A Circulating miRNA-Based Scoring System Established by WGCNA to Predict Colon Cancer.
ABSTRACT: Introduction:Circulation microRNAs (miRNAs) perform as potential diagnostic biomarkers of many kinds of cancers. This study is aimed at identifying circulation miRNAs as diagnostic biomarkers in colon cancer. Methods:We conducted a weighted gene coexpression network analysis (WGCNA) in miRNAs to find out the expression pattern among circulation miRNAs by using a "WGCNA" package in R. Correlation analysis was performed to find cancer-related modules. Differentially expressed miRNAs (DEmiRs) in colon cancer were identified by a "limma" package in R. Hub gene analysis was conducted for these DEmiRs in the cancer-related modules by the "closeness" method in cytoscape software. Then, logistic regression was performed to identify the independent risk factors, and a scoring system was constructed based on these independent risk factors. Then, we use data from the GEO database to confirm the reliability of this scoring system. Results:A total of 9 independent coexpression modules were constructed based on the expression levels of 848 miRNAs by WGCNA. After correlation analysis, green (cor = 0.77, p = 3 × 10-25) and yellow (cor = 0.65, p = 6 × 10-16) modules were strongly correlated with cancer development. 20 hub genes were found after hub gene analysis in these DEmiRs by cytoscape. Among all these hub genes, hsa-miR-23a-3p (OR = 2.6391, p = 6.23 × 10-5) and hsa-miR-663a (OR = 1.4220, p = 0.0069) were identified as an independent risk factor of colon cancer by multivariate regression. Furthermore, a scoring system was built to predict the probability of colon cancer based on both of these miRNAs, the area under the curve (AUC) of which was 0.828. Data from GSE106817 and GSE112264 was used to confirm this scoring system. And the AUC of them was 0.980 and 0.917, respectively. Conclusion:We built a scoring system based on circulation hub miRNAs found by WGCNA to predict the development of colon cancer.
Project description:Introduction:Colorectal cancer (CRC) is the fourth most common cause of cancer-related mortality worldwide. The tumor, node, metastasis (TNM) stage remains the standard for CRC prognostication. Identification of meaningful microRNA (miRNA) and gene modules or representative biomarkers related to the pathological stage of colon cancer helps to predict prognosis and reveal the mechanisms behind cancer progression. Materials and methods:We applied a systems biology approach by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA) to detect the pathological stage-related miRNA and gene modules and construct a miRNA-gene network. The Cancer Genome Atlas (TCGA) colon adenocarcinoma (CAC) RNA-sequencing data and miRNA-sequencing data were subjected to WGCNA analysis, and the GSE29623, GSE35602 and GSE39396 were utilized to validate and characterize the results of WGCNA. Results:Two gene modules (Gmagenta and Ggreen) and one miRNA module were associated with the pathological stage. Six hub genes (COL1A2, THBS2, BGN, COL1A1, TAGLN and DACT3) were related to prognosis and validated to be associated with the pathological stage. Five hub miRNAs were identified to be related to prognosis (hsa-miR-125b-5p, hsa-miR-145-5p, hsa-let-7c-5p, hsa-miR-218-5p and hsa-miR-125b-2-3p). A total of 18 hub genes and seven hub miRNAs were predominantly expressed in tumor stroma. Proteoglycans in cancer, focal adhesion, extracellular matrix (ECM)-receptor interaction and so on were common pathways of the three modules. Hsa-let-7c-5p was located at the core of miRNA-gene network. Conclusion:These findings help to advance the understanding of tumor stroma in the progression of CAC and provide prognostic biomarkers as well as therapeutic targets.
