Monitoring drug–target interactions through target engagement-mediated amplification on arrays and <i>in situ</i>

Al-Amin, Rasel A.; Johansson, Lars; Abdurakhmanov, Eldar; Landegren, Nils; Löf, Liza; Arngården, Linda; Blokzijl, Andries; Svensson, Richard; Hammond, Maria; Lönn, Peter; Haybaeck, Johannes; Kamali‐Moghaddam, Masood; Jensen, Annika Jenmalm; Danielson, U. Helena; Artursson, Per; Lundbäck, Thomas; Landegren, Ulf

doi:10.1093/nar/gkac842

Cited by 1 publication

(1 citation statement)

References 68 publications

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“…In cancer, mutations and copy number alterations coupled with epigenetic aberrations and altered gene expression are commonly focused points of study towards understanding the cause, heterogeneity, and drug resistance or defining other phenomena related to the aggressiveness and progression of cancers [ 10 , 11 ]. Accordingly, compared to normal cells, drastic changes occur in cancer cells at multiple levels, including molecular functions and cellular processes [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. In different cancer types, the pattern of aberrations behind the disrupted tissue and cell functions varies substantially [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation

Mobashir

Turunen

Izhari

et al. 2022

Cells

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To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes’/proteins’ connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein–protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways’ components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment.

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