Steller's sea cow, an extinct sirenian and one of the largest Quaternary mammals, was described by Georg Steller in 1741 and eradicated by humans within 27 years. Here, we complement Steller's descriptions with paleogenomic data from 12 individuals. We identified convergent evolution between Steller's sea cow and cetaceans but not extant sirenians, suggesting a role of several genes in adaptation to cold aquatic (or marine) environments. Among these are inactivations of lipoxygenase genes, which in humans and mouse models cause ichthyosis, a skin disease characterized by a thick, hyperkeratotic epidermis that recapitulates Steller's sea cows' reportedly bark-like skin. We also found that Steller's sea cows' abundance was continuously declining for tens of thousands of years before their description, implying that environmental changes also contributed to their extinction.

Background: RNA-seq emerges as a valuable method for clinical genetics. The transcriptome is "dynamic" and tissue-specific, but typically the probed tissues to analyze (TA) are different from the tissue of interest (TI) based on pathophysiology.

Results: We developed Phenotype-Tissue Expression and Exploration (PTEE), a tool to facilitate the decision about the most suitable TA for RNA-seq. We integrated phenotype-annotated genes, used 54 tissues from GTEx to perform correlation analyses and identify expressed genes and transcripts between TAs and TIs. We identified skeletal muscle as the most appropriate TA to inquire for cardiac arrhythmia genes and skin as a good proxy to study neurodevelopmental disorders. We also explored RNA-seq limitations and show that on-off switching of gene expression during ontogenesis or circadian rhythm can cause blind spots for RNA-seq-based analyses.

Conclusions: PTEE aids the identification of tissues suitable for RNA-seq for a given pathology to increase the success rate of diagnosis and gene discovery. PTEE is freely available at https://bioinf.eva.mpg.de/PTEE/.

MicroRNAs (miRNAs) are approximately 20-22 nucleotides in length, which are well known to participate in the post-transcriptional modification. The mature miRNAs were observed to be varied on 5' or 3' that raise another term-the isoforms of mature miRNAs (isomiRs), which have been proven not the artifacts and discussed widely recently. In our research, we focused on studying the 5' isomiRs in lung adenocarcinoma (LUAD) in The Cancer Genome Atlas (TCGA). We characterized 75 isomiRs significantly associated with better prognosis and 43 isomiRs with poor prognosis. The 75 protective isomiRs can successfully distinguish tumors from normal samples and are expressed differently between patients of early and late stages. We also found that most of the protective isomiRs tend to be with downstream shift and upregulated compared with those with upstream shift, implying that a possible selection occurs during cancer development. Among these protective isomiRs, we observed a highly positive and significant correlation, as well as in harmful isomiRs, suggesting cooperation within the group. However, between protective and harmful, there is no such a concordance but conversely more negative correlation, suggesting the possible antagonistic effect between protective and harmful isomiRs. We also identified that two isomiRs miR-181a-3p|-3 and miR-181a-3p|2, respectively, belong to the harmful and protective groups, suggesting a bidirectional regulation of their originated archetype-miR-181a-3p. Additionally, we found that the protective isomiRs of miR-21-5p, which is an oncomiR, may be evolved as the tumor suppressors through producing isomiRs to hinder metastasis. In summary, these results displayed the characteristics of the protective isomiRs and their potential for developing the treatment of lung cancer.

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

Background
Elucidation of lipid metabolism and accumulation mechanisms is of paramount importance to understanding obesity and unveiling therapeutic targets. In vitro cell models have been extensively used for these purposes, yet, they do not entirely reflect the in vivo setup. Conventional lipomas, characterized by the presence of mature adipocytes and increased adipogenesis, could overcome the drawbacks of cell cultures. Also, they have the unique advantage of easily accessible matched controls in the form of subcutaneous adipose tissue (SAT) from the same individual. We aimed to determine whether lipomas are a good model to understand lipid accumulation.

Methods
We histologically compared lipomas and control SAT, followed by assessment of the lipidome using high-resolution 1H NMR spectroscopy and ESI-IT mass spectrometry. RNA-sequencing was used to obtain the transcriptome of lipomas and the matched SAT.

