Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets

Aouiche, Chaima; Chen, Bolin; Shang, Xuequn

doi:10.1186/s12859-019-2740-6

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…Genes with high expression in advanced and metastatic cancer are associated with poor patient prognosis. 32 Therefore, we compared the gene expression between nonmetastatic stage II CRC and metastatic stage III CRC. RABGAP1L , MYH9 , and DRD4 were significantly more highly expressed in stage III CRC than in stage II CRC (Figure 1E ).…”

Section: Resultsmentioning

confidence: 99%

“…To examine whether the five candidate genes were related to survival and recurrence, we conducted further analysis by exploring the RNA‐sequencing data of CRC patients using a public database, R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl). Genes with high expression in advanced and metastatic cancer are associated with poor patient prognosis 32 . Therefore, we compared the gene expression between nonmetastatic stage II CRC and metastatic stage III CRC.…”

Section: Resultsmentioning

confidence: 99%

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Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients

et al. 2022

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Background Predicting the survival of cancer patients provides prognostic information and therapeutic guidance. However, improved prediction models are needed for use in diagnosis and treatment. Objective This study aimed to identify genomic prognostic biomarkers related to colon cancer (CC) based on computational data and to develop survival prediction models. Methods We performed machine‐learning (ML) analysis to screen pathogenic survival‐related driver genes related to patient prognosis by integrating copy number variation and gene expression data. Moreover, in silico system analysis was performed to clinically assess data from ML analysis, and we identified RABGAP1L , MYH9 , and DRD4 as candidate genes. These three genes and tumor stages were used to generate survival prediction models. Moreover, the genes were validated by experimental and clinical analyses, and the theranostic application of the survival prediction models was assessed. Results RABGAP1L , MYH9 , and DRD4 were identified as survival‐related candidate genes by ML and in silico system analysis. The survival prediction model using the expression of the three genes showed higher predictive performance when applied to predict the prognosis of CC patients. A series of functional analyses revealed that each knockdown of three genes reduced the protumor activity of CC cells. In particular, validation with an independent cohort of CC patients confirmed that the coexpression of MYH9 and DRD4 gene expression reflected poorer clinical outcomes in terms of overall survival and disease‐free survival. Conclusions Our survival prediction approach will contribute to providing information on patients and developing a therapeutic strategy for CC patients.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients

et al. 2022

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“…With regard to cancer, several studies have predicted patient survival and identified biomarkers for predicting cancer type and biological changes based on gene expression 22 – 24 . Aouiche et al 25 used a pathway network to extract stage-specific genes by constructing gene modules. Park et al 26 applied deep learning to stage prediction in gene expression profiles, and Rahimi et al 27 improved the performance of cancer stage prediction and identified gene sets that are commonly related across different cancer cohorts in TCGA.…”

Section: Introductionmentioning

confidence: 99%

Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data

Chung,

Lee

2023

Sci Rep

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The accurate prediction of patients with complex diseases, such as Alzheimer’s disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of clinical data. Deep metric learning has emerged as a promising approach for addressing these challenges by improving data representation. In this study, we propose a joint triplet loss model with a semi-hard constraint (JTSC) to represent data in a small number of samples. JTSC strictly selects semi-hard samples by switching anchors and positive samples during the learning process in triplet embedding and combines a triplet loss function with an angular loss function. Our results indicate that JTSC significantly improves the number of appropriately represented samples during training when applied to the gene expression data of AD and to cancer stage prediction tasks. Furthermore, we demonstrate that using an embedding vector from JTSC as an input to the classifiers for AD and cancer stage prediction significantly improves classification performance by extracting more accurate features. In conclusion, we show that feature embedding through JTSC can aid in classification when there are a small number of samples compared to a larger number of features.

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“…Detecting differentially expressed genes (DEGs) across different experiments conditions is an essential step and sometimes the major goal in the statistical analysis of expression data [ 2 ]. It helps to understand the function of genes when cells respond to different conditions [ 3 ]. In addition, detecting DEGs can be a pre-step for clustering gene expression profiles or testing gene set enrichments [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

A two-way rectification method for identifying differentially expressed genes by maximizing the co-function relationship

2021

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Background The identification of differentially expressed genes (DEGs) is an important task in many biological studies. The currently widely used methods often calculate a score for each gene by estimating the significance level in terms of the differential expression. However, biological experiments often have only three duplications, plus plenty of noises contain in gene expression datasets, which brings a great challenge to statistical analysis methods. Moreover, the abundance of gene expression levels are not evenly distributed. Thus, those low expressed genes are more easily to be detected by fold-change based methods, which may results in high false positives among the DEG list. Since phenotypical changes result from DEGs should be strongly related to several distinct cellular functions, a more robust method should be designed to increase the true positive rate of the functional related DEGs. Results In this study, we propose a two-way rectification method for identifying DEGs by maximizing the co-function relationships between genes and their enriched cellular pathways. An iteration strategy is employed to sequentially narrow down the group of identified DEGs and their associated biological functions. Functional analyses reveal that the identified DEGs are well organized in the form of functional modules, and the enriched pathways are very significant with lower p-value and larger gene count. Conclusions An integrative rectification method was proposed to identify key DEGs and their related functions simultaneously. The experimental validations demonstrate that the method has high interpretability and feasibility. It performs very well in terms of the identification of remarkable functional related genes.

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Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets

Cited by 5 publications

References 43 publications

Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients

Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients

Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data

A two-way rectification method for identifying differentially expressed genes by maximizing the co-function relationship

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