Comprehensive pan-cancer analysis of role of GPRASP1, associated with clinical outcomes, immune microenvironment, and immunotherapeutic efficiency in pancreatic cancer.

BACKGROUND: GPRASP1 (G-protein-coupled receptor-associated sorting protein 1) plays an important role in tumorigenesis. However, GPRASP1 specific role has not been clearly clarified in cancer, particularly in pancreatic cancer(PC). METHODS: Firstly, we utilized pan-cancer analysis based on RNA sequencing data from TCGA (The Cancer Genome Atlas) to evaluate the expression pattern and immunological role of GPRASP1. Then, through multiple transcriptome datasets (TCGA and Gene Expression Omnibus (GEO)) and multi-omics (RNA-seq, DNA methylation, copy number variations (CNV), somatic mutation data) in-depth analysis, we comprehensively explore the relationship of GPRASP1 expression with clinicopathologic characteristics, clinical outcomes, CNV, and DNA methylation in pancreatic cancer. Additionally, we employed immunohistochemistry (IHC) to further confirm GPRASP1 expression pattern between PC tissues and paracancerous tissues. Lastly, we systematically associated the GPRASP1 with immunological properties from numerous perspectives, such as immune cell infiltration, immune-related pathways, immune checkpoint inhibitors, immunomodulators, immunogenicity, and immunotherapy. RESULTS: Through pan-cancer analysis, we identified that GPRASP1 plays a critical role in the occurrence and prognosis of PC, and is closely related to immunological characteristics in PC. IHC analysis confirmed that GPRASP1 is significantly down-regulated in PC compared with normal tissues. The expression of GPRASP1 is significantly negatively correlated with clinical features (histologic grade, T stage, and TNM stage), and is an independent predictor of favorable prognosis, regardless of other clinicopathological features (HR: 0.69, 95% CI 0.54-0.92, p= 0.011). The etiological investigation found that the abnormal expression of GPRASP1 was related to DNA methylation and CNV frequency. Subsequently, the high expression of GPRASP1 was significantly correlated with immune cell infiltration (CD8 + T cell, tumor-infiltrating lymphocyte(TIL)), immune-related pathways(cytolytic activity, check-point, human leukocyte antigen (HLA)), immune checkpoint inhibitors (CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT), immunomodulators ( CCR4/5/6, CXCL9, CXCR4/5), and immunogenicity(immune score, neoantigen, TMB(tumor mutation burden)). Finally, IPS (immunophenoscore) and TIDE (tumor immune dysfunction and exclusion) analysis demonstrated that GPRASP1 expression levels can accurately predict the immunotherapeutic response. CONCLUSION: GPRASP1 is a promising candidate biomarker that plays a role in the occurrence, development, and prognosis of PC. Evaluating GPRASP1 expression will aid in the characterization of tumor microenvironment (TME) infiltration and orient more efficient immunotherapy strategies.

GPRASP1 is a candidate anti-oncogene and correlates with immune microenvironment and immunotherapeutic efficiency in head and neck cancer.

BACKGROUND: G-protein-coupled receptor-associated sorting protein 1 (GPRASP1) plays an important role in tumorigenesis. However, GPRASP1 specific role has not been clarified in head and neck cancer (HNC). METHODS: HNC RNA sequencing (RNA-seq) datasets, DNA methylation data, somatic mutation data, copy number variation (CNV) data, and corresponding clinicopathologic information were acquired from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive evaluation was performed to explore the relationship of GPRASP1 expression with clinicopathologic characteristics, CNV, and DNA methylation. Additionally, we employed HNC tissue microarray (TMA) to further confirm the relation between GPRASP1 expression and clinical features. Then, we systematically associated the GPRASP1 with immunological properties from numerous perspectives, such as immune cell infiltration, immune-related pathways, immune checkpoint inhibitors (ICIs), immunomodulators, immunogenicity, and immunotherapy. RESULTS: Analyzing TCGA, GEO, and TMA datasets, GPRASP1 is significantly down-regulated in HNC compared to normal tissues. The expression of GPRASP1 is significantly negatively correlated with clinical features (perineural invasion, histologic grade, T stage, and TNM stage), and is an independent predictor of favorable prognosis, regardless of other clinicopathological features (HR: 0.42, 95% CI 0.20-0.91, p = 0.028). The etiological investigation found that the abnormal expression of GPRASP1 was related to DNA methylation, not CMV. Subsequently, the high expression of GPRASP1 was significantly correlated with immune cell infiltration (CD8+  T cell, tumor infiltrating lymphocyte), immune-related pathways (cytolytic activity, check-point, human leukocyte antigen), ICIs (CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT), immunomodulators (CCR4/5, CXCL9, CXCR3/4/5), and immunogenicity (immune score, neoantigen, tumor mutation burden). Finally, immunophenoscore and tumor immune dysfunction and exclusion analysis demonstrated that GPRASP1 expression levels can accurately predict the immunotherapeutic response. CONCLUSION: GPRASP1 is a promising candidate biomarker that plays a role in the occurrence, development, and prognosis of HNC. Evaluating GPRASP1 expression will aid in the characterization of tumor microenvironment infiltration and orient more efficient immunotherapy strategies.

