Ubiquitin-specific peptidase 14 maintains estrogen receptor α stability via its deubiquitination activity in endometrial cancer.

USP14 deubiquitinates ERα to maintain its stability in ECEndometrial cancer (EC) is one of the common gynecological malignancies of which the incidence has been rising for decades. It is considered that continuously unopposed estrogen exposure is the main risk factor for EC initiation. Thus, exploring the modulation of estrogen/estrogen receptor α (ERα) signaling pathway in EC would be helpful to well understand the mechanism of EC development and find the potential target for EC therapy. Ubiquitin-specific peptidase 14 (USP14), a member of the proteasome-associated deubiquitinating enzyme family, plays a crucial role in a series of tumors. However, the function of USP14 in EC is still elusive. Here, our results have demonstrated that USP14 is highly expressed in EC tissues compared with that in normal endometrial tissues, and higher expression of USP14 is positively correlated with poor prognosis. Moreover, USP14 maintains ERα stability through its deubiquitination activity. Our results further demonstrate that USP14 depletion decreases the expression of ERα-regulated genes in EC-derived cell lines. Moreover, knockdown of USP14 or USP14-specific inhibitor treatment significantly suppresses cell growth and migration in EC cell lines or in mice. We further provide the evidence to show that the effect of USP14 on EC cell growth, if not all, at least is partially related to ERα pathway. Our study provides new sights for USP14 to be a potential therapeutic target for the treatment of EC, especially for EC patients with fertility preservation needs.

Research on Classification of Grassland Degeneration Indicator Objects Based on UAV Hyperspectral Remote Sensing and 3D_RNet-O Model.

Real-time and high-precision land cover classification is the foundation for efficient and quantitative research on grassland degradation using remote sensing techniques. In view of the shortcomings of manual surveying and satellite remote sensing, this study focuses on the identification and classification of grass species indicating grassland degradation. We constructed a UAV-based hyperspectral remote sensing system and collected field data in grassland areas. By applying artificial intelligence technology, we developed a 3D_RNet-O model based on convolutional neural networks, effectively addressing technical challenges in hyperspectral remote sensing identification and classification of grassland degradation indicators, such as low reflectance of vegetation, flat spectral curves, and sparse distribution. The results showed that the model achieved a classification accuracy of 99.05% by optimizing hyperparameter combinations based on improving residual block structures. The establishment of the UAV-based hyperspectral remote sensing system and the proposed 3D_RNet-O classification model provide possibilities for further research on low-altitude hyperspectral remote sensing in grassland ecology.

Combined Filtering Method for Offshore Oil and Gas Platform Point Cloud Data Based on KNN_PCF and Hy_WHF and Its Application in 3D Reconstruction.

With the increasing scale of deep-sea oil exploration and drilling platforms, the assessment, maintenance, and optimization of marine structures have become crucial. Traditional detection and manual measurement methods are inadequate for meeting these demands, but three-dimensional laser scanning technology offers a promising solution. However, the complexity of the marine environment, including waves and wind, often leads to problematic point cloud data characterized by noise points and redundancy. To address this challenge, this paper proposes a method that combines K-Nearest-Neighborhood filtering with a hyperbolic function-based weighted hybrid filtering. The experimental results demonstrate the exceptional performance of the algorithm in processing point cloud data from offshore oil and gas platforms. The method improves noise point filtering efficiency by approximately 11% and decreases the total error by 0.6 percentage points compared to existing technologies. Not only does this method accurately process anomalies in high-density areas-it also removes noise while preserving important details. Furthermore, the research method presented in this paper is particularly suited for processing large point cloud data in complex marine environments. It enhances data accuracy and optimizes the three-dimensional reconstruction of offshore oil and gas platforms, providing reliable dimensional information for land-based prefabrication of these platforms.

Sea Surface Height Measurements Based on Multi-Antenna GNSS Buoys.

