Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

Mosharaf, Md. Parvez; Reza, Md. Selim; Göv, Esra; Mahumud, Rashidul Alam; Mollah, Md. Nurul Haque

doi:10.3390/vaccines10050771

Cited by 8 publications

(4 citation statements)

References 88 publications

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“…Several studies have demonstrated that YY1 can act as a transcriptional activator and repressor, depending on its associated partners and sequences surrounding its binding sites [20,21]. Studies have also observed YY1 expression to be associated with tumor recurrence and tumorigenesis [22][23][24]. However, a study demonstrated that YY1 might have tumor-suppressing properties through the inhibition of c-Myc transforming activity [25].…”

Section: Discussionmentioning

confidence: 99%

Effects of methylation and transcription factor YY1 on ID2 expression in non-small cell lung carcinoma cells

Tseng

2024

Am J Cancer Res

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The inhibitor of DNA-binding 2 (ID2) plays a major role in tumor dedifferentiation in non-small cell lung cancer (NSCLC). Studies have indicated an inverse correlation between ID2 expression and NSCLC cell invasiveness. However, the mechanisms through which ID2 activation is regulated are currently unclear. We overexpressed ID2 in H1299 cells and extensively characterized their cellular behaviors. By employing a serial deletion approach combined with a reporter assay, we pinpointed the basal promoter region of ID2. We also examined the DNA methylation status of the ID2 promoter to elucidate the epigenetic mechanisms driving ID2 regulation. Our results revealed that ID2 overexpression effectively inhibited the migration, invasion, proliferation, and colony formation abilities of H1299 cells. The region from -243 to +202 played a major role in driving the transcriptional activity of ID2. Sequence analysis results indicated that the transcription factor Yin Yang 1 (YY1) might be crucial in the regulation of ID2 expression. The ectopically expressed YY1 activated both the expression levels of ID2 and the transcriptional activity of the ID2 promoter, potentially contributing to its repressive activity on cancer cell growth. Furthermore, site-directed mutagenesis and chromatin immunoprecipitation assays revealed that YY1 may target the -120 and -76 sites of the ID2 promoter, thereby activating its transcriptional activity. The ID2 promoter regions were also fully methylated in CL1-5 cells, and the methylation level was correlated with the expression levels of the ID2 promoter. Moreover, the YY1-induced suppression of colony formation was counteracted by ID2 knockdown, which suggests that YY1 represses cell colony growth through the regulation of ID2. Our results indicate that YY1 plays a role in transactivating ID2 expression and might also contribute to the repression of colony growth through the regulation of ID2.

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Section: Discussionmentioning

confidence: 99%

Effects of methylation and transcription factor YY1 on ID2 expression in non-small cell lung carcinoma cells

Tseng

2024

Am J Cancer Res

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“…High miR-335-5p expression promotes bone formation and regeneration ( Zhang et al, 2017 ) and angiogenesis ( Walz et al, 2019 ) and regulates cancer progression by targeting MAPK10 ( Gao et al, 2021 ) or NLRP1/7 ( Lin W. et al, 2021 ). Furthermore, a FOXC1 and miR-335-5p network was presented in non-small-cell lung cancer ( Mosharaf et al, 2022 ), type 2 diabetes ( Rahman et al, 2020 ), and vascular dementia ( Shu et al, 2022 ). The dermis of keloid and SSc is known to be made up of newly formed vessels ( Trojanowska, 2010 ; Ogawa, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Identification and characterization of bone/cartilage-associated signatures in common fibrotic skin diseases

Jin

Chen

et al. 2023

Front. Genet.

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Background: Fibrotic skin diseases are characterized by excessive accumulation of the extracellular matrix (ECM) and activation of fibroblasts, leading to a global healthcare burden. However, effective treatments of fibrotic skin diseases remain limited, and their pathological mechanisms require further investigation. This study aims to investigate the common biomarkers and therapeutic targets in two major fibrotic skin diseases, namely, keloid and systemic sclerosis (SSc), by bioinformatics analysis.Methods: The keloid (GSE92566) and SSc (GSE95065) datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then constructed a protein–protein interaction (PPI) network for the identification of hub genes. We explored the possibility of further functional enrichment analysis of hub genes on the Metascape, GeneMANIA, and TissueNexus platforms. Transcription factor (TF)–hub gene and miRNA–hub gene networks were established using NetworkAnalyst. We fixed GSE90051 and GSE76855 as the external validation datasets. Student’s t-test and receiver operating characteristic (ROC) curve were used for candidate hub gene validation. Hub gene expression was assessed in vitro by quantitative real-time PCR.Results: A total of 157 overlapping DEGs (ODEGs) were retrieved from the GSE92566 and GSE95065 datasets, and five hub genes (COL11A1, COL5A2, ASPN, COL10A1, and COMP) were identified and validated. Functional studies revealed that hub genes were predominantly enriched in bone/cartilage-related and collagen-related processes. FOXC1 and miR-335-5p were predicted to be master regulators at both transcriptional and post‐transcriptional levels.Conclusion: COL11A1, COL5A2, ASPN, COL10A1, and COMP may help understand the pathological mechanism of the major fibrotic skin diseases; moreover, FOXC1 and miR-355-5p could build a regulatory network in keloid and SSc.

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“…Finally, we recommended our proposed BC-causing five upregulated (COL11A1, COL10A1, CD24, PLK1, UBE2C) and three down-regulated (PDK4, CD36, ACACB) HubGsguided top-ranked six ligands/molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, TG.02) as the candidate drug molecules by molecular docking analysis. It should be mentioned here that both upregulated and down-regulated HubGs were used as drug targets in different studies [166][167][168][169]. Upregulated HubGs-guided drugs inhibit the upregulation of HubGs, while down-regulated HubGs-guided drugs activate the downregulation of HubGs [170].…”

Section: Discussionmentioning

confidence: 99%

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Tuly,

Hossen,

Islam

et al. 2023

Medicina

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Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study’s findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

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Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

Cited by 8 publications

References 88 publications

Effects of methylation and transcription factor YY1 on ID2 expression in non-small cell lung carcinoma cells

Effects of methylation and transcription factor YY1 on ID2 expression in non-small cell lung carcinoma cells

Identification and characterization of bone/cartilage-associated signatures in common fibrotic skin diseases

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

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