Potential of blood exosomal ENAH, SEPT9, EGF, MMP‑9 and CXCL8 for the early screening of breast cancer

Zhang, Zijing; Wang, Hongying; Jin, Yonglong; Chu, Chia‐Ching; Bai, Jinhe; Huang, Juntian; Yang, Lemei; Tang, Feng; Zou, Liping; Wang, Shuyang; Zou, Qiang

doi:10.3892/ol.2022.13580

Cited by 2 publications

(3 citation statements)

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“…In this study, the hybrid-based FS pipeline screened an optimal subset that includes three gene biomarkers, namely MAPK1, APOBEC3B, and ENAH, for earlier detection of primary breast tumors. In recent years, clinical studies have shown that these screened gene biomarkers, namely MAPK 1 [75][76][77][78][79][80], APOBEC3B [81][82][83][84], and ENAH [85][86][87][88], are known to be related to the pathophysiology of BC. Therefore, in addition to an unpaired t-test that characteristically focuses on each gene's differential expression between two classes of samples, we also focused on the differences in their ontology.…”

Section: Discussionmentioning

confidence: 99%

“…It assembles actin filaments for cell motility and adhesion [85]. Recent studies have shown the upregulated expression of ENAH in many human cancer types, namely breast cancer [86][87][88], melanoma [91], pancreatic cancer [92], and gastric cancer [93]. The overexpression of ENAH increases the chances of transformation and tumorigenesis in BC, consequently offering an innovative method of clinical evaluation of BC [94].…”

Section: Discussionmentioning

confidence: 99%

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A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

2023

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The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

2023

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“…Specifically, MMP9 was found to be highly upregulated in tumour cell exosomes 48 . It has previously been shown that hepatocellular carcinoma-derived exosomes could increase MMP9 secretion in hepatocytes 5 49 Also, Zhang et al 50 have studied the potential of the expression levels of MMP-9 and CXCL8 in the blood for the early screening of breast cancer.…”

Section: Discussionmentioning

confidence: 99%

Integrated computational screening and liquid biopsy approach to uncover the role of biomarkers for oral cancer lymph node metastasis

Shaikh,

Yadav,

Bhadresha

et al. 2023

Sci Rep

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Cancer is an abnormal, heterogeneous growth of cells with the ability to invade surrounding tissue and even distant organs. Worldwide, GLOBOCAN had an estimated 18.1 million new cases and 9.6 million death rates of cancer in 2018. Among all cancers, Oral cancer (OC) is the sixth most common cancer worldwide, and the third most common in India, the most frequent type, oral squamous cell carcinoma (OSCC), tends to spread to lymph nodes in advanced stages. Throughout the past few decades, the molecular landscape of OSCC biology has remained unknown despite breakthroughs in our understanding of the genome-scale gene expression pattern of oral cancer particularly in lymph node metastasis. Moreover, due to tissue variability in single-cohort studies, investigations on OSCC gene-expression profiles are scarce or inconsistent. The work provides a comprehensive analysis of changed expression and lays a major focus on employing a liquid biopsy base method to find new therapeutic targets and early prediction biomarkers for lymph node metastasis. Therefore, the current study combined the profile information from GSE9844, GSE30784, GSE3524, and GSE2280 cohorts to screen for differentially expressed genes, and then using gene enrichment analysis and protein–protein interaction network design, identified the possible candidate genes and pathways in lymph node metastatic patients. Additionally, the mRNA expression of discovered genes was assessed using real-time PCR, and the Human Protein Atlas database was utilized to determine the protein levels of hub genes in tumor and normal tissues. Angiogenesis was been investigated using the Chorioallentoic membrane (CAM) angiogenesis test. In a cohort of OSCC patients, fibronectin (FN1), C-X-C Motif Chemokine Ligand 8 (CXCL8), and matrix metallopeptidase 9 (MMP9) were significantly upregulated, corroborating these findings. Our identified significant gene signature showed greater serum exosome effectiveness in early detection and clinically linked with intracellular communication in the establishment of the premetastatic niche. Also, the results of the CAM test reveal that primary OC derived exosomes may have a function in angiogenesis. As a result, our study finds three potential genes that may be used as a possible biomarker for lymph node metastasis early detection and sheds light on the underlying processes of exosomes that cause a premetastatic condition.

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Potential of blood exosomal ENAH, SEPT9, EGF, MMP‑9 and CXCL8 for the early screening of breast cancer

Cited by 2 publications

References 43 publications

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

Integrated computational screening and liquid biopsy approach to uncover the role of biomarkers for oral cancer lymph node metastasis

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