Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer’s Disease
Abstract
:1. Introduction
- (i)
- The proposed approach integrates text mining and genomics together to find common genes and drugs for AD and its comorbid diseases. The existing works either use text mining or genomics to find new treatment options for a disease. The existing works do not consider the comorbid diseases too. Thus, we advance the existing research methods for finding new treatment options.
- (ii)
- The proposed approach queries PubMed abstracts to extract comorbid diseases for AD. The existing approaches extract the comorbid diseases for a disease of interest from patients’ electronic health records (EHRs). However, accessing EHRs requires institutional revenue board (IRB) approval. There are other challenges too. Unlike patients’ EHRs, PubMed is an open source and it is free to use.
- (iii)
- A comorbidity-guided approach is proposed to identify new candidate genes and drugs for AD and its comorbid diseases. To our knowledge, the existing works on identifying new candidate genes and drugs for a disease, especially AD, do not consider its comorbid diseases. However, the knowledge on comorbid diseases, their candidate genes, and drugs prescribed for treating these comorbid diseases is important to avoid drug–drug interaction.
2. Materials and Methods
2.1. Text Mining Approach
2.1.1. Lexicon of MeSH Diseases
2.1.2. Retrieving Comorbid Diseases from PubMed
- (i)
- Number of PubMed articles with AD and a co-occurring disease in the MeSH index;
- (ii)
- Number of PubMed articles with the co-occurring disease in the MeSH index;
- (iii)
- Number of PubMed articles with AD in the MeSH index;
- (iv)
- Total number of PubMed articles (125,924, access date 22 November 2022) with the MeSH term “Comorbidity” in the MeSH index.
- Boolean query (a) for count (i): “Alzheimer disease” [MeSH] AND “Comorbidity” [MeSH] AND “CO-OCCURRING DISEASE” [MeSH];
- Boolean query (b) for count (ii): “CO-OCCURRING DISEASE” [MeSH] AND “Comorbidity” [MeSH] NOT “Alzheimer disease” [MeSH];
- Boolean query (c) for count (iii): “Alzheimer disease” [MeSH] AND “Comorbidity” [MeSH] NOT “CO-OCCURRING DISEASE” [MeSH];
- Boolean query (d) for count (iv): “Comorbidity” [MeSH].
2.2. Genomics Approach
2.2.1. Identification of Common Genes for AD and Five Comorbid Diseases
2.2.2. Protein–Protein Interaction from STRING Database
2.2.3. Enrichment Analysis for Pathways
2.2.4. Drugs from Library of Integrated Network-Based Cellular Signatures (Sigcom LINCS)
3. Results
3.1. Comorbid Diseases for AD
3.2. Common Genes among AD and Comorbid Diseases
3.3. Network Analysis of Common Genes
3.4. Comorbid Diseases and Drug Perturbation
4. Discussion
4.1. Comorbid Diseases and Genetic Analysis
4.2. Comorbid Diseases and Pathway Analysis
4.3. Comorbid Diseases and Drug Perturbation
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Co-Occurring Disease | PubMed Articles with AD and Co-Occurring Disease (P) | PubMed Articles with AD and All Co-Occurring Diseases (T) | Sort Ratio (P/T) |
---|---|---|---|
Dementia | 142 | 843 | 0.1684 |
Type 2 diabetes | 95 | 843 | 0.1127 |
Hypertension | 60 | 843 | 0.0712 |
Parkinson’s disease | 51 | 843 | 0.0605 |
Down Syndrome | 43 | 843 | 0.0510 |
Comorbid Disease (CD) | PubMed Articles | FET p-Value | Literature Evidence (PMID) | |||
---|---|---|---|---|---|---|
AD and CD | CD | AD | Comorbidity | |||
Dementia | 142 | 220 | 1066 | 125,924 | 1.30 × 10−238 | 11406927 |
Type 2 diabetes | 95 | 11,209 | 1113 | 125,924 | 0.68 | 28922161 |
