LncRNA AC100826.1 regulated PLCB1 to promote progression in non-small cell lung cancer.

BACKGROUND: Lung cancer is the most common malignant tumor. In the present study, we identified a long non-coding RNA (lncRNA) AC100826.1 (simplify to Lnc1), which was highly expressed in non-small cell lung cancer (NSCLC) tissues compared with the paracancerous tissues. We also observed the critical role of Lnc1 in regulating the metastasis ability of NSCLC cells. METHODS: RNA sequencing was performed to detect differential expression levels of lncRNAs in NSCLC tissues and its paracancerous tissues. Effects of Lnc1 on cell proliferation, invasion, and migration were determined by CCK-8, transwell and scratch assays. The xenograft experiment confirmed the effect of Lnc1 on NSCLC cells proliferation and migration abilities in vivo. RT-qPCR and western blots were performed to determine the expression levels of mRNAs and proteins. RESULTS: The expression level of Lnc1 was related to multiple pathological results, knockdown of Lnc1 can inhibit the proliferation and metastasis abilities of NSCLC cells. silencing phospholipase C, ß1(PLCB1) can reverse the promoting effects of overexpression Lnc1 on NSCLC cells proliferation and migration abilities. In addition, the Rap1 signaling pathway was implicated in the regulation of Lnc1 in NSCLC metastasis. CONCLUSION: Our results suggest that Lnc1 regulated the metastatic ability of NSCLC cells through targeting the PLCB1/Rap1 signal pathway.

The Ion Channel Gene KCNAB2 Is Associated with Poor Prognosis and Loss of Immune Infiltration in Lung Adenocarcinoma.

The malignancy with the greatest global mortality rate is lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype. The evidence demonstrated that voltage-gated potassium channel subunit beta-2 (KCNAB2) significantly participated in the initiation of colorectal cancer and its progression. However, the biological function of KCNAB2 in LUAD and its effect on the tumor immune microenvironment are still unknown. In this study, we found that the expression of KCNAB2 in tissues of patients with LUAD was markedly downregulated, and its downregulation was linked to accelerated cancer growth and poor clinical outcomes. In addition, low KCNAB2 expression was correlated with a deficiency in immune infiltration. The mechanism behind this issue might be that KCNAB2 influenced the immunological process such that the directed migration of immune cells was affected. Furthermore, overexpression of KCNAB2 in cell lines promoted the expression of CCL2, CCL3, CCL4, CCL18, CXCL9, CXCL10, and CXCL12, which are necessary for the recruitment of immune cells. In conclusion, KCNAB2 may play a key function in immune infiltration and can be exploited as a predictive biomarker for evaluating prognosis and a possible immunotherapeutic target.

Fatty acid metabolism-related genes in bronchoalveolar lavage fluid unveil prognostic and immune infiltration in idiopathic pulmonary fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition with an unfavorable prognosis. A recent study has demonstrated that IPF patients exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with IPF pathogenesis. Therefore, in this study, we have explored whether the gene signature associated with fatty acid metabolism could be used as a reliable biological marker for predicting the survival of IPF patients. Methods: Data on the fatty acid metabolism-related genes (FAMRGs) were extracted from databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome pathway. The GSE70866 dataset with information on IPF patients was retrieved from the Gene Expression Omnibus (GEO). Next, the consensus clustering method was used to identify novel molecular subgroups. Gene Set Enrichment Analysis (GSEA) was performed to understand the mechanisms involved. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the level of immune cell infiltration in the identified subgroups based on gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression analysis were performed to develop a prognostic risk model. Results: The gene expression signature associated with fatty acid metabolism was used to create two subgroups with significantly different prognoses. GSEA reveals that immune-related pathways were significantly altered between the two subgroups, and the two subgroups had different metabolic characteristics. High infiltration of immune cells, mainly activated NK cells, monocytes, and activated mast cells, was observed in the subgroup with a poor prognosis. A risk model based on FAMRGs had an excellent ability to predict the prognosis of IPF. The nomogram constructed using the clinical features and the risk model could accurately predict the prognosis of IPF patients. Conclusion: The fatty acid metabolism-related gene expression signature could be used as a potential biological marker for predicting clinical outcomes and the level of infiltration of immune cells. This could eventually enhance the accuracy of the treatment of IPF patients.

Global and local feature fusion via long and short-term memory mechanism for dance emotion recognition in robot.

In recent years, there are more and more intelligent machines in people's life, such as intelligent wristbands, sweeping robots, intelligent learning machines and so on, which can simply complete a single execution task. We want robots to be as emotional as humans. In this way, human-computer interaction can be more natural, smooth and intelligent. Therefore, emotion research has become a hot topic that researchers pay close attention to. In this paper, we propose a new dance emotion recognition based on global and local feature fusion method. If the single feature of audio is extracted, the global information of dance cannot be reflected. And the dimension of data features is very high. In this paper, an improved long and short-term memory (LSTM) method is used to extract global dance information. Linear prediction coefficient is used to extract local information. Considering the complementarity of different features, a global and local feature fusion method based on discriminant multi-canonical correlation analysis is proposed in this paper. Experimental results on public data sets show that the proposed method can effectively identify dance emotion compared with other state-of-the-art emotion recognition methods.

A New Residual Dense Network for Dance Action Recognition From Heterogeneous View Perception.

At present, part of people's body is in the state of sub-health, and more people pay attention to physical exercise. Dance is a relatively simple and popular activity, it has been widely concerned. The traditional action recognition method is easily affected by the action speed, illumination, occlusion and complex background, which leads to the poor robustness of the recognition results. In order to solve the above problems, an improved residual dense neural network method is used to study the automatic recognition of dance action images. Firstly, based on the residual model, the features of dance action are extracted by using the convolution layer and pooling layer. Then, the exponential linear element (ELU) activation function, batch normalization (BN) and Dropout technology are used to improve and optimize the model to mitigate the gradient disappearance, prevent over-fitting, accelerate convergence and enhance the model generalization ability. Finally, the dense connection network (DenseNet) is introduced to make the extracted dance action features more rich and effective. Comparison experiments are carried out on two public databases and one self-built database. The results show that the recognition rate of the proposed method on three databases are 99.98, 97.95, and 0.97.96%, respectively. It can be seen that this new method can effectively improve the performance of dance action recognition.