as well. We performed trial sequential analysis (TSA) to determine adequacy of sample size for overall and subgroup meta-analyses. We performed network meta-analysis (NMA) to compare and rank included regimens. A total of 22 RCTs involving 9,968 MM patients and 15 regimens were included, the overall quality of which was adequate. Significant heterogeneity was detected for progression-free survival (PFS) but not overall survival (OS). Meta-analyses showed that maintenance containing new agents significantly improved PFS but not OS [PFS: Hazard Ratio (HR) = 0.59, 95% Confidence Interval (CI) = 0.54 to 0.64; OS: HR = 0.93, 95% CI = 0.87 to 1.00], compared with controls. Subgroup analyses revealed lenalidomide (Len)-based therapies better than thalidomide-based ones (HR = 0.50 and 0.66, respectively; P = 0.001). NMA revealed that most of the maintenance regimens containing new agents were significantly better than simple observation in terms of PFS but not OS. Len single agent was the most effective, considering PFS and OS both. We concluded that conventional maintenance has very limited effect. Maintenance containing new agents is highly effective in improving PFS, but has very limited effect on OS. Maintenance with Len may have the largest survival benefits. Emerging strategies may further change the landscape of maintenance of MM.

The Role of COL5A2 in Patients With Muscle-Invasive Bladder Cancer: A Bioinformatics Analysis of Public Datasets Involving 787 Subjects and 29 Cell Lines.

Bladder cancer (BC) is one of the most common malignancies. Two previous studies identified collagen type V alpha 2 (COL5A2) as a potential biomarker in BC, both are simple reanalysis of a single transcriptomic dataset without subgroup analysis for muscle-invasive BC (MIBC). We focused in MIBC patients and explored the role of COL5A2 from an integration perspective, using refined methodology covering individual participant data meta-analysis and bioinformatics analysis. Eight transcriptomic datasets of 787 MIBC patients (including one dataset containing genomic mutation information) and two drug sensitivity datasets of 29 cell lines in which more than 250 compounds were analyzed. We found subjects with increased COL5A2 gene expression exhibited poorer prognosis, and the power analysis confirmed adequate sample size. FGFR3 was the only gene differential mutated between the COL5A2 high and low expression groups. Differential expression and co-expression network analysis suggested potential association between COL5A2 expression and essential pathways involved in cancer invasion and dissemination, including cell adhesion, extracellular matrix organization, and epithelial-mesenchymal transition. Coordinately, analysis of drug screening datasets and gene-drug interaction also revealed COL5A2 expression linked to cell morphogenesis, angiogenesis, blood vessel development, and urogenital development. The utility and feasibility of COL5A2 for clinically applicable prognosis prediction and risk classification and the exact underlying molecular mechanism should be further investigated in subsequent studies.

Induction regimens for transplant-eligible patients with newly diagnosed multiple myeloma: a network meta-analysis of randomized controlled trials.

OBJECTIVE: The aim of this study was to compare the early efficacy and survivals of induction regimens for transplant-eligible patients with untreated multiple myeloma. MATERIALS AND METHODS: A comprehensive literature search in electronic databases was conducted for relevant randomized controlled trials (RCTs). Eligible studies were selected according to the predefined selection criteria, before they were evaluated for methodological quality. Basic characteristics and data for network meta-analysis (NMA) were extracted from included trials and pooled in our meta-analysis. The end points were the overall response rate (ORR), progression-free survival (PFS), and overall survival (OS). RESULTS: A total of 14 RCTs that included 4,763 patients were analyzed. The post-induction ORR was higher with bortezomib plus thalidomide plus dexamethasone (VTD) regimens, and VTD was better than the majority of other regimens. For OS, VTD plus cyclophosphamide (VTDC) regimens showed potential superiority over other regimens, but the difference was not statistically significant. The PFS was longer with thalidomide plus doxorubicin plus dexamethasone (TAD) regimens for transplant-eligible patients with newly diagnosed multiple myeloma (NDMM). CONCLUSION: The NMA demonstrated that the VTD, VTDC, and TAD regimens are most beneficial in terms of ORR, OS, and PFS for transplant-eligible patients with NDMM, respectively.