Nuclear transport maintenance of USP22-AR by Importin-7 promotes breast cancer progression.

The translocation of biological macromolecules between cytoplasm and nucleus is of great significance to maintain various life processes in both normal and cancer cells. Disturbance of transport function likely leads to an unbalanced state between tumor suppressors and tumor-promoting factors. In this study, based on the unbiased analysis of protein expression differences with a mass spectrometer between human breast malignant tumors and benign hyperplastic tissues, we identified that Importin-7, a nuclear transport factor, is highly expressed in breast cancer (BC) and predicts poor outcomes. Further studies showed that Importin-7 promotes cell cycle progression and proliferation. Mechanistically, through co-immunoprecipitation, immunofluorescence, and nuclear-cytoplasmic protein separation experiments, we discovered that AR and USP22 can bind to Importin-7 as cargoes to promote BC progression. In addition, this study provides a rationale for a therapeutic strategy to restream the malignant progression of AR-positive BC by inhibiting the high expression state of Importin-7. Moreover, the knockdown of Importin-7 increased the responsiveness of BC cells to the AR signaling inhibitor, enzalutamide, suggesting that targeting Importin-7 may be a potential therapeutic strategy.

SNS-023 sensitizes hepatocellular carcinoma to sorafenib by inducing degradation of cancer drivers SIX1 and RPS16.

Hepatocellular carcinoma (HCC) remains challenging due to the lack of efficient therapy. Promoting degradation of certain cancer drivers has become an innovative therapy. The nuclear transcription factor sine oculis homeobox 1 (SIX1) is a key driver for the progression of HCC. Here, we explored the molecular mechanisms of ubiquitination of SIX1 and whether targeting SIX1 degradation might represent a potential strategy for HCC therapy. Through detecting the ubiquitination level of SIX1 in clinical HCC tissues and analyzing TCGA and GEPIA databases, we found that ubiquitin specific peptidase 1 (USP1), a deubiquitinating enzyme, contributed to the lower ubiquitination and high protein level of SIX1 in HCC tissues. In HepG2 and Hep3B cells, activation of EGFR-AKT signaling pathway promoted the expression of USP1 and the stability of its substrates, including SIX1 and ribosomal protein S16 (RPS16). In contrast, suppression of EGFR with gefitinib or knockdown of USP1 restrained EGF-elevated levels of SIX1 and RPS16. We further revealed that SNS-023 (formerly known as BMS-387032) induced degradation of SIX1 and RPS16, whereas this process was reversed by reactivation of EGFR-AKT pathway or overexpression of USP1. Consequently, inactivation of the EGFR-AKT-USP1 axis with SNS-032 led to cell cycle arrest, apoptosis, and suppression of cell proliferation and migration in HCC. Moreover, we showed that sorafenib combined with SNS-032 or gefitinib synergistically inhibited the growth of Hep3B xenografts in vivo. Overall, we identify that both SIX1 and RPS16 are crucial substrates for the EGFR-AKT-USP1 axis-driven growth of HCC, suggesting a potential anti-HCC strategy from a novel perspective.

Methylome Variation Predicts Exemestane Resistance in Advanced ER+ Breast Cancer.

BACKGROUND: More than 30% of estrogen receptor-positive breast cancers are resistant to primary hormone therapy, and about 40% that initially respond to hormone therapy eventually acquire resistance. Although the mechanisms of hormone therapy resistance remain unclear, aberrant DNA methylation has been implicated in oncogenesis and drug resistance. PURPOSE: We investigated the relationship between methylome variations in circulating tumor DNA and exemestane resistance, to track hormone therapy efficacy. METHODS: We prospectively recruited 16 patients who were receiving first-line therapy in our center. All patients received exemestane-based hormone therapy after enrollment. We collected blood samples at baseline, first follow-up (after 2 therapeutic cycles) and at detection of disease progression. Disease that progressed within 6 months under exemestane treatment was considered exemestane resistance but was considered relatively exemestane-sensitive otherwise. We obtained circulating tumor DNA-derived methylomes using the whole-genome bisulfide sequencing method. Methylation calling was done by BISMARK software; differentially methylated regions for exemestane resistance were calculated afterward. RESULTS: Median follow-up for the 16 patients was 19.0 months. We found 7 exemestane resistance-related differentially methylated regions, located in different chromosomes, with both significantly different methylation density and methylation ratio. Baseline methylation density and methylation ratio of chromosome 6 [32400000-32599999] were both high in exemestane resistance. High baseline methylation ratios of chromosome 3 [67800000-67999999] (P = .013), chromosome 3 [140200000-140399999] (P = .037), and chromosome 12 [101200000-101399999] (P = .026) could also predict exemestane resistance. During exemestane treatment, synchronized changes in methylation density and methylation ratio in chromosome 6 [32400000-32599999] could accurately stratify patients in terms of progression-free survival (P = .000033). Cutoff values of methylation density and methylation ratio for chromosome 6 [149600000-149799999] were 0.066 and 0.076, respectively. CONCLUSION: Methylation change in chromosome 6 [149600000-149799999] is an ideal predictor of exemestane resistance with great clinical potential.

