Identification of immunological bioprocesses involved in peri-implantitis using weighted gene co-expression network analysis.

BACKGROUND: Peri-implantitis is an irreversible infectious disease that occurs with high incidence. Exploring the immune responses of peri-implantitis is key to developing targeted treatment strategies. However, there is limited research on the immune response of peri-implantitis. METHODS: This study performed a weighted gene co-expression network analysis to identify the peri-implantitis related gene network and conducted a functional enrichment analysis of the gene network. Thereafter, the candidate hub genes were selected by constructing a protein-protein interaction network and drawing an upset plot. The hub genes were identified through their significant associations with disease condition and validated using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis. Using the gene set variation analysis, the hub genes were further used to explore infiltrating immunocytes and immune factors in peri-implantitis. Finally, the immunocytes and immune factor related hub genes were intersected to obtain the therapeutic target, which was validated using histological staining. RESULTS: The peri-implantitis related gene network was enriched in innate and adaptive immune response. Subsequently, interleukin (IL)1B, IL10, ITGAM, ITGB1, STAT3, and TLR4 were identified as hub genes. Plasmacytoid dendritic cells, macrophages, myeloid-derived suppressor cells, natural killer T cells, and immature B cells were positively and significantly related to the hub genes IL1B, TLR4, ITGAM, and ITGB1 (correlation coefficient > 0.80). While immune factors CXCL10, IL6, and CXCL12 and hub genes IL10 and IL1B held the highest degree in the immune factors network. IL1B may be a promising therapeutic target. CONCLUSION: This study provides new insights into the hub genes, immunocytes, and immune factors underlying peri-implantitis immunological bioprocess.

Identification and characterization of circular RNAs as novel putative biomarkers to predict anti-PD-1 monotherapy response in metastatic melanoma patients - Knowledge from two independent international studies.

Melanoma is the most aggressive skin malignancy with high morbidity. Anti-programmed cell death protein 1 (PD-1) monotherapy has been applied in metastatic melanoma. However, still most of the patients do not respond to anti-PD-1 and the availability of the present approved biomarkers therefore is limited. Here we combined the transcriptomic and clinical data of 163 advanced melanoma patients receiving anti-PD-1 from NIH Melanoma Genome Sequencing Project (phs000452, 122 patients) as the training and internal validation cohort, and Melanoma Institute Australia cohort (PRJEB23709, 41 patients) as the external validation cohort, respectively. Circular RNAs (circRNAs) are an evolutionarily conserved novel class of noncoding endogenous RNAs (ncRNAs) found in the eukaryotic transcriptome and were used based on RNAseq data for our analyses. 74,243 circular RNAs (circRNAs) were identified with NCLscan and CIRCexplorer2. Thereof, 70 circRNAs significantly associated with progression-free survival and overall survival. Further, a prognostic circRNAs signature consisting of HSA_CIRCpedia_1497, HSA_CIRCpedia_12559, HSA_CIRCpedia_43640, HSA_CIRCpedia_43070, and HSA_CIRCpedia_21660 could be determined with LASSO regression. This signature was a prognostic factor of overall survival and progression-free survival among the analyzed advanced melanoma patients. The concordance indexes (C-index of OStraining: 0.61, C-index of PFStraining: 0.68) also confirmed its credibility and accuracy. First enrichment analysis indicated that immune response and pathways related to tumor immune microenvironment were enriched. In conclusion, we succeeded to construct and validate novel prognostic circRNAs signature for advanced melanoma patients treated with anti-PD-1 immunotherapy.

Definition of a new blood cell count score for early survival prediction for non-small cell lung cancer patients treated with atezolizumab: Integrated analysis of four multicenter clinical trials.

Importance: Blood cell count test (BCT) is a robust method that provides direct quantification of various types of immune cells to reveal the immune landscape to predict atezolizumab treatment outcomes for clinicians to decide the next phase of treatment. Objective: This study aims to define a new BCTscore model to predict atezolizumab treatment benefits in non-small lung cell cancer (NSCLC) patients. Design Setting and Participants: This study analyzed four international, multicenter clinical trials (OAK, BIRCH, POPLAR, and FIR trials) to conduct post-hoc analyses of NSCLC patients undergoing atezolizumab (anti-PD-L1) single-agent treatment (n = 1,479) or docetaxel single-agent treatment (n = 707). BCT was conducted at three time points: pre-treatment (T1), the first day of treatment cycle 3 (T2), and first day of treatment cycle 5 (T3). Univariate and multivariate Cox regression analyses were conducted to identify early BCT biomarkers to predict atezolizumab treatment outcomes in NSCLC patients. Main Outcomes and Measures: Overall survival (OS) was used as the primary end point, whereas progression-free survival (PFS) according to Response Evaluation Criteria in Solid Tumors (RECIST), clinical benefit (CB), and objective response rate (ORR) were used as secondary end points. Results: The BCT biomarkers of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) at time point T3 and neutrophil-to-monocyte ratio (NMR) at time point T2 with absolute cutoff values of NLR_T3 = 5, PLR_T3 = 180, and NMR_T2 = 6 were identified as strong predictive biomarkers for atezolizumab (Ate)-treated NSCLC patients in comparison with docetaxel (Dtx)-treated patients regarding OS (BCTscore low risk: HR Ate vs. Dtx = 1.54 (95% CI: 1.04-2.27), P = 0.031; high risk: HR Ate vs. Dtx = 0.84 (95% CI: 0.62-1.12), P = 0.235). The identified BCTscore model showed better OS AUC in the OAK (AUC12month = 0.696), BIRCH (AUC12month = 0.672) and POPLAR+FIR studies (AUC12month = 0.727) than that of each of the three single BCT biomarkers. Conclusion and Relevance: The BCTscore model is a valid predictive and prognostic biomarker for early survival prediction in atezolizumab-treated NSCLC patients.

Elucidation of the Application of Blood Test Biomarkers to Predict Immune-Related Adverse Events in Atezolizumab-Treated NSCLC Patients Using Machine Learning Methods.

Background: Development of severe immune-related adverse events (irAEs) is a major predicament to stop treatment with immune checkpoint inhibitors, even though tumor progression is suppressed. However, no effective early phase biomarker has been established to predict irAE until now. Method: This study retrospectively used the data of four international, multi-center clinical trials to investigate the application of blood test biomarkers to predict irAEs in atezolizumab-treated advanced non-small cell lung cancer (NSCLC) patients. Seven machine learning methods were exploited to dissect the importance score of 21 blood test biomarkers after 1,000 simulations by the training cohort consisting of 80%, 70%, and 60% of the combined cohort with 1,320 eligible patients. Results: XGBoost and LASSO exhibited the best performance in this study with relatively higher consistency between the training and test cohorts. The best area under the curve (AUC) was obtained by a 10-biomarker panel using the XGBoost method for the 8:2 training:test cohort ratio (training cohort AUC = 0.692, test cohort AUC = 0.681). This panel could be further narrowed down to a three-biomarker panel consisting of C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), and thyroid-stimulating hormone (TSH) with a small median AUC difference using the XGBoost method [for the 8:2 training:test cohort ratio, training cohort AUC difference = -0.035 (p < 0.0001), and test cohort AUC difference = 0.001 (p=0.965)]. Conclusion: Blood test biomarkers currently do not have sufficient predictive power to predict irAE development in atezolizumab-treated advanced NSCLC patients. Nevertheless, biomarkers related to adaptive immunity and liver or thyroid dysfunction warrant further investigation.

Identification of an endogenous retroviral signature to predict anti-PD1 response in advanced clear cell renal cell carcinoma: an integrated analysis of three clinical trials.

