Background: Parkinson’s disease (PD), Alzheimer’s disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the early stage of AD or PD. Blood biomarkers are considered to be less invasive, less cost and more convenient, and there is tremendous potential for the diagnosis and prediction of neurodegenerative diseases. As a recently mentioned field, artificial intelligence (AI) is often applied in biology and shows excellent results. In this article, we use AI to model PD, AD, MCI data and analyze the possible connections between them.Method: Human blood protein microarray profiles including 156 CT, 50 MCI, 132 PD, 50 AD samples are collected from Gene Expression Omnibus (GEO). First, we used bioinformatics methods and feature engineering in machine learning to screen important features, constructed artificial neural network (ANN) classifier models based on these features to distinguish samples, and evaluated the model’s performance with classification accuracy and Area Under Curve (AUC). Second, we used Ingenuity Pathway Analysis (IPA) methods to analyse the pathways and functions in early stage and late stage samples of different diseases, and potential targets for drug intervention by predicting upstream regulators.Result: We used different classifier to construct the model and finally found that ANN model would outperform the traditional machine learning model. In summary, three different classifiers were constructed to be used in different application scenarios, First, we incorporated 6 indicators, including EPHA2, MRPL19, SGK2, to build a diagnostic model for AD with a test set accuracy of up to 98.07%. Secondly, incorporated 15 indicators such as ERO1LB, FAM73B, IL1RN to build a diagnostic model for PD, with a test set accuracy of 97.05%. Then, 15 indicators such as XG, FGFR3 and CDC37 were incorporated to establish a four-category diagnostic model for both AD and PD, with a test set accuracy of 98.71%. All classifier models have an auc value greater than 0.95. Then, we verified that the constructed feature engineering filtered out fewer important features but contained more information, which helped to build a better model. In addition, by classifying the disease types more carefully into early and late stages of AD, MCI, and PD, respectively, we found that early PD may occur earlier than early MCI. Finally, there are 24 proteins that are both differentially expressed proteins and upstream regulators in the disease group versus the normal group, and these proteins may serve as potential therapeutic targets and targets for subsequent studies.Conclusion: The feature engineering we build allows better extraction of information while reducing the number of features, which may help in subsequent applications. Building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. Overall, it is important for us to study neurodegenerative diseases from both diagnostic and interventional aspects.

Background/Objectives: Bone metastasis is a frequent and life-threatening event in advanced cancers, affecting up to 70–85% of prostate cancer patients. Understanding the cellular and molecular mechanisms underlying bone metastasis is essential for developing targeted therapies. This study aimed to systematically characterize the heterogeneity and microenvironmental adaptation of prostate cancer bone metastases using single-cell transcriptomics. Methods: We integrated the largest single-cell transcriptome dataset to date, encompassing 124 samples from primary prostate tumors, various bone metastatic sites, and non-malignant tissues (e.g., benign prostatic hyperplasia, normal bone marrow). After quality control, 602,497 high-quality single-cell transcriptomes were analyzed. We employed unsupervised clustering, gene expression profiling, mutation analysis, and metabolic pathway reconstruction to characterize cancer cell subtypes and tumor microenvironmental remodeling. Results: Cancer epithelial cells dominated the tumor microenvironment but exhibited pronounced heterogeneity, posing challenges for conventional clustering methods. By integrating genetic and metabolic features, we revealed key evolutionary trajectories of epithelial cancer cells during metastasis. Notably, we identified a novel epithelial subpopulation, NEndoCs, characterized by unique differentiation patterns and distinct spatial distribution across metastatic niches. We also observed significant metabolic reprogramming and recurrent mutations linked to prostate-to-bone microenvironmental transitions. Conclusions: This study comprehensively elucidates the mutation patterns, metabolic reprogramming, and microenvironment adaptation mechanisms of bone metastasis in prostate cancer, providing key molecular targets and clinical strategies for the precise treatment of bone metastatic prostate cancer.

Rationale: The 2019 coronavirus disease (COVID-19) pandemic poses a significant threat to human health. After SARS-CoV-2 infection, major clinical concerns are organ damage and possible sequelae.

Male breast cancer (MBC) is rare and remains largely managed using knowledge derived from female breast cancer. Here, we generated a multiplatform single-cell and spatial transcriptomic atlas integrating scRNA-seq, SeekSpace, Visium HD, Xenium In Situ, and multiplex immunohistochemistry data from male and female breast cancer cohorts, covering more than 660,000 cells. We identified male-specific tumor cells (MSTCs) that were enriched in MBC, rare in female breast cancer, and associated with poorer disease-free survival. MSTCs exhibited neural transcriptional programs, increased transcriptome-inferred copy number variation burden, and spatial coupling with fatty acid metabolism signals. MSTC-enriched regions showed reduced antigen-presentation signatures and spatial association with APOE ⁺ /CD163 ⁺ macrophages, with GRN-SORT1 emerging as a candidate macrophage-tumor interaction axis. These findings define an MBC-associated malignant state and its immune-metabolic spatial context.

Breast cancer brain metastasis (BCBM) is a major cause of breast cancer-related mortality, but the molecular mechanisms underlying its progression remain poorly understood. Here, we profiled the tumor immune microenvironment of BCBM at single-cell resolution and identified candidate regulators associated with brain metastatic progression. Single-cell RNA sequencing (scRNA-seq) was performed on brain metastatic tissue, adjacent tumor tissue, cerebrospinal fluid, and circulating tumor cells (CTCs) from seven patients, and bulk DNA sequencing was conducted on primary breast tumors, peripheral blood, and brain metastases from 47 patients. Analysis of 131,880 single cells identified 63 distinct cell clusters, including 12 tumor-associated macrophage (TAM) subtypes and 5 metastatic tumor cell (MTC) subtypes. Circulating TAMs displayed an M1-like inflammatory phenotype, whereas tissue-resident TAMs were predominantly M2-like. MTCs exhibited substantial transcriptional heterogeneity, and a neuro-related subtype showed adaptive upregulation of neuronal signaling pathways. Brain metastases also harbored a higher mutational burden than primary tumors, with recurrent mutations in GABRB3 and NRXN1 associated with poorer patient survival. Functional experiments further showed that loss of GABRB3 or NRXN1 impaired tumor growth and brain colonization in xenograft models. These findings nominate GABRB3 and NRXN1 as candidate regulators of brain metastatic fitness in BCBM and support their further evaluation as biomarkers and therapeutic targets.