Investigating Potential Drug Targets for Non-small Cell Lung Cancer through STITCH Database Analysis
The aim of this study is to identify potential drug targets and repurposing opportunities by analyzing interactions between key proteins involved in non-small cell lung cancer (NSCLC) pathways, focusing on the inhibition of aberrant signaling in cancer therapy.
- Initial Hypothesis: Targeting key nodes in the Ras, PI3K-Akt, Cell Cycle, p53, and Retinoid signaling pathways may provide effective therapeutic strategies for NSCLC by inhibiting cancer cell proliferation and survival.
Non-small cell lung cancer (NSCLC) represents 85% of lung cancer cases. Key pathways involved in NSCLC pathogenesis include the Ras, PI3K-Akt, Cell Cycle, p53, and Retinoid signaling pathways. These pathways are often dysregulated in NSCLC, leading to uncontrolled cell proliferation, survival, and resistance to apoptosis.
- Primary Database: STRING (Search Tool for the Retrieval of Interacting Genes/Proteins)
- Secondary Database: STITCH (Search Tool for Interactions of Chemicals)
- Ras Signaling Pathway
STRING Network:
https://string-db.org/cgi/network?identifiers=KRAS%0aHRAS%0aNRAS%0aARAF%0aBRAF%0aRAF1%0aMAP2K1%0aMAP2K2%0aMAPK1%0aMAPK3&species=9606&required_score=900&network_flavor=evidence&add_white_nodes=10STITCH Interactors:
http://stitch.embl.de/api/tsv/interactors?identifiers=KRAS%0aHRAS%0aNRAS%0aARAF%0aBRAF%0aRAF1%0aMAP2K1%0aMAP2K2%0aMAPK1%0aMAPK3&species=9606&required_score=900&limit=20&chemicalmode=10.5&minprotchem=1- PI3K-Akt Signaling Pathway
STRING Network:
https://string-db.org/cgi/network?identifiers=PIK3CA%0aPIK3CB%0aPIK3CD%0aAKT1%0aAKT2%0aAKT3%0aPTEN%0aMTOR%0aFOXO3%0aBAD&species=9606&required_score=900&network_flavor=evidence&add_white_nodes=10STITCH Interactors:
http://stitch.embl.de/api/tsv/interactors?identifiers=PIK3CA%0aPIK3CB%0aPIK3CD%0aAKT1%0aAKT2%0aAKT3%0aPTEN%0aMTOR%0aFOXO3%0aBAD&species=9606&required_score=900&limit=20&chemicalmode=10.5&minprotchem=1- Calcium Signaling Pathway
STRING Network:
https://string-db.org/cgi/network?identifiers=PLCG1%0aPLCG2%0aIP3R%0aCAMK&species=9606&required_score=900&network_flavor=evidence&add_white_nodes=10STITCH Interactors:
http://stitch.embl.de/api/tsv/interactors?identifiers=PLCG1%0aPLCG2%0aIP3R%0aCAMK&species=9606&required_score=900&limit=20&chemicalmode=10.5&minprotchem=1- p53 Signaling Pathway
STRING Network:
https://string-db.org/cgi/network?identifiers=TP53%0aCDKN1A%0aBAX%0aGADD45A%0aGADD45B%0aGADD45G&species=9606&required_score=900&network_flavor=evidence&add_white_nodes=10STITCH Interactors:
http://stitch.embl.de/api/tsv/interactors?identifiers=TP53%0aCDKN1A%0aBAX%0aGADD45A%0aGADD45B%0aGADD45G&species=9606&required_score=900&limit=20&chemicalmode=10.5&minprotchem=1- Cell Cycle Pathway
STRING Network:
https://string-db.org/cgi/network?identifiers=CDK4%0aCDK6%0aCCND1%0aRB1%0aE2F1%0aE2F2%0aE2F3%0aCDKN2A&species=9606&required_score=900&network_flavor=evidence&add_white_nodes=10STITCH Interactors:
http://stitch.embl.de/api/tsv/interactors?identifiers=CDK4%0aCDK6%0aCCND1%0aRB1%0aE2F1%0aE2F2%0aE2F3%0aCDKN2A&species=9606&required_score=900&limit=20&chemicalmode=10.5&minprotchem=1- Retinoid Signaling Pathway
STRING Network:
https://string-db.org/cgi/network?identifiers=RARA%0aRARB%0aRARG%0aRXRA%0aRXRB%0aRXRG%0aCRABP1%0aCRABP2&species=9606&required_score=900&network_flavor=evidence&add_white_nodes=10STITCH Interactors:
http://stitch.embl.de/api/tsv/interactors?identifiers=RARA%0aRARB%0aRARG%0aRXRA%0aRXRB%0aRXRG%0aCRABP1%0aCRABP2&species=9606&required_score=900&limit=20&chemicalmode=10.5&minprotchem=1Non-small cell lung cancer - WP4255
@article{10.1093/nar/gkad960,
author = {Agrawal, Ayushi and Balcı, Hasan and Hanspers, Kristina and Coort, Susan L and Martens, Marvin and Slenter, Denise N and Ehrhart, Friederike and Digles, Daniela and Waagmeester, Andra and Wassink, Isabel and Abbassi-Daloii, Tooba and Lopes, Elisson N and Iyer, Aishwarya and Acosta, Javier Millán and Willighagen, Lars G and Nishida, Kozo and Riutta, Anders and Basaric, Helena and Evelo, Chris T and Willighagen, Egon L and Kutmon, Martina and Pico, Alexander R},
title = "{WikiPathways 2024: next generation pathway database}",
journal = {Nucleic Acids Research},
volume = {52},
number = {D1},
pages = {D679-D689},
year = {2023},
month = {11},
abstract = "{WikiPathways (wikipathways.org) is an open-source biological pathway database. Collaboration and open science are pivotal to the success of WikiPathways. Here we highlight the continuing efforts supporting WikiPathways, content growth and collaboration among pathway researchers. As an evolving database, there is a growing need for WikiPathways to address and overcome technical challenges. In this direction, WikiPathways has undergone major restructuring, enabling a renewed approach for sharing and curating pathway knowledge, thus providing stability for the future of community pathway curation. The website has been redesigned to improve and enhance user experience. This next generation of WikiPathways continues to support existing features while improving maintainability of the database and facilitating community input by providing new functionality and leveraging automation.}",
issn = {0305-1048},
doi = {10.1093/nar/gkad960},
url = {https://doi.org/10.1093/nar/gkad960},
eprint = {https://academic.oup.com/nar/article-pdf/52/D1/D679/55040703/gkad960.pdf},
}@article{10.1093/nar/gkac1000,
author = {Szklarczyk, Damian and Kirsch, Rebecca and Koutrouli, Mikaela and Nastou, Katerina and Mehryary, Farrokh and Hachilif, Radja and Gable, Annika L and Fang, Tao and Doncheva, Nadezhda T and Pyysalo, Sampo and Bork, Peer and Jensen, Lars J and von Mering, Christian},
title = "{The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest}",
journal = {Nucleic Acids Research},
volume = {51},
number = {D1},
pages = {D638-D646},
year = {2022},
month = {11},
abstract = "{Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein–protein interactions—both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.}",
issn = {0305-1048},
doi = {10.1093/nar/gkac1000},
url = {https://doi.org/10.1093/nar/gkac1000},
eprint = {https://academic.oup.com/nar/article-pdf/51/D1/D638/48440966/gkac1000.pdf},
}