Background: Endogenous retrovirus (ERV) elements are genomic footprints of ancestral retroviral infections within the human genome. Previous studies have demonstrated that dysregulated ERV transcription level is associated with immune cell infiltration in cancers, but the association between ERV expression and programmed cell death protein 1 (PD-1) blockade response is currently unraveled for solid cancers, such as advanced clear cell renal cell carcinoma (ccRCC). Methods: ERV mRNA profiles were obtained from three clinical trials of ccRCC where the patients were treated with anti-PD-1 (CM-009, CM-010, CM-025, and TCGA-KIRC data). Patients treated with nivolumab were divided into training and test cohort, while the TCGA-KIRC cohort was used as an external validation. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression were used to establish the signature. Immune cell infiltration analysis and gene set enrichment analysis were performed to explore potential biological mechanisms. Results: An ERV signature was established based on nine ERV expression patterns. In the training cohort, the median overall survival in the low- and high-risk group was 45.2 and 19.6 months [hazard ratio (HR) = 0.49, 0.32-0.75, p < 0.001], respectively. The results were confirmed in the test (HR = 0.41, 0.20-0.83, p = 0.013), and in the TCGA-KIRC cohort (HR = 0.55, 0.34-0.90, p = 0.017). Moreover, in the CM-025 cohort, the low-risk group that received nivolumab had a more favorable survival compared with those that received the mTOR inhibitor everolimus, while no significant differences were observed in the high-risk group. CD8+ T cells were enriched in the low-risk group, while immune suppressive pathways were suppressed. Conclusion: The newly identified ERV signature is not only a prognostic, but also a predictive biomarker for advanced ccRCC patients who received anti-PD-1 therapy, which can guide personalized treatment in cancer patients in the future.

Recombinant human insulin-like growth factor-1 promotes osteoclast formation and accelerates orthodontic tooth movement in rats.

BACKGROUND: IGF-1 may be an important factor in bone remodeling, but its mechanism of action on osteoclasts during orthodontic tooth movement is complex and unclear. METHODOLOGY: The closed-coil spring was placed between the left maxillary first molar and upper incisors with a force of 50 g to establish an orthodontic movement model. Eighty SD rats were randomized to receive phosphate buffer saline or 400 ng rhIGF-1 in the lateral buccal mucosa of the left maxillary first molar every two days. Tissue sections were stained for tartrate-resistant acidic phosphatase (TRAP), the number of TRAP-positive cells was estimated and tooth movement measured. RESULTS: The rhIGF-1 group exhibited evidential bone resorption and lacuna appeared on the alveolar bone compared to the control group. Moreover, the number of osteoclasts in compression side of the periodontal ligament in the rhIGF-1 group peaked at day 4 (11.37±0.95 compared to 5.28±0.47 in the control group) after the orthodontic force was applied and was significantly higher than that of the control group (p<0.01). Furthermore, the distance of tooth movement in the rhIGF-1 group was significantly larger than that of the control group from day 4 to day 14 (p<0.01), suggesting that rhIGF-1 accelerated orthodontic tooth movement. CONCLUSION: Our study has showed that rhIGF-1 could stimulate the formation of osteoclasts in the periodontal ligament, and accelerate bone remodeling and orthodontic tooth movement.

Identification of 15 lncRNAs Signature for Predicting Survival Benefit of Advanced Melanoma Patients Treated with Anti-PD-1 Monotherapy.

The blockade of programmed cell death protein 1 (PD-1) as monotherapy has been widely used in melanoma, but to identify melanoma patients with survival benefit from anti-PD-1 monotherapy is still a big challenge. There is an urgent need for prognostic signatures improving the prediction of immunotherapy responses of these patients. We analyzed transcriptomic data of pre-treatment tumor biopsies and clinical profiles in advanced melanoma patients receiving only anti-PD-1 monotherapy (nivolumab or pembrolizumab) from the PRJNA356761 and PRJEB23709 data sets as the training and validation cohort, respectively. Weighted gene co-expression network analysis was used to identify the key module, then least absolute shrinkage and selection operator was conducted to determine prognostic-related long noncoding RNAs (lncRNAs). Subsequently, the differentially expressed genes between different clusters were identified, and their function and pathway annotation were performed. In this investigation, 92 melanoma patients with complete survival information (51 from training cohort and 41 from validation cohort) were included in our analyses. We initiallyidentified the key module (skyblue) by weighted gene co-expression network analysis, and then identified a 15 predictive lncRNAs (AC010904.2, LINC01126, AC012360.1, AC024933.1, AL442128.2, AC022211.4, AC022211.2, AC127496.5, NARF-AS1, AP000919.3, AP005329.2, AC023983.1, AC023983.2, AC139100.1, and AC012615.4) signature in melanoma patients treated with anti-PD-1 monotherapy by least absolute shrinkage and selection operator in the training cohort. These results were then validated in the validation cohort. Finally, enrichment analysis showed that the functions of differentially expressed genes between two consensus clusters were mainly related to the immune process and treatment. In summary, the 15 lncRNAs signature is a novel effective predictor for prognosis in advanced melanoma patients treated with anti-PD-1 monotherapy.