Sea level monitoring is an essential foundational project for studying global climate change and the rise in sea levels. Satellite radar altimeters, which can sometimes provide inaccurate sea surface height data near the coast, are affected by both the instrument itself and geophysical factors. Buoys equipped with GNSS receivers offer a relatively flexible deployment at sea, allowing for long-term, high-precision measurements of sea surface heights. When operating at sea, GNSS buoys undergo complex movements with multiple degrees of freedom. Attitude measurements are a crucial source of information for understanding the motion state of the buoy at sea, which is related to the buoy's stability and reliability during its development. In this study, we designed and deployed a four-antenna GNSS buoy with both position and attitude measurement capabilities near Jimiya Wharf in Qingdao, China, to conduct offshore sea surface monitoring activities. The GNSS data were processed using the Precise Point Positioning (PPK) method to obtain a time series of sea surface heights, and the accuracy was evaluated using synchronous observation data from a small sea surface height radar. The difference between the GNSS buoy and the full-time radar was calculated, resulting in a root-mean-square error (RMSE) of 1.15 cm. Concurrently, the attitude of the GNSS buoy was calculated using multi-antenna technology, and the vertical elevation of the GNSS buoy antenna was corrected using the obtained attitude data. The RMSE between the corrected GNSS buoy data and the high ground radar was 1.12 cm, indicating that the four-antenna GNSS buoy can not only acquire high-precision coastal sea level data but also achieve synchronous measurement of the buoy's attitude. Furthermore, the data accuracy was also improved after the sea level attitude correction.

Assessment and machine learning prediction of heavy metals fate in mining farmland assisted by Positive Matrix Factorization.

The accurate pollutant prediction by Machine Learning (ML) is significant to efficient environmental monitoring and risk assessment. However, application of ML in soil is under studied. In this study, a Positive Matrix Factorization (PMF) assisted prediction method was developed with Support Vector Machine (SVM) and Random Forest (RF) for heavy metals (HMs) prediction in mining farmland. Principal Component Analysis (PCA) and Redundancy Analysis (RDA) were selected to pretreat data. Experiment results illustrated Cd was the main pollutant with heavy risks in the study area and Pb was easy to migrate. The method effects of HMs total concentration predicting were PMF > Simple > PCA > PCA - PMF, and RF predicted better than SVM. Data pretreatment by RDA prior inspection improved the model results. Characteristic HMs Tessier fractions prediction received good effects with average R value as 0.86. Risk classification prediction performed good in Cd, Cu, Ni and Zn, however, Pb showed weak effect by simple model. The best classifier method for Pb was PMF - RF method with relatively good effect (Area under ROC Curve = 0.896). Overall, our study suggested the combination between PMF and ML can assist the prediction of HMs in soil. Spatial weighted attribute of HMs can be provided by PMF.

A vaccine delivery system promotes strong immune responses against SARS-CoV-2 variants.

Global coronavirus disease 2019 (COVID-19) pandemics highlight the need of developing vaccines with universal and durable protection against emerging SARS-CoV-2 variants. Here we developed an extended-release vaccine delivery system (GP-diABZI-RBD), consisting the original SARS-CoV-2 WA1 strain receptor-binding domain (RBD) as the antigen and diABZI stimulator of interferon genes (STING) agonist in conjunction with yeast ß-glucan particles (GP-diABZI) as the platform. GP-diABZI-RBD could activate STING pathway and inhibit SARS-CoV-2 replication. Compared to diABZI-RBD, intraperitoneal injection of GP-diABZI-RBD elicited robust cellular and humoral immune responses in mice. Using SARS-CoV-2 GFP/ΔN transcription and replication-competent virus-like particle system (trVLP), we demonstrated that GP-diABZI-RBD-prototype vaccine exhibited the strongest and durable humoral immune responses and antiviral protection; whereas GP-diABZI-RBD-Omicron displayed minimum neutralization responses against trVLP. By using pseudotype virus (PsVs) neutralization assay, we found that GP-diABZI-RBD-Prototype, GP-diABZI-RBD-Delta, and GP-diABZI-RBD-Gamma immunized mice sera could efficiently neutralize Delta and Gamma PsVs, but had weak protection against Omicron PsVs. In contrast, GP-diABZI-RBD-Omicron immunized mice sera displayed the strongest neutralization response to Omicron PsVs. Taken together, the results suggest that GP-diABZI can serve as a promising vaccine delivery system for enhancing durable humoral and cellular immunity against broad SARS-CoV-2 variants. Our study provides important scientific basis for developing SARS-CoV-2 VOC-specific vaccines.

