Identification of the targets of hematoporphyrin derivative in lung adenocarcinoma using integrated network analysis

BACKGROUND: Hematoporphyrin derivative (HPD) has a sensibilization effect in lung adenocarcinoma. This study was conducted to identify the target genes of HPD in lung adenocarcinoma. METHODS: RNA sequencing was performed using the lung adenocarcinoma cell line A549 after no treatment or treatment with X-ray or X-ray + HPD. The differentially expressed genes (DEGs) were screened using Mfuzz package by noise-robust soft clustering analysis. Enrichment analysis was carried out using "BioCloud" online tool. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. Using WebGestalt tool and integrated transcription factor platform (ITFP), microRNA target and transcription factor (TF) target pairs were separately predicted. An integrated regulatory network was visualized with Cytoscape software. RESULTS: A total of 815 DEGs in the gene set G1 (continuously dysregulated genes along with changes in processing conditions [untreated-treated with X-ray-X-ray + treated with HPD]) and 464 DEGs in the gene set G2 (significantly dysregulated between X-ray + HPD-treated group and untreated/X-ray-treated group) were screened. The significant module identified from the PPI network for gene set G1 showed that ribosomal protein L3 (RPL3) gene could interact with heat shock protein 90 kDa alpha, class A member 1 (HSP90AA1). TFs AAA domain containing 2 (ATAD2) and protein inhibitor of activated STAT 1 (PIAS1) were separately predicted for the genes in gene set G1 and G2, respectively. In the integrated network for gene set G2, ubiquitin-specific peptidase 25 (USP25) was targeted by miR-200b, miR-200c, and miR-429. CONCLUSION: RPL3, HSP90AA1, ATAD2, and PIAS1 as well as USP25, which is targeted by miR-200b, miR-200c, and miR-429, may be the potential targets of HPD in lung adenocarcinoma.

Elementary screening of lymph node metastatic-related genes in gastric cancer based on the co-expression network of messenger RNA, microRNA and long non-coding RNA

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer-related deaths worldwide. The high mortality might be attributed to delay in detection and is closely related to lymph node metastasis. Therefore, it is of great importance to explore the mechanism of lymph node metastasis and find strategies to block GC metastasis. Messenger RNA (mRNA), microRNA (miRNA) and long non-coding RNA (lncRNA) expression data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 908 differentially expressed factors with variance >0.5 including 542 genes, 42 miRNA, and 324 lncRNA were screened using significant analysis microarray algorithm, and interaction networks were constructed using these differentially expressed factors. Furthermore, we conducted functional modules analysis in the network, and found that yellow and turquoise modules could separate samples efficiently. The groups classified in the yellow and turquoise modules had a significant difference in survival time, which was verified in another independent GC mRNA dataset (GSE62254). The results suggested that differentially expressed factors in the yellow and turquoise modules may participate in lymph node metastasis of GC and could be applied as potential biomarkers or therapeutic targets for GC.

Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle

Gene networks have been broadly used to predict gene functions based on guilt by association (GBA) principle. Thus, in order to better understand the molecular mechanisms of esophageal squamous cell carcinoma (ESCC), our study was designed to use a network-based GBA method to identify the optimal gene functions for ESCC. To identify genomic bio-signatures for ESCC, microarray data of GSE20347 were first downloaded from a public functional genomics data repository of Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) between ESCC patients and controls were identified using the LIMMA method. Afterwards, construction of differential co-expression network (DCN) was performed relying on DEGs, followed by gene ontology (GO) enrichment analysis based on a known confirmed database and DEGs. Eventually, the optimal gene functions were predicted using GBA algorithm based on the area under the curve (AUC) for each GO term. Overall, 43 DEGs and 67 GO terms were gained for subsequent analysis. GBA predictions demonstrated that 13 GO functions with AUC>0.7 had a good classification ability. Significantly, 6 out of 13 GO terms yielded AUC>0.8, which were determined as the optimal gene functions. Interestingly, there were two GO categories with AUC>0.9, which included cell cycle checkpoint (AUC=0.91648), and mitotic sister chromatid segregation (AUC=0.91597). Our findings highlight the clinical implications of cell cycle checkpoint and mitotic sister chromatid segregation in ESCC progression and provide the molecular foundation for developing therapeutic targets.