Recombinant human insulin-like growth factor-1 promotes osteoclast formation and accelerates orthodontic tooth movement in rats

Abstract Background: IGF-1 may be an important factor in bone remodeling, but its mechanism of action on osteoclasts during orthodontic tooth movement is complex and unclear. Methodology: The closed-coil spring was placed between the left maxillary first molar and upper incisors with a force of 50 g to establish an orthodontic movement model. Eighty SD rats were randomized to receive phosphate buffer saline or 400 ng rhIGF-1 in the lateral buccal mucosa of the left maxillary first molar every two days. Tissue sections were stained for tartrate-resistant acidic phosphatase (TRAP), the number of TRAP-positive cells was estimated and tooth movement measured. Results: The rhIGF-1 group exhibited evidential bone resorption and lacuna appeared on the alveolar bone compared to the control group. Moreover, the number of osteoclasts in compression side of the periodontal ligament in the rhIGF-1 group peaked at day 4 (11.37±0.95 compared to 5.28±0.47 in the control group) after the orthodontic force was applied and was significantly higher than that of the control group (p<0.01). Furthermore, the distance of tooth movement in the rhIGF-1 group was significantly larger than that of the control group from day 4 to day 14 (p<0.01), suggesting that rhIGF-1 accelerated orthodontic tooth movement. Conclusion: Our study has showed that rhIGF-1 could stimulate the formation of osteoclasts in the periodontal ligament, and accelerate bone remodeling and orthodontic tooth movement.

Co-expression gene modules involved in cisplatin-induced peripheral neuropathy according to sensitivity, status, and severity.

Chemotherapy-induced peripheral neuropathy (CIPN) is among the most disabling and frustrating problems for cancer survivors. The neurotoxicity caused by cisplatin varies greatly among patients, and few predictors of appearance, duration of symptoms, susceptibility, or severity are available. A deeper understanding of the mechanisms underlying individual differences in status, severity, or sensitivity in response to cisplatin treatment is therefore required. By analyzing the GSE64174 gene expression profile and constructing a weighted gene co-expression network analysis (WGCNA) network, we screened gene modules and hub genes related to CIPN status, severity and sensitivity. We first identified the transcriptome profile of mouse dorsal root ganglion (DRG) samples and transformed their genes to human DRG counterparts. We then constructed WGCNA gene modules via optimal soft-threshold power-identification and module-preservation analysis. Comprehensive analysis and identification of module hub genes were performed via functional-enrichment analysis and significant common hub genes were identified, including "Cytoscape_cytoHubba," "Cytoscape_MCODE," and "Metascape_MCODE." Brown, green, and blue modules were selected to represent CIPN sensitivity, status, and severity, respectively, via trait-module correlational analysis. Additionally, functional enrichment analysis results indicated that these three modules were associated with some crucial biological functions, such as neutrophil migration, chemokine-mediated signaling pathway, and PI3K-Akt signaling pathway. We then identified seven common hub genes via three methods, including CXCL10, CCL21, CCR2, CXCR4, TLR4, NPY1R, and GALR2, related to CIPN status, severity and sensitivity. Our results provide possible targets and mechanism insights into the development and progress of CIPN, which can guide further transformation and pre-clinical research.

Development and Validation of a Gene Signature for Prediction of Relapse in Stage I Testicular Germ Cell Tumors.

