Identification of the miRNA signature and key genes in colorectal cancer lymph node metastasis

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.

MicroRNA-nanoparticles against cancer: Opportunities and challenges for personalized medicine

“… 49 miR-99a, miR-100 Down Tissue Wang et al. 50 Diagnosis and Prognosis let-7c-5p, let-7e, miR-34c-5p, miR-133b Up Tissue Song et al. 51 miR-106a, miR-144 Down Tissue Song et al.…”

“… 48 The correlation between CRC and reduced miR-195, miR-99a, miR-100, and miR-139 expression has been reported, as well as miR-20a and miR-183 overexpression. 49 , 50 Clinical evidence correlated let-7c-5p, let-7e, miR-34c-5p, and miR-133b levels to CRC progression and poor prognosis, and described miR-106a and miR-144 as tumor suppressors counteracting the bad clinical outcome of CRC. 51 Similarly, miR-449 downregulation could act as both a diagnostic and prognostic marker in patients affected by CRC predicting a poorer clinical outcome.…”

Identification of hub genes and pathways in lung metastatic colorectal cancer

“…Recently, bioinformatics analyses emerged as an efficient and promising tool to screen significantly aberrantly expressed genes and genetic pathways involved in carcinogenesis, which could provide a rationale to identify potential therapeutic targets cancer and understand a cancer prognosis [11][12][13]. In particular, many studies utilized integrated microarrays analysis and reported that certain vital genes or pathways potentially are involved in CRC liver metastasis or lymph node metastasis [12,13]. However, studies were quite limited in CRC lung metastases.…”

Identification and verification of microRNA signature and key genes in the development of osteosarcoma with lung metastasis

“…It is a promising therapeutic research field to inhibit or replace microRNAs. [ 14 ] One study found that miRNA-424-5p regulated ferroptosis by targeting Acyl-CoA Synthetase Long Chain Family Member 4 in OC cells and indicated a potential therapeutic target for ovarian cancer. [ 15 ] Wu et al [ 16 ] found that the downregulated microRNA-1301-3p inhabited lung carcinoma cell proliferation and migration and has a strong negative correlation with Polymerase I and transcript release factor.…”