Results
We found a significant increase of small-size (maximal axis < 70 µm) and very big (maximal axis > 150 µm) adipocytes within lipomas. This suggests both enhanced adipocyte proliferation and increased lipid accumulation. We further show that there is no significant change in the lipid composition compared to matched SAT. To better delineate the pathophysiology of lipid accumulation, we considered two groups with different genetic backgrounds: (1) lipomas with HMGA2 fusions and (2) without gene fusions. To reduce the search space for genes that are relevant for lipid pathophysiology, we focused on the overlapping differentially expressed (DE) genes between the two groups. Gene Ontology analysis revealed that DE genes are enriched in pathways related to lipid accumulation.

Conclusions
We show that the common shared lipid accumulation mechanism in lipoma is a reduction in lipolysis, with most gene dysregulations leading to a reduced cAMP in the adipocyte. Superficial lipomas could thus be used as a model for lipid accumulation through altered lipolysis as found in obese patients.

The development of disease involves a systematic disturbance inside cells and is associated with changes in the interactions or regulations among genes forming biological networks. The bridges inside a network are critical in shortening the distances between nodes. We observed that, inside the human gene regulatory network, one strongly connected core bridged the whole network. Other regulations outside the core formed a weakly connected component surrounding the core like a peripheral structure. Furthermore, the regulatory feedback loops (FBLs) inside the core compose an interface-like structure between the core and periphery. We then denoted the regulatory FBLs as the interface core. Notably, both the cancer-associated and essential biomolecules and regulations were significantly overrepresented in the interface core. These results implied that the interface core is not only critical for the network structure but central in cellular systems. Furthermore, the enrichment of the cancer-associated and essential regulations in the interface core might be attributed to its bridgeness in the network. More importantly, we identified one regulatory FBL between HNF4A and NR2F2 that possesses the highest bridgeness in the interface core. Further investigation suggested that the disturbance of the HNF4A-NR2F2 FBL might protect tumor cells from apoptotic processes. Our results emphasize the relevance of the regulatory network properties to cellular systems and might reveal a critical role of the interface core in cancer.

Despite of the discovery of protein therapeutic targets and advancement in multimodal therapy, the survival chance of high-risk neuroblastoma (NB) patients is still less than 50%. MYCN amplification is a potent driver of NB, which exerts its oncogenic activity through either activating or inhibiting the transcription of target genes. Recently, long noncoding RNAs (lncRNAs) are reported to be altered in cancers including NB. However, lncRNAs that are altered by MYCN amplification and associated with outcome in high-risk NB patients are limitedly discovered. Herein, we examined the expression profiles of lncRNAs and protein-coding genes between MYCN amplified and MYCN non-amplified NB from microarray (n = 47) and RNA-seq datasets (n = 493). We identified 6 lncRNAs in common that were differentially expressed (adjusted P ≤ 0.05 and fold change ≥ 2) and subsequently validated by RT-qPCR. The co-expression analysis reveals lncRNA, SNHG1 and coding gene, TAF1D highly co-expressed in NB. Kaplan-Meier analysis shows that higher expression of SNHG1 is significantly associated with poor patient survival. Importantly, multivariate analysis confirms high expression of SNHG1 as an independent prognostic marker for event-free survival (EFS) (HR = 1.58, P = 2.36E-02). In conclusion, our study unveils that SNHG1 is up-regulated by MYCN amplification and could be a potential prognostic biomarker for high-risk NB intervention.

Transcription factor and microRNA (miRNA) can mutually regulate each other and jointly regulate their shared target genes to form feed-forward loops (FFLs). While there are many studies of dysregulated FFLs in a specific cancer, a systematic investigation of dysregulated FFLs across multiple tumor types (pan-cancer FFLs) has not been performed yet. In this study, using The Cancer Genome Atlas data, we identified 26 pan-cancer FFLs, which were dysregulated in at least five tumor types. These pan-cancer FFLs could communicate with each other and form functionally consistent subnetworks, such as epithelial to mesenchymal transition-related subnetwork. Many proteins and miRNAs in each subnetwork belong to the same protein and miRNA family, respectively. Importantly, cancer-associated genes and drug targets were enriched in these pan-cancer FFLs, in which the genes and miRNAs also tended to be hubs and bottlenecks. Finally, we identified potential anticancer indications for existing drugs with novel mechanism of action. Collectively, this study highlights the potential of pan-cancer FFLs as a novel paradigm in elucidating pathogenesis of cancer and developing anticancer drugs.