Background: Testicular germ cell tumors (TGCTs) are commonly diagnosed tumors in young men. However, a satisfactory approach to predict relapse of stage I TGCTs is still lacking. Therefore, this study aimed to develop a robust risk score model for stage I TGCTs. Method: RNA-sequence data of stage I TGCTs and normal testis samples were downloaded and analyzed to identify different expression genes. Gene-based prognostic model was constructed in The Cancer Genome Atlas (TCGA) using least absolute shrinkage and selection operator (LASSO) regression analysis and validated in GSE99420 dataset. Potential biological functions of the genes in prognostic model were determined via Gene Set Enrichment Analysis (GSEA) between high-risk and low-risk patients. Results: A total of 9,391 differentially expressed genes and 84 prognosis-related genes were identified. An eight-gene-based risk score model was constructed to divide patients into high or low risk of relapse. The low-risk patients had a significantly better relapse-free survival (RFS) than high-risk patients in both training and validation cohorts (HR = 0.129, 95% CI = 0.059-0.284, P < 0.001; HR = 0.277, 95% CI = 0.116-0.661, P = 0.004, respectively). The area under the receiver operating characteristic curve (AUC) values at 5 years was 0.805 and 0.724 in the training and validation cohorts, respectively. Functional enrichment analyses showed that DNA replication, ribosome, cell cycle, and TGF-beta signaling pathway may contribute to the relapse process. Conclusion: In summary, our analysis provided a novel eight-gene signature that could predict RFS in stage I TGCT patients.

Olanzapine combined with 5-hydroxytryptamine type 3 receptor antagonist (5-HT3 RA) plus dexamethasone for prevention and treatment of chemotherapy-induced nausea and vomiting in high and moderate emetogenic chemotherapy: a systematic review and meta-analysis of randomised controlled trials.

We performed a pooled analysis to evaluate the efficacy and adverse events (AEs) of olanzapine combined with dexamethasone plus 5-hydroxytryptamine type 3 receptor antagonist (5-HT3 RA) compared with 5-HT3 RA plus dexamethasone for the prevention and treatment of chemotherapy-induced nausea and vomiting (CINV) in high and moderate emetogenic chemotherapy based on randomised controlled trials (RCTs). PubMed, EMBASE, Web of Science, the Cochrane Library, China Biomedical Literature database (CBM), WanFang Database, China National Knowledge Infrastructure (CNKI), and Chinese Science and Technology Periodical Database (VIP) (from their inception to April 2019) were searched to capture relevant articles. Relative risk with 95% confidence intervals for CINV and AEs were all extracted or calculated. Eleven studies with 1107 cancer patients were involved in this review. The pooled RR of delayed CINV (RR 0.50, 95% CI 0.38 to 0.66; p<0.01) were significantly decreased in the olanzapine group. The occurrence of insomnia was also statistically decreased, as was the rate of acute CINV (RR 0.60, 95% CI 0.48 to 0.75; p<0.01). However, only the percentages of CINV III and CINV IV were significantly decreased in the acute and delayed phases. Subgroup analysis demonstrated that the efficacy was not statistically significantly different between 5 mg and 10 mg olanzapine. Olanzapine significantly decreased the occurrence of CINV III and IV and insomnia in high and moderately emetogenic chemotherapy. Compared with 10 mg per day, 5 mg oral olanzapine may be more appropriate for patients with cancer.

Identification of novel key lncRNAs involved in periodontitis by weighted gene co-expression network analysis.

