Identification of key biomarkers for STAD using filter feature selection approaches

Wang, Yangyang; Wang, Jihan; Hu, Ya; Shangguan, Jingbo; Song, Quansheng; Xu, Jing; Wang, Hanping; Xue, Mengju; Wang, Liping; Zhang, Yuanyuan

doi:10.1038/s41598-022-21760-w

Cited by 4 publications

(1 citation statement)

References 31 publications

(28 reference statements)

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“…Effective feature selection methods help to identify potential molecular biomarkers for further research and to train precise classifiers for different tumour type/subtype classifications or diagnoses [ 23 ]. Studies have demonstrated the application of feature selection in genomic analysis of STAD and COAD based on TCGA and Gene Expression Omnibus (GEO) cohorts [ 24 , 25 ]. In this study, we applied MIFS algorithms and screened the top 20 candidate protein markers in distinguishing ESCA, STAD, and CRC tumour samples.…”

Section: Discussionmentioning

confidence: 99%

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers

2023

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Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

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Section: Discussionmentioning

confidence: 99%

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers

2023

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Classification of high-dimensional imbalanced biomedical data based on spectral clustering SMOTE and marine predators algorithm

Qin,

Zhang,

Dong

et al. 2024

IFS

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The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm’s optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods.

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Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis

Janyasupab,

Singhanat,

Warnnissorn

et al. 2024

Biomolecules

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Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven genes (NOL4, STAR, C8G, NEIL1, SLC46A3, FRMD6, and SCARF2) that are potential biomarkers in breast cancer. To gain more relevant proteins, further investigation based on a protein–protein interaction network and the network diffusion technique revealed enrichment in the Hippo signaling and Wnt signaling pathways, promoting tumor initiation, invasion, and metastasis in several cancer types. In conclusion, these novel genes, recognized as overexpressed in high-TB samples, along with their associated pathways, offer promising therapeutic targets, thus advancing treatment and diagnosis for breast cancer.

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Identification of key biomarkers for STAD using filter feature selection approaches

Cited by 4 publications

References 31 publications

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers

Classification of high-dimensional imbalanced biomedical data based on spectral clustering SMOTE and marine predators algorithm

Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis

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