Bioinformatics Analysis of a Prognostic miRNA Signature and Potential Key Genes in Pancreatic Cancer.

In this study, miRNAs and their critical target genes related to the prognosis of pancreatic cancer were screened based on bioinformatics analysis to provide targets for the prognosis and treatment of pancreatic cancer. R software was used to screen differentially expressed miRNAs (DEMs) and genes (DEGs) downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. A miRNA Cox proportional hazards regression model was constructed based on the miRNAs, and a miRNA prognostic model was generated. The target genes of the prognostic miRNAs were predicted using TargetScan and miRDB and then intersected with the DEGs to obtain common genes. The functions of the common genes were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. A protein-protein interaction (PPI) network of the common genes was constructed with the STRING database and visualized with Cytoscape software. Key genes were also screened with the MCODE and cytoHubba plug-ins of Cytoscape. Finally, a prognostic model formed by the key gene was also established to help evaluate the reliability of this screening process. A prognostic model containing four downregulated miRNAs (hsa-mir-424, hsa-mir-3613, hsa-mir-4772 and hsa-mir-126) related to the prognosis of pancreatic cancer was constructed. A total of 118 common genes were enriched in two KEGG pathways and 33 GO functional annotations, including extracellular matrix (ECM)-receptor interaction and cell adhesion. Nine key genes related to pancreatic cancer were also obtained: MMP14, ITGA2, THBS2, COL1A1, COL3A1, COL11A1, COL6A3, COL12A1 and COL5A2. The prognostic model formed by nine key genes also possessed good prognostic ability. The prognostic model consisting of four miRNAs can reliably predict the prognosis of patients with pancreatic cancer. In addition, the screened nine key genes, which can also form a reliable prognostic model, are significantly related to the occurrence and development of pancreatic cancer. Among them, one novel miRNA (hsa-mir-4772) and two novel genes (COL12A1 and COL5A2) associated with pancreatic cancer have great potential to be used as prognostic factors and therapeutic targets for this tumor.

Chen S ，Gao C ，Yu T ，Qu Y ，Xiao GG ，Huang Z ... -  《Frontiers in Oncology》

Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis.

Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neoplasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. The microarray datasets GSE28735, GSE62165, and GSE91035 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein-protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI network and then identified key modules. From the PPI network, 225 nodes and 803 edges were selected. The most significant module, which comprised 11 DEGs, was identified using the Molecular Complex Detection plugin. The top 20 hub genes, which were filtered by the CytoHubba plugin, comprised FN1, COL1A1, COL3A1, BGN, POSTN, FBN1, COL5A2, COL12A1, THBS2, COL6A3, VCAN, CDH11, MMP14, LTBP1, IGFBP5, ALB, CXCL12, FAP, MATN3, and COL8A1. These genes were validated using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, and the encoded proteins were subsequently validated using the HPA website. The GO analysis results showed that the most significantly enriched biological process, cellular component, and molecular function terms among the 20 hub genes were cell adhesion, proteinaceous extracellular matrix, and calcium ion binding, respectively. The KEGG pathway analysis showed that the 20 hub genes were mainly enriched in ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. These findings indicated that FBN1 and COL8A1 appear to be involved in the progression of PDAC. Moreover, patient survival analysis performed via the GEPIA using TCGA and GTEx databases demonstrated that the expression levels of COL12A1 and MMP14 were correlated with a poor prognosis in PDAC patients (p < 0.05). The results demonstrated that upregulation of MMP14 and COL12A1 is associated with poor overall survival, and these might be a combination of prognostic biomarkers in PDAC.

