RESUMO
Esophageal cancer (ESCA) is a common malignant tumor with poor prognosis. Accumulating evidence indicates an important role of lysosomal-associated membrane protein 2 (LAMP2) in the progression and development of various cancers. In this study, we obtained RNA-sequencing raw count data and the corresponding clinical information for ESCA samples from The Cancer Genome Atlas and Gene Expression Omnibus databases. We comprehensively investigated the expression and prognostic significance of LAMP2 and relationships between LAMP2 expression and prognosis, different clinicopathological parameters, and immune cell infiltration in ESCA. We also obtained the differentially expressed genes between the high LAMP2 expression and low LAMP2 expression groups in ESCA and performed a functional enrichment analysis of the 250 linked genes most positively related to LAMP2 expression. Moreover, we performed the pan-cancer analysis of LAMP2 to further analyze the role of LAMP2 in 25 commonly occurring types of human cancer. We also verified and compared the expression of LAMP2 in 40 samples of human ESCA tissue and adjacent tissues. The results indicated that LAMP2 expression was significantly upregulated in ESCA and various human cancers. In addition, LAMP2 expression was associated with certain clinicopathological parameters, prognosis, and immune infiltration in ESCA and the other types of cancer. Our study represents a comprehensive pan-cancer analysis of LAMP2 and supports the potential use of the modulation of LAMP2 in the management of ESCA and various cancers.
RESUMO
In quantum mechanics, a norm-squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum. This statistical property is at the core of the fuzzy structure of microcosmos. Recently, hybrid neural structures raised intense attention, resulting in various intelligent systems with far-reaching influence. Here, we propose a probability-density-based deep learning paradigm for the fuzzy design of functional metastructures. In contrast to other inverse design methods, our probability-density-based neural network can efficiently evaluate and accurately capture all plausible metastructures in a high-dimensional parameter space. Local maxima in probability density distribution correspond to the most likely candidates to meet the desired performances. We verify this universally adaptive approach in but not limited to acoustics by designing multiple metastructures for each targeted transmission spectrum, with experiments unequivocally demonstrating the effectiveness and generalization of the inverse design.