Project description:The aberrant expression of microRNAs (miRNAs) and genes in tumor microenvironment (TME) has been associated with the pathogenesis of colon cancer. An integrative exploration of transcriptional markers (gene signatures) and miRNA-mRNA regulatory networks in colon tumor stroma (CTS) remains lacking. Using two datasets of mRNA and miRNA expression profiling in CTS, we identified differentially expressed miRNAs (DEmiRs) and differentially expressed genes (DEGs) between CTS and normal stroma. Furthermore, we identified the transcriptional markers which were both gene targets of DEmiRs and hub genes in the protein-protein interaction (PPI) network of DEGs. Moreover, we investigated the associations between the transcriptional markers and tumor immunity in colon cancer. We identified 17 upregulated and seven downregulated DEmiRs in CTS relative to normal stroma based on a miRNA expression profiling dataset. Pathway analysis revealed that the downregulated DEmiRs were significantly involved in 25 KEGG pathways (such as TGF-?, Wnt, cell adhesion molecules, and cytokine-cytokine receptor interaction), and the upregulated DEmiRs were involved in 10 pathways (such as extracellular matrix (ECM)-receptor interaction and proteoglycans in cancer). Moreover, we identified 460 DEGs in CTS versus normal stroma by a meta-analysis of two gene expression profiling datasets. Among them, eight upregulated DEGs were both hub genes in the PPI network of DEGs and target genes of the downregulated DEmiRs. We found that three of the eight DEGs were negative prognostic factors consistently in two colon cancer cohorts, including COL5A2, EDNRA, and OLR1. The identification of transcriptional markers and miRNA-mRNA regulatory networks in CTS may provide insights into the mechanism of tumor immune microenvironment regulation in colon cancer.
Project description:High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and malignant ovarian tumor.To identify co-expression modules and hub genes correlated with platinum-based chemotherapy resistant and sensitive HGSOC, we performed weighted gene co-expression network analysis (WGCNA) on microarray data of HGSOC with 12 resistant samples and 16 sensitive samples of GSE51373 dataset.A total of 5122 genes were included in WGCNA, and 16 modules were identified. Module-trait analysis identified that the module salmon (cor?=?0.50), magenta (cor?=?0.49), and black (cor?=?0.45) were discovered associated with chemotherapy resistant, and the significance for these platinum-resistant modules were validated in the GSE63885 dataset. Given that the black module was validated to be the most related one, hub genes of this module, alcohol dehydrogenase 1B, cadherin 11, and vestigial like family member 3were revealed to be expressional related with platinum resistance, and could serve as prognostic markers for ovarian cancer.Our analysis might provide insight for molecular mechanisms of platinum-based chemotherapy resistance and treatment response in ovarian cancer.
Project description:<b>Background:</b> Sensitive and specific non-invasive biomarkers are urgently needed in order to improve the survival of patients with pancreatic ductal adenocarcinoma (PDAC), which is the fourth leading cause of cancer-related death. We aim to identify serum hub miRNAs as potential diagnostic and prognostic biomarkers for PDAC. <b>Methods:</b> A total of 2578 serum miRNA expression data from 88 PDAC patients and 19 healthy subjects were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was constructed and significant modules were extracted from the network by WGCNA R package. Network modules and hub miRNAs closely related to PDAC were identified. The prognostic value of hub miRNAs was assessed by Kaplan-Meier overall survival analysis. <b>Results:</b> Two modules strongly associated with PDAC were identified by WGCNA, which were labeled as turquoise and brown respectively. Within each module, twenty hub miRNAs were found. At the functional level, turquoise module was mainly associated with tumorigenesis pathways such as P53 and WNT signaling pathway, while the brown module was mostly related to the pathways of cancer such as RNA transport and MAPK signaling pathway. Utilizing overall survival analyses, five "real" miRNAs were able to stratify PDAC patients into low-risk and high-risk groups. <b>Conclusions:</b> The association of specific Hub miRNAs with the development of pancreatic cancer was established by WGCNA analysis. Five miRNAs (mir-16-2-3p, mir-890, mir-3201, mir-602, and mir-877) were identified as potential diagnostic and prognostic biomarkers for PDAC.
