Mendelian randomization and transcriptomic analysis reveal an inverse causal relationship between Alzheimer's disease and cancer.


Journal

Journal of translational medicine
ISSN: 1479-5876
Titre abrégé: J Transl Med
Pays: England
ID NLM: 101190741

Informations de publication

Date de publication:
04 08 2023
Historique:
received: 21 04 2023
accepted: 14 07 2023
medline: 7 8 2023
pubmed: 5 8 2023
entrez: 4 8 2023
Statut: epublish

Résumé

Alzheimer's disease (AD) and cancer are common age-related diseases, and epidemiological evidence suggests an inverse relationship between them. However, investigating the potential mechanism underlying their relationship remains insufficient. Based on genome-wide association summary statistics for 42,034 AD patients and 609,951 cancer patients from the GWAS Catalog using the two-sample Mendelian randomization (MR) method. Moreover, we utilized two-step MR to identify metabolites mediating between AD and cancer. Furthermore, we employed colocalization analysis to identify genes whose upregulation is a risk factor for AD and demonstrated the genes' upregulation to be a favorable prognostic factor for cancer by analyzing transcriptomic data for 33 TCGA cancer types. Two-sample MR analysis revealed a significant causal influence for increased AD risk on reduced cancer risk. Two-step MR analysis identified very low-density lipoprotein (VLDL) as a key mediator of the negative cause-effect relationship between AD and cancer. Colocalization analysis uncovered PVRIG upregulation to be a risk factor for AD. Transcriptomic analysis showed that PVRIG expression had significant negative correlations with stemness scores, and positive correlations with antitumor immune responses and overall survival in pan-cancer and multiple cancer types. AD may result in lower cancer risk. VLDL is a significant intermediate variable linking AD with cancer. PVRIG abundance is a risk factor for AD but a protective factor for cancer. This study demonstrates a causal influence for AD on cancer and provides potential molecular connections between both diseases.

Sections du résumé

BACKGROUND
Alzheimer's disease (AD) and cancer are common age-related diseases, and epidemiological evidence suggests an inverse relationship between them. However, investigating the potential mechanism underlying their relationship remains insufficient.
METHODS
Based on genome-wide association summary statistics for 42,034 AD patients and 609,951 cancer patients from the GWAS Catalog using the two-sample Mendelian randomization (MR) method. Moreover, we utilized two-step MR to identify metabolites mediating between AD and cancer. Furthermore, we employed colocalization analysis to identify genes whose upregulation is a risk factor for AD and demonstrated the genes' upregulation to be a favorable prognostic factor for cancer by analyzing transcriptomic data for 33 TCGA cancer types.
RESULTS
Two-sample MR analysis revealed a significant causal influence for increased AD risk on reduced cancer risk. Two-step MR analysis identified very low-density lipoprotein (VLDL) as a key mediator of the negative cause-effect relationship between AD and cancer. Colocalization analysis uncovered PVRIG upregulation to be a risk factor for AD. Transcriptomic analysis showed that PVRIG expression had significant negative correlations with stemness scores, and positive correlations with antitumor immune responses and overall survival in pan-cancer and multiple cancer types.
CONCLUSION
AD may result in lower cancer risk. VLDL is a significant intermediate variable linking AD with cancer. PVRIG abundance is a risk factor for AD but a protective factor for cancer. This study demonstrates a causal influence for AD on cancer and provides potential molecular connections between both diseases.

Identifiants

pubmed: 37542274
doi: 10.1186/s12967-023-04357-3
pii: 10.1186/s12967-023-04357-3
pmc: PMC10403895
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

527

Informations de copyright

© 2023. BioMed Central Ltd., part of Springer Nature.

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Auteurs

Zehua Dong (Z)

Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China.

Mengli Xu (M)

Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China.

Xu Sun (X)

Department of Pharmacy, Nanjing Luhe People's Hospital, Nanjing, 211500, China. sunxu@njslhormyy2.wecom.work.
Department of Pharmacy, Luhe Hospital Affiliated with Yangzhou University Medical College, Nanjing, 211500, China. sunxu@njslhormyy2.wecom.work.

Xiaosheng Wang (X)

Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China. xiaosheng.wang@cpu.edu.cn.
Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China. xiaosheng.wang@cpu.edu.cn.
Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China. xiaosheng.wang@cpu.edu.cn.

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