A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications.

The utilization of multibeam sonar systems has significantly facilitated the acquisition of underwater bathymetric data. However, efficiently processing vast amounts of multibeam point cloud data remains a challenge, particularly in terms of rejecting massive outliers. This paper proposes a novel solution by implementing a cone model filtering method for multibeam bathymetric point cloud data filtering. Initially, statistical analysis is employed to remove large-scale outliers from the raw point cloud data in order to enhance its resistance to variance for subsequent processing. Subsequently, virtual grids and voxel down-sampling are introduced to determine the angles and vertices of the model within each grid. Finally, the point cloud data was inverted, and the custom parameters were redefined to facilitate bi-directional data filtering. Experimental results demonstrate that compared to the commonly used filtering method the proposed method in this paper effectively removes outliers while minimizing excessive filtering, with minimal differences in standard deviations from human-computer interactive filtering. Furthermore, it yields a 3.57% improvement in accuracy compared to the Combined Uncertainty and Bathymetry Estimator method. These findings suggest that the newly proposed method is comparatively more effective and stable, exhibiting great potential for mitigating excessive filtering in areas with complex terrain.

Genome-Wide Characterization and Functional Analysis of ABCG Subfamily Reveal Its Role in Cutin Formation in Cotton.

ATP-binding cassette transporter G (ABCG) has been shown to be engaged in export of broad-spectrum compounds with structural differences, but little is known concerning its role in cutin formation of cotton (Gossypium spp.). In this study, we conduct a genome-wide survey and detected 69, 71, 124 and 131 ABCG genes within G. arboretum, G. raimondii, G. hirsutum and G. barbadense, separately. The above ABCGs could be divided into four groups (Ia, Ib, Ic, II). Some ABCG genes such as GhABCG15, whose homologous gene transports cuticular lipid in Arabidopsis, was preferentially expressed in the development of fiber. A weighted gene co-expression network analysis (WGCNA) demonstrated that GhABCG expression was significantly associated with the amount of 16-Hydroxypalmitate (a main component of cutin precursor) in cotton fibers. Further, silencing of GhABCG15 by virus-induced gene silencing (VIGS) in cotton generated brightened and crinkled leaves as well as reduced thickness of cuticle and increased permeability. Chemical composition analysis showed the cutin content in GhABCG15-silenced leaves had decreased while the wax content had increased. Our results provide an insight for better understanding of the role of the Gossypium ABCG family and revealed the essential role of GhABCGs in cotton cutin formation.

Source identification and health risk assessment of heavy metals with mineralogy: the case of soils from a Chinese industrial and mining city.

Understanding the precise sources of heavy metals (HMs) in soil and the contribution of these sources to health risks has positive effects in terms of risk management. This study focused on the HMs in the soil of five land uses in an industrial and mining city. The sources of HMs in soils were identified, and the soil mineralogical characteristics and health risks of HMs were discussed. The results showed that the HMs (Cu, Zn, Ni, Cd, Pb) found in the soil of the five land uses were affected by human activities. For example, the Cu in grassland, gobi beach, woodland, green belt, and farmland is 22.3, 3.5, 22.5, 16.7, and 21.3 times higher than the soil background values in Gansu Province, respectively. The Positive Matrix Factorization model (PMF) results revealed that traffic emissions and industrial and agricultural activities were the primary sources of HMs in the soil, with industrial sources accounting for the largest share at 55.79%. Furthermore, various characteristics proved that the studied HMs were closely related to smelting products. Concentration-oriented health risk assessments showed that HMs in the different soil types held non-carcinogenic and carcinogenic risks for children and adults. Contamination source-oriented health risk assessments of children and adults found that industrial activities controlled non-carcinogenic and carcinogenic risks. This study highlighted the critical effects of smelting on urban soil and the contribution of pollution sources to health risks. Furthermore, this work is significant in respect of the risk control of HMs in urban soils.

