Identification and analysis of a key long non-coding RNAs (lncRNAs)-associated module reveal functional lncRNAs in cardiac hypertrophy.

Cardiac hypertrophy (CH) is a common disease that originates from long-term heart pressure overload and finally leads to heart failure. Recently, long non-coding RNAs (lncRNAs) have attracted attention because they have broad and crucial functions in regulating complex biological processes. Some studies had found that lncRNAs play vital roles in complex cardiovascular diseases. However, the function and mechanism of lncRNAs in CH have not been elucidated. In our study, to investigate the potential roles of lncRNAs in CH, the Cardiac Hypertrophy-associated LncRNAs-Protein coding genes Network (CHLPN) was constructed by integrating gene microarray re-annotation and subpathway enrichment analyses. After performing random walking with restart in CHLPN, we predicted 21 significant risk lncRNAs, of which 7 (Kis2, 1700110K17Rik, Gm17501, E330017L17Rik, C630043F03Rik, Gm9866 and Ube4bos1) formed a close module with their co-expressed protein-coding genes (PCGs). We found that the module might play crucial roles in the development of CH. In particular, 44 PCGs that were co-expressed with six lncRNAs were enriched in CH-related biological processes and pathways. We also found that some lncRNAs participated in the competitive endogenous RNA cross-talk that might be involved in CH. These results indicate that the functional lncRNAs are related to post-transcriptional regulation and could shed light on a new molecular diagnostic target of CH.

Integrative Pathway Analysis of Genes and Metabolites Reveals Metabolism Abnormal Subpathway Regions and Modules in Esophageal Squamous Cell Carcinoma.

Aberrant metabolism is one of the main driving forces in the initiation and development of ESCC. Both genes and metabolites play important roles in metabolic pathways. Integrative pathway analysis of both genes and metabolites will thus help to interpret the underlying biological phenomena. Here, we performed integrative pathway analysis of gene and metabolite profiles by analyzing six gene expression profiles and seven metabolite profiles of ESCC. Multiple known and novel subpathways associated with ESCC, such as 'beta-Alanine metabolism', were identified via the cooperative use of differential genes, differential metabolites, and their positional importance information in pathways. Furthermore, a global ESCC-Related Metabolic (ERM) network was constructed and 31 modules were identified on the basis of clustering analysis in the ERM network. We found that the three modules located just to the center regions of the ERM network-especially the core region of Module_1-primarily consisted of aldehyde dehydrogenase (ALDH) superfamily members, which contributes to the development of ESCC. For Module_4, pyruvate and the genes and metabolites in its adjacent region were clustered together, and formed a core region within the module. Several prognostic genes, including GPT, ALDH1B1, ABAT, WBSCR22 and MDH1, appeared in the three center modules of the network, suggesting that they can become potentially prognostic markers in ESCC.