Dataset Information

Identification of Serum MicroRNAs as Novel Biomarkers in Esophageal Squamous Cell Carcinoma Using Feature Selection Algorithms.

ABSTRACT: Introduction: Circulating microRNAs (miRNAs) are promising molecular biomarkers for the early detection of esophageal squamous cell carcinoma (ESCC). We investigated the serum miRNA expression profiles from microarray-based technologies and evaluated the diagnostic value of serum miRNAs as potential biomarkers for ESCC by using feature selection algorithms. Methods: Serum miRNA expression profiles were obtained from 52 ESCC patients and 52 age- and sex-matched controls via performing a high-throughput microarray assay. Five representative feature selection algorithms including the false discovery rate procedure, family-wise error rate procedure, Lasso logistic regression, hybrid huberized support vector machine (SVM), and SVM using the squared-error loss with the elastic-net penalty were jointly carried out to select the significantly differentially expressed miRNAs based on the miRNA profiles. Results: Three miRNAs including miR-16-5p, miR-451a, and miR-574-5p were identified as the powerful biomarkers for the diagnosis of ESCC. The diagnostic accuracy of the combination of these three miRNAs was evaluated by using logistic regression and the SVM. The averages of the area under the receiver operating curve and classification accuracies based on different classifiers were more than 0.80 and 0.79, respectively. The cross-validation results suggested that the three-miRNA-based classifiers could clearly distinguish ESCC patients from healthy controls. Moreover, the classifying performance of the miRNA panel persisted in discriminating the healthy group from patients with ESCC stage I-II (AUC > 0.76) and patients with ESCC stage III-IV (AUC > 0.80). Conclusions: These results in this study have moved forward the identification of novel biomarkers for the diagnosis of ESCC.

SUBMITTER: Zheng D

PROVIDER: S-EPMC6348251 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identification of Serum MicroRNAs as Novel Biomarkers in Esophageal Squamous Cell Carcinoma Using Feature Selection Algorithms.

Zheng Deqiang D Ding Yuanjie Y Ma Qing Q Zhao Lei L Guo Xudong X Shen Yi Y He Yan Y Wei Wenqiang W Liu Fen F

Frontiers in oncology 20190121

<b>Introduction:</b> Circulating microRNAs (miRNAs) are promising molecular biomarkers for the early detection of esophageal squamous cell carcinoma (ESCC). We investigated the serum miRNA expression profiles from microarray-based technologies and evaluated the diagnostic value of serum miRNAs as potential biomarkers for ESCC by using feature selection algorithms. <b>Methods:</b> Serum miRNA expression profiles were obtained from 52 ESCC patients and 52 age- and sex-matched controls via performi ...[more]

PMID: 30719423

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Project description:BackgroundMicroRNAs (miRNAs) have played important roles in the regulation of gene expression in many cancers, but their roles in esophageal squamous cell carcinoma (ESCC) are still unclear. The aim of this study was to determine the potential ESCC-specific key miRNAs from a large sample dataset in The Cancer Genome Atlas (TCGA).MethodsIntegrative bioinformatics analysis was used to identify key ESCC-specific miRNAs related to the ESCC patients' tumor histological grade and lymphatic metastasis from TCGA. Next, these key miRNA potential gene regulatory functions and relationships with ESCC patients' clinical characteristics and overall survival were analyzed. Finally, three key miRNAs were selected randomly and quantificational real-time polymerase chain reaction (qRT-PCR) was used to validate in 51 newly diagnosed ESCC patients' tissues samples (collected from Nov. 2017 to Feb. 2019, in Wuwei, China) whether the bioinformatics analyses results were reliable and valid. Two-tailed Student's t test, Pearson Chi-squared test and Kaplan-Meier survival analysis were used in this study.ResultsThirty-five ESCC-specific miRNAs from TCGA database were investigated (fold-change > 2.0, P < 0.05), and 28 participated in the miRNAs-mRNAs co-expression network construction, while 17 were related with ESCC patients' tumor histological grade, TNM stage, and lymphatic metastasis (P < 0.05). Meanwhile, six miRNAs (including miR-200b-3p, miR-31-5p, miR-15b-5p, miR-141-3p, miR-135b-5p, and miR-195-5p) were correlated with overall survival of ESCC patients (log-rank, P < 0.05). MiR-135b-5p, miR-15b-5p, and miR-195-5p were selected for verification of the expression levels in 51 ESCC patients' tissue samples by using qRT-PCR. We found that the fold-changes between qRT-PCR and TCGA were completely consistent. The results also suggested that miR-135b-5p, miR-15b-5p, and miR-195-5p were significantly correlated with tumor differentiation degrees (P < 0.05), miR-195-5p was significantly correlated with tumor TNM stage (P < 0.05), and miR-135b-5p was significantly correlated with lymph-node metastasis (P < 0.05). MiR-135b-5p, miR-15b-5p, and miR-195-5p expression levels, ESCC patient clinical features association analysis results and the aforementioned TCGA bioinformatics analyses were similar.ConclusionThis study identified key ESCC-related miRNAs. The key miRNAs are worthy of further investigation as potential novel biomarkers for diagnosis, classification, and prognosis of ESCC.

