Xi Wang1, Guangyu Gao2, Zhengrong Chen3, Zhihao Chen4, Mingxiao Han1, Xiaolu Xie1, Qiyuan Jin1, Hong Du1, Zhifei Cao5, Haifang Zhang6. 1. Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China. 2. Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China. 3. Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China. 4. Department of Orthopedics, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China. 5. Department of Pathology, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China. hunancao@163.com. 6. Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China. haifangzhang@suda.edu.cn.
Abstract
BACKGROUND: Because its metastasis to the lymph nodes are closely related to poor prognosis, miRNAs and mRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of colorectal cancer (CRC). This study aimed to identify novel gene signatures in the lymph node metastasis of CRC. METHODS: GSE56350, GSE70574, and GSE95109 datasets were downloaded from the Gene Expression Omnibus (GEO) database, while data from 569 colorectal cancer cases were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DE-miRNAs) were calculated using R programming language (Version 3.6.3), while gene ontology and enrichment analysis of target mRNAs were performed using FunRich ( http://www.funrich.org ). Furthermore, the mRNA-miRNA network was constructed using Cytoscape software (Version 3.8.0). Gene expression levels were verified using the GEO datasets. Similarly, quantitative real-time PCR (qPCR) was used to examine expression profiles from 20 paired non-metastatic and metastatic lymph node tissue samples obtained from patients with CRC. RESULTS: In total, five DE-miRNAs were selected, and 34 mRNAs were identified after filtering the results. Moreover, two key miRNAs (hsa-miR-99a, hsa-miR-100) and one gene (heparan sulfate-glucosamine 3-sulfotransferase 2 [HS3ST2]) were identified. The GEO datasets analysis and qPCR results showed that the expression of key miRNA and genes were consistent with that obtained from the bioinformatic analysis. A novel miRNA-mRNA network capable of predicting the prognosis and confirmed experimentally, hsa-miR-99a-HS3ST2-hsa-miR-100, was found after expression analysis in metastasized lymph node tissue from CRC samples. CONCLUSION: In summary, miRNAs and genes with potential as biomarkers were found and a novel miRNA-mRNA network was established for CRC lymph node metastasis by systematic bioinformatic analysis and experimental validation. This network may be used as a potential biomarker in the development of lymph node metastatic CRC.
BACKGROUND: Because its metastasis to the lymph nodes are closely related to poor prognosis, miRNAs and mRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of colorectal cancer (CRC). This study aimed to identify novel gene signatures in the lymph node metastasis of CRC. METHODS: GSE56350, GSE70574, and GSE95109 datasets were downloaded from the Gene Expression Omnibus (GEO) database, while data from 569 colorectal cancer cases were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DE-miRNAs) were calculated using R programming language (Version 3.6.3), while gene ontology and enrichment analysis of target mRNAs were performed using FunRich ( http://www.funrich.org ). Furthermore, the mRNA-miRNA network was constructed using Cytoscape software (Version 3.8.0). Gene expression levels were verified using the GEO datasets. Similarly, quantitative real-time PCR (qPCR) was used to examine expression profiles from 20 paired non-metastatic and metastatic lymph node tissue samples obtained from patients with CRC. RESULTS: In total, five DE-miRNAs were selected, and 34 mRNAs were identified after filtering the results. Moreover, two key miRNAs (hsa-miR-99a, hsa-miR-100) and one gene (heparan sulfate-glucosamine 3-sulfotransferase 2 [HS3ST2]) were identified. The GEO datasets analysis and qPCR results showed that the expression of key miRNA and genes were consistent with that obtained from the bioinformatic analysis. A novel miRNA-mRNA network capable of predicting the prognosis and confirmed experimentally, hsa-miR-99a-HS3ST2-hsa-miR-100, was found after expression analysis in metastasized lymph node tissue from CRC samples. CONCLUSION: In summary, miRNAs and genes with potential as biomarkers were found and a novel miRNA-mRNA network was established for CRC lymph node metastasis by systematic bioinformatic analysis and experimental validation. This network may be used as a potential biomarker in the development of lymph node metastatic CRC.
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