Ensemble learning model for identifying the hallmark genes of NFκB/TNF signaling pathway in cancers.

BACKGROUND: The nuclear factor kappa B (NFκB) regulatory pathways downstream of tumor necrosis factor (TNF) play a critical role in carcinogenesis. However, the widespread influence of NFκB in cells can result in off-target effects, making it a challenging therapeutic target. Ensemble learning is a machine learning technique where multiple models are combined to improve the performance and robustness of the prediction. Accordingly, an ensemble learning model could uncover more precise targets within the NFκB/TNF signaling pathway for cancer therapy. METHODS: In this study, we trained an ensemble learning model on the transcriptome profiles from 16 cancer types in the TCGA database to identify a robust set of genes that are consistently associated with the NFκB/TNF pathway in cancer. Our model uses cancer patients as features to predict the genes involved in the NFκB/TNF signaling pathway and can be adapted to predict the genes for different cancer types by switching the cancer type of patients. We also performed functional analysis, survival analysis, and a case study of triple-negative breast cancer to demonstrate our model's potential in translational cancer medicine. RESULTS: Our model accurately identified genes regulated by NFκB in response to TNF in cancer patients. The downstream analysis showed that the identified genes are typically involved in the canonical NFκB-regulated pathways, particularly in adaptive immunity, anti-apoptosis, and cellular response to cytokine stimuli. These genes were found to have oncogenic properties and detrimental effects on patient survival. Our model also could distinguish patients with a specific cancer subtype, triple-negative breast cancer (TNBC), which is known to be influenced by NFκB-regulated pathways downstream of TNF. Furthermore, a functional module known as mononuclear cell differentiation was identified that accurately predicts TNBC patients and poor short-term survival in non-TNBC patients, providing a potential avenue for developing precision medicine for cancer subtypes. CONCLUSIONS: In conclusion, our approach enables the discovery of genes in NFκB-regulated pathways in response to TNF and their relevance to carcinogenesis. We successfully categorized these genes into functional groups, providing valuable insights for discovering more precise and targeted cancer therapeutics.

Transcriptomic Profiling Reveals an Enhancer RNA Signature for Recurrence Prediction in Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) is one of the most fatal malignancies worldwide, and this is in part due to high rates of tumor recurrence in these patients. Currently, TNM staging remains the gold standard for predicting prognosis and recurrence in CRC patients; however, this approach is inadequate for identifying high-risk patients with the highest likelihood of disease recurrence. Recent evidence has revealed that enhancer RNAs (eRNAs) represent a higher level of cellular regulation, and their expression is frequently dysregulated in several cancers, including CRC. However, the clinical significance of eRNAs as recurrence predictor biomarkers in CRC remains unexplored, which is the primary aim of this study. RESULTS: We performed a systematic analysis of eRNA expression profiles in colon cancer (CC) and rectal cancer (RC) patients from the TCGA dataset. By using rigorous biomarker discovery approaches by splitting the entire dataset into a training and testing cohort, we identified a 22-eRNA panel in CC and a 19-eRNA panel in RC for predicting tumor recurrence. The Kaplan-Meier analysis showed that biomarker panels robustly stratified low and high-risk CC (p = 7.29 × 10-5) and RC (p = 6.81 × 10-3) patients with recurrence. Multivariate and LASSO Cox regression models indicated that both biomarker panels were independent predictors of recurrence and significantly superior to TNM staging in CC (HR = 11.89, p = 9.54 × 10-4) and RC (HR = 3.91, p = 3.52 × 10-2). Notably, the ROC curves demonstrated that both panels exhibited excellent recurrence prediction accuracy in CC (AUC = 0.833; 95% CI: 0.74-0.93) and RC (AUC = 0.834; 95% CI: 0.72-0.92) patients. Subsequently, a combination signature that included the eRNA panels and TNM staging achieved an even greater predictive accuracy in patients with CC (AUC = 0.85). CONCLUSIONS: Herein, we report a novel eRNA signature for predicting recurrence in patients with CRC. Further experimental validation in independent clinical cohorts, these biomarkers can potentially improve current risk stratification approaches for guiding precision oncology treatments in patients suffering from this lethal malignancy.