Achievements and perspectives of synthetic biology in botanical insecticides.

Botanical insecticides are the origin of all insecticidal compounds. They have been widely used to control pests in crops for a long time. Currently, the commercial production of botanical insecticides extracted from plants is limited because of insufficient raw material supply. Synthetic biology is a promising and effective approach for addressing the current problems of the production of botanical insecticides. It is an emerging biological research hotspot in the field of botanical insecticides. However, the biosynthetic pathways of many botanical insecticides are not completely elucidated. On the other hand, the cytotoxicity of botanical pesticides and low efficiency of these biosynthetic enzymes in new hosts make it still challenging for their heterologous production. In the present review, we summarized the recent developments in the heterologous production of botanical insecticides, analyzed the current challenges, and discussed the feasible production strategies, focusing on elucidating biosynthetic pathways, enzyme engineering, host engineering, and cytotoxicity engineering. Looking to the future, synthetic biology promises to further advance heterologous production of more botanical pesticides.

Effect of co-toxicity of lead and nanoplastics on the flavonoid biosynthetic pathway in dandelion (Taraxacum asiaticum Dahlst).

MAIN CONCLUSION: Negatively charged carboxy-polystyrene (CPS) and positively charged amino-polystyrene (NPS) could significantly inhibit the biomass and flavonoid content of dandelion roots and leaves, and the inhibitory effect of NPS was stronger than that of CPS. The increasingly serious pollution of microplastics and heavy metals is likely to affect the efficacy of flavonoids synthesized by dandelion in natural medicine fields. Therefore, we combined hydroponic experiments with computational chemistry (Gaussian and autodock analysis) to explore the mechanism by which amino-polystyrene (NPS), carboxy-polystyrene (CPS), and lead affect the flavonoid biosynthetic pathway in dandelion (Taraxacum asiaticum Dahlst). Our results show that CPS and NPS could significantly inhibit the biomass and flavonoid content of dandelion roots and leaves, and the inhibitory effect of NPS was stronger than that of CPS. Mechanistic studies showed that CPS and NPS increased the content of O2- and H2O2 in dandelion roots and leaves, causing membrane lipid peroxidation, resulting in cell damage and decreased biomass. CPS and NPS inhibited related enzymatic activities by affecting their tertiary structures, resulting in a decrease in phenolic acid, coumaroyl-CoA, and flavonoid content. Dandelion preferred to absorb positively charged NPS compared to negatively charged CPS, but CPS inhibited the uptake of Pb by dandelion more strongly than NPS. Pb promoted CPS agglomeration and increased the surface positive charge of CPS through coordination bonds and hydrogen bonds, so more CPS entered dandelion under CPS + Pb treatment than under CPS alone. Although NPS and CPS reduced the uptake of Pb by dandelion, the biomass and flavonoid contents of dandelion were lower than those of single Pb treatment because of the higher toxicity of NPS and CPS than Pb. Pb significantly increased the effect of CPS on the root biomass of dandelion compared with CPS alone by increasing the positive charge of CPS. We suggest that microplastics with different charges and lead composite pollution inhibit dandelion flavonoid biosynthesis and provide a reference for the loss of dandelion medicinal components and economic value.

Genome-wide association study reveals novel quantitative trait loci and candidate genes of lint percentage in upland cotton based on the CottonSNP80K array.

