A Voxel-Based Radiographic Analysis Reveals the Biological Character of Proneural-Mesenchymal Transition in Glioblastoma

Qi, Tengfei; Meng, Xiangqi; Wang, Zhenyu; Wang, Xinyu; Sun, Nan; Ming, Jianguang; Ren, Lejia; Jiang, Chuanlu; Cai, Jinquan

doi:10.3389/fonc.2021.595259

Cited by 4 publications

(3 citation statements)

References 74 publications

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“…The PMT occurs in gliomas, similar to the EMT process, and is considered to be an important mechanism for promoting metastatic 35 , 47 , 48 . The results of our study confirm that ALKBH5 contributes to formation of the MES phenotype and promotes PMT in GBM, which is consistent with the previous study that ALKBH5 can affect the malignant progression of GBM 49 .…”

Section: Discussionmentioning

confidence: 99%

ALKBH5-PYCR2 Positive Feedback Loop Promotes Proneural-Mesenchymal Transition Via Proline Synthesis In GBM

Li¹,

Meng-ting²,

Pu³

et al. 2023

J. Cancer

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AlkB homolog 5, RNA demethylase (ALKBH5) is abnormally highly expressed in glioblastoma multiforme (GBM) and is negatively correlated with overall survival in GBM patients. In this study, we found a new mechanism that ALKBH5 and pyrroline-5-carboxylate reductase 2 (PYCR2) formed a positive feedback loop involved in proline synthesis in GBM. ALKBH5 promoted PYCR2 expression and PYCR2-mediated proline synthesis; while PYCR2 promoted ALKBH5 expression through the AMPK/mTOR pathway in GBM cells. In addition, ALKBH5 and PYCR2 promoted GBM cell proliferation, migration, and invasion, as well as proneural-mesenchymal transition (PMT). Furthermore, proline rescued AMPK/mTOR activation and PMT after silencing PYCR2 expression. Our findings reveal an ALKBH5-PYCR2 axis linked to proline metabolism, which plays an important role in promoting PMT in GBM cells and may be a promising therapeutic pathway for GBM.

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Section: Discussionmentioning

confidence: 99%

ALKBH5-PYCR2 Positive Feedback Loop Promotes Proneural-Mesenchymal Transition Via Proline Synthesis In GBM

Li¹,

Meng-ting²,

Pu³

et al. 2023

J. Cancer

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“…Glioma patients further have boosted S100A8/9 serum quantities correlated with amplified arginase bustle in serum. S100A8/9 can be expressed by various myloid cells and tumor cells in glioma, where it can promote tumor cell growth and migration [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

Screening the Significant Hub Genes by Comparing Tumor Cells, Normoxic and Hypoxic Glioblastoma Stem-like Cell Lines Using Co-Expression Analysis in Glioblastoma

Güven

Afzal

Kazmi

2022

Genes

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Glioblastoma multiforme (GBM) is categorized by rapid malignant cellular growth in the central nervous system (CNS) tumors. It is one of the most prevailing primary brain tumors, particularly in human male adults. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate is on average 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and counteracting chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE45117 was retrieved and differentially expressed genes (DEGs) were spotted. The co-expression network analysis was employed on DEGs to find the significant modules. The most significant module resulting from co-expression analysis was the turquoise module. The turquoise module related to the tumor cells, hypoxia, normoxic treatments of glioblastoma tumor (GBT), and GSCs were screened. Sixty-one common genes in the turquoise module were selected generated through the co-expression analysis and protein–protein interaction (PPI) network. Moreover, the GO and KEGG pathway enrichment results were studied. Twenty common hub genes were screened by the NetworkAnalyst web instrument constructed on the PPI network through the STRING database. After survival analysis via the Kaplan–Meier (KM) plotter from The Cancer Genome Atlas (TCGA) database, we identified the five most significant hub genes strongly related to the progression of GBM. We further observed these five most significant hub genes also up-regulated in another GBM gene expression dataset. The protein–protein interaction (PPI) network of the turquoise module genes was constructed and a KEGG pathway enrichments study of the turquoise module genes was performed. The VEGF signaling pathway was emphasized because of the strong link with GBM. A gene–disease association network was further constructed to demonstrate the information of the progression of GBM and other related brain neoplasms. All hub genes assessed through this study would be potential markers for the prognosis and diagnosis of GBM.

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“…Spectral, textural, and contextual variables are commonly retrieved from mammography images and explain changes in color quality, spatial distribution of color, tone, and denote information from the surrounding area from the interest region [8]. The feature extraction technique is used to overcome some of the drawbacks of breast thermography, such as (i) failure to diagnose small tumor, (ii) identifying the cause of high temperature other than tumor, and (iii) differences in interpretation by different physicians [20]. As a result, this technique supports extracting the best features from large datasets by selecting and combining variables into features, hence minimizing the amount of data.…”

Section: Feature Extractionmentioning

confidence: 99%

An Automatic Breast Cancer Diagnostic System Based on Mammographic Images Using Convolutional Neural Network Classifier

Jubeen

Rahman

Wahid

et al. 2022

JCBI

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World’s second most occurring cancer is breast cancer. Prediction of disease is one of the most challenging tasks and there are many factors that effect this type of diagnosis like the ability of visual perception. This paper proposed a Convolutional Neural Network (CNN) based proper method for analyzing the earliest signs of breast cancer with the help of mammogram images. The main goal of proposed system is to identify the disease of breast cancer at early stages. Due to this reason, Mammographic image analysis society (MIAS) dataset is used. There are three hundred & twenty two (322) mammograms in the dataset, with 209 images of normal breasts and 133 images of abnormal breasts. While abnormal breasts are further classified as benign (62 images) and malignant (51 images). To implement this system, python library Keras and Tensor Flow libraries are used along with deep learning model CNN. Convolutional Neural Network (CNN) has been shown to be effective in detecting breast cancer in mammography images with 70% accuracy rate, according to promising testing data. The proposed system will enable the radiologist in detecting breast cancer in early stages.

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A Voxel-Based Radiographic Analysis Reveals the Biological Character of Proneural-Mesenchymal Transition in Glioblastoma

Cited by 4 publications

References 74 publications

ALKBH5-PYCR2 Positive Feedback Loop Promotes Proneural-Mesenchymal Transition Via Proline Synthesis In GBM

ALKBH5-PYCR2 Positive Feedback Loop Promotes Proneural-Mesenchymal Transition Via Proline Synthesis In GBM

Screening the Significant Hub Genes by Comparing Tumor Cells, Normoxic and Hypoxic Glioblastoma Stem-like Cell Lines Using Co-Expression Analysis in Glioblastoma

An Automatic Breast Cancer Diagnostic System Based on Mammographic Images Using Convolutional Neural Network Classifier

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