Comprehensive characterization of costimulatory molecule gene for diagnosis, prognosis and recognition of immune microenvironment features in sepsis.

The present study, which involved 10 GEO datasets and 3 ArrayExpress datasets, comprehensively characterized the potential effects of CMGs in sepsis. Based on machine learning algorithms (Lasso, SVM and ANN), the CMG classifier was constructed by integrating 6 hub CMGs (CD28, CD40, LTB, TMIGD2, TNFRSF13C and TNFSF4). The CMG classifier exhibit excellent diagnostic values across multiple datasets and time points, and was able to distinguish sepsis from other critical diseases. The CMG classifier performed better in predicting mortality than other clinical characteristics or endotypes. More importantly, from clinical specimens, the CMG classifier showed more superior diagnostic values than PCT and CRP. Alternatively, the CMG classifier/hub CMGs is significantly correlated with immune cells infiltration (B cells, T cells, Tregs, and MDSC), pivotal immune and molecular pathways (inflammation-promoting, complement and coagulation cascades), and several cytokines. Collectively, CMG classifier was a robust tool for diagnosis, prognosis and recognition of immune microenvironment features in sepsis.

Construction of Autophagy-Related Gene Classifier for Early Diagnosis, Prognosis and Predicting Immune Microenvironment Features in Sepsis by Machine Learning Algorithms.

Background: The immune system plays a fundamental role in the pathophysiology of sepsis, and autophagy and autophagy-related molecules are crucial in innate and adaptive immune responses; however, the potential roles of autophagy-related genes (ARGs) in sepsis are not comprehensively understood. Methods: A systematic search was conducted in ArrayExpress and Gene Expression Omnibus (GEO) cohorts from July 2005 to May 2022. Machine learning approaches, including modified Lasso penalized regression, support vector machine, and artificial neural network, were applied to identify hub ARGs, thereby developing a prediction model termed ARG classifier. Diagnostic and prognostic performance of the model was comprehensively analyzed using multi-transcriptome data. Subsequently, we systematically correlated the ARG classifier/hub ARGs with immunological characteristics of multiple aspects, including immune cell infiltration, immune and molecular pathways, cytokine levels, and immune-related genes. Further, we collected clinical specimens to preliminarily investigate ARG expression levels and to assess the diagnostic performance of ARG classifier. Results: A total of ten GEO and three ArrayExpress datasets were included in this study. Based on machine learning algorithms, eight key ARGs (ATG4C, BAX, BIRC5, ERBB2, FKBP1B, HIF1A, NCKAP1, and NFKB1) were integrated to establish ARG classifier. The model exhibited excellent diagnostic values (AUC > 0.85) in multiple datasets and multiple points in time and superiorly distinguished sepsis from other critical illnesses. ARG classifier showed significant correlations with clinical characteristics or endotypes and performed better in predicting mortality (AUC = 0.70) than other clinical characteristics. Additionally, the identified hub ARGs were significantly associated with immune cell infiltration (B, T, NK, dendritic, T regulatory, and myeloid-derived suppressor cells), immune and molecular pathways (inflammation-promoting pathways, HLA, cytolytic activity, apoptosis, type-II IFN response, complement and coagulation cascades), levels of several cytokines (PDGFRB, IL-10, IFNG, and TNF), which indicated that ARG classifier/hub ARGs adequately reflected the immune microenvironment during sepsis. Finally, using clinical specimens, the expression levels of key ARGs in patients with sepsis were found to differ significantly from those of control patients, and ARG classifier exhibited superior diagnostic performance, compared to procalcitonin and C-reactive protein. Conclusion: Collectively, a diagnostic and prognostic model (ARG classifier) based on eight ARGs was developed which may assist clinicians in diagnosis of sepsis and recognizing patient at high risk to guide personalized treatment. Additionally, the ARG classifier effectively reflected the immune microenvironment diversity of sepsis and may facilitate personalized counseling for specific therapy.

