A Network-Based Approach Exploiting Transcriptomics and Interactomics Data for Predicting Drug Repurposing Solutions Across Human Cancers
Simple Summary
Abstract
1. Introduction
- Text-mining-based approaches. They involve extracting new knowledge from existing scientific literature [9].
- Semantic-based approaches. They integrate multiple sources (chemical, pharmacological, biological, biomedical, etc.) to build a semantic network, from which it is possible to predict new drug–disease relationships. While these methods have the advantage of making use of a massive number of databases, it is still a challenge to integrate all of these different sources [9].
- Transcriptional signature-based approaches. Transcriptomics data provide us with a list of over- and under-expressed genes in a biological system, treated with a drug or affected by a disease/condition. Comparing the transcriptional signatures can give us new insights on relationships between drugs and diseases [10].
- Molecular Docking. Here, we try to score the interaction between a small-molecule ligand and a protein to evaluate if a drug can bind to a new target [10].
- Network-based approaches. These methods organize the relationships between biological entities in the form of a network. Nodes can be many kinds of biological entities, like drugs, diseases, or proteins, while edges represent a direct or indirect interaction between two nodes [10]. A well-established algorithm falling in this category is SAveRUNNER [11], broadly applied in several biological contexts from viral infections to complex human diseases [12,13,14,15,16].
2. Materials and Methods
2.1. Data Retrieval
2.1.1. Gene Expression Data
2.1.2. Drug–Target Interactions
2.1.3. The SIGnaling Network Open Resource
2.2. Preprocessing
2.2.1. Differentially Expressed Genes Analysis
2.2.2. Disease Genes
2.3. PALADIN—Functional Enrichment Analysis
2.4. PALADIN—Pathways Analyzer for Off-LAbel inDIcatioNs
3. Results and Discussion
3.1. PALADIN Analysis
3.2. SAveRUNNER Analysis and Comparison
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
PALADIN | Pathways Analyzer for off-LAbel inDIcatioNs |
DR | Drug Repurposing |
TCGA | The Cancer Genome Atlas |
HGCN | HUGO Gene Nomenclature |
UCEC | Uterine Corpus Endometrial Carcinoma |
THYM | Thymoma |
THCA | Thyroid carcinoma |
STAD | Stomach Adenocarcinoma |
SKCM | Skin Cutaneous Melanoma |
READ | Rectum Adenocarcinoma |
PRAD | Prostate Adenocarcinoma |
PAAD | Pancreatic Adenocarcinoma |
LUSC | Lung squamous cell carcinoma |
LUAD | Lung Adenocarcinoma |
LIHC | Liver Hepatocellular Carcinoma |
KIRP | Kidney renal papillary cell carcinoma |
KIRC | Kidney renal clear cell carcinoma |
KICH | Kidney chromophobe |
ESCA | Esophageal carcinoma |
COAD | Colon adenocarcinoma |
BRCA | Breast cancer |
BLCA | Bladder cancer |
SAveRUNNER | Searching off-lAbel dRUg aNd NEtwoRk |
SIGNOR | SIGnaling Network Open Resource |
DEGs | Differentially Expressed Genes |
SANTA | Spatial Analysis of Network Associations |
iml | Interpretable Machine Learning |
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Normal Samples | Tumor Samples | Cancer Acronym | Cancer Type |
---|---|---|---|
101 | 180 | UCEC | Uterine corpus |
Endometrial carcinoma | |||
339 | 119 | THYM | Thymoma |
338 | 512 | THCA | Thyroid carcinoma |
211 | 413 | STAD | Stomach adenocarcinoma |
557 | 468 | SKCM | Skin cutaneous melanoma |
317 | 92 | READ | Rectum adenocarcinoma |
151 | 495 | PRAD | Prostate adenocarcinoma |
171 | 179 | PAAD | Pancreatic adenocarcinoma |
338 | 498 | LUSC | Lung squamous |
Cell carcinoma | |||
347 | 513 | LUAD | Lung adenocarcinoma |
160 | 369 | LIHC | Liver hepatocellular |
Carcinoma | |||