Excavation of attractor modules for nasopharyngeal carcinoma via integrating systemic module inference with attract method

The molecular mechanism of nasopharyngeal carcinoma (NPC) is poorly understood and effective therapeutic approaches are needed. This research aimed to excavate the attractor modules involved in the progression of NPC and provide further understanding of the underlying mechanism of NPC. Based on the gene expression data of NPC, two specific protein-protein interaction networks for NPC and control conditions were re-weighted using Pearson correlation coefficient. Then, a systematic tracking of candidate modules was conducted on the re-weighted networks via cliques algorithm, and a total of 19 and 38 modules were separately identified from NPC and control networks, respectively. Among them, 8 pairs of modules with similar gene composition were selected, and 2 attractor modules were identified via the attract method. Functional analysis indicated that these two attractor modules participate in one common bioprocess of cell division. Based on the strategy of integrating systemic module inference with the attract method, we successfully identified 2 attractor modules. These attractor modules might play important roles in the molecular pathogenesis of NPC via affecting the bioprocess of cell division in a conjunct way. Further research is needed to explore the correlations between cell division and NPC.

Revealing potential long non-coding RNA biomarkers in lung adenocarcinoma using long non-coding RNA-mediated competitive endogenous RNA network

In our study, we aimed to reveal potential long non-coding RNAs (lncRNA) biomarkers in lung adenocarcinoma (LAD) using lncRNA-mediated competing endogenous RNAs (ceRNAs) network (LMCN). Competing lncRNA-mRNA interactions were identified using the hypergeometric test. Co-expression analysis for the competing lncRNA-mRNA interactions was implemented, and relying on the weight value >0.8, a highly competitive LMCN was further constructed. Degree distribution, betweenness and closeness for LMCN were carried out to analyze the network structure. Functional analyses of mRNAs in LMCN were carried out to further explore the biological functions of lncRNAs. Biclique algorithm was utilized to extract competing modules from the LMCN. Finally, we verified our findings in an independent sample set using qRT-PCR. Based on degrees >60, we identified 4 hubs, including DLEU2, SNHG12, HCP5, and LINC00472. Furthermore, 2 competing modules were identified, and LINC00472 in module 1 functioned as a hub in both LMCN and module. Functional implications of lncRNAs demonstrated that lncRNAs were related to histone modification, negative regulation of cell cycle, neuroactive ligand-receptor interaction, and regulation of actin cytoskeleton. qRT-PCR results demonstrated that lncRNAs LINC00472, and HCP5 were down-regulated in LAD tissues, while the expression level of SNHG12 was up-regulated in LAD tissues. Our study sheds novel light on the roles of lncRNA-related ceRNA network in LAD and facilitates the detection of potential lncRNA biomarkers for LAD diagnosis and treatment. Remarkably, in our study, LINC00472, HCP5, and SNHG12 might be potential biomarkers for LAD management.

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor.

Disease association and inter-connectivity analysis of human brain specific co-expressed functional modules

BACKGROUND: In the recent studies, it is suggested that the analysis of transcriptomic change of functional modules instead of individual genes would be more effective for system-wide identification of cellular functions. This could also provide a new possibility for the better understanding of difference between human and chimpanzee. RESULTS: In this study, we analyzed to find molecular characteristics of human brain functions from the difference of transcriptome between human and chimpanzee's brain using the functional module-centric co-expression analysis. We performed analysis of brain disease association and systems-level connectivity of species-specific co-expressed functional modules. CONCLUSIONS: Throughout the analyses, we found human-specific functional modules and significant overlap between their genes in known brain disease genes, suggesting that human brain disorder could be mediated by the perturbation of modular activities emerged in human brain specialization. In addition, the human-specific modules having neurobiological functions exhibited higher networking than other functional modules. This finding suggests that the expression of neural functions are more connected than other functions, and the resulting high-order brain functions could be identified as a result of consolidated inter-modular gene activities. Our result also showed that the functional module based transcriptome analysis has a potential to expand molecular understanding of high-order complex functions like cognitive abilities and brain disorders.

Bioinformatics analysis of biomarkers and transcriptional factor motifs in Down syndrome

In this study, biomarkers and transcriptional factor motifs were identified in order to investigate the etiology and phenotypic severity of Down syndrome. GSE 1281, GSE 1611, and GSE 5390 were downloaded from the gene expression ominibus (GEO). A robust multiarray analysis (RMA) algorithm was applied to detect differentially expressed genes (DEGs). In order to screen for biological pathways and to interrogate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, the database for annotation, visualization, and integrated discovery (DAVID) was used to carry out a gene ontology (GO) function enrichment for DEGs. Finally, a transcriptional regulatory network was constructed, and a hypergeometric distribution test was applied to select for significantly enriched transcriptional factor motifs. CBR1, DYRK1A, HMGN1, ITSN1, RCAN1, SON, TMEM50B, and TTC3 were each up-regulated two-fold in Down syndrome samples compared to normal samples; of these, SON and TTC3 were newly reported. CBR1, DYRK1A, HMGN1, ITSN1, RCAN1, SON, TMEM50B, and TTC3 were located on human chromosome 21 (mouse chromosome 16). The DEGs were significantly enriched in macromolecular complex subunit organization and focal adhesion pathways. Eleven significantly enriched transcription factor motifs (PAX5, EGR1, XBP1, SREBP1, OLF1, MZF1, NFY, NFKAPPAB, MYCMAX, NFE2, and RP58) were identified. The DEGs and transcription factor motifs identified in our study provide biomarkers for the understanding of Down syndrome pathogenesis and progression.