Construction of an HLA Classifier for Early Diagnosis, Prognosis, and Recognition of Immunosuppression in Sepsis by Multiple Transcriptome Datasets.

Background: Sepsis is a clinical syndrome, due to a dysregulated inflammatory response to infection. Accumulating evidence shows that human leukocyte antigen (HLA) genes play a key role in the immune responses to sepsis. Nevertheless, the effects of HLA genes in sepsis have still not been comprehensively understood. Methods: A systematical search was performed in the Gene Expression Omnibus (GEO) and ArrayExpress databases from inception to 10 September 2021. Random forest (RF) and modified Lasso penalized regression were conducted to identify hub genes in multi-transcriptome data, thus we constructed a prediction model, namely the HLA classifier. ArrayExpress databases, as external validation, were utilized to evaluate its diagnostic, prognostic, and predictive performance. Immune cell infiltration score was calculated via CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched in different subgroups. Next, we systematically correlated the HLA classifier with immunological characteristics from multiple perspectives, such as immune-related cell infiltration, pivotal molecular pathways, and cytokine expression. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to validate the expression level of HLA genes in clinical samples. Results: A total of nine datasets comprising 1,251 patients were included. Based on RF and modified Lasso penalized regression in multi-transcriptome datasets, five HLA genes (B2M, HLA-DQA1, HLA-DPA1, TAP1, and TAP2) were identified as hub genes, which were used to construct an HLA classifier. In the discovery cohort, the HLA classifier exhibited superior diagnostic value (AUC = 0.997) and performed better in predicting mortality (AUC = 0.716) than clinical characteristics or endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. In the E-MTAB-7581 dataset, the use of hydrocortisone in the HLA high-risk subgroup (OR: 2.84, 95% CI 1.07-7.57, p = 0.037) was associated with increased risk of mortality, but not in the HLA low-risk subgroup. Additionally, immune infiltration analysis by CIBERSORTx and ssGSEA revealed that B cells, activated dendritic cells, NK cells, T helper cells, and infiltrating lymphocytes (ILs) were significantly richer in HLA low-risk phenotypes, while Tregs and myeloid-derived suppressor cells (MDSCs) were more abundant in HLA high-risk phenotypes. The HLA classifier was significantly negatively correlated with B cells, activated dendritic cells, NK cells, T helper cells, and ILs, yet was significantly positively correlated with Tregs and MDSCs. Subsequently, molecular pathways analysis uncovered that cytokine-cytokine receptor (CCR) interaction, human leukocyte antigen (HLA), and antigen-presenting cell (APC) co-stimulation were significantly enriched in HLA low-risk endotypes, which was significantly negatively correlated with the HLA classifier in multi-transcriptome data. Finally, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different between the HLA subgroups, and the ratio of IL-10/TNF was significantly positively correlated with HLA score in multi-transcriptome data. Results of qRT-PCR validated the higher expression level of B2M as well as lower expression level of HLA-DQA1, HLA-DPA1, TAP1, and TAP2 in sepsis samples compared to control sample. Conclusion: Based on five HLA genes, a diagnostic and prognostic model, namely the HLA classifier, was established, which is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.

Characterization of Immune-Related Genes and Immune Infiltration Features in Epilepsy by Multi-Transcriptome Data.

Background: Epilepsy encompasses a group of heterogeneous brain diseases that afflict about 1% of the world's population. Accumulating evidence shows that the immune system plays a key role in epileptogenesis. Nevertheless, the immune-related mechanisms remain not been precisely understood. Methods: Three epilepsy datasets (GSE16969, GSE32534 and GSE143272) were screened to obtain differentially expressed immune-related genes (DEIRGs). Random forest (RF) and protein-protein interaction (PPI) network were constructed to identify core genes. Another dataset (GSE31718) and 60 clinical samples via quantitative real-time polymerase chain reaction (qRT-PCR) were utilized to validate core genes. Immune cell infiltration score was performed with CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched during normal and epilepsy. The correlation between hub genes, immune cells, and enriched molecular pathways was evaluated by Pearson correlation analysis. Results: Based on RF and PPI, 4 DEIRGs (CSF1R, IL6R, TLR2, and TNFRSF1A) were identified as hub genes. Results of qRT-PCR validated that higher expression levels of CSF1R, IL6R, TLR2, and TNFRSF1A in epilepsy samples compared to control sample. Immune infiltration analysis by CIBERSORTx displayed immune signatures that are significantly richer in epilepsy, T cell subsets in particular. Notably, ssGSEA found that Th1 signatures were more abundant in normal tissues; yet Th2 signatures were more abundant in epilepsy tissues. Cytokine cytokine receptor interaction (CCR) was significantly enriched in epilepsy based on multi-transcriptome data. Additionally, hub genes were significantly correlated with score of Th1/Th2 signatures and enrichment score of CCR in multi-transcriptome data. Conclusion: Four IRGs (CSF1R, IL6R, TLR2, and TNFRSF1A) were closely correlated pathogenesis of epilepsy, which may be by impacting CCR and the balance of Th1/Th2 signatures involved in the occurrence of epilepsy. Our data offer compelling insights into the pathogenesis and promising therapeutic targets for epilepsy.

