Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

MotieGhader, Habib; Tabrizi-Nezhadi, Parinaz; Paskeh, Mahshid Deldar Abad; Baradaran, Behzad; Mokhtarzadeh, Ahad; Hashemi, Mehrdad; Lanjanian, Hossein; Jazayeri, Seyed Mehdi; Maleki, Masoud; Khodadadi, Ehsan; Nematzadeh, Sajjad; Kiani, Farzad; Maghsoudloo, Mazaher; Masoudi‐Nejad, Ali

doi:10.1038/s41598-022-13719-8

Cited by 15 publications

(7 citation statements)

References 115 publications

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“…Upon analyzing biomarkers, the investigation of protein-protein interactions sheds light on cellular pathways integral to disease pathophysiology, guiding the creation of targeted therapies [ 41 ]. Moreover, comprehending drug-gene interactions becomes essential in formulating personalized medicine approaches, especially in the context of BC treatment [ 42 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Rakhshaninejad,

Fathian,

Shirkoohi

et al. 2024

BMC Bioinformatics

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Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

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Section: Literature Reviewmentioning

confidence: 99%

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Rakhshaninejad,

Fathian,

Shirkoohi

et al. 2024

BMC Bioinformatics

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“…The experiment looks at gene co‐expression and co‐occurrence networks. It is different from earlier research on NSCLC drug repositioning, which used a gene co‐expression network 15 . In addition, the co‐occurrence network considers the differential expression of genes between cancer cells and their adjacent normal cells.…”

Section: Introductionmentioning

confidence: 98%

“…It is different from earlier research on NSCLC drug repositioning, which used a gene co‐expression network. 15 In addition, the co‐occurrence network considers the differential expression of genes between cancer cells and their adjacent normal cells. This study aims to evaluate candidate drugs that are effective for NSCLC at the clinical level using a systems biology and network analysis approach.…”

Section: Introductionmentioning

confidence: 99%

Network‐based identification of key proteins and repositioning of drugs for non‐small cell lung cancer

Adeyemo,

Ashimiyu‐Abdusalam,

Adewunmi

et al. 2024

Cancer Reports

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BackgroundNSCLC is a lethal cancer that is highly prevalent and accounts for 85% of cases of lung cancer. Conventional cancer treatments, such as chemotherapy and radiation, frequently exhibit limited efficacy and notable adverse reactions. Therefore, a drug repurposing method is proposed for effective NSCLC treatment.AimsThis study aims to evaluate candidate drugs that are effective for NSCLC at the clinical level using a systems biology and network analysis approach.MethodsDifferentially expressed genes in transcriptomics data were identified using the systems biology and network analysis approaches. A network of gene co‐expression was developed with the aim of detecting two modules of gene co‐expression. Following that, the Drug–Gene Interaction Database was used to find possible drugs that target important genes within two gene co‐expression modules linked to non‐small cell lung cancer (NSCLC). The use of Cytoscape facilitated the creation of a drug–gene interaction network. Finally, gene set enrichment analysis was done to validate candidate drugs.ResultsUnlike previous research on repositioning drugs for NSCLC, which uses a gene co‐expression network, this project is the first to research both gene co‐expression and co‐occurrence networks. And the co‐occurrence network also accounts for differentially expressed genes in cancer cells and their adjacent normal cells. For effective management of non‐small cell lung cancer (NSCLC), drugs that show higher gene regulation and gene affinity within the drug–gene interaction network are thought to be important. According to the discourse, NSCLC genes have a lot of control over medicines like vincristine, fluorouracil, methotrexate, clotrimazole, etoposide, tamoxifen, sorafenib, doxorubicin, and pazopanib.ConclusionHence, there is a possibility of repurposing these drugs for the treatment of non‐small‐cell lung cancer.

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“…The weighted gene co-expression network analysis (WGCNA), widely employed in network construction analysis, uses hierarchical clustering to identify genes in each module [14][15][16]. However, it applies the eigengene network methodology for summarizing clusters and calculating module membership measures.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and mRNA-miRNA bipartite Network Analysis for Biomarker Development and Drug Repurposing in Colon Cancer

Zavarzadeh,

Abedi,

Abbasi

et al. 2024

Preprint

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Background: Colon cancer (CC) is the second cause of mortality among other non-skin cancers. Despite the progression in screening and diagnostic strategies, further information is still demanding for diagnosing, managing, and treating CC. In order to achieve this goal, computational analysis, including data mining algorithms are essential tools and the most widely used approach for achieving important features of gene expression data combined with clinical assessments which paves the way for diagnosis and treatment at the right time. Methods: This study aimed to identify the most critical genes and candidate drugs that may be helpful for CC treatment and develop novel microRNA biomarkers for CC. The gene expression data from CC tissues, including 98 CC and 98 normal samples, were used to reconstruct gene co-expression networks. Deferentially expressed genes (DEGs) were extracted after preprocessing and normalization. Then, DEGs were used to construct the weighted gene co-expression network (WGCNA), and low-preserved modules were obtained. The experimentally validated mRNA-miRNA interaction information was used to construct three bipartite mRNA-miRNA networks. From each bipartite network, ten hub miRNAs were extracted for further analysis. Finally, a drug-gene interaction network was constructed. Results: The most significant genes were ADH1B, AQP8, CA1, CLCA4, GUCA2B, MMP7, MS4A12, and TMIGD1 obtained from feature selection. The data included novel miRNAs (e.g., hsa-miR-665, hsa-miR-30c-3p, hsa-let-7b-5p, hsa-miR-3689d, hsa-miR-1233-5p, hsa-miR-1910-3p, and finally hsa-miR-6788-5p which was thoroughly novel without any investigation in other cancers) and might be potentially associated with CC. Also, the most important genes were targets for these miRNAs. Eventually, drugs such as Cytarabine, Levodopa, Methyldopa, Tretinoin, Hydralazine, Daunorubicin Hydrochloride, Olanzapine, and Idarubicin were proposed as potential novel drugs for CC treatment. Conclusion: The present study was performed to determine the most significant genes, develop biomarkers, and find novel potential drugs for colon cancer. All potential biomarkers and drugs obtained from this study could be practical after extensive experimental studies for using them in clinical experiments.

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Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

Cited by 15 publications

References 115 publications

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Network‐based identification of key proteins and repositioning of drugs for non‐small cell lung cancer

Feature Selection and mRNA-miRNA bipartite Network Analysis for Biomarker Development and Drug Repurposing in Colon Cancer

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