Dendritic cells (DCs) are specifically equipped with the G protein–coupled receptor 34 (GPR34). Tight regulation of GPR34 gene expression seems highly important for proper immunological functions, because the absence of this receptor leads to an alteration of the immune response, whereas overexpression was reported to be involved in neuroinflammation. However, the regulatory mechanism of GPR34 expression has not yet been investigated. Whole-transcriptome RNA sequencing analysis from spleens and DCs of GPR34 knockout and wild-type mice, combined with protein–protein interaction data, revealed functional modules affected by the absence of this receptor. Among these, NF-κB, MAPK, and apoptosis-signaling pathways showed high significance. Using murine DCs we experimentally show that NF-κB and MAPK pathways are involved in the downregulation of GPR34. DCs lacking GPR34 have a higher caspase-3/7 activity and increased apoptosis levels. Our study reveals a novel role of GPR34 in the fate of DCs and identifies a regulatory mechanism that could be relevant for treatment of GPR34-overexpressing pathologies, such as neuroinflammatory or cancer conditions.

A precursor microRNA (miRNA) has two arms: miR-5p and miR-3p (miR-5p/-3p). Depending on the tissue or cell types, both arms can become functional. However, little is known about their coregulatory mechanisms during the tumorigenic process. Here, by using the large-scale miRNA expression profiles of five cancer types, we revealed that several of miR-5p/-3p arms were concordantly dysregulated in each cancer. To explore possible coregulatory mechanisms of concordantly dysregulated miR-5p/-3p pairs, we developed a robust computational framework and applied it to lung cancer data. The framework deciphers miR-5p/-3p coregulated protein interaction networks critical to lung cancer development. As a novel part in the method, we uniquely applied the second-order partial correlation to minimize false-positive regulations. Using 279 matched miRNA and mRNA expression profiles extracted from tumor and normal lung tissue samples, we identified 17 aberrantly expressed miR-5p/-3p pairs that potentially modulate the gene expression of 35 protein complexes. Functional analyses revealed that these complexes are associated with cancer-related biological processes, suggesting the oncogenic potential of the reported miR-5p/-3p pairs. Specifically, we revealed that the reduced expression of miR-145-5p/-3p pair potentially contributes to elevated expression of genes in the “FOXM1 transcription factor network” pathway, which may consequently lead to uncontrolled cell proliferation. Subsequently, the regulation of miR-145-5p/-3p in the FOXM1signaling pathway was validated by a cohort of 104 matched miRNA and protein (reverse-phase protein array) expression profiles in lung cancer. In summary, our computational framework provides a novel tool to study miR-5p/-3p coregulatory mechanisms in cancer and other diseases.

MicroRNAs (miRNAs) are small non-coding RNAs that can regulate their target gene expressions at the post-transcriptional level. Moreover, they have been reported as either oncomirs or tumor suppressors and possess therapeutic potential in cancer. In this study, we investigated differential co-expression of miRNAs across four cancer types. We observed that the loss of positive co-expressions among miRNAs frequently occurs in the studied cancer types. This observation suggests that the disruption of positive co-expressions among miRNAs may be prevalent during tumorigenesis. By systematically collecting these lost positive co-expressions among miRNAs in cancer, we constructed a cross-cancer miRNA differential co-expression network. We observed that the influential miRNAs in the proposed network, i.e. hubs or in larger cliques, tended to be involved in more cancer types than other miRNAs. Moreover, we found that miRNAs which lose their positive co-expressions in cancers might co-contribute to cancer development, and even could be used to predict the cancer types in which miRNAs were involved. Finally, we identified two potential miRNA-regulated onco-modules, mitosis and DNA replication, that are associated with poor survival outcomes in patients across multiple cancers. Collectively, our study suggested that the disruption of miRNA positive co-expression in cancer might contribute to cancer development. Our findings also form an important basis for identifying miRNAs with potential co-contribution to carcinogenesis.

