MiRNA expression profiling in adenocarcinoma and squamous cell lung carcinoma reveals both common and specific deregulated microRNAs

Petkova, V.; Marinova, Dora; Kyurkchiyan, Silva; Stancheva, G.; Mekov, Evgeni; Kachakova, Darina; Slavova, Yanina; Kostadinov, Dimitar; Mitev, Vanio; Kaneva, Radka

doi:10.1097/md.0000000000030027

Cited by 11 publications

(9 citation statements)

References 97 publications

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“…For LUSC patients, Yang et al ( Yang et al, 2010 ) reported the significance of nine miRNAs (miR-30d, miR-185, miR-30a, miR-193a-3p, miR-125a, miR-101, let-7i, miR-126, and miR-15a) by using real-time polymerase chain reaction (qRT-PCR) in their studies. In another study, Petkova et al ( Petkova et al, 2022 ) validated 10 miRNAs (miR-144-3p, miR-4689-3p, miR-7-5p, miR-744-3p, miR-650, miR-375, miR-140-3p, miR-195-5p, miR-95-5p and miR-21-3p) related to LUSC.…”

Section: Discussionmentioning

confidence: 98%

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

et al. 2023

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During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified microRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: https://github.com/malikyousef/miRdisNET.

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Section: Discussionmentioning

confidence: 98%

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

et al. 2023

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“…33 Petkova et al reported the downregulation of several miRNAs in NSCLC tissues, including miR-140-3p. 34 Additionally, miR-140-3p downregulation predicted poor prognosis in lung adenocarcinoma. 19 On the contrary, Vykoukal et al demonstrated miR-140-3p was overexpressed in lung cancer.…”

Section: Discussionmentioning

confidence: 99%

“…Others are upregulated and act as oncogenes in NSCLC 33 . Petkova et al reported the downregulation of several miRNAs in NSCLC tissues, including miR‐140‐3p 34 . Additionally, miR‐140‐3p downregulation predicted poor prognosis in lung adenocarcinoma 19 .…”

Section: Discussionmentioning

confidence: 99%

MicroRNA‐140‐3p inhibits proliferation and promotes apoptosis in non‐small cell lung cancer by targeting MDIG

Yu,

Fan,

Zhao

et al. 2023

Environmental Toxicology

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BackgroundMicroRNAs (miRNAs) are associated with cancer progression. MiR‐140‐3p is a tumor suppressor. Nevertheless, its function in non‐small cell lung cancer (NSCLC) is unclear.MethodsMiR‐140‐3p expression in NSCLC clinical specimens was examined using the TCGA database and real‐time PCR. NSCLC cell proliferation and apoptosis were investigated after the miRNA overexpression. Then, mineral dust‐induced gene (MDIG) levels in NSCLC clinical specimens were monitored by real‐time PCR and western blotting. Bioinformatics predicated the binding of miR‐140‐3p to MDIG, and their relationship was validated by luciferase reporter assay. The miR‐140‐3p/MDIG axis was further validated through rescue experiments. The involvement of STAT3 signaling in the actions of miR‐140‐3p/MDIG axis was investigated.ResultsMiR‐140‐3p was decreased in NSCLC tissues and negatively correlated with MDIG expression. Additionally, it was also lower in high‐grade specimens than in low‐grade ones. MiR‐140‐3p restrained cell proliferation, facilitated apoptosis, and inhibited STAT3 signaling in NSCLC. Interestingly, MDIG was a target of this miRNA. Furthermore, MDIG upregulation abolished miR‐140‐3p's effect on cell proliferation, apoptosis, and STAT3 pathway in NSCLC cells.ConclusionMiR‐140‐3p restrained NSCLC development through the regulation of the STAT3 pathway by targeting MDIG. This axis may be a promising target for NSCLC treatment.

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“…In various studies, it has been also shown that the miR-193 family plays a role in the pathological processes underlying cardiovascular disorders [9]. It has been reported that miR-193b-3p is among the targets of PAPR1 [10]. miR-21-5p is highly expressed in the cardiovascular system.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Role of Molecules in DNA Repair Process in Coronary Artery Patients

Malikova

Karadağ

Durmaz

et al. 2023

Preprint

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Background The role of DNA damage in the progression of coronary artery disease (CAD) is widely recognized. Among the factors that determine the extent of DNA damage, genetic factors may be one of the determining factors in the pathogenesis of CAD. Methods & Results In our research, we investigated the expression levels of BRCA1 and PARP1, which are involved in the DNA repair process, as well as the regulators of gene expression for these molecules, namely miR-21-5p, miR-193b-3p, and miR-484, in lymphocyte samples collected from 55 patients with CAD and 55 healthy controls. The fold changes of BRCA1, PARP1, miR-21-5p, miR-193b-3p and miR-484 expression levels in the patient group, as determined by the 2−ΔΔCT calculation, were found to be 0.353, 0.332, 0.734, 0.876, and 1.231, respectively. In the patient group, a statistically significant negative correlation was observed only between PARP1 and miR-21 (r=-0.66, p=0.0001). ConclusionsThe expression levels in molecules related to the DNA repair systems of CAD patients are clearly related to the pathogenesis of the disease, and considering this situation, measures to be taken would be beneficial.

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MiRNA expression profiling in adenocarcinoma and squamous cell lung carcinoma reveals both common and specific deregulated microRNAs

Cited by 11 publications

References 97 publications

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

MicroRNA‐140‐3p inhibits proliferation and promotes apoptosis in non‐small cell lung cancer by targeting MDIG

Investigation of the Role of Molecules in DNA Repair Process in Coronary Artery Patients

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