Differentiated miRNA expression and validation of signaling pathways in apoE gene knockout mice by cross-verification microarray platform

The microRNA (miRNA) regulation mechanisms associated with atherosclerosis are largely undocumented. Specific selection and efficient validation of miRNA regulation pathways involved in atherosclerosis development may be better assessed by contemporary microarray platforms applying cross-verification methodology. A screening platform was established using both miRNA and genomic microarrays. Microarray analysis was then simultaneously performed on pooled atherosclerotic aortic tissues from 10 Apolipoprotein E (apoE) knockout mice (apoE-/-) and 10 healthy C57BL/6 (B6) mice. Differentiated miRNAs were screened and cross-verified against an mRNA screen database to explore integrative mRNA-miRNA regulation. Gene set enrichment analysis was conducted to describe the potential pathways regulated by these mRNA-miRNA interactions. High-throughput data analysis of miRNA and genomic microarrays of knockout and healthy control mice revealed 75 differentially expressed miRNAs in apoE-/- mice at a threshold value of 2. The six miRNAs with the greatest differentiation expression were confirmed by real-time quantitative reverse-transcription PCR (qRT-PCR) in atherosclerotic tissues. Significantly enriched pathways, such as the type 2 diabetes mellitus pathway, were observed by a gene-set enrichment analysis. The enriched molecular pathways were confirmed through qRT-PCR evaluation by observing the presence of suppressor of cytokine signaling 3 (SOCS3) and SOCS3-related miRNAs, miR-30a, miR-30e and miR-19b. Cross-verified high-throughput microarrays are optimally accurate and effective screening methods for miRNA regulation profiles associated with atherosclerosis. The identified SOCS3 pathway is a potentially valuable target for future development of targeted miRNA therapies to control atherosclerosis development and progression.

Integrative epigenomic and genomic analysis of malignant pheochromocytoma

Epigenomic and genomic changes affect gene expression and contribute to tumor development. The histone modifications trimethylated histone H3 lysine 4 (H3K4me3) and lysine 27 (H3K27me3) are epigenetic regulators associated to active and silenced genes, respectively and alterations of these modifications have been observed in cancer. Furthermore, genomic aberrations such as DNA copy number changes are common events in tumors. Pheochromocytoma is a rare endocrine tumor of the adrenal gland that mostly occurs sporadic with unknown epigenetic/genetic cause. The majority of cases are benign. Here we aimed to combine the genome-wide profiling of H3K4me3 and H3K27me3, obtained by the ChIP-chip methodology, and DNA copy number data with global gene expression examination in a malignant pheochromocytoma sample. The integrated analysis of the tumor expression levels, in relation to normal adrenal medulla, indicated that either histone modifications or chromosomal alterations, or both, have great impact on the expression of a substantial fraction of the genes in the investigated sample. Candidate tumor suppressor genes identified with decreased expression, a H3K27me3 mark and/or in regions of deletion were for instance TGIF1, DSC3, TNFRSF10B, RASSF2, HOXA9, PTPRE and CDH11. More genes were found with increased expression, a H3K4me3 mark, and/or in regions of gain. Potential oncogenes detected among those were GNAS, INSM1, DOK5, ETV1, RET, NTRK1, IGF2, and the H3K27 trimethylase gene EZH2. Our approach to associate histone methylations and DNA copy number changes to gene expression revealed apparent impact on global gene transcription, and enabled the identification of candidate tumor genes for further exploration.

Computational methods to dissect cis-regulatory transcriptional networks.

The formation of diverse cell types from an invariant set of genes is governed by biochemical and molecular processes that regulate gene activity. A complete understanding of the regulatory mechanisms of gene expression is the major function of genomics. Computational genomics is a rapidly emerging area for deciphering the regulation of metazoan genes as well as interpreting the results of high-throughput screening. The integration of computer science with biology has expedited molecular modelling and processing of large-scale data inputs such as microarrays, analysis of genomes, transcriptomes and proteomes. Many bioinformaticians have developed various algorithms for predicting transcriptional regulatory mechanisms from the sequence, gene expression and interaction data. This review contains compiled information of various computational methods adopted to dissect gene expression pathways.