Cancer germline antigen gene MAGEB2 promotes cell invasion and correlates with immune microenvironment and immunotherapeutic efficiency in laryngeal cancer.

By multiple transcriptome datasets (TCGA, GSE59102, GSE25727, GSE27020 and GSE65858) and multi-omics (RNA-seq, SNP, CNV, DNA methylation) in-depth analysis, we found that cancer germline antigen (CGA) family/genes MAGEB2 is involved in the imitation, progression and prognosis in LC as well as correlated positively with lymphatic metastasis and negatively with DNA methylation. Then, in vitro experiment verified that MAGEB2 expression renders significant alteration in LC tissues and cells via immunohistochemical (IHC), qRT-PCR and western blot (WB), and up-regulation of MAGEB2 expression could facilitate the proliferation, migration and invasion of LC cells and vice versa. Subsequently, MAGEB2 knockdown suppressed tumor growth and lung metastasis in vivo animal experiment, while MAGEB2 overexpression promoted tumor growth and lung metastasis. Lastly, MAGEB2 is significantly associated with immune cell infiltration (CD8+ T cells particularly, IHC staining confirmed that as the protein expression of MAGEB2 increased, the protein level of CD8 (representing tumor-infiltrating CD8 + T cells) decreased in vitro), immunomodulators (knockdown or overexpression of MAGEB2 on LC cell lines can significantly affect the chemokine/cytokine secretion in vitro), and immunogenicity(TMB) in LC, which hints that MAGEB2 is tightly correlated with immune characteristics and might guide more effective immunotherapy strategies for LC patients.

Characterization of immune-related genes andimmune infiltration features for early diagnosis, prognosis and recognition of immunosuppression in sepsis.

Among the body systems, the immune system plays a fundamental role in the pathophysiology of sepsis. The effects of immunogenomic and immune cell infiltration in sepsis were still not been systematically understood. Based on modified Lasso penalized regression and RF, 8 DEIRGs (ADM, CX3CR1, DEFA4, HLA-DPA1, MAPK14, ORM1, RETN, and SLPI) were combined to construct an IRG classifier. In the discovery cohort, IRG classifier exhibited superior diagnostic efficacy and performed better in predicting mortality than clinical characteristics or MARS/SRS endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. The use of hydrocortisone in IRG high-risk subgroup was associated with increased risk of mortality. In IRG low-risk phenotypes, NK cells, T helper cells, and infiltrating lymphocyte (IL) are significantly richer, while T cells regulatory (Tregs) and myeloid-derived suppressor cells (MDSC) are more abundant in IRG high-risk phenotypes. IRG score were significantly negatively correlated with Cytokine cytokine receptor interaction (CCR) and human leukocyte antigen (HLA). Between the IRG subgroups, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different, and IRG score was significantly positively correlated with ratio of IL-10/TNF. Results of qRT-PCR validated that higher expression level of ADM, DEFA4, MAPK14, ORM1, RETN, and SLPI as well as lower expression level of CX3CR1 and HLA-DPA1 in sepsis samples compared to control sample. A diagnostic and prognostic model, namely IRG classifier, was established based on 8 IRGs that is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.

Effects of Terlipressin on Management of Hypotensive Brain-Dead Patients Who are Potential Organ Donors: A Retrospective Study.

