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Gene expression sample data, which usually contains massive expression profiles of genes, is commonly used for disease related gene analysis. The selection of relevant genes from huge amount of genes is always a fundamental process in applications of gene expression data. As more and more genes have been detected, the size of gene expression data becomes larger and larger; this challenges the computing efficiency for extracting the relevant and important genes from gene expression data. In this paper, we provide a novel Bi-dimensional Principal Feature Selection (BPFS) method for efficiently extracting critical genes from big gene expression data. It applies the principal component analysis (PCA) method on sample and gene domains successively, aiming at extracting the relevant gene features and reducing redundancies while losing less information. The experimental results on four real-world cancer gene expression datasets show that the proposed BPFS method greatly reduces the data size and achieves a nearly double processing speed compared to the counterpart methods, while maintaining better accuracy and effectiveness.

The refractory of castration-resistant prostate cancer (CRPC) is mainly reflected in drug resistance. The current research on the resistance mechanism of CRPC is still in its infancy. In this study, we revealed for the first time the key role of LncRNA PCBP1-AS1 in CRPC drug resistance. Through detailed in vivo and in vitro studies, we found that PCBP1-AS1 may enhance the deubiquitination of AR/AR-V7 by stabilizing the USP22-AR/AR-V7 complex, thereby preventing AR/AR-V7 from being degraded through the ubiquitin–proteasome pathway. Targeting PCBP1-AS1 can significantly restore the drug sensitivity of enzalutamide-resistant tumors in vivo and in vitro. Our research further expands the function of LncRNA in castration-resistant prostate cancer, which may provide new potential for clinical diagnosis and targeted therapy.

Gene expression sample data, which usually contains massive expression profiles of genes, is commonly used for disease related gene analysis. The selection of relevant genes from huge amount of genes is always a fundamental process in applications of gene expression data. As more and more genes have been detected, the size of gene expression data becomes larger and larger; this challenges the computing efficiency for extracting the relevant and important genes from gene expression data. In this paper, we provide a novel Bi-dimensional Principal Feature Selection (BPFS) method for efficiently extracting critical genes from big gene expression data. It applies the principal component analysis (PCA) method on sample and gene domains successively, aiming at extracting the relevant gene features and reducing redundancies while losing less information. The experimental results on four real-world cancer gene expression datasets show that the proposed BPFS method greatly reduces the data size and achieves a nearly double processing speed compared to the counterpart methods, while maintaining better accuracy and effectiveness.

Background Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. Gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. Gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification results. Methods An innovative Gene Selection Programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on Gene Expression Programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. . Support Vector Machine (SVM) with a linear kernel serves as a classifier of the GSP. Results Experimental results on ten microarray cancer datasets demonstrate that Gene Selection Programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection methods. Conclusion Gene subset selected by GSP can achieve a higher classification accuracy with less processing time.

For the problem of passive location in mobile cellular network, base stations (BSs) selection can improve positioning accuracy. Through the analysis of base station layout in cellular networks, using Geometric Dilution of Precision (GDOP) as the optimization objective, we propose a Dynamic Base Stations Selection (DBSS) method in a cellular unit. This method enables the system to dynamically select the positioning base station when positioning target in the detection area. DBSS mainly include three steps: nearest base station calculation, layout of base stations analysis, and base station selection based on the target location. We mainly focus on the derivation of four-base station dynamic selection (DBSS4) and five-base station dynamic selection (DBSS5) algorithms. In simulation experiments, DBSS4 algorithm and DBSS5algorithm were compared with the state-of-the-art of BSs selection methods. The results show that our proposed method can achieve the exhaustive search in cellular cells and reduce more than 20% of the GDOP cumulative positioning error compared with the fixed four-base station selection algorithm. Meanwhile, the proposed method is more efficient, requires less running time and floating-point operations (FLOPs) than other comparison algorithm, and is independent of localization algorithms.

Background Microarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis. Results The gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost. Conclusions We provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples’ classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

