A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection

Yin, Qingyan; Chen, Wangwang; Zhang, Chunxia; Wei, Zhi

doi:10.1038/s41374-022-00801-y

Cited by 28 publications

(29 citation statements)

References 47 publications

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“…The original contributions presented in the study are included in the article/Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20. Further inquiries can be directed to the corresponding authors. The Additional files 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 for this article can be found online.…”

Section: Supplementary Informationmentioning

confidence: 99%

“…In recent years, machine learning and deep learning algorithms have been used to train many models represented by feature gene sets to predict the prognosis of NSCLC patients based on high-throughput gene expression data and survival data, including the short-term efficacy and long-term survival prediction of immunotherapy. However, the prediction effect is uneven and there is no unified measurement standard, so there are limitations in clinical transformation and popularization [ 17 , 18 ]. Therefore, we tried to find differential genes that may affect the response to immunotherapy, construct target genes that have a significant impact on the prognosis of early-stage lung cancer, and then analyze the relationship between the multi-omics changes of these genes and the tumor microenvironment of all stages of LUAD.…”

Section: Introductionmentioning

confidence: 99%

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Genomic and immunogenomic analysis of three prognostic signature genes in LUAD

et al. 2023

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Background Searching for immunotherapy-related markers is an important research content to screen for target populations suitable for immunotherapy. Prognosis-related genes in early stage lung cancer may also affect the tumor immune microenvironment, which in turn affects immunotherapy. Results We analyzed the differential genes affecting lung cancer patients receiving immunotherapy through the Cancer Treatment Response gene signature DataBase (CTR-DB), and set a threshold to obtain a total of 176 differential genes between response and non-response to immunotherapy. Functional enrichment analysis found that these differential genes were mainly involved in immune regulation-related pathways. The early-stage lung adenocarcinoma (LUAD) prognostic model was constructed through the cancer genome atlas (TCGA) database, and three target genes (MMP12, NFE2, HOXC8) were screened to calculate the risk score of early-stage LUAD. The receiver operating characteristic (ROC) curve indicated that the model had good prognostic value, and the validation set (GSE50081, GSE11969 and GSE42127) from the gene expression omnibus (GEO) analysis indicated that the model had good stability, and the risk score was correlated with immune infiltrations to varying degrees. Multi-type survival analysis and immune infiltration analysis revealed that the transcriptome, methylation and the copy number variation (CNV) levels of the three genes were correlated with patient prognosis and some tumor microenvironment (TME) components. Drug sensitivity analysis found that the three genes may affect some anti-tumor drugs. The mRNA expression of immune checkpoint-related genes showed significant differences between the high and low group of the three genes, and there may be a mutual regulatory network between immune checkpoint-related genes and target genes. Tumor immune dysfunction and exclusion (TIDE) analysis found that three genes were associated with immunotherapy response and maybe the potential predictors to immunotherapy, consistent with the CTR-DB database analysis. Conclusions From the perspective of data mining, this study suggests that MMP12, NFE2, and HOXC8 may be involved in tumor immune regulation and affect immunotherapy. They are expected to become markers of immunotherapy and are worthy of further experimental research.

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Section: Supplementary Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genomic and immunogenomic analysis of three prognostic signature genes in LUAD

et al. 2023

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“…4) Feature selector: The high dimensionality of the dataset is one of the main issues with machine learning [24]. The weighting features reduce processing time and redundant data, boosting algorithm performance because the analysis of several features uses a lot of memory and results in overfitting [20]. The method of eliminating all unnecessary and irrelevant genes while also identifying the most informative genes [28].…”

Section: ) Feature Mappingmentioning

confidence: 99%

Feedforward Deep Learning Optimizer-based RNA-Seq Women's cancers Detection with a hybrid Classification Models for Biomarker Discovery

Ead¹,

Abdelazim²,

Nasr³

2022

IJACSA

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Women's cancers, signified by breast adenocarcinoma and non-small-cell lung cancers, are a significant threat to women's health. Across the globe, the leading cause of death for women is a group of tumors referred to as "female-oriented cancers". The most recent researches in the classification of molecular tumors is the analysis of women's cancers using RNA-Seq data for precision cancer diagnoses. Furthermore, discovering the different genes' patterns behaviors will lead to predict the cancer-specific biomarkers to early diagnosis and detection of cancer-specific in women. An overfit model will be resulted due to the high-dimensional data of RNA-Seq from a small samples of data. In this work, we propose a filter-based selection approach for a deep learning-based classification model. In addition, hybrid classification models have been proposed to be compared with the new modified deep learning model. The Experiments' analysis showed that the proposed filter-based selection approach for a deep learningbased classification model performed better than the other hybrid models in terms of performance evaluation metrics, with an accuracy of 96.7% for RNA-Seq breast adenocarcinoma data and 95.5% for RNA-Seq non-small-cell lung cancer data.

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“…Hao et al [ 27 ] introduced gene pathways as prior information to build a locally connected NN. Yin et al [ 28 ] combined a convolutional NN with prognosis-related cascaded Wx feature selection to achieve survival prediction.…”

Section: Introductionmentioning

confidence: 99%

Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach

Fan

Zhang

2022

Genes

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Analysis of data with a censored survival response and high-dimensional omics measurements is now common. Most of the existing analyses are based on specific (semi)parametric models, in particular the Cox model. Such analyses may be limited by not having sufficient flexibility, for example, in accommodating nonlinearity. For categorical and continuous responses, neural networks (NNs) have provided a highly competitive alternative. Comparatively, NNs for censored survival data remain limited. Omics measurements are usually high-dimensional, and only a small subset is expected to be survival-associated. As such, regularized estimation and selection are needed. In the existing NN studies, this is usually achieved via penalization. In this article, we propose adopting the threshold gradient descent regularization (TGDR) technique, which has competitive performance (for example, when compared to penalization) and unique advantages in regression analysis, but has not been adopted with NNs. The TGDR-based NN has a highly sensible formulation and an architecture different from the unregularized and penalization-based ones. Simulations show its satisfactory performance. Its practical effectiveness is further established via the analysis of two cancer omics datasets. Overall, this study can provide a practical and useful new way in the NN paradigm for survival analysis with high-dimensional omics measurements.

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A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection

Cited by 28 publications

References 47 publications

Genomic and immunogenomic analysis of three prognostic signature genes in LUAD

Genomic and immunogenomic analysis of three prognostic signature genes in LUAD

Feedforward Deep Learning Optimizer-based RNA-Seq Women's cancers Detection with a hybrid Classification Models for Biomarker Discovery

Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach

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