BACKGROUND AND OBJECTIVE: Periodontitis is a multifactorial disease that can lead to the progressive destruction of dental support tissue. However, the detailed mechanisms and specific biomarkers involved in periodontitis remain to be further studied. Recently, long non-coding RNAs (lncRNAs) have been found to play a more important role than other types of RNAs. In our study, we analysed the expression of lncRNAs in periodontitis by analysing GSE16134. MATERIAL AND METHODS: We identified highly correlated genes by analysing GSE16134 with weighted gene co-expression network analysis (WGCNA) and identified 50 hub lncRNAs that were dysregulated. Then, we used the Linear Models for Microarray Data (Limma) package to identify the hub lncRNAs that were differentially expressed (DElncRNAs). The ceRNA co-expression network data were obtained from several sites, including miRcode, and were used to assess the potential WGCNA function of hub DElncRNAs in periodontitis. Besides, we divided the samples into LBX2-AS1 high and low expression group by the expression level of LBX2-AS1 and calculated DEG by Limma package. Furthermore, we performed GO function, KEGG pathway and GSEA enrichment of DEGs. RESULTS: In the analysis, we identified 50 hub lncRNAs that may play important roles in periodontitis. Then, we used the Limma package to identify 3 hub DElncRNAs (LINC00687, LBX2-AS1 and LINC01566). We elucidated the potential function of the hub DElncRNA LBX2-AS1 in periodontitis by constructing a co-expression network of lncRNA-miRNA-mRNA interactions. Totally, 573 DEGs (354 up- and 219 downregulated) in periodontitis samples were identified. DEGs were enriched in different GO terms and pathways, such as neutrophil degranulation, neutrophil activation, neutrophil activation involved in immune response, neutrophil-mediated immunity, antigen processing and presentation, JAK-STAT signalling pathway, natural killer cell-mediated cytotoxicity, EGFR tyrosine kinase inhibitor resistance, phosphatidylinositol signalling system and Vascular Endothelial Growth Factor (VEGF) signalling pathway. CONCLUSION: In our study, we found that 3 hub DElncRNAs (LINC00687, LBX2-AS1 and LINC01566) may be involved in the pathogenesis of periodontitis based on WGCNA and Limma analysis. Our study aimed to elucidate the mechanisms involved in periodontitis at the genetic and epigenetic levels by constructing a ceRNA network associated with lncRNA. Besides, identification DEGs of differential LBX2-AS1 and functional annotation showed that LBX2-AS1 might be associated with periodontitis.

Development of an miRNA-Array-Based Diagnostic Signature for Periodontitis.

Periodontitis progression is accompanied by irreversible alveolar bone absorption and leads to tooth loss. Early diagnosis is important for tooth stability and periodontal tissue preservation. However, there is no recognized miRNA diagnostic signature with convincing sensitivity and specificity for periodontitis. In this study, we obtained miRNA array expression profiles of periodontitis from the Gene Expression Omnibus (GEO) database. After screening for differentially expressed miRNAs, the least absolute shrinkage and selection operator (LASSO) method was performed to identify and construct a 17-miRNA-based diagnostic signature (hsa-miR-3917, hsa-mir-4271, hsa-miR-3156, hsa-miR-3141, hsa-miR-1246, hsa-miR-125a-5p, hsa-miR-671-5p, hcmv-mir-UL70, hsa-miR-650, hsa-miR-497-3p, hsa-miR-145-3p, hsa-miR-141-3p, hsa-miR-210-3p, hsa-miR-204-3p, hsa-miR-203a-5p, hsa-miR-99a-3p, and hsa-miR-30a-3p). Periodontal tissue samples with higher risk scores were more likely to show symptoms of periodontitis. Then, the receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the miRNA signature, which indicated that the optimum cutoff value in periodontitis diagnosis was 0.5056 with an area under the ROC curve (AUC) of 0.996, a sensitivity of 97.3%, a specificity of 100.0% in the training cohort; in the testing cohort, the corresponding values were as follows: an AUC of 0.998, a sensitivity of 97.9%, and a specificity of 91.7%. We next evaluated the efficacy of the signature in differentiating disease subtype and affected range. Furthermore, we conducted functional enrichment analysis of the 17 miRNA-targeted mRNAs, including the regulation of mTOR activity and cell autophagy, Th1/Th2 cell balance and immunoregulation, cell apoptosis, and so on. In summary, our study identified and validated a 17-miRNA diagnostic signature with convincing AUC, sensitivity, and specificity for periodontitis.

Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma.

Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods. Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events. Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.

Identification of a RNA-seq-based signature to improve prognostics for uterine sarcoma.

