Prognostic Significance of mRNA Expression RBBP8 or Its Methylation in Gliomas.

Retinoblastoma-binding protein 8 (RBBP8) affects the prognosis of patients with malignancies through various mechanisms. However, its function in gliomas is unknown. Our study explored the effects of RBBP8 on the prognosis of glioma patients, as well as its regulatory role in the glioma immune microenvironment. We used various bioinformatics methods to analyze the transcriptional profiles and methylation data of RBBP8 in gliomas from multiple databases. Our results showed that the mRNA and protein expression of RBBP8 in gliomas was higher than that in normal tissues and positively correlated with malignant clinical features such as age and WHO grade. A Kaplan-Meier analysis showed that patients with high RBBP8 expression had a poor prognosis. Cox regression demonstrated that RBBP8 was an independent risk indicator and had good diagnostic value for the poor prognosis of glioma. Importantly, RBBP8 was positively correlated with many well-known immune checkpoints (e.g., CTLA4 and PDL-1). Finally, a gene set enrichment analysis revealed that RBBP8 was remarkably enriched in cancer-related pathways such as cell cycle, DNA replication and so on. In conclusion, this study is the first to elaborate on the value of RBBP8 in the pathological process of glioma for anti-tumor immunotherapy. In addition, the expression of RBBP8 and its methylation site, cg05513509, may provide potential targets for glioma therapy.

Expression and clinical significance of CXCL17 and GPR35 in endometrial carcinoma.

Endometrial carcinoma is one of the most common gynecologic malignancies. CXCL17-CXCR8 (GPR35) axis is reported to play an indispensability role in tumors. Our purpose is to screen possible prognostic and immune-related factors in endometrial carcinoma by detecting the mRNA and protein expression of CXCL17 and CXCR8. We use the qRT-PCR method to test the mRNA expression of CXCL17 and CXCR8 in 35 pairs of endometrial carcinoma and adjacent tissue. The protein expression of CXCL17 and CXCR8 in 30 cases of normal proliferative endometrium, 30 cases of endometrial atypical hyperplasia and 50 cases of endometrial carcinoma was detected by tissue microarray immunohistochemistry. There was no significant difference in the positive expression rate between endometrial adenocarcinoma tissue and endometrial atypical hyperplasia tissue (P > 0.05). But significantly better than normal proliferative tissue (P < 0.001). Correlation analysis of CXCR8 and CXCL17 in endometrial carcinoma showed a positive correlation (r = 0.9123, P < 0.0001). For patients with endometrial cancer, the overall survival (OS) of patients with high CXCL17 expression was significantly higher than that low CXCL17 expression (log-rank test, P < 0.0001), whereas CXCR8 had no statistical significance. But the expression of CXCR8 is an independent prognostic factor of OS in endometrial carcinoma patients. Our study showed that CXCL17 and CXCR8 may be involved in the occurrence and development of endometrial cancer. High expression of CXCL17 may be used as a biomarker for predicting survival. Because CXCL17 and CXCL18 are related to lymphocytes and immune regulation, they are expected to become potential targets for immunotherapy.

Robust meal delivery service for the elderly: a case study in Hong Kong.

Service providers in a community center in Hong Kong deliver meals to community-dwelling elderly, first from a central kitchen to intermediate depots by a van and then to the homes of the elderly via walking. We propose a modified two-echelon vehicle routing model with concerns of both delivery efficiency and workload fairness among workers, incorporating important practical aspects, such as continuity of care and unique features of buildings and served elderly. Notably, we employ robust optimization to address service time uncertainties that differentiate between frail and ordinary elderly. The robust model can be transformed into a mixed integer program, for which we provide two decomposition-based approaches to accelerate computation. Through a real-data case study, we verify the effectiveness of the proposed models. We show that robust solutions can protect against service time variations and achieve better performance while incurring a small additional cost over deterministic ones. We provide insights into choosing the level of conservatism and human resource planning for practitioners.

Study on the application and imaging characteristics of optical coherence tomography in vulva lesions.

In this study, a prospective study was conducted by using optical coherence tomography (OCT) in the in vivo detection of vulvar diseases. The clinical efficacy of the OCT we investigated in the detection of vulvar diseases, and the characteristics of the OCT images were defined. Overall, this study recruited 63 patients undergoing the colposcopy for vulvar lesions in three Chinese hospitals from December 20th, 2018 and September 24th, 2019. The colposcopy and the OCT examination were performed successively, and the OCT images were compared with the relevant tissue sections to characterize different lesions. The OCT diagnoses where categorized into 7 types, including normal and inflammatory vulva, condyloma acuminata, papilloma, lichen sclerosus, atrophic sclerosing lichen, fibrous epithelial polyp as well as cysts. The structural characteristics of the vulva tissue can be clearly observed in the OCT image, which are consistent with the characteristics of the tissue section. Compared with the pathological results, the sensitivity, specificity and accuracy of the OCT examination reached 83.82% (95% confidence interval, CI 72.5%-91.3%), 57.89% (95% CI 34.0%-78.9%) and 78.16%, respectively. The OCT is found with the advantages of being noninvasive, real-time and sensitive and with high resolution. It is of high significance to screening vulva diseases, and it is expected as one of the methods to clinically diagnose vulva diseases.

CLSPN is a potential biomarker associated with poor prognosis in low-grade gliomas based on a multi-database analysis.

