Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms

Wang, Deling; Li, Jiarui; Zhang, Yuhang; Lei, Chen; Huang, Tao; Cai, Yu-Dong

doi:10.3390/genes9030155

Cited by 64 publications

(43 citation statements)

References 81 publications

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“…Since the tumor microenvironment of nude mice and patients varies, studying the expression differences of GSCs between xenografts and the corresponding original tumors has an important role in preclinical research of gliomas (47,48), for example, this may be the reason why some drugs have notable antitumor effects on animal models (49,50), but did not exhibit a desirable response in clinical trials (46,51), which will be one of the future directions of our lab. The lack of comparison between GSC providers is also one of the limitations of the present study.…”

Section: Discussionmentioning

confidence: 99%

Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor

et al. 2020

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Patient-derived orthotopic glioma xenograft models are important platforms used for pre-clinical research of glioma. In the present study, the diagnostic ability of magnetic resonance imaging (MRI) was examined with regard to the identification of biomarkers obtained from patient-derived glioma xenografts and human tumors. Conventional MRI, diffusion weighted imaging and dynamic contrast-enhanced (DCE)-MRI were used to analyze seven pairs of high grade gliomas with their corresponding xenografts obtained from non-obese diabetic-severe-combined immunodeficiency nude mice. Tumor samples were collected for transcriptome sequencing and histopathological staining, and differentially expressed genes were screened between the original tumors and the corresponding xenografts. Gene Ontology (GO) analysis was performed to predict the functions of these genes. In 6 cases of xenografts with diffuse growth, the degree of enhancement was significantly lower compared with the original tumors. Histopathological staining indicated that the microvascular area and microvascular diameter of the xenografts were significantly lower compared with the original tumors (P=0.009 and P=0.007, respectively). In one case, there was evidence of nodular tumor growth in the mouse. Both MRI and histopathological staining showed a clear demarcation between the transplanted tumors and the normal brain tissues. The relative apparent diffusion coefficient values of the 7 cases examined were significantly higher compared with the corresponding original tumors (P=0.001) and transfer coefficient values derived from DCE-MRI of the tumor area was significantly lower compared with the original tumors (P=0.016). GO analysis indicated that the expression levels of extracellular matrix-associated genes, angiogenesis-associated genes and immune function-associated genes in the original tumors were higher compared with the corresponding xenografts. In conclusion, the data demonstrated that the MRI features of patient-derived xenograft glioma models in mice were different compared with those of the original patient tumors. Differential gene expression may underlie the differences noted in the MRI features between original tumors and corresponding xenografts. The results of the present study highlight the precautions that should be taken when extrapolating data from patient-derived xenograft studies, and their applicability to humans.

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Section: Discussionmentioning

confidence: 99%

Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor

et al. 2020

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“…Notably,

consists the first i features in F . Then, for each FS i , all RNAs mentioned in Section 4.1 were encoded by features in FS i , and a classification algorithm was performed on this dataset, evaluated by a 10-fold cross-validation [ 81 , 82 , 83 , 84 , 85 ]. After all feature subsets had been tested, the feature subset that can yield the best performance was selected.…”

Section: Methodsmentioning

confidence: 99%

“…RF integrates these results by using majority voting. RF is deemed to be an effective classification algorithm and has been adopted to tackle different biological problems to date [ 85 , 88 , 89 , 90 , 91 , 92 ].…”

Section: Methodsmentioning

confidence: 99%

Tissue Expression Difference between mRNAs and lncRNAs

Lei

Zhang

Pan

et al. 2018

IJMS

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Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon’s entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups.

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“…The prediction abilities of the constructed classification models were evaluated by a 10-fold cross-validation (10-CV) [ 16 , 28 , 29 , 30 ], which yields similar results to the stricter test called the Jackknife cross-validation (J-CV) [ 31 , 32 ] but saves significant computational resources.…”

Section: Methodsmentioning

confidence: 99%

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

Lei

Zhang

et al. 2018

Genes

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Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function.

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Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms

Cited by 64 publications

References 81 publications

Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor

Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor

Tissue Expression Difference between mRNAs and lncRNAs

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

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