OBJECTIVE: Uterine sarcoma (US) is a highly malignant cancer with poor prognosis and high mortality. This study focused on the identification of a RNA-Seq expression signature for prognosis prediction in uterine sarcoma. METHODS: We obtained RNA-Seq expression profiles from The Cancer Genome Atlas database, and differentially expressed genes were identified between US tissues and normal tissues. Univariate Cox proportional hazards regression analysis and LASSO Cox model were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic, Kaplan-Meier curve and multivariate Cox regression analysis were used to assess the prognostic capacity of the six-gene signature. The nomogram was developed including prognostic signature and independent clinical factors to predict the overall survival (OS) of US patients. The functional enrichment and somatic mutation analysis were also analyzed by bioinformatics to understand the molecular mechanisms. RESULTS: This study identified a prognostic signature based on 6 genes: FGF23, TLX2, TIFAB, RNF223, HIST1H3A and AADACL4. In the training group, the median OS in the high- and low-risk groups was 19.6 vs 88.1 months (HR, 0.1412, 95% CI: 0.03295 - 0.6054; P = 0.002), respectively. In the testing group, the median OS in the high- and low-risk groups were 30 vs NR (not reach) months (HR, <0.0001, 95% CI: 0 - inf; P = 0.03). In all of patients, the low-risk group showed significant better survival compared with the high-risk group in OS, PFI, DSS and DFI. The nomogram based on the gene signature and radiation therapy was developed and successfully predicted the OS of US patients. The patients in the high-risk group displayed distinct mutation signatures comparing to patients in the low-risk group. Functional enrichment analysis indicated that the signature can play a vital role in cancer-related biological processes. CONCLUSION: Our study established a novel 6-gene signature and nomogram which could improve prognosis prediction in patients with US.

Development and Validation of a Prognostic Signature for Malignant Pleural Mesothelioma.

Introduction: Dysregulated genes play a critical role in the development and progression of cancer, suggesting their potential as novel independent biomarkers for cancer diagnosis and prognosis. Prognostic model-based gene expression profiles are not widely utilized in clinical medicine. We investigated the prognostic significance of an expression profile-based gene signature for outcome prediction in patients with malignant pleural mesothelioma (MPM). Methods: The gene expression profiles of a large cohort of patients with MPM were obtained and analyzed by repurposing publicly available microarray data. A gene-based risk score model was developed with the training dataset and then validated with the TCGA-MESO (mesothelioma) dataset. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance of survival prediction. The biological function of the prognostic genes was predicted using bioinformatics analysis. Results: Three genes in the training dataset (GSE2549) were identified as significantly associated with the overall survival (OS) of patients with MPM and were combined to develop a three-gene prognostic signature to stratify patients into low-risk and high-risk groups. The MPM patients of the training dataset in the low-risk group exhibited longer OS than those in the high-risk group (HR = 0.25, 95% CI = 0.11-0.56, P < 0.001). Similar prognostic values for the three-gene signature were observed in the validated TCGA-MESO cohort (HR = 0.53 95% CI = 0.33-0.85, P = 0.008). ROC analysis also demonstrated the good performance in predicting 3-year OS in the GEO and TCGA cohorts (KM-AUC for GEO = 0.989, KM-AUC for TCGA = 0.618). The C-statistic for the 3-gene model was 0.761. Validation with TCGA-MESO confirmed the model's ability to discriminate between risk groups in an alternative data set with fair performance (C-statistic: 0.68). Functional enrichment analysis suggested that these three genes may be involved in genetic and epigenetic events with known links to MPM. Conclusions: This study has identified and validated a novel 3-gene model to reliably discriminate patients at high and low risk of death in unselected populations of patients with MPM. Further larger, prospective multi-institutional cohort studies are necessary to validate this model.

Influence of marital status on overall survival in patients with ovarian serous carcinoma: finding from the surveillance epidemiology and end results (SEER) database.

OBJECTIVE: This study is to investigate the relationship between marital status and prognosis of patients with ovarian serous carcinoma. RESULTS: We performed data analysis from 19,276 patients identified from the SEER database of the National Cancer Center of the United States. 57.8% of the patients were married, 13.0% unmarried, and 29.2% separated/ divorced/widowed (SDW). The median overall survival time ofthe unmarried group and the married group are 48 months and 52 months respectively. Univariate Cox regression analysis showed that the patients with serous ovarian cancer in the unmarried group resulted in a hazard ratio (HR) of 1.14 (95% CI: 1.08-1.19%; P < 0.001), comparing to SDW group with a HR of 1.02 (95% CI: 0.98-1.19%; P = 0.26). However, the SDW group was not statistically significantly different from the married group. (median 32 vs 52 months). Multivariate Cox regression analysis presented the unmarried group leading to a HR of 1.05 (95% CI: 1.00-1.11%; P = 0.05), and the SDW group was not significant with a HR of 0.99 (95% CI: 0.95-1.03%; P = 0.57). CONCLUSION: Unmarried patients with ovarian serous carcinoma have higherHRof overall survival. After controlling age, race, grade, radiation and year of diagnosis, unmarried patients were found to have a significantly higher risk of OS. Consequently, these patients are suggested to obtain more focused healthcare for the management of ovarian serous carcinoma.

