Prognostic and predictive value of a metabolic risk score model in breast cancer: an immunogenomic landscape analysis

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes

Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.

Atractylenolide-1 affects glycolysis/gluconeogenesis by downregulating the expression of TPI1 and GPI to inhibit the proliferation and invasion of human triple-negative breast cancer cells

Atractylenolide-1 (AT-1) is a major octanol alkaloid isolated from Atractylodes Rhizoma and is widely used to treat various diseases. However, few reports have addressed the anticancer potential of AT-1, and the underlying molecular mechanisms of its anticancer effects are unclear. This study aimed to assess the effect of AT-1 on triple-negative breast cancer (TNBC) cell proliferation and migration and explore its potential molecular mechanisms. Cell invasion assays confirmed that the number of migrating cells decreased after AT-1 treatment. Colony formation assays showed that AT-1 treatment impaired the ability of MDA-MB-231 cells to form colonies. AT-1 inhibited the expression of p-p38, p-ERK, and p-AKT in MDA-MB-231 cells, significantly downregulated the proliferation of anti-apoptosis-related proteins CDK1, CCND1, and Bcl2, and up-regulated pro-apoptotic proteins Bak, caspase 3, and caspase 9. The gas chromatography-mass spectroscopy results showed that AT-1 downregulated the metabolism-related genes TPI1 and GPI through the glycolysis/gluconeogenesis pathway and inhibited tumor growth in vivo. AT-1 affected glycolysis/gluconeogenesis by downregulating the expression of TPI1 and GPI, inhibiting the proliferation, migration, and invasion of (TNBC) MDA-MB-231 cells and suppressing tumor growth in vivo.

ATGPred-FL: sequence-based prediction of autophagy proteins with feature representation learning

Autophagy plays an important role in biological evolution and is regulated by many autophagy proteins. Accurate identification of autophagy proteins is crucially important to reveal their biological functions. Due to the expense and labor cost of experimental methods, it is urgent to develop automated, accurate and reliable sequence-based computational tools to enable the identification of novel autophagy proteins among numerous proteins and peptides. For this purpose, a new predictor named ATGPred-FL was proposed for the efficient identification of autophagy proteins. We investigated various sequence-based feature descriptors and adopted the feature learning method to generate corresponding, more informative probability features. Then, a two-step feature selection strategy based on accuracy was utilized to remove irrelevant and redundant features, leading to the most discriminative 14-dimensional feature set. The final predictor was built using a support vector machine classifier, which performed favorably on both the training and testing sets with accuracy values of 94.40% and 90.50%, respectively. ATGPred-FL is the first ATG machine learning predictor based on protein primary sequences. We envision that ATGPred-FL will be an effective and useful tool for autophagy protein identification, and it is available for free at http://lab.malab.cn/~acy/ATGPred-FL , the source code and datasets are accessible at https://github.com/jiaoshihu/ATGPred .