Hypertension | 60 | 6726 | 1148 | 125,924 | 0.59 | 33888050 |
Parkinson’s disease | 51 | 683 | 1157 | 125,924 | 6.28 × 10−30 | 26820182 |
Down syndrome | 43 | 346 | 1165 | 125,924 | 1.88 × 10−34 | 28009725 |
Multimorbid Diseases | Number of Common Genes | Common Gene(s) |
---|---|---|
AD, dementia, Parkinson’s disease, Down syndrome | 4 | MAPT, PSEN1, APP, APOE |
AD, dementia, Parkinson’s disease | 5 | A2M, ABCA7, PLAU, PSEN2, MPO |
AD, dementia, Down syndrome | 2 | COL18A1, SYNJ1 |
AD, dementia, hypertension, Parkinson’s disease | 2 | HFE, NOS3 |
AD, Parkinson’s disease, Down syndrome | 1 | SORL1 |
AD, type 2 diabetes, hypertension | 1 | NOTCH2 |
AD, dementia, hypertension | 1 | TGFB2 |
AD, dementia, type 2 diabetes | 1 | LAMA1 |
Comorbid Diseases | Common Genes | Pathways for Common Genes (Reactome) (p < 0.05) | PPI Enrichment (STRING) (p-Value) |
---|---|---|---|
AD, dementia | 41 | 28 | <1.0 × 10−16 |
AD, type 2 diabetes | 5 | 36 | 3.56 × 10−5 |
AD, hypertension | 26 | 33 | 3.33 × 10−16 |
AD, Parkinson’s disease | 22 | 27 | <1.0 × 10−16 |
AD, Down syndrome | 33 | 92 | <1.0 × 10−16 |
AD and Comorbid Disease | Drug (Top 10) | p-Value |
---|---|---|
AD and Dementia | axitinib | 2.19 × 10−7 |
lamotrigine | 8.11 × 10−7 | |
ataluren | 1.99 × 10−6 | |
vandetanib | 2.21 × 10−6 | |
etofylline | 2.49 × 10−6 | |
BRD-K18100239 | 3.92 × 10−6 | |
AS-605240 | 4.53 × 10−6 | |
velnacrine | 5.96 × 10−6 | |
BRD-K30758067 | 6.15 × 10−6 | |
quiflapon | 7.10 × 10−6 | |
AD and Type 2 Diabetes | BMS-777607 | 0.0021 |
trifluoperazine | 0.0022 | |
BRD-K18972207 | 0.0025 | |
pirenperone | 0.0025 | |
lonidamine | 0.0026 | |
ARRY-334543 | 0.0026 | |
BRD-K84094241 | 0.0027 | |
BRD-K89952884 | 0.0028 | |
CYT-997 | 0.0029 | |
roflumilast | 0.0030 | |
AD and Hypertension | vernakalant | 5.73 × 10−7 |
BRD-K40853697 | 1.38 × 10−6 | |
marimastat | 1.64 × 10−6 | |
LXR-623 | 2.09 × 10−6 | |
regorafenib | 2.54 × 10−6 | |
aclidinium | 6.77 × 10−6 | |
α-estradiol | 7.31 × 10−6 | |
BRD-K38373457 | 7.37 × 10−6 | |
pidotimod | 7.95 × 10−6 | |
ifenprodil | 8.46 × 10−6 | |
AD and Parkinson’s disease | bortezomib | 1.30 × 10−6 |
maprotiline | 2.52 × 10−5 | |
flupirtine | 8.11 × 10−5 | |
PD-0325901 | 8.70 × 10−5 | |
salmeterol | 9.98 × 10−5 | |
quizartinib | 0.0001 | |
dapagliflozin | 0.0001 | |
gefitinib | 0.0001 | |
lomitapide | 0.0002 | |
SB-216763 | 0.0002 | |
AD and Down syndrome | BRD-A01960364 | 3.77 × 10−8 |
MG-132 | 3.20 × 10−7 | |
BRD-A06909528 | 6.13 × 10−7 | |
teniposide | 1.24 × 10−6 | |
MD-049 | 1.64 × 10−6 | |
BRD-A95820578 | 1.89 × 10−6 | |
pravastatin | 5.51 × 10−6 | |
gatifloxacin | 5.74 × 10−6 | |
VU-0418942-1 | 6.26 × 10−6 | |
marimastat | 6.74 × 10−6 |
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Oviya, I.R.; Sankar, D.; Manoharan, S.; Prabahar, A.; Raja, K. Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer’s Disease. Genes 2024, 15, 614. https://doi.org/10.3390/genes15050614
Oviya IR, Sankar D, Manoharan S, Prabahar A, Raja K. Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer’s Disease. Genes. 2024; 15(5):614. https://doi.org/10.3390/genes15050614
Chicago/Turabian StyleOviya, Iyappan Ramalakshmi, Divya Sankar, Sharanya Manoharan, Archana Prabahar, and Kalpana Raja. 2024. "Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer’s Disease" Genes 15, no. 5: 614. https://doi.org/10.3390/genes15050614
APA StyleOviya, I. R., Sankar, D., Manoharan, S., Prabahar, A., & Raja, K. (2024). Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer’s Disease. Genes, 15(5), 614. https://doi.org/10.3390/genes15050614