Opioid-Induced Constipation Relief From Fixed-Ratio Combination Prolonged-Release Oxycodone/Naloxone Compared With Oxycodone and Morphine for Chronic Nonmalignant Pain: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

CONTEXT: Opioid-induced constipation (OIC) is one of the most frequent and severe adverse events (AEs) after treatment with opioids. Recent studies have indicated that fixed-ratio combination prolonged-release oxycodone/naloxone (OXN PR) could decrease OIC with similar pain relief compared with other opioids. OBJECTIVES: We systematically reviewed (PROSPERO registration numbers: CRD42016036244) the constipation relief of OXN PR compared with other opioids regardless of formulation, prolonged release, or extended release used for the relief of chronic pain. METHODS: Relevant studies were identified by searching PubMed, EMBASE, Web of Science, and the Cochrane library from inception to May 2016, with an update to December 2016. We quantitatively analyzed OIC (assessed by bowel function index [BFI]), pain intensity, and AEs. RESULTS: A total of 167 articles were identified from the databases. Finally seven studies with 3217 patients were included in our meta-analysis, including 1322 patients in OXN PR treatment groups and 1885 patients in prolonged-release oxycodone (OXY PR) or prolonged-release morphine (MOR PR) control group. The relative risk (RR) of OIC was decreased in OXN PR (RR 0.52, 95% CI 0.44; 0.62). Whether BFI was better or worse at baseline, the mean difference (MD) of BFI -17.48 95% CI -21.60; -13.36) was better after treatment with OXN PR with clinical importance at the end of intervention; moreover, the BFI of the OXN PR-treated group was closer to normal BFI scores. However, clinical BFI change from baseline to the end measurement only existed in patients when the baseline BFI was high (mean [SDs] 61.0 [23.39]-67.40 [19.51]), and the MD of the BFI was -15.96 (95% CI -25.56; -15.48). The RR of AEs was also smaller (RR 0.80; 95% CI 0.69-0.93), but the severity or duration of AEs was not reported. Pain intensity was also significantly decreased in the OXN PR treatment groups (MD -3.84, 95% CI -7.14; -0.55), although there was no clinically meaningful difference. CONCLUSION: For people with chronic pain, treatment with OXN PR decreases the incidence of OIC and provides intermediate-term bowel function improvement with clinical importance; in addition, pain relief is not weakened. The OIC after treatment with OXN PR for cancer-related pain and over the long term remains unknown.

Efficacy of D5F3 IHC for detecting ALK gene rearrangement in NSCLC patients: a systematic review and meta-analysis.

We conducted a pooled analysis comparing the efficacy of an immunohistochemistry (IHC) assay using the D5F3 antibody with that of fluorescence in situ hybridization (FISH) for detecting ALK gene rearrangement in NSCLC patients. A total of 32 studies involving 5805 samples were included in this review. Pooled sensitivity for D5F3 IHC was 0.97 (95%CI: 0.93-0.98), specificity was 0.99 (95%CI: 0.98-1.00), PLR was 119.20 (95%CI: 57.79-245.89), NLR was 0.03 (95%CI: 0.02-0.07), DOR was 3526.66 (95%CI: 1344.71-9249.03), and AUROC was 1.00 (95%CI: 0.99-1.00). Meta-regression revealed that specimen type was a source of heterogeneity for specificity, and specimen type and FISH signal distance were sources of heterogeneity in the joint model. Subgroup analysis revealed that sensitivity and specificity were higher when the FISH signal distance standard was ≥ 2 than when it was ≥ 1. Sensitivity was higher for tumor specimens than for cell specimens; specificity was higher for cell specimens than for tumor specimens. In conclusion, the D5F3 IHC assay was nearly as effective as FISH for detection of ALK gene rearrangement in NSCLC patients.