Ding J ，Liu Y ，Lai Y 《PeerJ》

Identification of Prognostic miRNA Signature and Lymph Node Metastasis-Related Key Genes in Cervical Cancer.

miRNAs and genes can serve as biomarkers for the prognosis and therapy of cervical tumors whose metastasis into lymph nodes is closely associated with disease progression and poor prognosis. R software and Bioconductor packages were employed to identify differentially expressed miRNAs (DEMs) from The Cancer Genome Atlas (TCGA) database. GEO2R detected differentially expressed genes (DEGs) in the GSE7410 dataset originating from the Gene Expression Omnibus (GEO). A Cox proportional hazard regression model was established to select prognostic miRNA biomarkers. Online tools such as TargetScan and miRDB predicted target genes, and overlapping DEGs and target genes were defined as consensus genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) function annotations were performed to discern the potential functions of consensus genes. STRING and Cytoscape screened key genes and constructed a regulatory network. A combination of four miRNAs (down-regulated miR-502 and miR-145, up-regulated miR-142 and miR-33b) was identified as an independent prognostic signature of cervical cancer. A total of 94 consensus genes were significantly enriched in 7 KEGG pathways and 19 GO function annotations including the cAMP signaling pathway, the plasma membrane, integral components of the plasma membrane, cell adhesion, etc. The module analysis suggested that CXCL12, IGF1, PTPRC CDH5, RAD51B, REV3L, and WDHD1 are key genes that significantly correlate with cervical cancer lymph node metastasis. This study demonstrates that a four-miRNA signature can be a prognostic biomarker, and seven key genes are significantly associated with lymph node metastasis in cervical cancer patients. These miRNAs and key genes have the potential to be therapeutic targets for cervical cancer. Among them, two miRNAs (miR-502 and miR-33b) and two key genes (PTPRC and CDH5) were first reported to be potential novel biomarkers for cervical cancer. The current study further characterizes the progression of lymph node metastasis and mechanism of cervical tumors; therefore, it provides a novel diagnostic indicator and therapeutic targets for future clinical treatments.

Chen S ，Gao C ，Wu Y ，Huang Z ... -  《Frontiers in Pharmacology》

Bioinformatics Analysis of Prognostic miRNA Signature and Potential Critical Genes in Colon Cancer.

This study aims to lay a foundation for studying the regulation of microRNAs (miRNAs) in colon cancer by applying bioinformatics methods to identify miRNAs and their potential critical target genes associated with colon cancer and prognosis. Data of differentially expressed miRNAs (DEMs) and genes (DEGs) downloaded from two independent databases (TCGA and GEO) and analyzed by R software resulted in 472 DEMs and 565 DEGs in colon cancers, respectively. Next, we developed an 8-miRNA (hsa-mir-6854, hsa-mir-4437, hsa-mir-216a, hsa-mir-3677, hsa-mir-887, hsa-mir-4999, hsa-mir-34b, and hsa-mir-3189) prognostic signature for patients with colon cancer by Cox proportional hazards regression analysis. To predict the target genes of these miRNAs, we used TargetScan and miRDB. The intersection of DEGs with the target genes predicted for these eight miRNAs retrieved 112 consensus genes. GO and KEGG pathway enrichment analyses showed these 112 genes were mainly involved in protein binding, one-carbon metabolic process, nitrogen metabolism, proteoglycans in cancer, and chemokine signaling pathways. The protein-protein interaction network of the consensus genes, constructed using the STRING database and imported into Cytoscape, identified 14 critical genes in the pathogenesis of colon cancer (CEP55, DTL, FANCI, HMMR, KIF15, MCM6, MKI67, NCAPG2, NEK2, RACGAP1, RRM2, TOP2A, UBE2C, and ZWILCH). Finally, we verified the critical genes by weighted gene co-expression network analysis (WGCNA) of the GEO data, and further mined the core genes involved in colon cancer. In summary, this study identified an 8-miRNA model that can effectively predict the prognosis of colon cancer patients and 14 critical genes with vital roles in colon cancer carcinogenesis. Our findings contribute new ideas for elucidating the molecular mechanisms of colon cancer carcinogenesis and provide new therapeutic targets and biomarkers for future treatment and prognosis.

Chen W ，Gao C ，Liu Y ，Wen Y ，Hong X ，Huang Z ... -  《Frontiers in Genetics》

Identification of Differentially Expressed Genes and miRNAs Associated with Esophageal Squamous Cell Carcinoma by Integrated Analysis of Microarray Data.

To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.

Zhang L ，Chen J ，Cheng T ，Yang H ，Pan C ，Li H ... -  《-》