Classification of retinoblastoma-1 gene mutation with machine learning-based models in bladder cancer

İnce, Okan; Yıldız, Hülya; Kisbet, Tanju; Ertürk, Şükrü Mehmet; Önder, Hakan

doi:10.1016/j.heliyon.2022.e09311

Cited by 7 publications

(3 citation statements)

References 35 publications

(35 reference statements)

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“…A wrapper-based sequencing feature extraction approach and Pearson's correlation analysis were used for feature selection. Models were created using XGBoost, random forest (RF), and KNN [ 6 ]. Priya proposed a CNN-based retinoblastoma detection methods with some segmentation techniques of image processing and calculates the regression of tumors using convex polygon and convex area with the accuracy of 87% [ 7 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A Multi‐Thresholding‐Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models

Kumar

Suganthi

Valarmathi³

et al. 2023

BioMed Research International

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Cancer is one of the vital diseases which lead to the uncontrollable growth of the cell, and it affects the body tissue. A type of cancer that affects the children below five years and adults in a rare case is called retinoblastoma. It affects the retina in the eye and the surrounding region of eye like the eyelid, and sometimes, it leads to vision loss if it is not diagnosed at the early stage. MRI and CT are widely used scanning procedures to identify the cancerous region in the eye. Current screening methods for cancer region identification needs the clinicians’ support to spot the affected regions. Modern healthcare systems develop an easy way to diagnose the disease. Discriminative architectures in deep learning can be viewed as supervised deep learning algorithms which use classification/regression techniques to predict the output. A convolutional neural network (CNN) is a part of the discriminative architecture which helps to process both image and text data. This work suggests the CNN-based classifier which classifies the tumor and nontumor regions in retinoblastoma. The tumor-like region (TLR) in retinoblastoma is identified using the automated thresholding method. After that, ResNet and AlexNet algorithms are used to classify the cancerous region along with classifiers. In addition, the comparison of discriminative algorithm along with its variants is experimented to produce the better image analysis method without the intervention of clinicians. The experimental study reveals that ResNet50 and AlexNet yield better results compared to other learning modules.

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Section: Related Workmentioning

confidence: 99%

[Retracted] A Multi‐Thresholding‐Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models

Kumar

Suganthi

Valarmathi³

et al. 2023

BioMed Research International

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“…İnce O etc. applied radiomics features to predict retinoblastoma-1 mutation status in bladder cancer; the model yielded an accuracy of 84% ( 26 ). Cui Y etc.…”

Section: Discussionmentioning

confidence: 99%

Construction and prognostic value of enhanced CT image omics model for noninvasive prediction of HRG in bladder cancer based on logistic regression and support vector machine algorithm

Luo²,

Liu³

et al. 2023

Front. Oncol.

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BackgroundUrothelial Carcinoma of the bladder (BLCA) is the most prevalent cancer of the urinary system. In cancer patients, HRG fusion is linked to a poor prognosis. The prediction of HRG expression by imaging omics in BLCA has not yet been fully investigated.MethodsHRG expression in BLCA and healthy adjoining tissues was primarily identified utilizing data sourced from The Cancer Genome Atlas (TCGA). Using Kaplan–Meier survival curves and Landmark analysis, the relationship between HRG expression, clinicopathological parameters, and overall survival (OS) was investigated. Additionally, gene set variation analysis (GSVA) was conducted and CIBERSORTx was used to investigate the relationship between HRG expression and immune cell infiltration. The Cancer Imaging Archive (TCIA) provided CT images that were used for prognostic analysis, radiomic feature extraction, and construction of the model, respectively. The HRG expression levels were predicted using the constructed and evaluated LR and SMV models.ResultsHRG expression was shown to be substantially lower in BLCA tumors as opposed to that observed in normal tissues (p < 0.05). HRG expression had a close positive relationship with Eosinophils and a close negative relationship with B cells naive. The findings of the Landmark analysis illustrated that higher HRG was associated with improved patient survival at an early stage (P=0.048). The predictive performance of the two models, based on logistic regression analysis and support vector machine, was outstanding in the training and validation sets, yielding AUCs of 0.722 and 0.708, respectively, in the SVM model, and 0.727 and 0.662, respectively, in the LR.The models have good predictive efficiency.ConclusionHRG expression levels can have a significant impact on BLCA patients’ prognoses. The radiomic characteristics can successfully predict the pre-surgical HRG expression levels, based on CT- Image omics.