Background: Administration of terlipressin can reverse hypotension in potential organ donors with norepinephrine-resistance. The aim of this study was to determine the effects of terlipressin on the hemodynamics, liver function, and renal function of hypotensive brain-dead patients who were potential organ donors. Methods: A retrospective study was conducted by using the ICU database of one hospital. 18 patients in a total of 294 brain-dead cases were enrolled and administered terlipressin intravenously. All physiological parameters of recruited patients were obtained at baseline, 24 and 72 h after administration, and immediately before organ procurement. Results: Terlipressin induced significant increases in mean arterial pressure (MAP) from 69.56 ± 10.68 mm Hg (baseline) to 101.82 ± 19.27 mm Hg (immediately before organ procurement) and systolic blood pressure (SBP) from 89.78 ± 8.53 mm Hg (baseline) to 133.42 ± 26.11 mm Hg (immediately before organ procurement) in all patients. The increases in MAP were accompanied by significant decreases in heart rate (HR) from 113.56 ± 28.43 bpm (baseline) to 83.89 ± 11.70 bpm (immediately before organ procurement), which resulted in the decrease of norepinephrine dose over time from 0.8 ± 0.2 µg/kg/min (baseline) to 0.09 ± 0.02 µg/kg/min (immediately before organ procurement). There were no changes in central venous pressure, liver function including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and bilirubin. Renal function, assessed by serum creatinine (SCr), urine output (UOP), creatinine clearance rate (CCr), and estimated glomerular filtration rate (eGFR), improved significantly. Conclusion: Our analysis of brain-dead patients with hypotension indicates that administration of terlipressin can significantly increases MAP, SBP, UOP, CCr, and eGFR, while decreases HR and Scr. Terlipressin appears to help maintain hemodynamic stability, reduce vasoactive support, and improve renal function.

Development and Validation of Epigenetic Signature Predict Survival for Patients with Laryngeal Squamous Cell Carcinoma.

Establishing epigenetic signature to improve the accuracy of survival prediction and optimize therapeutic strategies for laryngeal squamous cell carcinoma (LSCC) by a genome-wide integrated analysis of methylation and the transcriptome. LSCC DNA methylation datasets and RNA sequencing datasets were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs), which developed an epigenetic signature. The predictive accuracy and clinical value of the epigenetic signature were evaluated by receiver operating characteristic and decision curve analysis, and compared with tumor-node-metastasis (TNM) stage system. In addition, prognostic value of the epigenetic signature was validated by external Gene Expression Omnibus (GEO) database. According to five MDGs of epigenetic signature, the candidate small molecules for LSCC were screen out by the CMap database. A total of 88 DNA MDGs were identified, five of which (MAGEB2, SUSD1, ZNF382, ZNF418, and ZNF732) were chosen to construct an epigenetic signature. The epigenetic signature can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.8 (5-year overall survival [OS]) and AUC of 0.745 (3-year OS). Stratification analysis affirmed that the epigenetic signature was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated that the efficacy of epigenetic signature appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the epigenetic signature was superior to traditional TNM stage. In addition, the epigenetic signature was confirmed in external LSCC cohorts from GEO. Finally, CMap matched the 10 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. An epigenetic signature, with five DNA MDGs, was identified and validated in LSCC patients by integrating multidimensional genomic data, which may offer novel research directions and prospects for individualized treatment of patients with LSCC.

Identification and validation of methylation-driven genes prognostic signature for recurrence of laryngeal squamous cell carcinoma by integrated bioinformatics analysis.

BACKGROUND: Recurrence remains a major obstacle to long-term survival of laryngeal squamous cell carcinoma (LSCC). We conducted a genome-wide integrated analysis of methylation and the transcriptome to establish methylation-driven genes prognostic signature (MDGPS) to precisely predict recurrence probability and optimize therapeutic strategies for LSCC. METHODS: LSCC DNA methylation datasets and RNA sequencing (RNA-seq) dataset were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs). By univariate and multivariate Cox regression analyses, five genes of DNA MDGs was developed a recurrence-free survival (RFS)-related MDGPS. The predictive accuracy and clinical value of the MDGPS were evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA), and compared with TNM stage system. Additionally, prognostic value of MDGPS was validated by external Gene Expression Omnibus (GEO) database. According to 5 MDGs, the candidate small molecules for LSCC were screen out by the CMap database. To strengthen the bioinformatics analysis results, 30 pairs of clinical samples were evaluated by digoxigenin-labeled chromogenic in situ hybridization (CISH). RESULTS: A total of 88 DNA MDGs were identified, and five RFS-related MDGs (LINC01354, CCDC8, PHYHD1, MAGEB2 and ZNF732) were chosen to construct a MDGPS. The MDGPS can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.738 (5-year RFS) and AUC of 0.74 (3-year RFS). Stratification analysis affirmed that the MDGPS was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated the efficacy of MDGPS appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the MDGPS was superior to traditional TNM stage. Additionally, the MDGPS was confirmed in external LSCC cohorts from GEO. CMap matched the 9 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. Finally, CISH analysis in 30 LSCC tissues and paired adjacent normal tissues revealed that MAGEB2 has significantly higher expression of LSCC compared to adjacent non-neoplastic tissues; LINC01354, CCDC8, PHYHD1, and ZNF732 have significantly lower expression of LSCC compared to adjacent non-neoplastic tissues, which were in line with bioinformatics analysis results. CONCLUSION: A MDGPS, with five DNA MDGs, was identified and validated in LSCC patients by combining transcriptome and methylation datasets analysis. Compared TNM stage alone, it generates more accurate estimations of the recurrence prediction and maybe offer novel research directions and prospects for individualized treatment of patients with LSCC.