Transcription factors (TFs) and microRNAs (miRNAs) form a gene regulatory network (GRN) at the transcriptional and post-transcriptional level in living cells. However, this network has not been well characterized, especially in regards to the mutual regulations between TFs and miRNAs in cancers. In this study, we collected those regulations inferred by ChIP-Seq or CLIP-Seq to construct the GRN formed by TFs, miRNAs and target genes. To increase the reliability of the proposed network and examine the regulation activity of TFs and miRNAs, we further incorporated the mRNA and miRNA expression profiles in seven cancer types using The Cancer Genome Atlas data. We observed that regulation rewiring was prevalent during tumorigenesis and found that the rewired regulatory feedback loops formed by TFs and miRNAs were highly associated with cancer. Interestingly, we identified one regulatory feedback loop between STAT1 and miR-155-5p that is consistently activated in all seven cancer types with its function to regulate tumor-related biological processes. Our results provide insights on the losing equilibrium of the regulatory feedback loop between STAT1 and miR-155-5p influencing tumorigenesis.

Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics.

Summary: Exploring microRNA (miRNA) regulations and protein–protein interactions could reveal the molecular mechanisms responsible for complex biological processes. Mirin is a web-based application suitable for identifying functional modules from protein–protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers. The analysis involves combining miRNA regulations, protein–protein interactions between target genes, as well as mRNA and miRNA expression profiles provided by users. Mirin has successfully uncovered oncomirs and their regulatory networks in various cancers, such as gastric and breast cancer.

Availability and implementation: Mirin is freely available at http://mirin.ym.edu.tw/.

Contact: hsuancheng@ym.edu.tw or yukijuan@ntu.edu.tw

Supplementary information: Supplementary Data are available at Bioinformatics online.

Background
Recently, a number of large-scale cancer genome sequencing projects have generated a large volume of somatic mutations; however, identifying the functional consequences and roles of somatic mutations in tumorigenesis remains a major challenge. Researchers have identified that protein pocket regions play critical roles in the interaction of proteins with small molecules, enzymes, and nucleic acid. As such, investigating the features of somatic mutations in protein pocket regions provides a promising approach to identifying new genotype-phenotype relationships in cancer.

Methods
In this study, we developed a protein pocket-based computational approach to uncover the functional consequences of somatic mutations in cancer. We mapped 1.2 million somatic mutations across 36 cancer types from the COSMIC database and The Cancer Genome Atlas (TCGA) onto the protein pocket regions of over 5,000 protein three-dimensional structures. We further integrated cancer cell line mutation profiles and drug pharmacological data from the Cancer Cell Line Encyclopedia (CCLE) onto protein pocket regions in order to identify putative biomarkers for anticancer drug responses.

Results
We found that genes harboring protein pocket somatic mutations were significantly enriched in cancer driver genes. Furthermore, genes harboring pocket somatic mutations tended to be highly co-expressed in a co-expressed protein interaction network. Using a statistical framework, we identified four putative cancer genes (RWDD1, NCF1, PLEK, and VAV3), whose expression profiles were associated with overall poor survival rates in melanoma, lung, or colorectal cancer patients. Finally, genes harboring protein pocket mutations were more likely to be drug-sensitive or drug-resistant. In a case study, we illustrated that the BAX gene was associated with the sensitivity of three anticancer drugs (midostaurin, vinorelbine, and tipifarnib).

Conclusions
This study provides novel insights into the functional consequences of somatic mutations during tumorigenesis and for anticancer drug responses. The computational approach used might be beneficial to the study of somatic mutations in the era of cancer precision medicine.

MicroRNAs are small non-coding RNAs that can regulate expressions of their target genes at the post-transcriptional level. In this study, we propose a tri-component strategy that combines the conservation of microRNAs, homology of mRNA coding regions, and conserved microRNA binding sites in the 3′ untranslated regions to discover conserved microRNA-mRNA interactions. To validate the performance of our conservation strategy, we collected the experimentally validated microRNA-mRNA interactions from three databases as the golden standard. We found that the proposed strategy can improve the performance of existing target prediction algorithms by approximately 2–4 fold. In addition, we demonstrated that the proposed strategy could efficiently retain highly confident interactions from the intersection results of the existing algorithms and filter out the possible false positive predictions in the union one. Furthermore, this strategy can facilitate our ability to trace the homologues in different species that are targeted by the same miRNA family because it combines these three features to identify the conserved miRNA-mRNA interactions during evolution. Through an extensive application of the proposed conservation strategy to a study of the miR-1/206 regulatory network, we demonstrate that the target mRNA recruiting process could be associated with expansion of miRNA family during its evolution. We also uncovered the functional evolution of the miR-1/206 regulatory network. In this network, the early targeted genes tend to participate in more general and development-related functions. In summary, the conservation strategy is capable of helping to highlight the highly confident miRNA-mRNA interactions and can be further applied to reveal the evolutionary features of miRNA regulatory network and functions.

MicroRNAs (miRNAs) are a class of endogenous small noncoding RNAs that regulate gene expression either by degrading target mRNAs or by suppressing protein translation. miRNAs have been found to be involved in many biological processes, such as development, differentiation, and growth. However, the evolution of miRNA regulatory functions and networks has not been well studied. In this study, we conducted a cross-species analysis to study the evolution of cardiac miRNAs and their regulatory functions and networks. We found that conserved cardiac miRNA target genes have maintained highly conserved cardiac functions. Additionally, most of cardiac miRNA target genes in human with annotations of cardiac functions evolved from the corresponding homologous targets, which are also involved in heart development-related functions. On the basis of these results, we investigated the functional evolution of cardiac miRNAs and presented a functional evolutionary map. From this map, we identified the evolutionary time at which the cardiac miRNAs became involved in heart development or function and found that the biological processes of heart development evolved earlier than those of heart functions, for example, heart contraction/relaxation or cardiac hypertrophy. Our study of the evolution of the cardiac miRNA regulatory networks revealed the emergence of new regulatory functional branches during evolution. Furthermore, we discovered that early evolved cardiac miRNA target genes tend to participate in the early stages of heart development. This study sheds light on the evolution of developmental features of genes regulated by cardiac miRNAs.

Cells govern biological functions through complex biological networks. Perturbations to networks may drive cells to new phenotypic states, for example, tumorigenesis. Identifying how genetic lesions perturb molecular networks is a fundamental challenge. This study used large-scale human interactome data to systematically explore the relationship among network topology, somatic mutation, evolutionary rate, and evolutionary origin of cancer genes. We found the unique network centrality of cancer proteins, which is largely independent of gene essentiality. Cancer genes likely have experienced a lower evolutionary rate and stronger purifying selection than those of noncancer, Mendelian disease, and orphan disease genes. Cancer proteins tend to have ancient histories, likely originated in early metazoan, although they are younger than proteins encoded by Mendelian disease genes, orphan disease genes, and essential genes. We found that the protein evolutionary origin (age) positively correlates with protein connectivity in the human interactome. Furthermore, we investigated the network-attacking perturbations due to somatic mutations identified from 3,268 tumors across 12 cancer types in The Cancer Genome Atlas. We observed a positive correlation between protein connectivity and the number of nonsynonymous somatic mutations, whereas a weaker or insignificant correlation between protein connectivity and the number of synonymous somatic mutations. These observations suggest that somatic mutational network-attacking perturbations to hub genes play an important role in tumor emergence and evolution. Collectively, this work has broad biomedical implications for both basic cancer biology and the development of personalized cancer therapy.

Background
Gene expression profiles have been frequently integrated with the human protein interactome to uncover functional modules under specific conditions like disease state. Beyond traditional differential expression analysis, differential co-expression analysis has emerged as a robust approach to reveal condition-specific network modules, with successful applications in a few human disease studies. Hepatocellular carcinoma (HCC), which is often interrelated with the Hepatitis C virus, typically develops through multiple stages. A comprehensive investigation of HCC progression-specific differential co-expression modules may advance our understanding of HCC's pathophysiological mechanisms.

Results
Compared with differentially expressed genes, differentially co-expressed genes were found more likely enriched with Hepatitis C virus binding proteins and cancer-mutated genes, and they were clustered more densely in the human reference protein interaction network. These observations indicated that a differential co-expression approach could outperform the standard differential expression network analysis in searching for disease-related modules. We then proposed a differential co-expression network approach to uncover network modules involved in HCC development. Specifically, we discovered subnetworks that enriched differentially co-expressed gene pairs in each HCC transition stage, and further resolved modules with coherent co-expression change patterns over all HCC developmental stages. Our identified network modules were enriched with HCC-related genes and implicated in cancer-related biological functions. In particular, APC and YWHAZ were highlighted as two most remarkable genes in the network modules, and their dynamic interaction partnership was resolved in HCC development.

Conclusions
We demonstrated that integration of differential co-expression with the protein interactome could outperform the traditional differential expression approach in discovering network modules of human diseases. In our application of this approach to HCC's gene expression data, we successfully identified subnetworks with marked differential co-expression in individual HCC stage transitions and network modules with coherent co-expression change patterns over all HCC developmental stages. Our results shed light on subtle HCC mechanisms, including temporal activation and dismissal of pivotal functions and dynamic interaction partnerships of key genes.

Many biological networks are signed molecular networks which consist of positive and negative links. To reveal the distinct features between links with different signs, we proposed signed link-clustering coefficients that assess the similarity of inter-action profiles between linked molecules. We found that positive links tended to cluster together, while negative links usually behaved like bridges between positive clusters. Positive links with higher adhesiveness tended to share protein domains, be associated with protein-protein interactions and make intra-connections within protein complexes. Negative links that were more bridge-like tended to make interconnections between protein complexes. Utilizing the proposed measures to group positive links, we observed hierarchical modules that could be well characterized by functional annotations or known protein complexes. Our results imply that the proposed sign-specific measures can help reveal the network structural characteristics and the embedded biological contexts of signed links, as well as the functional organization of signed molecular networks.

Alternative polyadenylation (APA) could result in mRNA isoforms with variable lengths of 3′ UTRs. Gain of microRNA target sites in the 3′ UTR of a long mRNA isoform may cause different regulation from the corresponding short isoform. It has been known that cancer cells globally exhibit a lower ratio of long and short isoforms (LSR); that is, they tend to express larger amounts of short isoforms. The objective of this study is to illustrate the relationship between microRNA differential regulation and LSR. We retrieved public APA annotations and isoform expression profiles of breast cancer and normal cells from a high-throughput sequencing method study specific for the mRNA 3′ end. Combining microRNA expression profiles, we performed statistical analysis to reveal and estimate microRNA regulation on APA patterns in a global scale. First, we found that the amount of microRNA target sites in the alternative UTR (aUTR), the region only present in long isoforms, could affect the LSR of the target genes. Second, we observed that the genes whose aUTRs were targeted by up-regulated microRNAs in cancer cells had an overall lower LSR. Furthermore, the target sites of up-regulated microRNAs tended to appear in aUTRs. Finally, we demonstrated that the amount of target sites for up-regulated microRNAs in aUTRs correlated with the LSR change between cancer and normal cells. The results indicate that up-regulation of microRNAs might cause lower LSRs of target genes in cancer cells through degradation of their long isoforms. Our findings provide evidence of how microRNAs might play a crucial role in APA pattern shifts from normal to cancerous or proliferative states.

MicroRNAs, which are small endogenous RNA regulators, have been associated with various types of cancer. Breast cancer is a major health threat for women worldwide. Many miRNAs were reported to be associated with the progression and carcinogenesis of breast cancer. In this study, we aimed to discover novel breast cancer-related miRNAs and to elucidate their functions. First, we identified confident miRNA-target pairs by combining data from miRNA target prediction databases and expression profiles of miRNA and mRNA. Then, miRNA-regulated protein interaction networks (PINs) were constructed with confident pairs and known interaction data in the human protein reference database (HPRD). Finally, the functions of miRNA-regulated PINs were elucidated by functional enrichment analysis. From the results, we identified some previously reported breast cancer-related miRNAs and functions of the PINs, e.g., miR-125b, miR-125a, miR-21, and miR-497. Some novel miRNAs without known association to breast cancer were also found, and the putative functions of their PINs were also elucidated. These include miR-139 and miR-383. Furthermore, we validated our results by receiver operating characteristic (ROC) curve analysis using our miRNA expression profile data, gene expression-based outcome for breast cancer online (GOBO) survival analysis, and a literature search. Our results may provide new insights for research in breast cancer-associated miRNAs.

Fluorescent liposomal nanovesicles (liposomes) are commonly used for lipid research and/or signal enhancement. However, the problem of self-quenching with conventional fluorescent liposomes limits their applications because these liposomes must be lysed to detect the fluorescent signals. Here, we developed a nonquenched fluorescent (NQF)1 liposome by optimizing the proportion of sulforhodamine B (SRB) encapsulant and lissamine rhodamine B-dipalmitoyl phosphatidylethanol (LRB-DPPE) on a liposomal surface for signal amplification. Our study showed that 0.3% of LRB-DPPE with 200 μm of SRB provided the maximal fluorescent signal without the need to lyse the liposomes. We also observed that the NQF liposomes largely eliminated self-quenching effects and produced greatly enhanced signals than SRB-only liposomes by 5.3-fold. To show their application in proteomics research, we constructed NQF liposomes that contained phosphatidylinositol 3,5-bisphosphate (PI(3,5)P2) and profiled its protein interactome using a yeast proteome microarray. Our profiling led to the identification of 162 PI(3,5)P2-specific binding proteins (PI(3,5)P2-BPs). We not only recovered many proteins that possessed known PI(3,5)P2-binding domains, but we also found two unknown Pfam domains (Pfam-B_8509 and Pfam-B_10446) that were enriched in our dataset. The validation of many newly discovered PI(3,5)P2-BPs was performed using a bead-based affinity assay. Further bioinformatics analyses revealed that the functional roles of 22 PI(3,5)P2-BPs were similar to those associated with PI(3,5)P2, including vesicle-mediated transport, GTPase, cytoskeleton, and kinase. Among the 162 PI(3,5)P2-BPs, we found a novel motif, HRDIKP[ES]NJLL that showed statistical significance. A docking simulation showed that PI(3,5)P2 interacted primarily with lysine or arginine side chains of the newly identified PI(3,5)P2-binding kinases. Our study showed that this new tool would greatly benefit profiling lipid–protein interactions in high-throughput studies.

Background
Gene regulatory networks control the global gene expression and the dynamics of protein output in living cells. In multicellular organisms, transcription factors and microRNAs are the major families of gene regulators. Recent studies have suggested that these two kinds of regulators share similar regulatory logics and participate in cooperative activities in the gene regulatory network; however, their combinational regulatory effects and preferences on the protein interaction network remain unclear.

Methods
In this study, we constructed a global human gene regulatory network comprising both transcriptional and post-transcriptional regulatory relationships, and integrated the protein interactome into this network. We then screened the integrated network for four types of regulatory motifs: single-regulation, co-regulation, crosstalk, and independent, and investigated their topological properties in the protein interaction network.

Results
Among the four types of network motifs, the crosstalk was found to have the most enriched protein-protein interactions in their downstream regulatory targets. The topological properties of these motifs also revealed that they target crucial proteins in the protein interaction network and may serve important roles of biological functions.

Conclusions
Altogether, these results reveal the combinatorial regulatory patterns of transcription factors and microRNAs on the protein interactome, and provide further evidence to suggest the connection between gene regulatory network and protein interaction network.

Deregulation of microRNAs (miRNAs) is common in advanced human hepatocellular carcinoma (HCC); however, the ones involved in early carcinogenesis have not yet been investigated. By examining the expression of 22 HCC‐related miRNAs between precancerous and cancerous liver tissues, we found miR‐216a and miR‐224 were significantly up‐regulated, starting from the precancerous stage. Furthermore, the elevation of miR‐216a was mainly identified in male patients. To study this gender difference, we demonstrated that pri‐miR‐216a is activated transcriptionally by the androgen pathway in a ligand‐dependent manner and is further enhanced by the hepatitis B virus X protein. The transcription initiation site for pri‐miR‐216a was delineated, and one putative androgen‐responsive element site was identified within its promoter region. Mutation of this site abolished the elevation of pri‐miR‐216a by the androgen pathway. One target of miR‐216a was shown to be the tumor suppressor in lung cancer‐1 gene (TSLC1) messenger RNA (mRNA) through the three target sites at its 3′ untranslated region. Finally, the androgen receptor level increased in male liver tissues during hepatocarcinogenesis, starting from the precancerous stage, with a concomitant elevation of miR‐216a but a decrease of TSLC1. Conclusion: The current study discovered the up‐regulation of miRNA‐216a by the androgen pathway and a subsequent suppression of TSLC1 as a new mechanism for the androgen pathway in early hepatocarcinogenesis. (HEPATOLOGY 2012)

Gastric cancer is the second most common cause of cancer deaths worldwide and due to its poor prognosis, it is important that specific biomarkers are identified to enable its early detection. Through 2‐D gel electrophoresis and MALDI‐TOF‐TOF‐based proteomics approaches, we found that 14‐3‐3β, which was one of the proteins that were differentially expressed by 5‐fluorouracil‐treated gastric cancer SC‐M1 cells, was upregulated in gastric cancer cells. 14‐3‐3β levels in tissues and serum were further validated in gastric cancer patients and controls. The results showed that 14‐3‐3β levels were elevated in tumor tissues (n=40) in comparison to normal tissues (n=40; p<0.01), and serum 14‐3‐3β levels in cancer patients (n=145) were also significantly higher than those in controls (n=63; p<0.0001). Elevated serum 14‐3‐3β levels highly correlated with the number of lymph node metastases, tumor size and a reduced survival rate. Moreover, overexpression of 14‐3‐3β enhanced the growth, invasiveness and migratory activities of tumor cells. Twenty‐eight proteins involved in anti‐apoptosis and tumor progression were also found to be differentially expressed in 14‐3‐3β‐overexpressing gastric cancer cells. Overall, these results highlight the significance of 14‐3‐3β in gastric cancer cell progression and suggest that it has the potential to be used as a diagnostic and prognostic biomarker in gastric cancer.

Background
MicroRNAs (miRNAs) are a class of endogenous, small and highly conserved noncoding RNAs that control gene expression either by degradation of target mRNAs or by inhibition of protein translation. They play important roles in cancer progression. A single miRNA can provoke a chain reaction and further affect protein interaction network (PIN). Therefore, we developed a novel integrative approach to identify the functional roles and the regulated PIN of oncomirs.

Results
We integrated the expression profiles of miRNA and mRNA with the human PIN to reveal miRNA-regulated PIN in specific biological conditions. The potential functions of miRNAs were determined by functional enrichment analysis and the activities of miRNA-regulated PINs were evaluated by the co-expression of protein-protein interactions (PPIs). The function of a specific miRNA, miR-148a, was further examined by clinical data analysis and cell-based experiments. We uncovered several miRNA-regulated networks which were enriched with functions related to cancer progression. One miRNA, miR-148a, was identified and its function is to decrease tumor proliferation and metastasis through its regulated PIN. Furthermore, we found that miR-148a could reduce the invasiveness, migratory and adhesive activities of gastric tumor cells. Most importantly, elevated miR-148a level in gastric cancer tissues was strongly correlated with distant metastasis, organ and peritoneal invasion and reduced survival rate.

Conclusions
This study provides a novel method to identify active oncomirs and their potential functions in gastric cancer progression. The present data suggest that miR-148a could be a potential prognostic biomarker of gastric cancer and function as a tumor suppressor through repressing the activity of its regulated PIN.

Background
Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear.

Results
We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy.

Conclusions
We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.

Essential genes are responsible for the viability of an organism. Global protein interaction network analysis provides an effective way to understand the relationships between protein products of genes. By means of large-scale identification of essential genes and protein−protein interactions, we investigated the substructure of the protein interaction network in Escherichia coli and identified all the cliques in the network. Our analysis showed that larger cliques tend to have larger fractions of proteins encoded by essential genes. By merging the maximum clique with overlapping neighboring cliques, we observed a dense core of the protein interaction network in Escherichia coli with significantly higher ratio of essential genes. The protein network of Saccharomyces cerevisiae also shows strong correlation between clique and essentiality, and there exist similar dense clusters with high essentiality. Our results indicated that the observed structure of essential cores might exist in higher organisms and play important roles in their respective protein networks.

Essential genes are indispensable to the viability of an organism. Identification and analysis of essential genes is key to understanding the systems level organization of living cells. On the other hand, the ability to predict these genes in pathogens is of great importance for directed drug development. Global analysis of protein interaction networks provides an effective way to elucidate the relationships between genes. It has been found that essential genes tend to be highly connected and generally have more interactions than nonessential ones. With recent large-scale identifications of essential genes and protein–protein interactions in Saccharomyces cerevisiae and Escherichia coli, we have systematically investigated the topological properties of essential and nonessential genes in the protein–protein interaction networks. Essential genes tend to play topologically more important roles in protein interaction networks. Many topological features were found to be statistically discriminative between essential and nonessential genes. In addition, we have also examined sequence properties such as open reading frame length, strand, and phyletic retention for their association with the gene essentiality. Employing the topological features in the protein interaction network and the sequence properties, we have built a machine learning classifier capable of predicting essential genes. Computational prediction of essential genes circumvents expensive and difficult experimental screens and will help antimicrobial drug development.