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“…RB1, also recognized as the "retinoblastoma 1" gene, is a crucial tumor suppressor gene encoding the Retinoblastoma protein (RB protein) that plays an essential role in maintaining normal cell growth and controlling the cell cycle [20,21]. In certain cancers, the RB1 gene may undergo regulatory disruptions, often involving direct interaction with DNMT1 [22,23].…”

Section: Introductionmentioning

confidence: 99%

Regulation of the RB1 Gene through DNMT1 by SGI-1027 and its Impact on the Growth and Metastasis of Gastric Cancer Cells

Gu,

Huang,

et al. 2024

Discovery Medicine

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Background: SGI-1027 is a recognized inhibitor of DNA methyltransferase 1 (DNMT1), and earlier investigations have indicated an inverse correlation between dysregulated DNMT1 expression in gastric cancer (GC) and retinoblastoma 1 (RB1) gene expression. Despite this knowledge, the precise mechanisms underlying the action of SGI-1027 in GC cells remain inadequately comprehended. The primary objective of this study is to elucidate the impact of SGI-1027 on the behavior of GC cells, encompassing aspects such as growth and metastatic potential, by intervening in DNMT1, thereby influencing the regulation of RB1 gene expression. Method: The acquisition of the normal gastric mucosal cell line GES-1 and the human gastric cancer cell line MKN45 was followed by employing Western blot (WB) and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) techniques to evaluate the expression levels of RB1 and DNMT1 in these two cell lines. Subsequently, the MKN45 cell line was cultured in medium containing varying concentrations of SGI-1027, and the impact of SGI-1027 on the regulation of RB1 and DNMT1 in GC cells was reassessed using WB and qRT-PCR techniques. To scrutinize the effect of SGI-1027 on GC cells, we utilized the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H tetrazolium bromide (MTT) assay to determine cell proliferation and performed Transwell experiments to assess cell migration and invasion capabilities. Throughout this process, we also employed WB to assess the levels of cell cycle-associated proteins (Cyclin D1, Cyclin E1, and Cyclin B1) and proteins related to apoptosis (BCL-2 associated protein X apoptosis regulator (BAX) and B-cell lymphoma 2 apoptosis regulator (BCL-2)). Furthermore, we injected the MKN45 cell line and MKN45 cell line cultured with the optimal concentration of SGI-1027 for 5 days and 10 days into mice subcutaneously and through the tail vein, dividing them into the Model group, Model+SGI-1027 5d group, and Model+SGI-1027 10d group. We monitored changes in tumor size and volume in mice, and tumor tissues as well as lung tissues were collected for hematoxylin and eosin (HE) staining. Finally, DNMT1 expression levels in GC tissues were detected using both WB and immunohistochemistry (IHC) techniques. Additionally, RB1 expression levels in GC tissues were assessed using WB. Result: In contrast to GES-1 cells, MKN45 cells displayed a distinctive profile characterized by increased DNMT1 expression and decreased RB1 expression (p < 0.05). However, upon the introduction of SGI-1027, a notable decrease in DNMT1 levels within GC cells was observed, concomitant with an elevation in RB1 gene expression, with 25 µmol/L SGI-1027 identified as the optimal concentration (p < 0.05). Functional assays demonstrated that SGI-1027-treated GC cells exhibited pronounced features of inhibited proliferation, migration, and invasion when compared to untreated MKN45 cells (p < 0.05). Moreover, in SGI-1027treated GC cells, the levels of Cyclin D1, Cyclin E1, Cyclin B1, and BCL-2 were significantly reduced,...

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Classification of retinoblastoma-1 gene mutation with machine learning-based models in bladder cancer

Cited by 7 publications

References 35 publications

[Retracted] A Multi‐Thresholding‐Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models

[Retracted] A Multi‐Thresholding‐Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models

Construction and prognostic value of enhanced CT image omics model for noninvasive prediction of HRG in bladder cancer based on logistic regression and support vector machine algorithm

Regulation of the RB1 Gene through DNMT1 by SGI-1027 and its Impact on the Growth and Metastasis of Gastric Cancer Cells

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