Pan-cancer analysis identified OAS1 as a potential prognostic biomarker for multiple tumor types

Background2’,5’-oligoadenylate synthetase 1 (OAS1), has been reported as a tumor driver gene in breast carcinoma and pancreatic carcinoma. However, the role of OAS1 in most tumors has not been reported.MethodsThe original data of 35 tumor types were down load from the TCGA (The Cancer Genome Atlas) database and Human Protein Atlas (HPA) database. TIMER2, Kmplot, UALCAN, and TISIDB tools were used to investigate the expression and function of OAS1, and the role of OAS1 in prognosis, diagnostic value, and immune characteristics of pan-cancer. LUAD and PRAD cell lines, A549, H1975, PC-3 and C4-2 were utilized to perform cell function tests.ResultsOAS1 expression was up-regulated in 12 tumor types and down-regulated in 2 tumor types. High OAS1 expression was correlated with poor prognosis in 6 tumor types, while high OAS1 expression was correlated with good prognosis in 2 tumor types. OAS1 was correlated with molecular subtypes in 8 tumor types and immune subtypes in 12 tumor types. OAS1 was positively associated with the expression of numerous immune checkpoint genes and tumor mutational burden (TMB). OAS1 had potential diagnostic value in 15 tumor types. Silence of OAS1 significantly inhibited the cell proliferation ability, and promoted G2/M cell cycle arrest of LUAD and PRAD cells. Meanwhile, silence of OAS1 enhanced cisplatin-induced apoptosis of LUAD and PRAD cells, but weakened cell migration.ConclusionThis pan-cancer study suggests that OAS1can be used as a molecular biomarker for prognosis in pan-cancer and may play an important role in tumor immune response

Anderson Localization from Berry-Curvature Interchange in Quantum Anomalous Hall System

We theoretically investigate the localization mechanism of the quantum anomalous Hall effect (QAHE) in the presence of spin-flip disorders. We show that the QAHE keeps quantized at weak disorders, then enters a Berry-curvature mediated metallic phase at moderate disorders, and finally goes into the Anderson insulating phase at strong disorders. From the phase diagram, we find that at the charge neutrality point although the QAHE is most robust against disorders, the corresponding metallic phase is much easier to be localized into the Anderson insulating phase due to the \textit{interchange} of Berry curvatures carried respectively by the conduction and valence bands. At the end, we provide a phenomenological picture related to the topological charges to better understand the underlying physical origin of the QAHE Anderson localization.Comment: 6 pages, 4 figure

Connecting Subspace Learning and Extreme Learning Machine in Speech Emotion Recognition

Speech Emotion Recognition (SER) is a powerful tool for endowing computers with the capacity to process information about the affective states of users in human-machine interactions. Recent research has shown the effectiveness of graph embedding based subspace learning and extreme learning machine applied to SER, but there are still various drawbacks in these two techniques that limit their application. Regarding subspace learning, the change from linearity to nonlinearity is usually achieved through kernelisation, while extreme learning machines only take label information into consideration at the output layer. In order to overcome these drawbacks, this paper leverages extreme learning machine for dimensionality reduction and proposes a novel framework to combine spectral regression based subspace learning and extreme learning machine. The proposed framework contains three stages - data mapping, graph decomposition, and regression. At the data mapping stage, various mapping strategies provide different views of the samples. At the graph decomposition stage, specifically designed embedding graphs provide a possibility to better represent the structure of data, through generating virtual coordinates. Finally, at the regression stage, dimension-reduced mappings are achieved by connecting the virtual coordinates and data mapping. Using this framework, we propose several novel dimensionality reduction algorithms, apply them to SER tasks, and compare their performance to relevant state-of-the-art methods. Our results on several paralinguistic corpora show that our proposed techniques lead to significant improvements