Association Use of Bisphosphonates with Risk of Breast Cancer: A Meta-Analysis.

BACKGROUND: Previous studies have investigated the association between the use of bisphosphonates and the development of breast cancer, which presented controversial results. Thus, this meta-analysis was conducted to summarize the current evidence of the association of bisphosphonate use with breast cancer risk. METHODS: A comprehensive search was conducted in PubMed, ISI Web of Knowledge, the Cochrane Library, and Embase from inception to March 2019 by two researches, who independently selected trials, retrieved relevant data, and assessed study quality. The summary relative risk (RR) for the use of bisphosphonates on the risks of developing breast cancer was calculated using a random-effect model. RESULTS: The present meta-analysis, which included four case-control studies, involving 55052 breast cancer cases, and seven retrospective cohort studies, involving 14641 breast cancer cases, assessed the effect of bisphosphonates on breast cancer risk. The random-effect model meta-analysis found a reduced risk of breast cancer with exposure to bisphosphonates with pooled RR of 0.87 (95% confidence interval [CI]: 0.80 to 0.94). The short-term use of bisphosphonates (<1 year) did not render significant alteration (RR = 0.92, 95% CI: 0.82 to 1.03), while a significant 26% risk reduction of breast cancer was noted with long-term use (>1 year) (RR = 0.74, 95% CI: 0.62 to 0.90). A protective effect of bisphosphonates was shown in contralateral breast cancer (RR = 0.41, 95% CI: 0.20 to 0.84). In terms of the type of bisphosphonates, a significant inverse relationship was noted for etidronate, with pooled RR of 0.87 (95% CI: 0.80 to 0.96). CONCLUSION: This meta-analysis suggested that the use of bisphosphonates was associated with reduced risk of breast cancer, including contralateral breast cancer. Compared to other types of bisphosphonates, only etidronate showed a significant inverse relationship. Additionally, the long-term use (>1 year) of bisphosphonates was more significant in lowering breast cancer risk. Further randomized controlled trials are needed to verify this association. This trial is registered with PROSPERO (registration number: CRD42018105024) (registered on 29 August 2018).

Development and validation of nomogram to predict risk of survival in patients with laryngeal squamous cell carcinoma.

To the best of our knowledge, this is the first study established a nomogram to predict survival probability in Asian patients with LSCC. A risk prediction nomogram for patients with LSCC, incorporating easily assessable clinicopathologic factors, generates more precise estimations of the survival probability when compared TNM stage alone, but still need additional data before being used in clinical application. BACKGROUND: Due to a wide variation of tumor behavior, prediction of survival in laryngeal squamous cell carcinoma (LSCC) patients received curative-intent surgery is an important but formidable challenge. We attempted to establish a nomogram to precisely predict survival probability in LSCC patients. METHODS: A total of 369 consecutive LSCC patients underwent curative resection between 2008 and 2012 at Hunan Province Cancer Hospital were included in the present study. Subsequently, 369 LSCC patients were assigned to a training set (N=261) and a validation set (N=108) at random. On the basis of multivariable Cox regression analysis results, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. RESULTS: Six independent parameters to predict prognosis were age, pack years, N-stage, lymph node ratio (LNR), anemia and albumin, which were all assembled into the nomogram. The calibration curve verified excellent models' concordance. The C-index of the nomogram was 0.73 (0.68-0.78), and the area under curve (AUC) of nomogram in predicting overall survival (OS) was 0.766, which were significantly higher than traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage. CONCLUSION: A risk prediction nomogram for patients with LSCC, incorporating easily assessable clinicopathologic factors, generates more precise estimations of the survival probability when compared TNM stage alone, but still need additional data before being used in clinical application.

Initial surgical versus non-surgical treatments for advanced hypopharyngeal cancer: A meta-analysis with trial sequential analysis.

