Histone demethylase PHF8 promotes prostate cancer metastasis via the E2F1-SNAI1 axis.

Metastasis is the primary culprit behind cancer-related fatalities in multiple cancer types, including prostate cancer. Despite great advances, the precise mechanisms underlying prostate cancer metastasis are far from complete. By using a transgenic mouse prostate cancer model (TRAMP) with and without Phf8 knockout, we have identified a crucial role of PHF8 in prostate cancer metastasis. By complexing with E2F1, PHF8 transcriptionally upregulates SNAI1 in a demethylation-dependent manner. The upregulated SNAI1 subsequently enhances epithelial-to-mesenchymal transition (EMT) and metastasis. Given the role of the abnormally activated PHF8/E2F1-SNAI1 axis in prostate cancer metastasis and poor prognosis, the levels of PHF8 or the activity of this axis could serve as biomarkers for prostate cancer metastasis. Moreover, targeting this axis could become a potential therapeutic strategy for prostate cancer treatment. © 2024 The Pathological Society of Great Britain and Ireland.

Essential genes identification model based on sequence feature map and graph convolutional neural network.

BACKGROUND: Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes. RESULTS: In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training. CONCLUSIONS: Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research.

An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems.

An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.

A twinning bare bones particle swarm optimization algorithm.

A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.

Clinical Effect and Rotational Stability of TICL in the Treatment of Myopic Astigmatism.

PURPOSE: To investigate the clinical outcomes and possible risk factors associated with rotational stability after the implantation of a V4c toric implantable Collamer lens (TICL) for the correction of moderate to high myopic astigmatism. METHODS: A total of 112 eyes of 66 patients with moderate to high myopic astigmatism underwent TICL implantation. All patients were followed up for more than 1 year. The uncorrected and best-corrected visual acuity (UCVA and BCVA), astigmatism and spherical equivalent, intraocular pressure, vault, endothelial cell morphometry, and rotation of the TICL axis were assessed at l day, 1 week, 1 month, 3 months, 6 months, and 12 months postoperatively. Postoperative rotation was defined as the angle between the intended axis and the achieved axis. Regression analysis was used to investigate the possible risk factors for TICL rotation postoperatively. RESULTS: The mean efficacy index and safety index 12 months postoperatively were 1.03 ± 0.09 and 1.05 ± 0.10, respectively. All patients had the same or better visual acuity than preoperatively. The mean astigmatism value decreased from -1.86 ± 0.79 D preoperatively to -0.37 ± 0.35 D. The mean absolute axis deviation of the TICL at the last follow-up was 2.75 ± 2.04° (range, 0°âˆ¼11°). The mean manifest refraction spherical equivalent (MRSE) changed from -9.04 ± 2.67 D preoperatively to -0.67 ± 0.51 D postoperatively. The logistic regression demonstrated that the absolute degree of TICL rotation had a significant association with the fixation angle of the TICL and the size of the lens (P=0.003, P=0.026, resp.). CONCLUSION: The results of our study support that TICL implantation is safe, effective, and predictable in the treatment of moderate to high myopic astigmatism, with relatively good postoperative rotational stability.