MiRFFS: a functional group-based feature selection method for the identification of microRNA biomarkers

Yang, Yang; Xiao, Yiqun; Cao, Tianyu; Kong, Wei

doi:10.1504/ijdmb.2017.10007184

Cited by 2 publications

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“…Some studies have analyzed the connection between mutation alterations and immune filtration to discover those alterations that influenced relative immune infiltration in several cancers (Rooney et al, 2015;Safonov et al, 2017;Thorsson et al, 2018;Li and Cai, 2019;Zeng et al, 2019). Incorporating link information of microRNA and gene in feature selection was usually applied to identify the potential tumor biomarkers (Yang et al, 2017(Yang et al, , 2018Liu and Yang, 2018). Meanwhile, the crosstalk between the tumor cells and tumor-infiltrating immune cells is generally modulated by the competing endogenous RNA (ceRNA) networks composed of microRNAs (miRNAs), messenger RNAs (mRNAs), and long non-coding RNAs (lncRNAs; Leonardo et al, 2011;Deng et al, 2018b;Huang et al, 2019).…”

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confidence: 99%

The Study of Sarcoma Microenvironment Heterogeneity Associated With Prognosis Based on an Immunogenomic Landscape Analysis

Deng

Zeng

Kong

et al. 2020

Front. Bioeng. Biotechnol.

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Microenvironment-driven tumor heterogeneity causes the limitation of immunotherapy of sarcomas. Nonetheless, systematical studies of various molecular levels can enhance the understanding of tumor microenvironment (TME) related to prognosis and provide novel insights of precision immunotherapy. Three prognostic-related TME phenotypes were identified by consensus clustering of the relative infiltration of 22 immune cells from 869 samples of sarcomas. Additionally, integrative immunogenomic analysis is applied to explore the characteristics of different TME groups. The results revealed that most of the immune cell infiltration is higher in the better prognostic group, which are more affected by lower DNA methylation levels and fewer copy number variations in the worse prognostic group. The signaling pathway crosstalk analysis suggested that the changes in the TME will cause considerable variation in the flow of information between pathways, especially when the degree of relative infiltration of immune cells is low, patient's endocrine system may also be significantly affected. Also, the endogenous competitive network analysis indicated that several differentially expressed long noncoding RNAs (lncRNAs) associated with the prognosis or tumor recurrence of sarcoma patients affected the regulatory relationship between miRNAs and different genes when the sarcoma microenvironment changes. In summary, the significant relationship between genetic alterations and prognostic-related TME characteristics in sarcomas were determined in this study. These findings may provide new clues for the treatment of sarcomas.

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Section: Introductionmentioning

confidence: 99%

The Study of Sarcoma Microenvironment Heterogeneity Associated With Prognosis Based on an Immunogenomic Landscape Analysis

Deng

Zeng

Kong

et al. 2020

Front. Bioeng. Biotechnol.

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Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification

Feng

Jiang

et al. 2021

Front. Genet.

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The multiple sources of cancer determine its multiple causes, and the same cancer can be composed of many different subtypes. Identification of cancer subtypes is a key part of personalized cancer treatment and provides an important reference for clinical diagnosis and treatment. Some studies have shown that there are significant differences in the genetic and epigenetic profiles among different cancer subtypes during carcinogenesis and development. In this study, we first collect seven cancer datasets from the Broad Institute GDAC Firehose, including gene expression profile, isoform expression profile, DNA methylation expression data, and survival information correspondingly. Furthermore, we employ kernel principal component analysis (PCA) to extract features for each expression profile, convert them into three similarity kernel matrices by Gaussian kernel function, and then fuse these matrices as a global kernel matrix. Finally, we apply it to spectral clustering algorithm to get the clustering results of different cancer subtypes. In the experimental results, besides using the P-value from the Cox regression model and survival analysis as the primary evaluation measures, we also introduce statistical indicators such as Rand index (RI) and adjusted RI (ARI) to verify the performance of clustering. Then combining with gene expression profile, we obtain the differential expression of genes among different subtypes by gene set enrichment analysis. For lung cancer, GMPS, EPHA10, C10orf54, and MAGEA6 are highly expressed in different subtypes; for liver cancer, CMYA5, DEPDC6, FAU, VPS24, RCBTB2, LOC100133469, and SLC35B4 are significantly expressed in different subtypes.

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MiRFFS: a functional group-based feature selection method for the identification of microRNA biomarkers

Cited by 2 publications

References 0 publications

The Study of Sarcoma Microenvironment Heterogeneity Associated With Prognosis Based on an Immunogenomic Landscape Analysis

The Study of Sarcoma Microenvironment Heterogeneity Associated With Prognosis Based on an Immunogenomic Landscape Analysis

Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification

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