BACKGROUND: There is no consensus on whether initial surgical or non-surgical treatments should be the standard treatment pattern for advanced hypopharyngeal cancer. The aim of the meta-analysis was systematically and quantitatively compare the relative efficacy between initial surgical and non-surgical therapies in patients with advanced-stage hypopharyngeal carcinoma. METHODS: A comprehensive search was performed in PubMed, the ISI Web of Knowledge, the Cochrane Library, and Embase databases from inception to April 10, 2019. Citation screening, data abstraction and quality assessment were performed in duplicate. Meta-analysis with trial sequential analysis (TSA) was used to assess the primary and secondary outcomes. Besides, we used the Grading of Recommendations Assessment Development and Evaluation (GRADE) to evaluate the certainty of the body of evidence. RESULTS: A total of 17 trials was appraised with 2539 patients that complied with inclusion and exclusion criterion. Pooled analyses indicated patients receiving primary surgical and non-surgical therapy did not significantly differ in overall survival (OS) (relative risk [RR] = 1.04, 95% confidence interval [CI] = 0.94 to 1.15), with TSA inconclusive. Additionally, patients treated with primary surgical experienced an increased disease free survival (DFS) probability compared with non-surgical treatment (RR 1.20, 95% CI = 1.05 to1.37), while TSA is inconclusive. Notably, non-surgical management did have a beneficial efficacy on larynx preservation (RR 0.48, 95% CI = 0.33 to 0.70), and TSA also provided conclusive evidence. GRADE indicated the level of evidence was low or very low for primary or secondary outcomes. CONCLUSION: The results of our meta-analysis indicated when compared to surgical treatments, non-surgical therapy for patients with advanced hypopharyngeal carcinoma appears to have equivalent efficacy, and it offers an opportunity to preserve laryngeal function. Due to inconclusive evidence by TSA, further investigation with large randomized clinical trials (RCTs) using modern approaches should be undertaken to verify the results of this meta-analysis. TRIAL REGISTRATION: PROSPERO registration number: CRD42018118563. Registered on December 19, 2018.

Development and validation of nomograms to accurately predict risk of recurrence for patients with laryngeal squamous cell carcinoma: Cohort study.

BACKGROUND: Recurrence is still major obstacle to long-term survival in laryngeal squamous cell carcinoma (LSCC). We aimed to establish and validate a nomogram to precisely predict recurrence probability in patients with LSCC. METHODS: A total of 283 consecutive patients with LSCC received curative-intend surgery between 2011 and 2014 at were enrolled in this study. Subsequently, 283 LSCC patients were randomly assigned to a training cohort (N = 171) and a validation cohort (N = 112) in a 3:2 ratio. According to the results of multivariable Cox regression analysis in the training cohort, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by calibration curve and concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram. RESULTS: Six independent factors rooted in multivariable analysis of the training cohort to predict recurrence were age, tumor site, smoking, alcohol, N stage and hemoglobin, which were all integrated into the nomogram. The calibration curve for the probability of recurrence presented that the nomogram-based predictions were in good correspondence with actual observations. The C-index of the nomogram was 0.81 (0.75-0.88), and the area under curve (AUC) of nomogram in predicting recurrence free survival (RFS) was 0.894, which were significantly better than traditional TNM stage. Decision curve analysis further affirmed that our nomogram had a larger net benefit than TNM stage. The results were confirmed in the validation cohort. CONCLUSION: A risk prediction nomogram for patients with LSCC, incorporating readily assessable clinicopathologic variables, generates more accurate estimations of the recurrence probability when compared TNM stage alone, but still needs additional data before being used in clinical implications.

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma.

BACKGROUND: Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients. METHODS: The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram. RESULTS: Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally. CONCLUSION: Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

The Accuracy of Urinary TIMP-2 and IGFBP7 for the Diagnosis of Cardiac Surgery-Associated Acute Kidney Injury: A Systematic Review and Meta-Analysis.

BACKGROUND: Tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) are recent promising markers for identification of cardiac surgery-associated acute kidney injury (CSA-AKI). The aim of this study was systematically and quantitatively to evaluate the accuracy of urinary TIMP-2 and IGFBP7 for the diagnosis of CSA-AKI. METHODS: Three databases including PubMed, ISI web of knowledge, and Embase were systematically searched from inception to March 2018. Two investigators conducted the processes of literature search study selection, data extraction, and quality evaluation independently. Meta-DiSc and STATA were used for all statistical analyses. RESULTS: A total of 8 studies comprising 552 patients were included in this meta-analysis. Pooled sensitivity and specificity with corresponding 95% confidence intervals (CIs) were 0.79 (95% CI, 0.71-0.86, I 2 = 74.2%) and 0.76 (95% CI, 0.72-0.80, I 2 = 80.8%), respectively. Pooled positive likelihood ratio (LR), negative LR, and diagnostic odds ratio were 3.49 (95% CI, 2.44-5.00, I 2 = 61.5%), 0.31(95% CI, 0.19-0.51, I 2 = 51.8%), and 14.89 (95% CI, 7.31-30.32, I 2 = 27.9%), respectively. The area under curve estimated by summary receiver operating characteristic was 0.868 (standard error [SE] 0.032) with a Q* value of 0.799 (SE 0.032). Sensitivity analysis demonstrated that one study notably affected the stability of pooled results. One of the subgroups investigated-AKI threshold-could account for partial heterogeneity. CONCLUSION: Urinary TIMP-2 and IGFBP7 is a helpful biomarker for early diagnosis of CSA-AKI. And, the potential of this biomarker with a broader spectrum of clinical settings may be the focus of future studies.

