&lt;p&gt;Integrated Bioinformatics Data Analysis Reveals Prognostic Significance Of SIDT1 In Triple-Negative Breast Cancer&lt;/p&gt;

Wang, Ya; Li, Hanning; Ma, Jingjing; Fang, Tian; Li, Xiaoting; Afewerky, Henok Kessete; Xiong, Lei; Gao, Qinglei

doi:10.2147/ott.s215898

Cited by 21 publications

(22 citation statements)

References 42 publications

(50 reference statements)

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“…ESR1 (i.e., ER), one of the three key genes of TNBC, was among them. The other four, CA12 [ 21 ], AGR2 [ 22 ], T FF1 [ 23 ], and AGR3 [ 24 ] have been reported to be up- or down-regulated in TNBC.…”

Section: Resultsmentioning

confidence: 99%

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

et al. 2020

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Background Previous studies have reported that labeling errors are not uncommon in omics data. Potential outliers may severely undermine the correct classification of patients and the identification of reliable biomarkers for a particular disease. Three methods have been proposed to address the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the least trimmed square (enetLTS), and Ensemble. Ensemble is an ensembled classification based on distinct feature selection and modeling strategies. The accuracy of biomarker selection and outlier detection of these methods needs to be evaluated and compared so that the appropriate method can be chosen. Results The accuracy of variable selection, outlier identification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated and an RNA-seq dataset. On simulated datasets, Ensemble had the highest variable selection accuracy, as measured by a comprehensive index, and lowest false discovery rate among the three methods. When the sample size was large and the proportion of outliers was ≤5%, the positive selection rate of Ensemble was similar to that of enetLTS. However, when the proportion of outliers was 10% or 15%, Ensemble missed some variables that affected the response variables. Overall, enetLTS had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and enetLTS still performed well when the proportion of outliers was relatively large. With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but with higher proportions of outliers Ensemble missed many mislabeled samples. Rlogreg and Ensemble were less accurate in identifying outliers than enetLTS. The prediction accuracy of enetLTS was better than that of Rlogreg. Running Ensemble on a subset of data after removing the outliers identified by enetLTS improved the variable selection accuracy of Ensemble. Conclusions When the proportion of outliers is ≤5%, Ensemble can be used for variable selection. When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset after removing outliers identified by enetLTS. For outlier identification, enetLTS is the recommended method. In practice, the proportion of outliers can be estimated according to the inaccuracy of the diagnostic methods used.

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Section: Resultsmentioning

confidence: 99%

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

et al. 2020

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“…Then differentially expressed circRNAs, miRNAs and mRNAs were obtained from GSE140256, GSE28423, and GSE99671, respectively. Differentially expressed circRNAs, miRNAs, mRNAs were defined as adjusted P < 0.05 and | log2 fold-change (FC)| > 1.5 (Wang et al, 2019).…”

Section: The Acquisition Of Differential Expression Of Circrnas Mirnas and Mrnasmentioning

confidence: 99%

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

Zhou

Wang

et al. 2021

Front. Genet.

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BackgroundOsteosarcoma is a common malignant primary bone tumor in adolescents and children. Numerous studies have shown that circRNAs were involved in the proliferation and invasion of various tumors. However, the role of circRNAs in osteosarcoma remains unclear. Here, we aimed to explore the regulatory network among circRNA-miRNA-mRNA in osteosarcoma.MethodsThe circRNA (GSE140256), microRNA (GSE28423), and mRNA (GSE99671) expression profiles of osteosarcoma were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs, miRNAs and mRNAs were identified. CircRNA-miRNA interactions and miRNA-mRNA interactions were determined by Circular RNA Interactome (CircInteractome) database and microRNA Data Integration Portal (mirDIP) database, respectively. Then, we constructed a regulatory network. Function enrichment analysis of miRNA and mRNA was performed by DIANA-miRPath v3.0 and Metascape database, respectively. mRNAs with significant prognostic value were identified based on expression profiles from The Cancer Genome Atlas (TCGA) database, and we constructed a subnetwork for them. To make the most of the network, we used the CLUE database to predict potential drugs for the treatment of osteosarcoma based on mRNA expression in the network. And we used the STITCH database to analyze and validate the interactions among these drugs and mRNAs, and to further screen for potential drugs.ResultsA total of 9 circRNAs, 19 miRNAs, 67 mRNAs, 54 pairs of circRNA-miRNA interactions and 110 pairs of miRNA-mRNA interactions were identified. A circRNA-miRNA-mRNA network was constructed. Function enrichment analysis indicated that these miRNAs and mRNAs in the network were involved in the process of tumorigenesis and immune response. Among these mRNAs, STC2 and RASGRP2 with significantly prognostic value were identified, and we constructed a subnetwork for them. Based on mRNA expression in the network, three potential drugs, quinacridine, thalidomide and zonisamide, were screened for the treatment of osteosarcoma. Among them, quinacridine and thalidomide have been proved to have anti-tumor effects in previous studies, while zonisamide has not been reported. And a corresponding drug-protein interaction network was constructed.ConclusionOverall, we constructed a circRNA-miRNA-mRNA regulatory network to investigate the possible mechanism in osteosarcoma, and predicted that quinacridine, thalidomide and zonisamide could be potential drugs for the treatment of osteosarcoma.

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“…[ 49 ] Besides, as is shown in this study and another study on TNBC, the abundance of ANKRD30A in breast cancer tissues may be used for better prognosis prediction. [ 51 ] Moreover, based on data from the human protein atlas, the expression of ANKRD30A should be extremely low in 19 types of blood cells in a breast‐cancer‐free population. [ 52 ] Therefore, like serum PSA for prostate cancer screening or Tg for post‐thyroidectomy surveillance, ANKRD30A may be valuable for breast cancer early diagnosis and monitoring recurrence by detecting its expression in blood.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatic Identification of a Breast‐Specific Transcript Profile

Chen

Yang

Rivandi

et al. 2020

Proteomics Clinical Apps

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Purpose: To identify a breast-specific transcript profile for the first time, and present an updated bioinformatics strategy for searching tissue-specific transcripts and predicting their significance in cancer. Experimental Design: The RNA-seq data of 49 311 transcripts in 88 human tissues from the GTEx, the Illumina Body Map, and the RIKEN FANTOM5 project are integrated to screen breast-specific transcripts. Gene Expression Profiling Interactive Analysis, TGCA, and Kaplan-Meier Plotter are used to examine their expression in cancer tissues and values for prognosis prediction. Results: Only 96 transcripts in human genome are breast-specific for women. Among them, ankyrin repeat domain 30A (ANKRD30A) and long intergenic non-protein coding RNA 993 (LINC00993) are further analyzed. The two transcripts are also breast-specific in 33 types of common female cancer and are often dysregulated in breast cancer tissues. Their expression is higher in the luminal breast cancer while significantly downregulated in triple-negative breast cancer. Moreover, the high expression levels of ANKRD30A and LINC0993 in breast cancer tissues indicate a better prognosis of patients with breast cancer. Conclusions and Clinical Relevance: Breast-specific transcripts in human genome are rare and poorly understood currently. The data indicate that these breast-specific biomarkers are promising candidates for screening early cancer, assessing treatment response, monitoring recurrence, identifying metastatic tumor origin, and serving as potential targets for immunotherapy.

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<p>Integrated Bioinformatics Data Analysis Reveals Prognostic Significance Of SIDT1 In Triple-Negative Breast Cancer</p>

Cited by 21 publications

References 42 publications

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

Bioinformatic Identification of a Breast‐Specific Transcript Profile

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