KEY MESSAGE: Thirty-four SNPs corresponding with 22 QTLs for lint percentage, including 13 novel QTLs, was detected via GWAS. Two candidate genes underlying this trait were also identified. Cotton (Gossypium spp.) is an important natural textile fiber and oilseed crop cultivated worldwide. Lint percentage (LP, %) is one of the important yield components, and increasing LP is a core goal of cotton breeding improvement. However, the genetic and molecular mechanisms underlying LP in upland cotton remain unclear. Here, we performed a genome-wide association study (GWAS) for LP based on 254 upland cotton accessions in four environments as well as the best linear unbiased predictors using the high-density CottonSNP80K array. In total, 41,413 high-quality single-nucleotide polymorphisms (SNPs) were screened, and 34 SNPs within 22 quantitative trait loci (QTLs) were significantly associated with LP. In total, 175 candidate genes were identified from two major genomic loci (GR1 and GR2), and 50 hub genes were identified through GO enrichment and weighted gene co-expression network analysis. Two candidate genes (Gh_D01G0162 and Gh_D07G0463), which may participate in early fiber development to affect the number of fiber protrusions and LP, were also identified. Their genetic variation and expression were verified by linkage disequilibrium blocks, haplotypes, and quantitative real-time polymerase chain reaction, respectively. The weighted gene interaction network analysis showed that the expression of Gh_D07G0463 was significantly correlated with that of Gh_D01G0162. These identified SNPs, QTLs and candidate genes provide important insights into the genetic and molecular mechanisms underlying variations in LP and serve as a foundation for LP improvement via marker-assisted breeding.

An H3K4me3 reader, BAP18 as an adaptor of COMPASS-like core subunits co-activates ERα action and associates with the sensitivity of antiestrogen in breast cancer.

Estrogen receptor alpha (ERα) signaling pathway is essential for ERα-positive breast cancer progression and endocrine therapy resistance. Bromodomain PHD Finger Transcription Factor (BPTF) associated protein of 18kDa (BAP18) has been recognized as a crucial H3K4me3 reader. However, the whole genomic occupation of BAP18 and its biological function in breast cancer is still elusive. Here, we found that higher expression of BAP18 in ERα-positive breast cancer is positively correlated with poor prognosis. ChIP-seq analysis further demonstrated that the half estrogen response elements (EREs) and the CCCTC binding factor (CTCF) binding sites are the significant enrichment sites found in estrogen-induced BAP18 binding sites. Also, we provide the evidence to demonstrate that BAP18 as a novel co-activator of ERα is required for the recruitment of COMPASS-like core subunits to the cis-regulatory element of ERα target genes in breast cancer cells. BAP18 is recruited to the promoter regions of estrogen-induced genes, accompanied with the enrichment of the lysine 4-trimethylated histone H3 tail (H3K4me3) in the presence of E2. Furthermore, BAP18 promotes cell growth and associates the sensitivity of antiestrogen in ERα-positive breast cancer. Our data suggest that BAP18 facilitates the association between ERα and COMPASS-like core subunits, which might be an essential epigenetic therapeutic target for breast cancer.

The role of Fe-oxidizing bacteria (FeOB) and organic matters in As removal in the heavy-polluted arid soil.

The bio-remediation of As-polluted farmlands in the arid area is seldomly reported. This study aimed at understanding the impact of DOM, Fe-oxides, and FeOB biogeochemical processes on As remediation. The approaches used included: FeOB strain Pseudomonas flavescens LZU-3; Batch-experiment. Our results showed that all FeOB tested effectively immobilized As (>95%) during microbial mineralization; DOM play an important role in the reduction of Fe(III)(hydr)oxides and As(V); Less-crystallized ferrihydrite transform to more-crystallized goethite and secondary minerals; Under the reaction of FeOB and DOM, the As-Fe-OM ternary compound were formed, containing N, S, C and O functional group; The addition of OM can clearly reduce soil Eh, promoting dissolution of As in bound to iron oxides, co-precipitation of the amorphous iron oxide in Fe(III)-OM-FeOB, closely related to As in bound to insoluble organics and sulfides and mineral residues, which plays an important role in controlling the mobilization of As. This study provides controlling of As transportation and transformation in the As-DOM-Bio-Fe ternary system as As-remediation technology in the arid soil.

Composition, environmental implication and source identification of elements in soil and moss from a pristine spruce forest ecosystem, Northwest China.