[Optimizing MaxEnt model in the prediction of species distribution.] / 最大熵模型在物种分布预测中的优化.

Maximum Entropy (MaxEnt) model has been widely used in recent years. However, MaxEnt is highly inclined to produce misleading results if it is not well optimized. We summarized the researches about the model optimization for sampling bias correction, model complexity tuning, presence-absence threshold selection, and model evaluation. Spatial filtering performs best for sampling bias correction, while restricted background method shows the lowest efficacy. Model complexi-ty is mainly determined by three factors: The number of environmental variables, model feature types, and regularization multiplier. Variables filtering is needed when sample size is less than the number of environment variables. The criterion of variables selection should focus on their ecological significance rather than the co-linearity between them. The choice of feature types has relatively limi-ted effects on predictive performance of the model, therefore it is advised to choose simpler models. To control overfitting, it is necessary to conduct species-specific tuning on regularization multiplier, which was usually bigger than the default setting. There are three criteria called objectivity, equality and discriminability for selecting threshold to convert continuous predication (e.g. probability of presence) into binary results. Maximizing the sum of sensitivity and specificity is a sound method for threshold selection. Model evaluation methods could be classified into two main types: Threshold-independent and threshold-dependent. Among the threshold-independent evaluations, information criteria may offer significant advantages over AUC and COR. True Skill Statistics is a better index for threshold-dependent evaluations, because it takes both omission and commission errors into account, and is robust to pseudo-absence assumption and species prevalence.

Identification of high independent prognostic value of nanotechnology based circulating tumor cell enumeration in first-line chemotherapy for metastatic breast cancer patients.

Enumeration of circulating tumor cells (CTCs) is a promising tool in the management of metastatic breast cancer (MBC). This study investigated the capturing efficiency and prognostic value of our previously reported peptide-based nanomagnetic CTC isolation system (Pep@MNPs). We counted CTCs in blood samples taken at baseline (n = 102) and later at patients' first clinical evaluation after starting firstline chemotherapy (n = 72) in a cohort of women treated for MBC. Their median follow-up was 16.3 months (range: 9.0-31.0 months). The CTC detection rate was 69.6 % for the baseline samples. Patients with ≤2 CTC/2 ml at baseline had longer median progression-free survival (PFS) than did those with >2 CTC/2 ml (17.0 months vs. 8.0 months; P = 0.002). Patients with ≤2 CTC/2 ml both at baseline and first clinical evaluation had longest PFS (18.2 months) among all patient groups (P = 0.004). Particularly, among patients with stable disease (SD; per imaging evaluation) our assay could identify those with longer PFS (P < 0.001). Patients with >2 CTC/2 ml at baseline were also significantly more likely to suffer liver metastasis (P = 0.010). This study confirmed the prognostic value of Pep@MNPs assays for MBC patients who undergo firstline chemotherapy, and offered extra stratification regarding PFS for patients with SD, and a possible indicator for patients at risk for liver metastasis.

[Foraging habitat selection of Siberian Crane (Grus leucogeranus) during autumn migration period in the Momoge Nature Reserve].

In autumn of 2008 and 2009, we studied the foraging habitat selection of Siberian Crane (Grus leucogeranus) in the Momoge Nature Reserve of Jilin province. Using the resource selection index, resource selection functions, and the chi-squared test, we found that the Siberian Crane exhibited selectivity in their preferred foraging environments in relation to the distance to human disturbances, vegetation density, coverage and height, foraging vegetation density and water level. Interestingly, this selectivity in regards to large scale disturbances was lower than other factors. The characteristics of favorite foraging habits of Siberian Cranes include a variety of factors: a distance >5 000 m from a national highway, >1 500 m from a non-gravel road, >1 000 m from the nearest road, >1 000 m from a residential area, >1 000 m from farmland; plant density between 20 and 50 grass/m(2); plant coverage lower than 10%; plant height lower than 20 cm; Scirpus planiclmis density between 1 and 50 grass/m(2); Scirpus triqueter density between 1 and 10 grass/m(2); and the water level between 40 and 60 cm. The resource selection functions of Siberian Crane foraging habitat in autumn can be described thusly: Logistic (P) = 0.663 + 0.565×distance to national highway + 0.042×distance to non-gravel road + 0.519×distance to the nearest road + 0.353×distance to residential area + 0.169×distance to farmland - 0.455×vegetation density - 0.618×vegetation coverage - 0.548×vegetation height - 0.158×Scirpus planiclmis density - 0.404×Scirpus triqueter density + 0.920×water level,T (x) =e(Logistic(p)) / [1 + e(Logistic(p))], with an overall prediction accuracy of 82.9%.