Project description:Heat stress has a detrimental effect on the physiological and production performance of buffaloes. Elucidating the underlying mechanisms of heat stress is challenging, therefore identifying candidate genes is urgent and necessary. We evaluated the response of buffaloes (n = 30) to heat stress using the physiological parameters, ELISA indexes, and hematological parameters. We then performed mRNA and microRNA (miRNA) expression profiles analysis between heat tolerant (HT, n = 4) and non-heat tolerant (NHT, n = 4) buffaloes, as well as the specific modules, significant genes, and miRNAs related to the heat tolerance identified using the weighted gene co-expression network analysis (WGCNA). The results indicated that the buffaloes in HT had a significantly lower rectal temperature (RT) and respiratory rate (RR) and displayed a higher plasma heat shock protein (HSP70 and HSP90) and cortisol (COR) levels than those of NHT buffaloes. Differentially expressed analysis revealed a total of 753 differentially expressed genes (DEGs) and 16 differentially expressed miRNAs (DEmiRNAs) were identified between HT and NHT. Using the WGCNA analysis, these DEGs assigned into 5 modules, 4 of which were significantly correlation with the heat stress indexes. Interestingly, 158 DEGs associated with heat tolerance in the turquoise module were identified, 35 of which were found within the protein-protein interaction network. Several hub genes (IL18RAP, IL6R, CCR1, PPBP, IL1B, and IL1R1) were identified that significantly enriched in the Cytokine-cytokine receptor interaction. The findings may help further elucidate the underlying mechanisms of heat tolerance in buffaloes.
Project description:The present bioinformatics analysis was performed using a multi?step approach to identify a microRNA (miR)?mRNA regulatory network in Down syndrome. miR (GSE69210) and mRNA (GSE70573) data was downloaded and collected from the thymic tissues of both Down syndrome and karyotypically normal subjects and placed in a public repository. Then, weighted gene co?expression network analysis (WGCNA) was performed to screen for miRs and mRNAs associated with Down syndrome. Subsequently, differentially expressed miRs (DEmiRs) and mRNAs/differentially expressed genes (DEGs) were identified following screening and mapping to RNA data. Bidirectional hierarchical clustering analysis was then performed to distinguish DEmiRs and DEGs between Down syndrome samples and normal control samples. DEmiR targets were retrieved using the miRanda database and mapped to the mRNA module screen by WGCNA. A gene co?expression network was constructed and subjected to functional enrichment analysis. During WGCNA, a total of 6 miR modules and 20 mRNA modules associated with Down syndrome were identified. Following mapping of these miRs and mRNAs to the miR and mRNA modules screened using WGNCA, a total of 12 DEmiRs and 237 DEGs were collected. Following comparison with DEmiR targets retrieved from the miRanda database, a total of 255 DEmiR?DEG pairs, including 6 DEmiRs and 106 DEGs were obtained. At expression correlation coefficient >0.9, a total of 231 gene pairs were selected. These gene pairs were enriched in response to stress and response to stimuli following functional annotation and module division. An integrated analysis of miR and mRNA expression in the thymus in Down syndrome is reported in the present study. miR?30c, miR?145, miR?183 and their targets may serve important roles in the pathogenesis and development of complications in Down syndrome. However, further experimental studies are required to verify these results.
Project description:Not much is known about the roles of long non-coding RNAs (lncRNAs) for chronic kidney disease (CKD). In this study, we included CKD patient cohorts and normal controls as a discovery cohort to identify putative lncRNA biomarkers associated with CKD. We first compared the lncRNA expression profiles of CKD patients with normal controls, and identified differentially expressed lncRNAs and mRNAs. Co-expression network based on the enriched differentially expressed mRNAs and lncRNAs was constructed using WGCNA to identify important modules related to CKD. A lncRNA-miRNA-mRNA pathway network based on the hub lncRNAs and mRNAs, related miRNAs, and overlapping pathways was further constructed to reveal putative biomarkers. A total of 821 significantly differentially expressed mRNAs and lncRNAs were screened between CKD and control samples, which were enriched in nine modules using weighted correlation network analysis (WGCNA), especially brown and yellow modules. Co-expression network based on the enriched differentially expressed mRNAs and lncRNAs in brown and yellow modules uncovered 7 hub lncRNAs and 53 hub mRNAs. A lncRNA-miRNA-mRNA pathway network further revealed that lncRNAs of HCP5 and NOP14-AS1 and genes of CCND2, COL3A1, COL4A1, and RAC2 were significantly correlated with CKD. The lncRNAs of NOP14-AS1 and HCP5 were potential prognostic biomarkers for predicting the risk of CKD.