The environmental quality of remote alpine ecosystem has been drawn increasing attention owing to the increasingly severe atmospheric pollution. This study investigated the composition and sources of elements in the soil and moss collected from a pristine spruce forest in the Qilian Mountains, Northwest China. The order of mean concentrations of elements investigated in soil was Fe > K > Na > Mg > Ca > Mn > Cr > Zn > Pb > Ni > Cu > As > Cd > Hg, and that of moss was Ca > Fe > Mg > K > Na > Mn > Cr > Zn > Pb > Ni > Cu > As > Cd > Hg. The concentrations of trace metals (except for As) in soil were greater than the soil background values, with Pb contamination more serious than the other elements. The Nemerow integrated pollution index (NIPI) values indicated that the soils were heavily polluted by Pb, Cd and Ni. The potential ecological risk index (PERI) suggested that the soils were at moderate risk. In particular, Hg and Cd were the most critically potential factors for ecological risk. According to the bioaccumulation factors (BAF), the accumulated concentrations of Ca, Hg, Cd, Pb, Ni, Mg, Cr and Zn in moss were higher than those in soil. By performing the multivariate analyses, natural sources (airborne soil particles) were identified to be the major contributors for all elements, whereas anthropogenic sources also contributed to the accumulations of Pb and Cd in the soil and moss in this region.

MOF upregulates the estrogen receptor α signaling pathway by its acetylase activity in hepatocellular carcinoma.

The histone acetyltransferase MOF (KAT8) is mainly involved in the acetylation of histone H4 at lysine 16 (H4K16) and some non-histone proteins. The MOF expression level is significantly reduced in many cancers, however the biological function of MOF and its underlying mechanism are still elusive in hepatocellular carcinoma (HCC). Estrogen receptor α (ERα) has been considered as a tumor suppressor in HCC. Here, we demonstrated that MOF expression is significantly reduced in HCC samples, and is positively correlated with that of ERα. MOF interacts with ERα, and participates in acetylation of ERα at K266, K268, K299, thereby inhibiting ERα ubiquitination to maintain the stability of ERα. In addition, MOF participates in the upregulation of ERα-mediated transactivation. Depletion of MOF significantly promotes cell growth, migration, and invasion in HCC cell lines. Taken together, our results provide new insights to understand the mechanism underlying the modulation function of MOF on ERα action in HCC, suggesting that MOF might be a potential therapeutic target for HCC.

miR-122-5p regulates hepatocytes damage caused by BaP and DBP co-exposure through SOCS1/STAT3 signaling in vitro.

BaP and DBP are ubiquitously and contemporaneously present in the environment. However, Current studies largely concentrate on the effects of a single pollutant (BaP or DBP). The liver is vital for biogenic activities. The effects of BaP and DBP co-exposure on liver remain unclear. Thus, we treated human normal liver cell (L02 cell) with BaP or/and DBP. We found that compared to individual exposure, co-exposure to BaP and DBP induced further increased levels of AST and ALT. BaP and DBP co-exposure caused further increased levels of IL-2, IL-6, and TNF-α, decreased IL-10 level, and a higher percentage of apoptotic cells and S-phase arrest cells. BaP and DBP co-exposure worsen the decrease of miR-122-5p level and chaos of SOCS1/STAT3 signaling. Dual-luciferase reporter gene assays showed that SOCS1 was a validated target of miR-122-5p. miR-122-5p overexpression alleviated the increased SOCS1 expression, decreased phospho-STAT3 expression, decreased IL-10 level, increased TNF-α levels, increased percentage of apoptosis and S-phase arrest, and cytotoxicity induced by BaP and DBP co-exposure in hepatocytes. These results suggested that miR-122-5p negatively regulated the synergistic effects on apoptosis and disorder of inflammatory factor secretion involved in hepatocyte injury caused by BaP and DBP co-exposure through targeting SOCS1/STAT3 signaling.

Splicing factor PRPF6 upregulates oncogenic androgen receptor signaling pathway in hepatocellular carcinoma.

