Identification of Potential Prognostic Biomarkers for Breast Cancer Based on lncRNA-TF-Associated ceRNA Network and Functional Module

Breast cancer leads to most of cancer deaths among women worldwide. Systematically analyzing the competing endogenous RNA (ceRNA) network and their functional modules may provide valuable insight into the pathogenesis of breast cancer. In this study, we constructed a lncRNA-TF-associated ceRNA network via combining all the significant lncRNA-TF ceRNA pairs and TF-TF PPI pairs. We computed important topological features of the network, such as degree and average path length. Hub nodes in the lncRNA-TF-associated ceRNA network were extracted to detect differential expression in different subtypes and tumor stages of breast cancer. MCODE was used for identifying the closely connected modules from the ceRNA network. Survival analysis was further used for evaluating whether the modules had prognosis effects on breast cancer. TF motif searching analysis was performed for investigating the binding potentials between lncRNAs and TFs. As a result, a lncRNA-TF-associated ceRNA network in breast cancer was constructed, which had a scale-free property. Hub nodes such as MDM4, ZNF410, AC0842-19, and CTB-89H12 were differentially expressed between cancer and normal sample in different subtypes and tumor stages. Two closely connected modules were identified to significantly classify patients into a low-risk group and high-risk group with different clinical outcomes. TF motif searching analysis suggested that TFs, such as NFAT5, might bind to the promoter and enhancer regions of hub lncRNAs and function in breast cancer biology. The results demonstrated that the synergistic, competitive lncRNA-TF ceRNA network and their functional modules played important roles in the biological processes and molecular functions of breast cancer.

Predicting Antimicrobial and Other Cysteine-Rich Peptides in 1267 Plant Transcriptomes

Antimicrobial peptides (AMPs) are a key component of innate immunity in various organisms including bacteria, insects, mammals, and plants. Their mode of action decreases the probability of developing resistance in pathogenic organisms, which makes them a promising object of study. However, molecular biology methods for searching for AMPs are laborious and expensive, especially for plants. Earlier, we developed a computational pipeline for identifying potential AMPs based on the cysteine motifs they usually possess. Since most motifs are too species-specific, a wide-scale screening of novel data is required to maintain the accuracy of searching algorithms. We have performed a search for potential AMPs in 1267 plant transcriptomes using our pipeline. On average, 50–150 peptides were revealed in each transcriptome. The data was verified by a BLASTp search in nr database to confirm peptide functions and by using random nucleotide sequences to estimate the fraction of erroneous predictions. The datasets obtained will be useful both for molecular biologists investigating AMPs in various organisms and for bioinformaticians developing novel algorithms of motif searching in transcriptomic and genomic sequences. The results obtained will represent a good reference point for future investigations in the fields mentioned above.

Sequence and comparative analysis of the MIP gene in Chinese straw mushroom, Volvariella volvacea

The mitochondrial intermediate peptidase (MIP) gene is conserved in fungi. It is linked closely with the mating-type A (mtA) gene. In this study, a fragment of the MIP gene in Volvariella volvacea (Bull. ex Fr.) Singer was first cloned by homologue-based cloning technology. Subsequently, the entire MIP DNA sequence (PYd21-MIP) was obtained after the fragment was compared with the genomic data through BLAST analysis. The PYd21-MIP sequence appeared to be homologous with the MIP gene in other fungi. Phylogenetic analysis of PYd21-MIP and other MIP sequences from diverse fungi agreed with the current organism phylogeny. Analysis of protein domains by InterProScan software and motif searching demonstrated that PYd21-MIP encodes a homologous MIP protein. These data support the hypothesis that the PYd21-MIP protein is a Hog-MIP protein homologue from V. volvacea.