Project description:With a 5?year survival rate of only 8%, pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer?associated mortality worldwide. Unfortunately, even following radical surgery, patient outcomes remain poor. Emerging as a new class of biomarkers in human cancer, microRNAs (miRNAs/miRs) have been reported to have various tumor suppressor and oncogenic functions. In the present study, miRNA expression profiles of patients with PDAC and corresponding clinical data with survival profiles were obtained from The Cancer Genome Atlas database. A co?expression network was constructed to detect the modules significantly associated with clinical features by weighted gene co?expression network analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on the hub miRNAs in the module of interest for functional annotation. A prognosis model consisting of hub miRNAs was generated using the R package 'rbsurv' and validated in survival analysis. The expression data of 523 miRNAs in 124 patients with PDAC were analyzed in a co?expression network. The turquoise module containing 131 miRNAs was identified to be associated with pathological T stage (cor=?0.21; P=0.02). The 39 hub miRNAs of the turquoise module were then detected using the 'networkScreening' function in R. These miRNAs were predominantly involved in biological processes including 'regulation of transcription', 'apoptotic process', 'TGF?? receptor signaling pathway', 'Ras protein signal transduction' and significantly enriched in 'cell cycle', 'adherens junction', 'FoxO', 'Hippo' and 'PI3K?Akt signaling' pathways. A prognostic signature consisting of four hub miRNAs (miR?1197, miR?218?2, miR?889 and miR?487a) associated with pathological T stage was identified to stratify the patients with early?stage PDAC into high and low risk groups. The signature may serve as a potential prognostic biomarker for patients with early?stage PDAC who undergo radical resection.
Project description:Idiopathic pulmonary fibrosis (IPF) is a fibrotic interstitial lung disease with lesions confined to the lungs. To identify meaningful microRNA (miRNA) and gene modules related to the IPF progression, GSE32537 (RNA-sequencing data) and GSE32538 (miRNA-sequencing data) were downloaded and processed, and then weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression networks and miRNA co-expression networks. GSE10667, GSE70866, and GSE27430 were used to make a reasonable validation for the results and evaluate the clinical significance of the genes and the miRNAs. Six hub genes (COL3A1, COL1A2, OGN, COL15A1, ASPN, and MXRA5) and seven hub miRNAs (hsa-let-7b-5p, hsa-miR-26a-5p, hsa-miR-25-3p, hsa-miR-29c-3p, hsa-let-7c-5p, hsa-miR-29b-3p, and hsa-miR-26b-5p) were clarified and validated. Meanwhile, iteration network of hub miRNAs-hub genes was constructed, and the emerging role of the network being involved in non-small cell lung cancer (NSCLC) was also analyzed by several webtools. The expression levels of hub genes were different between normal lung tissues and NSCLC tissues. Six genes (COL3A1, COL1A2, OGN, COL15A1, ASPN, and MXRA5) and three miRNAs (hsa-miR-29c-3p, hsa-let-7c-5p, and hsa-miR-29b-3p) were related to the survival time of lung adenocarcinoma (LUAD). The interaction network of hub miRNAs-hub genes might provide common mechanisms involving in IPF and NSCLC. More importantly, useful clues were provided for clinical treatment of both diseases based on novel molecular advances.
Project description:Serous ovarian cancer (SOC) is the most lethal gynecological cancer. Clinical studies have revealed an association between tumor stage and grade and clinical prognosis. Identification of meaningful clusters of co-expressed genes or representative biomarkers related to stage or grade may help to reveal mechanisms of tumorigenesis and cancer development, and aid in predicting SOC patient prognosis. We therefore performed a weighted gene co-expression network analysis (WGCNA) and calculated module-trait correlations based on three public microarray datasets (GSE26193, GSE9891, and TCGA), which included 788 samples and 10402 genes. We detected four modules related to one or more clinical features significantly shared across all modeling datasets, and identified one stage-associated module and one grade-associated module. Our analysis showed that MMP2, COL3A1, COL1A2, FBN1, COL5A1, COL5A2, and AEBP1 are top hub genes related to stage, while CDK1, BUB1, BUB1B, BIRC5, AURKB, CENPA, and CDC20 are top hub genes related to grade. Gene and pathway enrichment analyses of the regulatory networks involving hub genes suggest that extracellular matrix interactions and mitotic signaling pathways are crucial determinants of tumor stage and grade. The relationships between gene expression modules and tumor stage or grade were validated in five independent datasets. These results could potentially be developed into a more objective scoring system to improve prediction of SOC outcomes.