BACKGROUND: The oncogene CLSPN, also known as claspin, has regulatory effects in a variety of tumours; however, it is not clear whether CLSPN is a therapeutic target in low-grade gliomas (LGG). In this study, the prognostic value of CLSPN in LGG and its role as an immunotherapeutic target were evaluated. METHODS: Transcriptome and methylation data for thousands of patients with glioma were collected from various databases, including The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gene Expression Omnibus. Subsequently, a series of bioinformatics methods were used to evaluate the relationships between CLSPN and prognosis, clinical features, methylation status, immune cells, and molecular signaling pathways in LGG. RESULTS: CLSPN expression levels were positively correlated with major malignant characteristics of LGG, and low expression of CLSPN was associated with a better prognosis. The methylation sites cg04263115 and cg06100291 negatively regulated the expression of CLSPN, and increased methylation levels at these sites were related to a longer survival time in patients with LGG. CLSPN was positively correlated with tumour-infiltrating immune cells and showed high copy number variation in these cells. There was a positive regulatory relationship between CLSPN expression and programmed death-1 (PD-1) and programmed cell death ligand 1 (PD-L1). A gene set enrichment analysis revealed that CLSPN activates a variety of cancer signaling pathways. CONCLUSION: CLSPN was identified as an independent risk factor for LGG with excellent prognostic value.

Identification of Prognostic Biomarkers Among FAM83 Family Genes in Human Ovarian Cancer Through Bioinformatic Analysis and Experimental Verification.

PURPOSE: Family with sequence similarity 83 (FAM83) is a newly discovered oncogene family, and the members of which can affect the prognosis of patients with malignant tumors via various mechanisms. However, the functions and molecular mechanisms of FAM83 genes in ovarian cancer (OC) have not yet been investigated. This study aimed to explore the clinical significance and prognostic value of FAM83 genes in OC. MATERIALS AND METHODS: We used a series of bioinformatics databases (Oncomine, GEPIA, cBioPortal, Kaplan-Meier plotter, DAVID and TIMER) to investigate the expression status, prognostic value, genetic alteration and biological function of all eight FAM83 genes in OC. In addition, a tissue microarray cohort (TMA) comprising 99 ovarian tumor tissues and 19 normal ovarian tissues was used to validate the protein expression and clinicopathological significance of FAM83H. RESULTS: Several datasets demonstrated the mRNA levels of FAM83A/D/E/F/H were significantly higher in OC compared with that in normal tissue. Moreover, the upregulation of FAM83D/H has been mutually confirmed in the Oncomine and GEPIA datasets. Kaplan-Meier survival analysis indicated that the FAM83D/H upregulation could predict poor prognosis of OC patients who had shorter overall survival (OS) and progression-free survival (PFS). In addition, cBioportal analysis indicated that the genetic alterations of FAM83 genes might affect the survival outcomes of patients with OC. Furthermore, KEGG analysis suggested that FAM83D/H are involved in the progression of OC through the cell cycle signaling pathway, and they had significant co-expression relationship with cell cycle-related genes. Finally, immunohistochemistry analysis confirmed the high expression of FAM83H protein in OC tissue, suggesting that its expression is positively correlated with the FIGO stage and pathological subtype of OC. CONCLUSION: This study elucidated the expression status and prognostic value of FAM83 genes in OC and identified that FAM83D/H might be potential targets for the prognostic monitoring and targeted therapy of OC.

Smooth Bayesian network model for the prediction of future high-cost patients with COPD.

INTRODUCTION: The clinical course of chronic obstructive pulmonary disease (COPD) is marked by acute exacerbation events that increase hospitalization rates and healthcare spending. The early identification of future high-cost patients with COPD may decrease healthcare spending by informing individualized interventions that prevent exacerbation events and decelerate disease progression. Existing studies of cost prediction of other chronic diseases have applied regression and machine-learning methods that cannot capture the complex causal relationships between COPD factors. Thus, the exploration of these factors through nonlinear, high-dimensional but explainable modeling is greatly needed. OBJECTIVES: We aimed to develop a machine-learning model to identify future high-cost patients with COPD. Such a model should incorporate expert knowledge about causal relationships, and the method for estimating the model could provide more accurate predictions than other machine learning methods. METHODS: We used the 2011-2013 medical insurance data of patients with COPD in a large city. The data set included demographic information and admission records. Leveraging on developments in graphical modeling methods, we proposed a smooth Bayesian network (SBN) model for the prediction of high-cost individuals using medical insurance data. The modeling method incorporated some expert knowledge about causal relationships (i.e., about the Bayesian network structure). We employed a smoothing kernel based on the weighted nearest neighborhood method in the SBN model to address overfitting, case-mix effect, and data sparsity (i.e., using data about "similar patients"). RESULTS: The proposed SBN achieved the area under curve (AUC) of 0.80 and showed considerable improvement over the baseline machine-learning methods. Besides confirming the known factors from the literature, we found "region" (i.e., a suburban or urban area) to be a significant factor, and that in a 3-tier system with primary, secondary and tertiary hospitals, COPD patients who had been admitted to primary hospitals were more likely to develop into future high-cost patients than patients who had been admitted to tertiary hospitals. CONCLUSION: The proposed SBN model not only obtained higher prediction accuracy and stronger generalizability than a number of benchmark machine-learning methods, but also used the Bayesian network to capture the complex causal relationships between different predictors by incorporating expert knowledge. Furthermore, a framework was developed to establish the relationships between exposure to historical trajectory and future outcome, which can also be applied to other temporal data to model different trajectory information and predict other outcomes.