PurposeTo develop a prediction model that combined magnetic resonance images (MRI)-based radiomics features with clinical factors to predict recurrence-free survival (RFS) of hepatocellular carcinoma (HCC) patients treated with surgical resection.MethodsHCC patients treated with surgical resection (n = 153) were randomly divided into training (n = 107) and validation (n = 46) datasets. The volumes of interest were manually outlined around the lesion and additional 2 mm and 5 mm peritumoral areas were created with automated dilatation in MRI to extract tumoral (T) and peritumoral (PT) radiomics features. The radiomics models were constructed using least absolute shrinkage and selection operator Cox regression. The combined model incorporated clinical factors and radiomics features using multivariable Cox regression based on the Akaike information criterion principle. Predictive performance of different models were evaluated by receiver operating characteristic (ROC) curves, decision curves, and calibration curves.ResultsAmong the radiomics models, similar performance was observed in the 2 mm and 5 mm PT models (C-index both 0.657), which were better than the T model or T + PT model (C-index 0.607 and 0.641, respectively) in the validation dataset, whereas the model combined with the three identified clinical risk factors showed the best performance (C-index 0.725). Results of the ROC curves, decision curves, and the calibration curves indicated that the combined model and the derived nomogram had better prediction performance, greater clinical benefits, and fair calibration efficiency.ConclusionThe prediction model that combined MRI radiomics signatures with clinical factors can effectively predict the prognosis of patients with HCC treated with surgical resection.

Mounting research papers have suggested that long non‐coding RNAs (lncRNAs) elicit important functions in the progression of osteosarcoma (OS). This study focused on the role of TNK2‐AS1 in OS. TNK2‐AS1 was powerfully expressed in OS tissues and cell lines. In addition, TNK2‐AS1 downregulation inhibited proliferative, migratory, and invasive capacities while promoting apoptosis in OS cells. miR‐4319 was removed by TNK2‐AS1 and therefore TNK2‐AS1 elevated WDR1 expression in OS cells. miR‐4319 had an inhibitory influence on OS progression, while WDR1 was a contributor to OS progression. Rescue assays certified that TNK2‐AS1 promoted malignant phenotypes in vitro and the growth in vivo of OS cells by upregulating WDR1. In depth, we found that YY1 accelerated the transcription of TNK2‐AS1 in OS cells, and that its role in OS also depended on TNK2‐AS1‐regulated WDR1. In conclusion, TNK2‐AS1 was positively modulated by YY1 and aggravated the development of OS by ‘sponging’ miR‐4319 to elevate WDR1. The findings highlighted that TNK2‐AS1 might be a promising target for the treatment of OS. TNK2‐AS1 was upregulated and facilitated the progression of OS cells. miR‐4319 was expressed at low levels in OS cells and hampered proliferation, migration, and invasion. WDR1 could bind to miR‐4319 in OS cells. WDR1 facilitated the progression of OS cells. TNK2‐AS1 downregulation repressed OS growth in vivo. YY1 accelerated the transcription of TNK2‐AS1.

Obesity, one of the most serious public health issues, is caused by the imbalance of energy intake and energy expenditure. N(6)-methyladenosine (m 6 A) RNA modification has been recently identified as a key regulator of obesity, while the detailed mechanism is elusive. Here, we find that YTH RNA binding protein 1 (YTHDF1), an m 6 A reader, acts as an essential regulator of white adipose tissue metabolism. The expression of YTHDF1 decreases in adipose tissue of male mice fed a high-fat diet. Adipocyte-specific Ythdf1 deficiency exacerbates obesity-induced metabolic defects and inhibits beiging of inguinal white adipose tissue (iWAT) in male mice. By contrast, male mice with WAT-specific YTHDF1 overexpression are resistant to obesity and shows promotion of beiging. Mechanistically, YTHDF1 regulates the translation of diverse m 6 A-modified mRNAs. In particular, YTHDF1 facilitates the translation of bone morphogenetic protein 8b ( Bmp8b ) in an m 6 A-dependent manner to induce the beiging process. Here, we show that YTHDF1 may be an potential therapeutic target for the management of obesity-associated diseases. Activation of white adipose tissue (WAT) thermogenesis alleviates obesity-associated metabolic disorders in rodents. Here the authors report that the m6 A RNA modification reader YTHDF1 promotes WAT thermogenesis in a study with male mice, and may be a potential target for the treatment of obesity.

RNA N6-methyladenosine (m6A) modification plays a crucial role in the DNA damage response, while the detailed mechanisms remain to be explored. In this study, we report the involvement of the m6A demethylase ALKBH5 in X-ray-induced DNA damage response. Depletion of ALKBH5 reduces X-ray-induced DNA damage, induces G2/M phase arrest and reduces cell apoptosis. RNA sequencing and m6A sequencing analysis reveal that ALKBH5 removes m6A modifications from its target mRNAs and suppresses their expression. A subset of mRNAs encoding cyclin dependent kinase inhibitors, such as CDKN1A and CDKN2B, show increased stability and expression upon ALKBH5 knockdown. Subsequently, the upregulation of CDKN1A and CDKN2B contributes to G2/M phase arrest to facilitate DNA repair. Our findings unveil the epigenetic regulation of cell cycle checkpoint by ALKBH5 in X-ray-induced DNA damage, offering potential targets for DNA damage-based therapy for cancers.