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

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

doi:10.1504/ijdmb.2017.086099

Cited by 5 publications

(5 citation statements)

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“…Many computational methods have been applied in bioinformatics research, such as lncRNA–miRNA interaction predictions ( Liu et al, 2020 ; Zhang et al, 2021a , b ), the identification of microRNA combinatorial biomarkers ( Yang et al, 2017a , b ; Liu and Yang, 2018 ), and so on ( Wang et al, 2020 ; Zhang et al, 2020 ). In the next step, we will consider the use of the machine learning and deep learning methods in the research of CLDN6.…”

Section: Discussionmentioning

confidence: 99%

Identification of Claudin-6 as a Molecular Biomarker in Pan-Cancer Through Multiple Omics Integrative Analysis

Zhang

Guo

et al. 2021

Front. Cell Dev. Biol.

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Claudin-6 (CLDN6) is one of the 27 family members of claudins and majorly involved in the tight junction and cell-to-cell adhesion of epithelial cell sheets, playing a significant role in cancer initiation and progression. To provide a more systematic and comprehensive dimension of identifying the diverse significance of CLDN6 in a variety of malignant tumors, we explored CLDN6 through multiple omics data integrative analysis, including gene expression level in pan-cancer and comparison of CLDN6 expression in different molecular subtypes and immune subtypes of pan-cancer, targeted protein, biological functions, molecular signatures, diagnostic value, and prognostic value in pan-cancer. Furthermore, we focused on uterine corpus endometrial carcinoma (UCEC) and further investigated CLDN6 from the perspective of the correlations with clinical characteristics, prognosis in different clinical subgroups, co-expression genes, and differentially expressed genes (DEGs), basing on discussing the validation of its established monoclonal antibody by immunohistochemical staining and semi-quantification reported in the previous study. As a result, CLDN6 expression differs significantly not only in most cancers but also in different molecular and immune subtypes of cancers. Besides, high accuracy in predicting cancers and notable correlations with prognosis of certain cancers suggest that CLDN6 might be a potential diagnostic and prognostic biomarker of cancers. Additionally, CLDN6 is identified to be significantly correlated with age, stage, weight, histological type, histologic grade, and menopause status in UCEC. Moreover, CLDN6 high expression can lead to a worse overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in UCEC, especially in different clinical subgroups of UCEC. Taken together, CLDN6 may be a remarkable molecular biomarker for diagnosis and prognosis in pan-cancer and an independent prognostic risk factor of UCEC, presenting to be a promising molecular target for cancer therapy.

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Section: Discussionmentioning

confidence: 99%

Identification of Claudin-6 as a Molecular Biomarker in Pan-Cancer Through Multiple Omics Integrative Analysis

Zhang

Guo

et al. 2021

Front. Cell Dev. Biol.

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“…Broad exploration and deep verification of molecular biology experiments are still need to perform and investigate in the individual cancer study, due to the heterogeneity and complexity of tumours. Furthermore, diverse computational methods can provide novel insights of bioinformatics exploration, such as lncRNA-miRNA interaction predictions, [42][43][44] the identification of microRNA combinatorial biomarkers [45][46][47] and predictive model. 48,49 In summary, our study contributes to uncovering cancer promoting or suppressing effects of YTHDC2 in various cancer types comprehensively and provides evidence on the role of YTHDC2 in tumour cell immune infiltration, diagnostic value and clinical prognosis.…”

Section: Discussionmentioning

confidence: 99%

The role of YTH domain containing 2 in epigenetic modification and immune infiltration of pan‐cancer

Zhang

Guo

et al. 2021

J Cellular Molecular Medi

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YTH domain containing 2 (YTHDC2) is the largest N6‐Methyladenosine (m6A) binding protein of the YTH protein family and the only member containing ATP‐dependent RNA helicase activity. For further analysing its biological role in epigenetic modification, we comprehensively explored YTHDC2 from gene expression, genetic alteration, protein‐protein interaction (PPI) network, immune infiltration, diagnostic value and prognostic value in pan‐cancer, using a series of databases and bioinformatic tools. We found that YTHDC2 with Missense mutation could cause a different prognosis in uterine corpus endometrial carcinoma (UCEC), and its different methylation level could lead to a totally various prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), lung squamous cell carcinoma (LUSC) and UCEC. The main molecular mechanisms of YTHDC2 focused on catalytic activity, helicase activity, snRNA binding, spliceosome and mRNA surveillance. Additionally, YTHDC2 was notably correlated with tumour immune infiltration. Moreover, YTHDC2 had a high diagnostic value for seven cancer types and a prognostic value for brain lower grade glioma (LGG), rectum adenocarcinoma (READ) and skin cutaneous melanoma (SKCM). Collectively, YTHDC2 plays a significant role in epigenetic modification and immune infiltration and maybe a potential biomarker for diagnosis and prognosis in certain cancers.

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“…Recently, many methods for cancer subtypes recognition and marker extraction have been proposed ( Yeoh et al, 2002 ; Lapointe et al, 2004 ; Figueroa et al, 2010 ; Yang et al, 2017a ; Pan et al, 2019 ). Some models are based on single expression data, including gene expression ( Yeoh et al, 2002 ; Lapointe et al, 2004 ), microRNA (miRNA) expression ( Yang et al, 2017a , b ; Liu and Yang, 2018 ), copy number variation ( Pan et al, 2019 ), and DNA methylation ( Figueroa et al, 2010 ). Lapointe et al (2004) identified three subclasses of prostate tumors based on distinct patterns of gene expression.…”

Section: Introductionmentioning

confidence: 99%

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 5 publications

References 0 publications

Identification of Claudin-6 as a Molecular Biomarker in Pan-Cancer Through Multiple Omics Integrative Analysis

Identification of Claudin-6 as a Molecular Biomarker in Pan-Cancer Through Multiple Omics Integrative Analysis

The role of YTH domain containing 2 in epigenetic modification and immune infiltration of pan‐cancer

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

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