60 | 288 | KIRP | Kidney renal papillary cell carcinoma |
100 | 530 | KIRC | Kidney renal clear cell carcinoma |
53 | 66 | KICH | Kidney chromophobe |
286 | 182 | ESCA | Esophageal carcinoma |
348 | 288 | COAD | Colon adenocarcinoma |
291 | 1099 | BRCA | Breast cancer |
28 | 407 | BLCA | Bladder cancer |
4196 | 6698 | TOTAL |
Disease | Kappa log2fc | Kappa Sign Genes | Accuracy log2fc | Accuracy Sign Genes | AUC log2fc | AUC Sign Genes |
---|---|---|---|---|---|---|
KICH | 0.8799 | 0.966 | 0.9403 | 0.9832 | 0.9841 | 0.9994 |
BLCA | 0.1423 | 0.3485 | 0.9352 | 0.9386 | 0.9471 | 0.8906 |
BRCA | 0.9428 | 0.9539 | 0.9809 | 0.9847 | 0.9974 | 0.9987 |
COAD | 0.9169 | 0.974 | 0.9588 | 0.9871 | 0.992 | 0.999 |
ESCA | 0.9277 | 0.9005 | 0.9654 | 0.9521 | 0.983 | 0.9913 |
KIRC | 0.7563 | 0.9253 | 0.9251 | 0.9803 | 0.9783 | 0.9923 |
KIRP | 0.7814 | 0.9208 | 0.9282 | 0.9776 | 0.9854 | 0.997 |
LIHC | 0.8763 | 0.8351 | 0.9474 | 0.931 | 0.989 | 0.9756 |
LUAD | 0.931 | 0.9623 | 0.967 | 0.9819 | 0.9969 | 0.9979 |
LUSC | 0.9501 | 0.9675 | 0.9761 | 0.9843 | 0.997 | 0.996 |
PAAD | 0.8634 | 0.9514 | 0.9317 | 0.9757 | 0.9861 | 0.989 |
PRAD | 0.6914 | 0.8429 | 0.8872 | 0.9446 | 0.9046 | 0.9781 |
READ | 0.7492 | 0.8917 | 0.8985 | 0.9601 | 0.9888 | 0.9971 |
SKCM | 0.9418 | 0.9736 | 0.971 | 0.9869 | 0.9947 | 0.997 |
STAD | 0.9255 | 0.9255 | 0.9665 | 0.967 | 0.9943 | 0.9939 |
THCA | 0.9315 | 0.8763 | 0.9673 | 0.9408 | 0.9937 | 0.9901 |
THYM | 0.9887 | 0.9543 | 0.9956 | 0.9825 | 0.999 | 0.9976 |
UCEC | 0.9572 | 0.9259 | 0.9804 | 0.9655 | 0.9981 | 0.9984 |
Drug TTD ID | Distance to READ Module | FDA Indication | p-Value | Drug Names |
---|---|---|---|---|
D0U3ED | -5,660,390 | Diabetic neuropathy | 0.030376 | E-2007 |
D01EVT | -1,436,887 | NA | 0.03374 | Ethinyl-pyrazole derivative 1 |
D02RRE | -1,436,887 | NA | 0.03374 | Pyrazole derivative 78 |
D03KYL | -1,436,887 | NA | 0.03374 | Quinoline derivative 4 |
D03PRA | -1,436,887 | NA | 0.03374 | Heteroaryl-pyrazole derivative 2 |
D04LRT | -1,436,887 | NA | 0.03374 | Quinoline derivative 9 |
D04VAE | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-26 |
D05CBS | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-37 |
D06NZY | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-44 |
D08JPW | -1,436,887 | NA | 0.03374 | N-substituted pyrazole derivative 1 |
D08PHH | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-45 |
D09JZA | -1,436,887 | NA | 0.03374 | Quinoline derivative 8 |
D09NRH | -1,436,887 | NA | 0.03374 | Quinoline derivative 5 |
D09OQZ | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-49 |
D09WXH | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-25 |
D0B6DN | -1,436,887 | NA | 0.03374 | Ethinyl-pyrazole derivative 3 |
D0BP6R | -1,436,887 | NA | 0.03374 | Ethinyl-pyrazole derivative 2 |
D0C2ZJ | -1,436,887 | NA | 0.03374 | Tetra-hydro-imidazo[1,5-d][1,4]oxazepin-3-yl derivative 3 |
D0CZ6T | -1,436,887 | NA | 0.03374 | Tetra-hydro-imidazo[1,5-d][1,4]oxazepin-3-yl derivative 2 |
D0E5QO | -1,436,887 | NA | 0.03374 | Quinoline derivative 7 |
D0H2BU | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-53 |
D0H2LD | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-43 |
D0HD8F | -1,436,887 | NA | 0.03374 | Pyrazole derivative 76 |
D0HG1W | -1,436,887 | NA | 0.03374 | 2-(substituted ethynyl)quinoline derivative 4 |
D0I6LO | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-47 |
D0IL4Z | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-38 |
D0IY8T | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-46 |
D0K0ZX | -1,436,887 | NA | 0.03374 | Heteroaryl-pyrazole derivative 3 |
D0K1RU | -1,436,887 | NA | 0.03374 | Quinoline derivative 3 |
