Prostate cancer progression to castration refractory disease is associated with anomalous transcriptional activity of the androgen receptor (AR) in an androgen-depleted milieu. To identify novel gene products whose downregulation transactivates AR in prostate cancer cells, we performed a screen of enzymatically-generated shRNA lenti-libraries selecting for transduced LNCaP cells with elevated expression of a fluorescent reporter gene under the control of an AR-responsive promoter. The shRNAs present in selected populations were analyzed using high-throughput sequencing to identify target genes. Highly enriched gene targets were then validated with siRNAs against selected genes, testing first for increased expression of luciferase from an AR-responsive promoter and then for altered expression of endogenous androgen-regulated genes in LNCaP cells. We identified 20 human genes whose silencing affected the expression of exogenous and endogenous androgen-responsive genes in prostate cancer cells grown in androgen-depleted medium. Knockdown of four of these genes upregulated the expression of endogenous AR targets and siRNAs targeting two of these genes (IGSF8 and RTN1) enabled androgen-independent proliferation of androgen-dependent cells. The effects of IGSF8 appear to be mediated through its interaction with a tetraspanin protein, CD9, previously implicated in prostate cancer progression. Remarkably, homozygous deletions of IGSF8 are found almost exclusively in prostate cancers but not in other cancer types. Our study shows that androgen independence can be achieved through the inhibition of specific genes and reveals a novel set of genes that regulate AR signaling in prostate cancers.
Working distance and background radiation greatly affect the signal-to-noise ratio of avalanche photodiode (APD) in the lidar detection system. The traditional method cannot adapt to a complex environment by offline compensation or pre-compensation according to the influence factors of the external environment. In this paper, an avalanche photodiode voltage compensation method based on the improved random forest is designed. Firstly, the distance image data is de-noised. Then the weight of each decision tree in the random forest was changed to improve the classification performance. The particle swarm optimization (PSO) algorithm was used to search for the optimal combination of parameters affecting classification accuracy and performance. Finally, the improved random forest algorithm is used to judge the current working state of APD at different distances, compensate for the bias voltage, and make APD work in the optimal state. The proposed method is compared with the k-nearest neighbor, support vector machine, and other commonly used classification algorithms, and the results verify the effectiveness of the proposed method.
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