Analysis of single-cell RNA-sequencing data identifies a hypoxic tumor subpopulation associated with poor prognosis in triple-negative breast cancer

Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer

Ferroptosis, a commonly observed type of programmed cell death caused by abnormal metabolic and biochemical mechanisms, is frequently triggered by cellular stress. The occurrence of ferroptosis is predominantly linked to pathophysiological conditions due to the substantial impact of various metabolic pathways, including fatty acid metabolism and iron regulation, on cellular reactions to lipid peroxidation and ferroptosis. This mode of cell death serves as a fundamental factor in the development of numerous diseases, thereby presenting a range of therapeutic targets. Single-cell sequencing technology provides insights into the cellular and molecular characteristics of individual cells, as opposed to bulk sequencing, which provides data in a more generalized manner. Single-cell sequencing has found extensive application in the field of cancer research. This paper reviews the progress made in ferroptosis-associated cancer research using single-cell sequencing, including ferroptosis-associated pathways, immune checkpoints, biomarkers, and the identification of cell clusters associated with ferroptosis in tumors. In general, the utilization of single-cell sequencing technology has the potential to contribute significantly to the investigation of the mechanistic regulatory pathways linked to ferroptosis. Moreover, it can shed light on the intricate connection between ferroptosis and cancer. This technology holds great promise in advancing tumor-wide diagnosis, targeted therapy, and prognosis prediction.

Advances in computational methods for process and data mining in healthcare

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Glucocorticoids in lung cancer: Navigating the balance between immunosuppression and therapeutic efficacy

Glucocorticoids (GCs), a class of hormones secreted by the adrenal glands, are released into the bloodstream to maintain homeostasis and modulate responses to various stressors. These hormones function by binding to the widely expressed GC receptor (GR), thereby regulating a wide range of pathophysiological processes, especially in metabolism and immunity. The role of GCs in the tumor immune microenvironment (TIME) of lung cancer (LC) has been a focal point of research. As immunosuppressive agents, GCs exert a crucial impact on the occurrence, progression, and treatment of LC. In the TIME of LC, GCs act as a constantly swinging pendulum, simultaneously offering tumor-suppressive properties while diminishing the efficacy of immune-based therapies. The present study reviews the role and mechanisms of GCs in the TIME of LC.

GATLGEMF: A graph attention model with line graph embedding multi-complex features for ncRNA-protein interactions prediction

Non-coding RNA (ncRNA) plays an important role in many fundamental biological processes, and it may be closely associated with many complex human diseases. NcRNAs exert their functions by interacting with proteins. Therefore, identifying novel ncRNA-protein interactions (NPIs) is important for understanding the mechanism of ncRNAs role. The computational approach has the advantage of low cost and high efficiency. Machine learning and deep learning have achieved great success by making full use of sequence information and structure information. Graph neural network (GNN) is a deep learning algorithm for complex network link prediction, which can extract and discover features in graph topology data. In this study, we propose a new computational model called GATLGEMF. We used a line graph transformation strategy to obtain the most valuable feature information and input this feature information into the attention network to predict NPIs. The results on four benchmark datasets show that our method achieves superior performance. We further compare GATLGEMF with the state-of-the-art existing methods to evaluate the model performance. GATLGEMF shows the best performance with the area under curve (AUC) of 92.41% and 98.93% on RPI2241 and NPInter v2.0 datasets, respectively. In addition, a case study shows that GATLGEMF has the ability to predict new interactions based on known interactions. The source code is available at https://github.com/JianjunTan-Beijing/GATLGEMF.

Single-cell transcriptomics provide insight into metastasis-related subsets of breast cancer

Breast cancer metastasis is a complex, multi-step process, with high cellular heterogeneity between primary and metastatic breast cancer, and more complex interactions between metastatic cancer cells and other cells in the tumor microenvironment. High-resolution single-cell transcriptome sequencing technology can visualize the heterogeneity of malignant and non-malignant cells in the tumor microenvironment in real time, especially combined with spatial transcriptome analysis, which can directly compare changes between different stages of metastatic samples. Therefore, this study takes single-cell analysis as the first perspective to deeply explore special or rare cell subpopulations related to breast cancer metastasis, systematically summarizes their functions, molecular features, and corresponding treatment strategies, which will contribute to accurately identify, understand, and target tumor metastasis-related driving events, provide a research basis for the mechanistic study of breast cancer metastasis, and provide new clues for its personalized precision treatment.

Insights into the potential of Sanguinarine as a promising therapeutic option for breast cancer

Breast cancer (BC) is one of the leading causes of cancer-related deaths in women worldwide. The tumor microenvironment (TME) plays a crucial role in the progression and metastasis of BC. A significant proportion of BC is characterized by a hypoxic TME, which contributes to the development of drug resistance and cancer recurrence. Sanguinarine (SAN), an isoquinoline alkaloid found in Papaver plants, has shown promise as an anticancer agent. The present review focuses on exploring the molecular mechanisms of hypoxic TME in BC and the potential of SAN as a therapeutic option. The review presents the current understanding of the hypoxic TME, its signaling pathways, and its impact on the progression of BC. Additionally, the review elaborates on the mechanisms of action of SAN in BC, including its effects on vital cellular processes such as proliferation, migration, drug resistance, and tumor-induced immune suppression. The review highlights the importance of addressing hypoxic TME in treating BC and the potential of SAN as a promising therapeutic option.

Recent advances in predicting lncRNA-disease associations based on computational methods

Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.

Recent advances in machine learning methods for predicting LncRNA and disease associations

Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.