The correlation of miRNA expression and tumor mutational burden in uterine corpus endometrial carcinoma

Background:The relationship between microRNA(miRNA)expression patterns and tumor mutation burden(TMB)in uterine corpus endometrial carcinoma(UCEC)was investigated in this study.Methods:The UCEC dataset from The Cancer Genome Atlas(TCGA)database was used to identify the miRNAs that differ in expression between high TMB and low TMB sample sets.The total sample sets were divided into a training set and a test set.TMB levels were predicted using miRNA-based signature classifiers developed by Lasso Cox regression.Test sets were used to validate the classifier.This study investigated the relationship between a miRNA-based signature classifier and three immune checkpoint molecules(programmed cell death protein 1[PD-1],programmed cell death ligand 1[PD-L1],cytotoxic T lymphocyte-associated antigen 4[CTLA-4]).For the miRNA-based signature classifier,functional enrichment analysis was performed on the miRNAs.An analysis of the relationship between PD-1,PD-L1,and CTLA-4 immune checkpoint genes was carried out using the miRNA-based signature classifier.Results:We identified 27 differentially expressed miRNAs in miRNA-base signature.For predicting the TMB level,27-miRNA-based signature classifiers had accuracies of 0.8689 in the training cohort,0.8276 in the test cohort,and 0.8524 in the total cohort.The correlation between the miRNA-based signature classifier and PD-1 was negative,while the correlation between PD-L1 and CTLA4 was positive.Based on the miRNA profiling described above,we validated the expression levels of 9 miRNAs in clinical samples by quantitative reverse transcription PCR(qRT-PCR).Four of them were highly expressed and many cancer-related and immune-associated biological processes were linked to these 27 miRNAs.Thus,the developed miRNA-based signature classifier was correlated with TMB levels that could also predict TMB levels in UCEC samples.Conclusion:In this study,we investigated the relationship between a miRNAbased signature classifier and TMB levels in Uterine Corpus Endometrial Carcinoma.Further,this is the first study to confirm their relationship in clinical samples,which may provide more evidence support for immunotherapy of endometrial cancer.

Neural Network Based Feasible Region Approximation Model for Optimal Operation of Integrated Electricity and Heating System

This paper proposes a neural network based feasible region approximation model of a district heating system(DHS),and it is intended to be used for optimal operation of integrated electricity and heating system(IEHS)considering privacy protection.In this model,a neural network is trained to approximate the feasible region of the DHS operation and then is reformulated as a set of mixed-integer linear constraints.Based on the received approximation models of DHSs and detailed electricity system model,the electricity operator conducts centralized optimization,and then sends specific heating generation plans back to corresponding heating operators.Furthermore,subsequent optimization is formulated for each DHS to obtain detailed operation strategy based on received heating generation plan.In this scheme,optimization of the IEHS could be achieved and privacy protection requirement is satisfied since the feasible region approximation model does not contain detailed system parameters.Case studies conducted on a small-scale system demonstrate accuracy of the proposed strategy and a large-scale system verify its application possibility.

鼻咽病变的影像诊断思路

鼻咽解剖位置深在,病变种类繁多,CT及MRI是鼻咽病变治疗前主要评估手段。不同鼻咽病变影像表现不同,但也存在较多重叠,临床工作中需从病变位置、形态、边界、信号及密度特征、强化方式、与邻近组织结构关系、有无骨质破坏或淋巴结转移等方面进行综合分析,并结合临床鼻咽镜及实验室检查进一步明确诊断,从而提升诊断准确性。笔者系统总结鼻咽病变影像诊断思路,并归纳分析鼻咽病变的影像诊断要点,在治疗前明确诊断及病变范围,从而为临床治疗决策提供重要支持证据。