Bioinformatics Identification of ZNFs/LINC00520/miR-181d/BCL2 Axis as a Novel Network in Cisplatin-Resistant Lung Adenocarcinoma Cells

Background: Resistance to cisplatin (DDP) leads to poor prognosis in patients with Lung Adenocarcinoma (LUAD) and limits its clinical application. It has been confirmed that autophagy promotes chemoresistance and, therefore, novel strategies to reverse chemoresistance by regulating autophagy are desperately needed. Methods: The differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) between A549 and A549/DDP cell lines were identified using the limma package in R, after gene expression profiles were obtained from Gene Expression Omnibus (GEO) database. By combining Autophagy-Related Genes (ARGs) from Human Autophagy Database (HADb), the interactions lncRNA-miRNAs and the interactions miRNAs-mRNAs respectively predicted by miRcode and miRDB/Targetscan database, the autophagy-related ceRNA network was constructed. Then, extraction of ceRNA subnetwork and Cox regression analyses were performed. A prognosis-related ceRNA subnetwork was constructed, and the upstream Transcription Factors (TFs) regulating lncRNAs were predicted by the JASPAR database. Finally, the expression patterns of candidate genes were further verified by quantitative real-time polymerase chain reaction (qRT-PCR) experiments. Results: A total of 3179 DEmRNAs, 180 DEmiRNAs, and 160 DElncRNAs were identified, and 35 DEmRNAs were contained in the HADb. Based on the ceRNA hypothesis, we established a ceRNA network, including 10 autophagy-related DEmRNAs, 9 DEmiRNAs, and 14 DElncRNAs. Then, LINC00520, miR-181d, and BCL2 were identified to construct a risk score model, which was confirmed to be a well-predicting prognostic factor. Furthermore, 5 TF ZNF family members were predicted to regulate LINC00520, whereas the RT-PCR results showed that the 5 ZNFs were consistent with the bioinformatics analysis. Finally, a ZNF regulatory LINC00520/miR-181d/BCL2 ceRNA subnetwork was constructed. Conclusions: An ZNFs/LINC00520/miR-181d/BCL2 axis as a novel network in DDP-resistant LUAD has been constructed successfully, which may provide potential therapeutic targets for LUAD.

Positive Correlation between PMS2 Deficiency and PD-L1 Expression in Pancreatic Cancer

Background: Pancreatic cancer is one of the most lethal types of cancer, and immunotherapy has become a promising remedy with advancements in tumor immunology. However, predicting the clinical response to immunotherapy in pancreatic cancer remains a dilemma for clinicians. Methods: GEPIA database was used to analyze the differential expression of MMR and PD-L1 genes in 33 common cancer types including pancreatic cancer. The expression levels of MMR and PD-L1 genes were downloaded from the GEPIA and GEO databases to analyze the correlation between MMR genes and PD-L1, and the clinicopathological and survival information were downloaded from the TCGA databases to analyze the relationship between the expression of MMR, PD-L1 and clinicopathological characteristics, prognosis. Meanwhile, the tumor tissue samples of 41 patients with pancreatic cancer were collected, and the protein expression levels of MMR and PD-L1 were detected by immunohistochemical assay. Furthermore, we analyzed the correlation between MMR and PD-L1, and the correlation between the expression of MMR, PD-L1 and clinicopathological characteristics, prognosis of pancreatic cancer patients. Results: Bioinformatics analysis showed that MLH1, MLH3, MSH2, MSH3, and PMS2 were highly expressed in most cancer types including pancreatic cancer (P P = 0.012), clinical stage (I vs II: P = 0.016), MSH2 expression was related to clinical stage (P < 0.05), T stage (T3 vs T4: P = 0.039), and MSH3 expression was related to T stage (P < 0.05). Besides, both MSH2 expression (P P = 0.044) were significantly associated with prognosis. GEPIA data also showed that MSH2 expression was related to prognosis (P = 0.008). The correlation analysis revealed that the expressions MSH2, MLH1, PMS2 had strong correlations with PD-L1 both in GEPIA and GEO databases. Real-world data indicated that of the 41 pancreatic cancer patients, 5 cases had MLH1 deletion, 5 cases had MSH2 deletion, 4 cases had PMS2 deletion, and 12 cases had PD-L1 positive expression. Notably, PMS2 deletion was associated with PD-L1 positive expression (P = 0.035). In addition, MLH1 was related to clinical stage (P = 0.033), age (P = 0.048), and MSH2 was related to clinical stage (P = 0.033). However, MLH1 (P = 0.697), MSH2 (P = 0.956), PMS2 (P = 0.341), and PD-L1 (P = 0.734) appeared to have no impact on overall survival among patients with pancreatic cancer. Conclusion: Both bioinformatics and real-world data showed that there were correlation between PMS2 deletion and PD-L1 expression, and correlation between MLH1, MSH2 and clinical stage.

复杂地质条件下登楼山隧道施工技术研究

登楼山隧道是弥勒–玉溪高速公路中最重要的控制性工程,本文将对登楼山隧道的断面布置形式与工法选择进行研究。通过对登楼山隧道的工程概况以及国内特长隧道断面形式的调研情况进行分析,推荐本隧道拟采用设置平行导洞的三洞方案进行设计。针对钻爆法和TBM工法各自的优缺点,从地层适应性、施工安全性、技术可行性、经济性、施工工期等5个方面进行对比分析,推荐平行导洞工法选择使用钻爆法施工。分析过程为类似工程案例提供参考依据。

An Age-Dependent and State-Dependent Adaptive Prognostic Approach for Hidden Nonlinear Degrading System

Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL.Most current researches focus on age-dependent degradation models,but it has been found that some degradation processes in engineering are also related to the degradation states themselves.In addition,due to different working conditions and complex environments in engineering,the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL.In order to solve the above issues jointly,we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states.Then,the Kalman filter(KF)is utilized to update the hidden degradation states in real time,and the expectation-maximization(EM)algorithm is applied to adaptively estimate the unknown model parameters.Besides,the approximate analytical RUL distribution can be obtained from the concept of the first hitting time.Once the new observation is available,the RUL distribution can be updated adaptively on the basis of the updated degradation states and model parameters.The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.

A new remaining useful life estimation method for equipment subjected to intervention of imperfect maintenance activities

As the key part of Prognostics and Health Management（PHM）, Remaining Useful Life（RUL） estimation has been extensively investigated in recent years. Current RUL estimation studies considering the intervention of imperfect maintenance activities usually assumed that maintenance activities have a single influence on the degradation level or degradation rate, but not on both.Aimed at this problem, this paper proposes a new degradation modeling and RUL estimation method taking the influence of imperfect maintenance activities on both the degradation level and the degradation rate into account. Toward this end, a stochastic degradation model considering imperfect maintenance activities is firstly constructed based on the diffusion process. Then, the Probability Density Function（PDF） of the RUL is derived by the convolution operator under the concept of First Hitting Time（FHT）. To implement the proposed RUL estimation method,the Maximum Likelihood Estimation（MLE） is utilized to estimate the degradation related parameters based on the Condition Monitoring（CM） data, while the Bayesian method is utilized to estimate the maintenance related parameters based on the maintenance data. Finally, a numerical example and a practical case study are provided to demonstrate the superiority of the proposed method. The experimental results show that the proposed method could greatly improve the RUL estimation accuracy for the degrading equipment subjected to imperfect maintenance activities.