Construction of an miRNA-mRNA regulatory network in colorectal cancer with bioinformatics methods

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths. The five-year relative survival rate for CRC is estimated to be approximately 90% for patients diagnosed with early stages and 14% for those diagnosed at an advanced stages of disease, respectively. Hence, the development of accurate prognostic markers is required. Bioinformatics enables the identification of dysregulated pathways and novel biomarkers. RNA expression profiling was performed in CRC patients from the TCGA database using a Machine Learning approach to identify differential expression genes (DEGs). Survival curves were assessed using Kaplan-Meier analysis to identify prognostic biomarkers. Furthermore, the molecular pathways, protein-protein interaction, the co-expression of DEGs, and the correlation between DEGs and clinical data have been evaluated. The diagnostic markers were then determined based on machine learning analysis. The results indicated that key upregulated genes are associated with the RNA processing and heterocycle metabolic process, including C10orf2, NOP2, DKC1, BYSL, RRP12, PUS7, MTHFD1L, and PPAT. Furthermore, the survival analysis identified NOP58, OSBPL3, DNAJC2, and ZMYND19 as prognostic markers. The combineROC curve analysis indicated that the combination of C10orf2 -PPAT- ZMYND19 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.98, 1.00, and 0.99, respectively. Eventually, ZMYND19 gene was validated in CRC patients. In conclusion, novel biomarkers of CRC have been identified that may be a promising strategy for early diagnosis, potential treatment, and better prognosis.

Comprehensive analysis of negatively correlated miRNA-mRNA regulatory pairs associated with microsatellite instability in colorectal cancer

BACKGROUND: Several studies have demonstrated that microRNAs (miRNAs) and target mRNAs are associated with different frequencies of microsatellite instability. OBJECTIVE: The study aimed to elucidate the profiles of miRNAs and target mRNAs expression and their associations with the phenotypic hallmarks of microsatellite instability in colorectal cancers (CRC) by integrating transcriptomic, immunophenotype, methylation, mutation, and survival data. METHODS: Differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) were screened out and then the miRNA-mRNA regulatory pairs were identified through two databases. We verified that the expression levels were detected in 40 microsatellite instable (MSI) and 40 microsatellite stable (MSS) CRC samples and used the logistic regression and the Cox regression method to evaluate the diagnostic and prognostic value of negative regulatory pairs respectively. RESULTS: The best diagnostic model that combines miR-31-5p, PLAGL2, miR-361-5p, and RAB27B, which were associated with immune microenvironment, tumor mutation burden (TMB), and overall DNA methylation, could significantly predict microsatellite instability in colon tissues. MiR-31-5p and RAB27B could also predict the overall survival of MSS CRCs. CONCLUSION: This study generated a predictive model of the combination of miRNAs and mRNAs to distinguish MSI versus MSS CRCs and elaborated their potential molecular mechanisms and biological functions.

Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer

Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis.

Construction of a Potential Breast Cancer-Related miRNA-mRNA Regulatory Network

Background

Breast cancer is a malignant tumor that occurs in the epithelial tissue of the breast gland and has become the most common malignancy in women. The regulation of the expression of related genes by microRNA (miRNA) plays an important role in breast cancer. We constructed a comprehensive breast cancer-miRNA-gene interaction map.

Methods

Three miRNA microarray datasets (GSE26659, GSE45666, and GSE58210) were obtained from the GEO database. Then, the R software “LIMMA” package was used to identify differential expression analysis. Potential transcription factors and target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted. The BRCA GE-mRNA datasets (GSE109169 and GSE139038) were downloaded from the GEO database for identifying differentially expressed genes (DE-genes). Next, GO annotation and KEGG pathway enrichment analysis were conducted. A PPI network was then established, and hub genes were identified via Cytoscape software. The expression and prognostic roles of hub genes were further evaluated.

Results

We found 6 upregulated differentially expressed- (DE-) miRNAs and 18 downregulated DE-miRNAs by analyzing 3 Gene Expression Omnibus databases, and we predicted the upstream transcription factors and downstream target genes for these DE-miRNAs. Then, we used the GEO database to perform differential analysis on breast cancer mRNA and obtained differentially expressed mRNA. We found 10 hub genes of upregulated DE-miRNAs and 10 hub genes of downregulated DE-miRNAs through interaction analysis.

Conclusions

In this study, we have performed an integrated bioinformatics analysis to construct a more comprehensive BRCA-miRNA-gene network and provide new targets and research directions for the treatment and prognosis of BRCA.

Identification of Key mRNAs and Pathways in Colorectal Cancer

Colorectal cancer (CRC) is the third most cancer-related death worldwide. This work aimed to identify potential hub genes and dysregulated pathways in the CRC by bioinformatics analysis. Three gene expression datasets were collected from GEO datasets, including tumor sample (N = 242) and adjacent nontumor tissue sample (N = 59). RankProd was used to discover the differential expressed genes between tumor and adjacent nontumor tissues for datasets generated by different laboratories. The gene set enrichment analysis conducted on the DE genes, followed by the protein-protein interaction (PPI) network. In total, 2,007 significant differential expression (DE) genes between tumor and adjacent nontumor tissues, include 1,090 upregulated genes and 917 downregulated genes in the tumor. The DE mRNAs are involved in cancer-related pathways. We comprehensively identified the CRC-related key mRNAs. Our data demonstrated combined different resources of transcriptomes will promote the understanding of the molecular mechanisms underlying CRC development and may be useful in discovering candidate molecular biomarkers for diagnosing, prognosis, and treating of CRC.

Identification of Key Genes and Circular RNAs in Human Gastric Cancer

Background

Globally, gastric cancer (GC) is the third most common source of cancer-associated mortality. The aim of this study was to identify key genes and circular RNAs (circRNAs) in GC diagnosis, prognosis, and therapy and to further explore the potential molecular mechanisms of GC.

Material/Methods

Differentially expressed genes (DEGs) and circRNAs (DE circRNAs) between GC tissues and adjacent non-tumor tissues were identified from 3 mRNA and 3 circRNA expression profiles. Functional analyses were performed, and protein–protein interaction (PPI) networks were constructed. The significant modules and key genes in the PPI networks were identified. Kaplan-Meier analysis was performed to evaluate the prognostic value of these key genes. Potential miRNA-binding sites of the DE circRNAs and target genes of these miRNAs were predicted and used to construct DE circRNA–miRNA–mRNA networks.

Results

A total of 196 upregulated and 311 downregulated genes were identified in GC. The results of functional analysis showed that these DEGs were significantly enriched in a variety of functions and pathways, including extracellular matrix-related pathways. Ten hub genes (COL1A1, COL3A1, COL1A2, COL5A2, FN1, THBS1, COL5A1, SPARC, COL18A1, and COL11A1) were identified via PPI network analysis. Kaplan-Meier analysis revealed that 7 of these were associated with a poor overall survival in GC patients. Furthermore, we identified 2 DE circRNAs, hsa_circ_0000332 and hsa_circ_0021087. To reveal the potential molecular mechanisms of circRNAs in GC, DE circRNA–microRNA–mRNA networks were constructed.

Conclusions

Key candidate genes and circRNAs were identified, and novel PPI and circRNA–microRNA–mRNA networks in GC were constructed. These may provide useful information for the exploration of potential biomarkers and targets for the diagnosis, prognosis, and therapy of GC.