Efficacy and safety of haloperidol for delirium prevention in adult patients: An updated meta-analysis with trial sequential analysis of randomized controlled trials.

STUDY OBJECTIVE: To identify the efficacy and safety of haloperidol prophylaxis in adult patients with a high risk for delirium. DESIGN: A meta-analysis with trial sequential analysis of randomized controlled trials. INTERVENTION: A comprehensive search was performed in PubMed, the ISI Web of Knowledge, the Cochrane Library, and Embase databases from inception through to March 2019.Citation screening, data abstraction and quality assessment were performed in duplicate. Meta-analysis with trial sequential analysis (TSA) were used to assess the primary and secondary outcomes. In addition, we used the Grading of Recommendations Assessment Development and Evaluation (GRADE) to evaluate the certainty of the body of evidence. MAIN RESULTS: We appraised 8 RCTs involving 3034 patients that that were in compliance with inclusion and exclusion criterion. Pooled analyses indicated patients receiving haloperidol prophylaxis and placebo or normal saline did not significantly differ in incidence of delirium (relative risk [RR] = 0.90, 95% confidence interval [CI] = 0.70 to 1.15), with TSA inconclusive. Notably, compared with the control group, use of haloperidol significantly decreased the duration of delirium (Mean difference [MD] -0.94; 95% CI -1.82 to -0.06 days), with a marked heterogeneity. Additionally, haloperidol prophylaxis does not significantly affect duration of mechanical ventilation, length of intensive care unit (ICU) stay, length of hospital stay and mortality. In terms of safety profiles, haloperidol was not associated with increased risk for QTc prolongation, extrapyramidal symptoms, or adverse events. GRADE indicated the level of evidence was very low for a benefit from haloperidol prophylaxis. CONCLUSIONS: The results of our meta-analysis suggested the use of prophylactic haloperidol compared with placebo had no beneficial impacts on incidence of delirium, duration of mechanical ventilation, length of intensive care unit (ICU) stay, length of hospital stay and mortality in adult patients. It appeared to have a positive effect on duration of delirium, while with a significant heterogeneity. These findings do not support the routine usage of haloperidol for delirium prevention. TRIAL REGISTRATION: PROSPERO registration number: CRD42018100511. Registered on 17 July 2018.

A Genomic-Clinicopathologic Nomogram Predicts Survival for Patients with Laryngeal Squamous Cell Carcinoma.

BACKGROUND: Long noncoding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. We aimed to establish a lncRNA signature and a nomogram incorporating the genomic and clinicopathologic factors to improve the accuracy of survival prediction for laryngeal squamous cell carcinoma (LSCC). METHODS: A LSCC RNA-sequencing (RNA-seq) dataset and the matched clinicopathologic information were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a thirteen-lncRNA signature related to prognosis. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with the TNM staging system by C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate the clinical value of our nomogram. RESULTS: Thirteen overall survival- (OS-) related lncRNAs were identified, and the signature consisting of the selected thirteen lncRNAs could effectively divide patients into high-risk and low-risk subgroups, with area under curves (AUC) of 0.89 (3-year OS) and 0.885 (5-year OS). Independent factors derived from multivariable analysis to predict survival were margin status, tumor status, and lncRNA signature, which were all assembled into the nomogram. The calibration curve for the survival probability showed that the predictions based on the nomogram coincided well with actual observations. The C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of the nomogram in predicting overall survival (OS) was 0.938, both of which were significantly higher than the traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage. CONCLUSION: An inclusive nomogram for patients with LSCC, comprising genomic and clinicopathologic variables, generates more accurate estimations of the survival probability when compared with TNM stage alone, but more data are needed before the nomogram is used in clinical practice.

An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma.

Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72-0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.