Multiomics reveal the central role of pentose phosphate pathway in resident thymic macrophages to cope with efferocytosis-associated stress.

Tissue-resident macrophages (TRMs) are heterogeneous cell populations found throughout the body. Depending on their location, they perform diverse functions maintaining tissue homeostasis and providing immune surveillance. To survive and function within, TRMs adapt metabolically to the distinct microenvironments. However, little is known about the metabolic signatures of TRMs. The thymus provides a nurturing milieu for developing thymocytes yet efficiently removes those that fail the selection, relying on the resident thymic macrophages (TMφs). This study harnesses multiomics analyses to characterize TMφs and unveils their metabolic features. We find that the pentose phosphate pathway (PPP) is preferentially activated in TMφs, responding to the reduction-oxidation demands associated with the efferocytosis of dying thymocytes. The blockade of PPP in Mφs leads to decreased efferocytosis, which can be rescued by reactive oxygen species (ROS) scavengers. Our study reveals the key role of the PPP in TMφs and underscores the importance of metabolic adaptation in supporting Mφ efferocytosis.

Stratification of lncRNA modulation networks in breast cancer.

BACKGROUND: Recently, non-coding RNAs are of growing interest, and more scientists attach importance to research on their functions. Long non-coding RNAs (lncRNAs) are defined as non-protein coding transcripts longer than 200 nucleotides. We already knew that lncRNAs are related to cancers and will be dysregulated in them. But most of their functions are still left to further study. A mechanism of RNA regulation, known as competing endogenous RNAs (ceRNAs), has been proposed to explain the complex relationships among mRNAs and lncRNAs by competing for binding with shared microRNAs (miRNAs). METHODS: We proposed an analysis framework to construct the association networks among lncRNA, mRNA, and miRNAs based on their expression patterns and decipher their network modules. RESULTS: We collected a large-scale gene expression dataset of 1,061 samples from breast invasive carcinoma (BRCA) patients, each consisted of the expression profiles of 4,359 lncRNAs, 16,517 mRNAs, and 534 miRNAs, and applied the proposed analysis approach to interrogate them. We have uncovered the underlying ceRNA modules and the key modulatory lncRNAs for different subtypes of breast cancer. CONCLUSIONS: We proposed a modulatory analysis to infer the ceRNA effects among mRNAs and lncRNAs and performed functional analysis to reveal the plausible mechanisms of lncRNA modulation in the four breast cancer subtypes. Our results might provide new directions for breast cancer therapeutics and the proposed method could be readily applied to other diseases.

Identification and Elucidation of the Protective isomiRs in Lung Cancer Patient Prognosis.

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.

Characterization of the Survival Influential Genes in Carcinogenesis.

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.

Reduced lipolysis in lipoma phenocopies lipid accumulation in obesity.

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.

Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis.

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.

Co-expression analysis identifies long noncoding RNA SNHG1 as a novel predictor for event-free survival in neuroblastoma.

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.

Dendritic Cells Regulate GPR34 through Mitogenic Signals and Undergo Apoptosis in Its Absence.

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.

Systematic dissection of dysregulated transcription factor-miRNA feed-forward loops across tumor types.

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.

A cross-cancer differential co-expression network reveals microRNA-regulated oncogenic functional modules.

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.

A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types.

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.

Regulation rewiring analysis reveals mutual regulation between STAT1 and miR-155-5p in tumor immunosurveillance in seven major cancers.

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.

Concordant dysregulation of miR-5p and miR-3p arms of the same precursor microRNA may be a mechanism in inducing cell proliferation and tumorigenesis: a lung cancer study.

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.

Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach.

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.

Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome.

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.

Mirin: identifying microRNA regulatory modules in protein-protein interaction networks.

UNLABELLED: 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/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MicroRNA-regulated protein-protein interaction networks and their functions in breast cancer.

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.

Differential microRNA regulation correlates with alternative polyadenylation pattern between breast cancer and normal cells.

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.