D0KZ6Q | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-40 |
D0LI2E | -1,436,887 | NA | 0.03374 | Tetra-hydro-imidazo[1,5-d][1,4]oxazepin-3-yl derivative 1 |
D0MB0V | -1,436,887 | NA | 0.03374 | 2-(substituted ethynyl)quinoline derivative 2 |
D0N0FA | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-51 |
D0N1CC | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-42 |
D0N9IA | -1,436,887 | NA | 0.03374 | Tetra-hydro-imidazo[1,5-d][1,4]oxazepin-3-yl derivative 4 |
D0NE0W | -1,436,887 | NA | 0.03374 | Tetra-hydro-imidazo[1,5-d][1,4]oxazepin-3-yl derivative 6 |
D0O6QC | -1,436,887 | NA | 0.03374 | Heteroaryl-pyrazole derivative 1 |
D0O9TL | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-52 |
D0OT4O | -1,436,887 | NA | 0.03374 | Quinoline derivative 6 |
D0P7DA | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-10 |
D0PJ8X | -1,436,887 | NA | 0.03374 | 2-(substituted ethynyl)quinoline derivative 1 |
D0Q2FC | -1,436,887 | NA | 0.03374 | Tetra-hydro-imidazo[1,5-d][1,4]oxazepin-3-yl derivative 5 |
D0QM1R | -1,436,887 | NA | 0.03374 | Pyrazole derivative 77 |
D0RX0M | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-39 |
D0S5SK | -1,436,887 | NA | 0.03374 | Pyrazole derivative 79 |
D0T3DG | -1,436,887 | NA | 0.03374 | BCI-632 |
D0V0BC | -1,436,887 | NA | 0.03374 | N-substituted pyrazole derivative 2 |
D0WP5S | -1,436,887 | NA | 0.03374 | N-substituted pyrazole derivative 3 |
D0XQ4T | -1,436,887 | NA | 0.03374 | 2-(substituted ethynyl)quinoline derivative 3 |
D0Y5DN | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-41 |
D0YZ7D | -1,436,887 | NA | 0.03374 | PMID25435285-Compound-50 |
D00GEG | -1,415,097 | NA | 0.035841 | Ralfinamide |
D01NLB | -1,415,097 | Pain | 0.035841 | Ziconotide |
D07VDZ | -1,415,097 | Epilepsy; alcohol use disorders | 0.035841 | Topiramate |
D08EOD | -1,415,097 | Epileptic seizures | 0.035841 | Methsuximide |
D09JBP | -1,415,097 | Paramethadione syndrome; seizures | 0.035841 | Paramethadione |
D0A3MJ | -1,415,097 | NA | 0.035841 | XEN007 |
D0CQ0Z | -1,415,097 | Schizophrenia | 0.035841 | Penfluridol |
D0I9HF | -1,415,097 | Capillary fragility | 0.035841 | Hesperidin |
D0M8AB | -1,415,097 | Dietary shortage | 0.035841 | Glycine |
D0N3SR | -1,415,097 | NA | 0.035841 | Cilnidipine |
D0Q4XQ | -1,415,097 | Epilepsy | 0.035841 | Ethosuximide |
D0R0FE | -1,415,097 | Hypertension; angina | 0.035841 | Verapamil |
D0U4VT | -1,415,097 | Epileptic conditions; pancreatic cancer | 0.035841 | Trimethadione |
D0U7GP | -1,415,097 | NA | 0.035841 | Rauwolfia Serpentina root |
D0W8XT | -1,415,097 | Granted orphan drug status by FDA for ovarian cancer, pancreatic cancer, and glioblastoma multiforme | 0.035841 | Mibefradil |
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Galimi, A.; Fiscon, G. A Network-Based Approach Exploiting Transcriptomics and Interactomics Data for Predicting Drug Repurposing Solutions Across Human Cancers. Cancers 2025, 17, 1144. https://doi.org/10.3390/cancers17071144
Galimi A, Fiscon G. A Network-Based Approach Exploiting Transcriptomics and Interactomics Data for Predicting Drug Repurposing Solutions Across Human Cancers. Cancers. 2025; 17(7):1144. https://doi.org/10.3390/cancers17071144
Chicago/Turabian StyleGalimi, Alessio, and Giulia Fiscon. 2025. "A Network-Based Approach Exploiting Transcriptomics and Interactomics Data for Predicting Drug Repurposing Solutions Across Human Cancers" Cancers 17, no. 7: 1144. https://doi.org/10.3390/cancers17071144
APA StyleGalimi, A., & Fiscon, G. (2025). A Network-Based Approach Exploiting Transcriptomics and Interactomics Data for Predicting Drug Repurposing Solutions Across Human Cancers. Cancers, 17(7), 1144. https://doi.org/10.3390/cancers17071144