Androgen receptor (AR) signaling is considered to be crucial for the pathogenesis of hepatocellular carcinoma (HCC) with obvious sexual dimorphism. Pre-mRNA processing factor 6 (PRPF6) was identified as a coactivator of AR. However, the molecular mechanism underlying the modulation function of PRPF6 on AR-mediated transcriptional activity in HCC needs to be further clarified. In this study, we analyzed data from The Cancer Genome Atlas to show that PRPF6 is highly expressed in HCC. . Our data indicated that PRPF6 interacts with AR/AR splice variants (AR-Vs) and upregulates AR/AR splice variant 7-mediated transcriptional activity even without dihydrotestosterone treatment. We observed that AR is obviously induced by androgen treatment and is mainly expressed in the nucleus in HCC-derived cell lines. Moreover, overexpression of PRPF6 enhances AR expression accompanied with the increase of AR-Vs expression. We provided evidence that PRPF6 participates in upregulating AR self-transcription. PRPF6 facilitates the recruitment of AR to the androgen responsive element region of the AR gene. Finally, PRPF6 depletion inhibits cell proliferation in HCC cells and mouse xenografts. Taken together, our results suggest that PRPF6 as a splicing factor enhances AR self-transcription, thereby coactivating oncogenic AR/AR-Vs actions in HCC.

ASH2L is involved in promotion of endometrial cancer progression via upregulation of PAX2 transcription.

Absent, small or homeotic 2-like protein (ASH2L) is a core component of a multimeric histone methyltransferase complex that is involved in the maintenance of active transcription, participating in several cancers, however the biological function and molecular mechanism of ASH2L in endometrial cancer (ECa) are largely unknown. Endometrial cancer is a common malignant tumor in women and the incidence of this cancer is on the rise. Estrogen-ERα signaling, as an oncogenic pathway, plays a crucial role in endometrial carcinogenesis. Therefore, further exploration of the molecular mechanisms around ERα-mediated gene transcription in ECa would be helpful to the understanding of tumor development and to finding a new therapeutic target for ECa. Here, our study demonstrated that ASH2L was highly expressed in ECa samples, and higher expression of ASH2L was positively correlated with a poor prognosis. Moreover, we identified that ASH2L associated with ERα and that knockdown of ASH2L resulted in decreased expression of a subset of the estrogen-induced target genes, including paired box 2 (PAX2), an oncogenic gene in ECa. ASH2L was recruited to cis-regulatory elements in PAX2, thereby altering histone H3K4me3 and H3K27me3 levels, to enhance ERα-mediated transactivation. Finally, depletion of ASH2L suppressed endometrial cancer cell proliferation and migration. Our findings suggest that ASH2L participates in the promotion of ECa progression, if not totally at least partially, via upregulation of PAX2 transcription.

Association between polymorphism in CDKN2B-AS1 gene and its interaction with smoking on the risk of lung cancer in a Chinese population.

BACKGROUND: Long non-coding RNAs became the hot spots in the carcinogenesis of various tumors. This case-control study evaluated the association between the rs2151280 in lncRNA CDKN2B-AS1 and lung cancer risk. METHODS: This study included 507 lung cancer patients and 542 healthy individuals. Odds ratios and their 95% confidence intervals were calculated by unconditional logistic regression analysis to evaluate the association between the rs2151280 and lung cancer risk. RESULTS: Compared with individuals carrying TT genotype, individuals carrying CC genotype of rs2151280 had a decreased risk of lung cancer (OR = 0.640, 95%CI = 0.421-0.972, P = 0.036). In the recessive model, rs2151280 CC genotype was observed to reduce the risk of lung cancer (OR = 0.684). C allele was associated with non-small cell lung cancer risk (OR = 0.674). The rs2151280 was significantly associated with lung adenocarcinoma risk (CCvsTT: OR = 0.567, 95%CI = 0.333-0.965, P = 0.037; CCvsTC+TT: OR = 0.543, 95%CI 0.330-0.893, P = 0.016, respectively). However, there was no significant association between rs2151280 and lung squamous cell carcinoma risk in five models. The quantitative analysis suggested that there were no significant interactions of rs2151280 with smoking exposure to lung cancer susceptibility. CONCLUSIONS: This hospital-based case-control study suggested that CDKN2B-AS1 rs2151280 T>C was associated with the risk of lung cancer.