Dataset Information

Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets.

ABSTRACT: Alzheimer's disease (AD), the most common form of dementia, is recognized as a heterogeneous disease with diverse pathophysiologic mechanisms. In this study, we interrogate the molecular heterogeneity of AD by analyzing 1543 transcriptomes across five brain regions in two AD cohorts using an integrative network approach. We identify three major molecular subtypes of AD corresponding to different combinations of multiple dysregulated pathways, such as susceptibility to tau-mediated neurodegeneration, amyloid-? neuroinflammation, synaptic signaling, immune activity, mitochondria organization, and myelination. Multiscale network analysis reveals subtype-specific drivers such as GABRB2, LRP10, MSN, PLP1, and ATP6V1A We further demonstrate that variations between existing AD mouse models recapitulate a certain degree of subtype heterogeneity, which may partially explain why a vast majority of drugs that succeeded in specific mouse models do not align with generalized human trials across all AD subtypes. Therefore, subtyping patients with AD is a critical step toward precision medicine for this devastating disease.

SUBMITTER: Neff RA

PROVIDER: S-EPMC7787497 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets.

Neff Ryan A RA Wang Minghui M Vatansever Sezen S Guo Lei L Ming Chen C Wang Qian Q Wang Erming E Horgusluoglu-Moloch Emrin E Song Won-Min WM Li Aiqun A Castranio Emilie L EL Tcw Julia J Ho Lap L Goate Alison A Fossati Valentina V Noggle Scott S Gandy Sam S Ehrlich Michelle E ME Katsel Pavel P Schadt Eric E Cai Dongming D Brennand Kristen J KJ Haroutunian Vahram V Zhang Bin B

Science advances 20210106 2

Alzheimer's disease (AD), the most common form of dementia, is recognized as a heterogeneous disease with diverse pathophysiologic mechanisms. In this study, we interrogate the molecular heterogeneity of AD by analyzing 1543 transcriptomes across five brain regions in two AD cohorts using an integrative network approach. We identify three major molecular subtypes of AD corresponding to different combinations of multiple dysregulated pathways, such as susceptibility to tau-mediated neurodegenerat ...[more]

PMID: 33523961

Similar Datasets

Project description:The main challenge in treating stomach adenocarcinoma (STAD) is chemotherapy resistance, which is characterized by changes in the immune microenvironment. Disulfidptosis, a novel form of programmed cell death, is involved in STAD but its mechanism is not fully understood. Long non-coding RNAs (LncRNAs) may play a role in regulating disulfidptosis and influencing the immune microenvironment and chemotherapy resistance in STAD. This study aims to establish disulfidptosis-related lncRNA (DRL) features and explore their significance in the immune microenvironment and chemotherapy resistance in STAD patients. By analyzing RNA sequencing and clinical data from STAD patients and extracting disulfidptosis-related genes, we identified DRLs through co-expression, single-factor and multi-factor Cox regression, and Lasso regression analyses. We also investigated differences in the immune microenvironment, immune function, immune checkpoint gene expression, and chemotherapy resistance between different risk groups using various algorithms. A prognostic risk model consisting of 2 DRLs was constructed, with a strong predictive value for patient survival, outperforming other clinical-pathological factors in predicting 3-year and 5-year survival. Immune-related analysis revealed a strong positive correlation between T cell CD4+ cells and risk score across all algorithms, and higher expression of immune checkpoint genes in the high-risk group. In addition, high-risk patients showed better sensitivity to Erlotinib, Oxaliplatin, and Gefitinib. Furthermore, three novel molecular subtypes of STAD were identified based on the 2-DRLs features, with evaluation of the immune microenvironment and chemotherapy drug sensitivity for each subgroup, which holds significant implications for achieving precise treatment in STAD. Overall, our 2-DRLs prognostic model demonstrates high predictive value for patient survival in STAD, potentially providing new targets for individualized immune and chemical therapy.

Dataset Information

Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets.

Publications

Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets