Determining Potential Therapeutic Targets for Venous Thrombosis through Network Pharmacology, Cluster Typing, WGCNA, Machine Learning, Immune Infiltration Analysis, and ceRNA Networks
Abstract BACKGROUND: Venous thrombosis presents a significant global health challenge, characterized by its high incidence and limited therapeutic effectiveness. Our objective is to leverage the pharmacological insights offered by traditional Chinese medicine networks to identify potential therapeutic targets for venous thrombosis treatment and elucidate their underlying mechanisms of action. METHODS: We initiated our study by isolating the active ingredients and targets of Chinese medicine compounds from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Concurrently, we acquired venous thromboembolism (VTE) patient data from the Gene Expression Omnibus (GEO) dataset. Utilizing Cytoscape, we constructed a network connecting TCM ingredients, disease targets, and related interactions. Molecular subtypes were determined through target information clustering and typing using the "ConsensusClusterPlus" software package. Key genes were subsequently identified through a combination of weighted gene co-expression network analysis (WGCNA) and machine learning techniques. Functional enrichment analysis was performed on these key genes. Subsequently, we investigated immune cell and immune function differences related to the identified key genes. Lastly, we constructed the competing endogenous RNA (ceRNA) network associated with PPP2R1A. RESULTS: Our initial analysis identified several key genes, including FOS, ICAM1, CASP3, and HSP90AA1. Subsequent functional and downstream analyses revealed that two central hub genes, PPP2R1A and RPL9, may represent novel targets for VTE therapy. Notably, these hub genes are not only associated with thrombospondin and platelet function regulation but also play a role in modulating T cell expression in immunoassays. In conclusion, our findings suggest that ten lncRNAs may compete with two miRNAs for binding, thereby regulating PPP2R1A target genes, with potential implications for improving VTE treatment efficacy. CONCLUSION: Our current findings offer a novel direction and serve as a theoretical foundation for identifying therapeutic targets in the treatment of VTE. Furthermore, these findings hold the potential to facilitate the translation of our research into clinical applications in the future..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
ResearchSquare.com - (2024) vom: 08. März Zur Gesamtaufnahme - year:2024 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Li, Zhuoji [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
|---|
| doi: |
10.21203/rs.3.rs-3894556/v1 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XRA042324955 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XRA042324955 | ||
| 003 | DE-627 | ||
| 005 | 20240309130953.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240130s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.21203/rs.3.rs-3894556/v1 |2 doi | |
| 035 | |a (DE-627)XRA042324955 | ||
| 035 | |a (ResearchSquare)10.21203/rs.3.rs-3894556/v1 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Li, Zhuoji |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Determining Potential Therapeutic Targets for Venous Thrombosis through Network Pharmacology, Cluster Typing, WGCNA, Machine Learning, Immune Infiltration Analysis, and ceRNA Networks |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract BACKGROUND: Venous thrombosis presents a significant global health challenge, characterized by its high incidence and limited therapeutic effectiveness. Our objective is to leverage the pharmacological insights offered by traditional Chinese medicine networks to identify potential therapeutic targets for venous thrombosis treatment and elucidate their underlying mechanisms of action. METHODS: We initiated our study by isolating the active ingredients and targets of Chinese medicine compounds from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Concurrently, we acquired venous thromboembolism (VTE) patient data from the Gene Expression Omnibus (GEO) dataset. Utilizing Cytoscape, we constructed a network connecting TCM ingredients, disease targets, and related interactions. Molecular subtypes were determined through target information clustering and typing using the "ConsensusClusterPlus" software package. Key genes were subsequently identified through a combination of weighted gene co-expression network analysis (WGCNA) and machine learning techniques. Functional enrichment analysis was performed on these key genes. Subsequently, we investigated immune cell and immune function differences related to the identified key genes. Lastly, we constructed the competing endogenous RNA (ceRNA) network associated with PPP2R1A. RESULTS: Our initial analysis identified several key genes, including FOS, ICAM1, CASP3, and HSP90AA1. Subsequent functional and downstream analyses revealed that two central hub genes, PPP2R1A and RPL9, may represent novel targets for VTE therapy. Notably, these hub genes are not only associated with thrombospondin and platelet function regulation but also play a role in modulating T cell expression in immunoassays. In conclusion, our findings suggest that ten lncRNAs may compete with two miRNAs for binding, thereby regulating PPP2R1A target genes, with potential implications for improving VTE treatment efficacy. CONCLUSION: Our current findings offer a novel direction and serve as a theoretical foundation for identifying therapeutic targets in the treatment of VTE. Furthermore, these findings hold the potential to facilitate the translation of our research into clinical applications in the future. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Zhong, Ming |4 aut | |
| 700 | 1 | |a Fu, Kaili |4 aut | |
| 700 | 1 | |a Pang, Lingpin |4 aut | |
| 700 | 1 | |a Sun, Jie |4 aut | |
| 700 | 1 | |a Huang, Tao |4 aut | |
| 700 | 1 | |a Song, Lingyue |4 aut | |
| 700 | 1 | |a Guo, Dingyu |4 aut | |
| 700 | 1 | |a Cheng, Junfen |4 aut | |
| 700 | 1 | |a Sun, Xishi |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t ResearchSquare.com |g (2024) vom: 08. März |
| 773 | 1 | 8 | |g year:2024 |g day:08 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.21203/rs.3.rs-3894556/v1 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XRA | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 08 |c 03 | ||