Project description:The poor outcome of patients with esophageal squamous cell carcinoma (ESCC) highlights the importance of the identification of novel effective prognostic biomarkers. Long non‑coding RNAs (lncRNAs) serve regulatory roles in various types of cancer. The aim of the present study was to investigate the lncRNA expression profile in ESCC and to identify lncRNAs associated with the prognosis of ESCC by performing comprehensive bioinformatics analyses. The RNA‑sequencing (Seq) expression dataset GSE53625 generated from ESCC samples was used as a training dataset. Additional RNA‑Seq datasets relative to ESCC samples were downloaded from The Cancer Genome Atlas and used as a validation dataset. Data were screened using the limma package, and differentially expressed lncRNAs between early‑ and late‑stage ESCC were identified. A random forest algorithm was used to select the optimal lncRNA biomarkers, which were then analyzed using the support vector machine (SVM) algorithm with R software. The identified lncRNA biomarkers were examined in the validation dataset by bidirectional hierarchical clustering and using an SVM classifier. Subsequently, univariate and multivariate Cox regression analyses were performed to analyze the potential ability lncRNAs to predict the survival rate of patients with ESCC. By examining the training group, 259 deregulated lncRNAs between early‑ and advanced‑stage ESCC were identified. Further bioinformatics analyses identified a nine‑lncRNA signature, including AC098973, AL133493, RP11‑51M24, RP11‑317N8, RP11‑834C11, RP11‑69C17, LINC00471, LINC01193 and RP1‑124C. This nine‑lncRNA signature was used to predict the tumor stage and patient survival rate with high reliability and accuracy in the training and validation datasets. Furthermore, these nine lncRNA biomarkers were primarily involved in regulating the cell cycle and DNA replication, and these processes were previously identified to be associated with the progression of ESCC. The identified nine‑lncRNA signature was identified to be associated with the tumor stage, and could be used as predictor of the survival rate of patients with ESCC.

Project description:Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature selection are no longer practical in the context of high-dimensional data due to the computationally complex. Ensemble learning, a prominent learning method in machine learning, has demonstrated exceptional performance, particularly in classification problems. To address the issue, we propose a three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI). Firstly, highly linearly correlated features are eliminated using the Spearman coefficient. Then, a feature selector based on the F-test is employed for the first stage selection. For the second stage, four feature subsets are formed using mutual information (MI), ReliefF, SURF, and SURF* filters in parallel. The third stage involves feature selection using a combinator based on GINI coefficient. Finally, a soft voting approach is proposed to employ for classification, including decision tree, naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN) and random forest classifiers. To demonstrate the effectiveness and efficiency of the proposed algorithm, eight high-dimensional datasets are used and five feature selection methods are employed to compare with our proposed algorithm. Experimental results show that our method effectively enhances the accuracy and speed of feature selection. Moreover, to explore the biological significance of the proposed algorithm, we apply it on the renal cell carcinoma dataset GSE40435 from the Gene Expression Omnibus database. Two feature genes, NOP2 and NSUN5, are selected by our proposed algorithm. They are directly involved in regulating m5c RNA modification, which reveals the biological importance of EFS-GINI. Through bioinformatics analysis, we shows that m5C-related genes play an important role in the occurrence and progression of renal cell carcinoma, and are expected to become an important marker to predict the prognosis of patients.

Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is a common digestive tract malignant tumor with high incidence and dismal prognosis worldwide. However, the reliable biomarkers for clinical diagnosis and the underlying signaling pathways insights of ESCC are not unequivocally understood yet. The serum proteome may provide valuable clues for the early diagnosis of ESCC and the discovery of novel molecular insights.MethodsIn the current study, an optimized proteomics approach was employed to discover novel serum-based biomarkers for ESCC, and unveil abnormal signal pathways. Gene ontology (GO) enrichment analysis was done by Gene Set Enrichment Analysis (GSEA) and Metascape database, respectively. Pathway analysis was accomplished by GeneCards database. The correlation coefficient was assessed using Pearson and distance correlation analyses. Prioritized candidates were further verified in two independent validation sets by enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) staining.ResultsA total of 633 non-redundant proteins were identified in the serum of patients with ESCC, of which 59 and 10 proteins displayed a more than 1.5-fold increase or decrease compared with healthy controls. Verification was performed for six candidate biomarkers, including S100A8/A9, SAA1, ENO1, TPI1 and PGAM1. Receiver operating characteristics (ROC) curve plotting showed the high diagnostic sensitivity and specificity of these six protein molecules as a biomarker panel: the area under characteristic curve (AUC) is up to 0.945. Differentially expressed proteins were subjected to functional enrichment analysis, which revealed the dysregulation of signaling pathways mainly involved in glycolysis, TLR4, HIF-1α, Cori cycle, TCA cycle, folate metabolism, and platelet degranulation. The latter finding was all the more noteworthy as a strong positive correlation was discovered between activated glycolysis and TLR4 pathways and unfavorable clinicopathological TNM stages in ESCC.ConclusionsOur findings propose a potential serum biomarker panel for the early detection and diagnosis of ESCC, which could potentially broaden insights into the characteristics of ESCC from the proteomic perspective.

Dataset Information

Identification of Serum MicroRNAs as Novel Biomarkers in Esophageal Squamous Cell Carcinoma Using Feature Selection Algorithms.

Publications

Identification of Serum MicroRNAs as Novel Biomarkers in Esophageal Squamous Cell Carcinoma Using Feature Selection Algorithms.

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