Cancer driver drug interaction explorer

Nogales, Cristian; Zolotareva, Olga; List, Markus

doi:10.1093/nar/gkac384

Cited by 17 publications

(10 citation statements)

References 42 publications

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“…A plethora of computational methods have been developed for drug target identification and drug repurposing [46]. Many of these methods already use networks as input [47–49] and embrace concepts of network pharmacology [50]. Typically, such methods extract disease modules after connecting patient- or disease-specific seed genes.…”

Section: Discussionmentioning

confidence: 99%

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

Lazareva

Louadi

Kersting

et al. 2022

Preprint

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Gene regulation is frequently altered in diseases in unique and often patient-specific ways. Hence, personalized strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not focus on disease-specific dysregulation or lack assessments of statistical significance. Moreover, they do not account for clinically important confounders such as age, sex or treatment history.To overcome these shortcomings, we present DysRegNet, a novel method for inferring patient-specific regulatory alterations (dysregulations) from gene expression profiles. We compared DysRegNet to state-of-the-art methods and demonstrated that DysRegNet produces more interpretable and biologically meaningful networks. Independent information on promoter methylation and single nucleotide variants further corroborate our results. We apply DysRegNet to eleven TCGA cancer types and illustrate how the inferred networks can be used for down-stream analysis. We show that unique as well as cancer-type-specific dysregulation patterns exist and highlight immune-related mechanisms that are not obvious when focusing on individual genes rather than their interactions.DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package) and analysis results for eleven TCGA cancer types are further available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).

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Section: Discussionmentioning

confidence: 99%

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

Lazareva

Louadi

Kersting

et al. 2022

Preprint

Self Cite

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“…As shown in Figure 5 , in the CADDIE database ( 59 ), we found that the downstream genes STAT3, AKT1, IGF1R, and BCAT1 of LINC00324 have multiple targeted drugs. They include STAT3-targeted drugs (Digoxin, Acitretin, Ouabain, Digitoxin, and Niclosamide), AKT1-targeted drugs (Gefitinib, Sorafenib, Erlotinib, Imatinib, and Risperidone), IGF1R-targeted drugs (Insulin human, Sorafenib, Masoprocol, Erlotinib, and Gefitinib), and BCAT1-targeted drugs (Pyridoxal phosphate, Glutamic Acid, L-Leucine, L-Valine, and Isoleucine).…”

Section: Clinical Value Of Linc00324 In Cancermentioning

confidence: 94%

LINC00324 in cancer: Regulatory and therapeutic implications

et al. 2022

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LINC00324 is a 2082 bp intergenic noncoding RNA. Aberrant expression of LINC00324 was associated with the risk of 11 tumors and was closely associated with clinicopathological features and prognostic levels of 7 tumors. LINC00324 can sponge multiple miRNAs to form complex ceRNA networks, and can also recruit transcription factors and bind RNA-binding protein HuR, thereby regulating the expression of a number of downstream protein-coding genes. LINC00324 is involved in 4 signaling pathways, including the PI3K/AKT signaling pathway, cell cycle regulatory pathway, Notch signaling pathway, and Jak/STAT3 signaling pathway. High expression of LINC00324 was associated with larger tumors, a higher degree of metastasis, a higher TNM stage and clinical stage, and shorter OS. Currently, four downstream genes in the LINC00324 network have targeted drugs. In this review, we summarize the molecular mechanisms and clinical value of LINC00324 in tumors and discuss future directions and challenges for LINC00324 research.

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“…miR-1294 and cancer therapy As shown in Fig. 3, we found that currently listed drugs can target 6 downstream genes of miR-1294 via the CADDIE website (https://exbio.wzw.tum.de/caddie/drug-lookup) [49]. These drugs are Palifermin, Heparin, Regorafenib, Ponatinib, and Lenvatinib targeting FGFR1, R788 (Fostamatinib) targeting PLK1 and PIM1, C225 (Cetuximab), LIDO (Lidocaine), (ZD1839) Gefitinib targeting EGFR, Erlotinib, and Lapatinib; Insulin and Mecasermin targeting IGF1R, and ATO and RESV (Resveratrol) targeting AKT1.…”

Section: The Relationship Between Mir-1294 and Clinicopathological Ch...mentioning

confidence: 95%

The tumor suppressor role and ceRNA network of miR-1294 in cancer

Mao¹,

Shen²,

FANG³

et al. 2023

Oncology Research

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miRNAs are endogenous small RNAs that are important regulators of gene expression. miR-1294 was found to be significantly down-regulated in 15 cancers and regulated by 21 upstream regulators. miR-1294 affects the proliferation, migration, invasion, and apoptosis of cancer cells. The target genes of miR-1294 are involved in the PI3K/AKT/mTOR, RAS, and JAK/STAT signaling pathways. Six target genes of miR-1294 are the targets of a variety of drugs. Low expression of miR-1294 is associated with resistance to cisplatin and TMZ and a poorer prognosis in patients with ESCC, GC, EOC, PDAC, or NSCLC. Therefore, this work outlines the molecular mechanisms and provides a basis for the clinical significance of the tumor suppressor miR-1294 in cancer.

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Cancer driver drug interaction explorer

Cited by 17 publications

References 42 publications

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

LINC00324 in cancer: Regulatory and therapeutic implications

The tumor suppressor role and ceRNA network of miR-1294 in cancer

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