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.

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.

Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less explored whether metastatic events can be identified through genomic mutational signatures, which are concise descriptions of the mutational processes. Here, we developed MetaWise, a Deep Neural Network (DNN) model, by applying mutational signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts. This model can accurately classify metastatic tumors from primary tumors and outperform traditional machine learning (ML) models and a deep learning (DL) model, DiaDeL. Signatures of non-coding mutations also have a major impact on the model's performance. SHapley Additive exPlanations (SHAP) and Local Surrogate (LIME) analyses identify several mutational signatures which are directly correlated to metastatic spread in cancers, including APOBEC-mutagenesis, UV-induced signatures, and DNA damage response deficiency signatures.

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.

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

Triple-negative breast cancer (TNBC) is an aggressive subtype of mammary carcinoma characterized by low expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Along with the rapid development of the single-cell RNA-sequencing (scRNA-seq) technology, the heterogeneity within the tumor microenvironment (TME) could be studied at a higher resolution level, facilitating an exploration of the mechanisms leading to poor prognosis during tumor progression. In previous studies, hypoxia was considered as an intrinsic characteristic of TME in solid tumors, which would activate downstream signaling pathways associated with angiogenesis and metastasis. Moreover, hypoxia-related genes (HRGs) based risk score models demonstrated nice performance in predicting the prognosis of TNBC patients. However, it is essential to further investigate the heterogeneity within hypoxic TME, such as intercellular communications. In the present study, utilizing single-sample Gene Set Enrichment Analysis (ssGSEA) and cell-cell communication analysis on the scRNA-seq data retrieved from Gene Expression Omnibus (GEO) database with accession number GSM4476488, we identified four tumor subpopulations with diverse functions, particularly a hypoxia-related one. Furthermore, results of cell-cell communication analysis revealed the dominant role of the hypoxic tumor subpopulation in angiogenesis- and metastasis-related signaling pathways as a signal sender. Consequently, regard the TNBC cohorts acquired from The Cancer Genome Atlas (TCGA) and GEO as train set and test set respectively, we constructed a risk score model with reliable capacity for the prediction of overall survival (OS), where ARTN and L1CAM were identified as risk factors promoting angiogenesis and metastasis of tumors. The expression of ARTN and L1CAM were further analyzed through tumor immune estimation resource (TIMER) platform. In conclusion, these two marker genes of the hypoxic tumor subpopulation played vital roles in tumor development, indicating poor prognosis in TNBC patients.