Search

Axillary strategy decisions have become more complex and controversial in considering minimally traumatic therapy instead of sentinel lymph node biopsy, axillary lymph node dissection or regional nodal irradiation for people with breast cancer. The purpose of this study was to noninvasively predict sentinel lymph node (SLN) and non-sentinel lymph node (NSLN) status based on pre-operative sonographic and clinicopathologic features to determine optimal decisions regarding axillary therapy. In total, 701 patients with breast cancer from two independent centers were retrospectively analyzed. The SLN model (SLNM) for predicting SLN status and the NSLN model (NSLNM) for predicting NSLN status were trained based on a training set using the random-forest algorithm, and their performance was validated using an independent external test set. A receiver operating characteristic curve was drawn to obtain the area under the curve, which was used to assess performance. The area under the curve for the SLNM in the training and test, respectively, was 94.2% and 83.0%, and for the NSLNM, 99.5% and 92.7%. The SLNM and NSLNM accurately predicted that 61.46% (319/519) and 17.53% (91/519), respectively, of our participants were non-metastatic. The overall benefit of the three models was 78.99% in our participants. The two models for predicting SLN and NSLN status showed excellent application potential in optimizing axillary strategies.

The clonal mutations in driver genes enable cells to gradually acquire growth advantage in tumor development. Therefore, revealing the functions of clonal driver gene mutations is important. Here, we proposed the method FCMP that considered evolutionary dependencies to analyze the functions of clonal driver gene mutations in a single patient. Applying our method to five cancer types from The Cancer Genome Atlas, we identified specific functions and common functions of clonal driver gene mutations. We found that the clonal driver gene mutations in the same patient played multiple functions. We also found that clonal mutations in the same driver gene performed different functions in different patients. These findings suggested that the clonal driver gene mutations showed strong tumor heterogeneity. In the pan-cancer analysis, the immune-related functions for clonal driver gene mutations were shared by multiple cancer types. In addition, clonal mutations in some driver genes predicted the survival of patients in cancers.

Background Lung adenocarcinoma (LUAD), as the most common subtype of lung cancer, is the leading cause of cancer deaths in the world. The accumulation of driver gene mutations enables cancer cells to gradually acquire growth advantage. Therefore, it is important to understand the functions and interactions of driver gene mutations in cancer progression. Methods We obtained gene mutation data and gene expression profile of 506 LUAD tumors from The Cancer Genome Atlas (TCGA). The subtypes of tumors with driver gene mutations were identified by consensus cluster analysis. Results We found 21 significantly mutually exclusive pairs consisting of 20 genes among 506 LUAD patients. Because of the increased transcriptomic heterogeneity of mutations, we identified subtypes among tumors with non‐silent mutations in driver genes. There were 494 mutually exclusive pairs found among driver gene mutations within different subtypes. Furthermore, we identified functions of mutually exclusive pairs based on the hypothesis of functional redundancy of mutual exclusivity. These mutually exclusive pairs were significantly enriched in nuclear division and humoral immune response, which played crucial roles in cancer initiation and progression. We also found 79 mutually exclusive triples among subtypes of tumors with driver gene mutations, which were key roles in cell motility and cellular chemical homeostasis. In addition, two mutually exclusive triples and one mutually exclusive triple were associated with the overall survival and disease‐specific survival of LUAD patients, respectively. Conclusions We revealed novel mutual exclusivity and generated a comprehensive functional landscape of driver gene mutations, which could offer a new perspective to understand the mechanisms of cancer development and identify potential biomarkers for LUAD therapy., We revealed novel mutual exclusivity and generated a comprehensive functional landscape of driver gene mutations, which could offer a new perspective to understand the mechanisms of cancer development and identify biomarkers for LUAD therapy.

Systematically identifying cancer cell functional states, especially their associations, is key to understanding the pathogenesis of cancers.Here, we systematically identified six cancer-related states, including epithelial-mesenchymal transition (EMT), immune response, epithelial differentiation, stress, G1/S and G2/M phases, in head and neck squamous cell carcinoma (HNSCC) based on single-cell RNA-sequencing (scRNA-seq).We defined the association patterns between these functional states and found the patterns were correlated with the state activity. Particularly, immune response and EMT were negatively, positively, or non-significantly correlated in samples with the highest immune response activity, the lowest activity of the two states, or with the highest EMT activity, respectively. Combining scRNA-seq data of immune cells and four independent HNSCC cohorts, we found the negative relationship between EMT and immune response was correlated with an activated immune microenvironment and a longer survival, while the non-significant relationship was correlated with an immunosuppressed microenvironment and a poor prognosis. Collectively, our results provide insight into the association patterns between functional states in HNSCC, and may facilitate the elucidation of the interactions between cancer cells and immune system during cancer progression.

Over the past decade, thousands of long noncoding RNAs (lncRNAs) have been identified, many of which play crucial roles in normal physiology and human disease. LncRNAs can interact with chromatin and then recruit protein complexes to remodel chromatin states, thus regulating gene expression. However, how lncRNA-chromatin interactions contribute to their biological functions is largely unknown. Here, we collected and constructed an atlas of 188,647 lncRNA-chromatin interactions in human and mouse. All lncRNAs showed diverse epigenetic modification patterns at their binding sites, especially the marks of enhancer activity. Functional analysis of lncRNA target genes further revealed that lncRNAs could exert their functions by binding to both promoter and distal regulatory elements, especially the distal regulatory elements. Intriguingly, many important pathways were observed to be widely regulated by lncRNAs through distal binding. For example, NEAT1, a cancer lncRNA, controls 13.3% of genes in the PI3K-AKT signaling pathway by interacting with distal regulatory elements. In addition, “two-gene” signatures composed of a lncRNA and its distal target genes, such as HOTAIR-CRIM1, provided significant clinical benefits relative to the lncRNA alone. In summary, our findings underscored that lncRNA-distal interactions were essential for lncRNA functions, which would provide new clues to understand the molecular mechanisms of lncRNAs in complex disease.

Epigenetic modifications play critical roles in modulating gene expression, yet their roles in regulatory networks in human cell lines remain poorly characterized. We integrated multiomics data to construct directed regulatory networks with nodes and edges labeled with chromatin states in human cell lines. We observed extensive association of diverse chromatin states and network motifs. The gene expression analysis showed that diverse chromatin states of coherent type-1 feedforward loop (C1-FFL) and incoherent type-1 feedforward loops (I1-FFL) contributed to the dynamic expression patterns of targets. Notably, diverse chromatin state compositions could help C1- or I1-FFL to control a large number of distinct biological functions in human cell lines, such as four different types of chromatin state compositions cooperating with K562-associated C1-FFLs controlling “regulation of cytokinesis,” “G1/S transition of mitotic cell cycle,” “DNA recombination,” and “telomere maintenance,” respectively. Remarkably, we identified six chromatin state-marked C1-FFL instances (HCFC1-NFYA-ABL1, THAP1-USF1-BRCA2, ZNF263-USF1-UBA52, MYC-ATF1-UBA52, ELK1-EGR1-CCT4, and YY1-EGR1-INO80C) could act as prognostic biomarkers of acute myelogenous leukemia though influencing cancer-related biological functions, such as cell proliferation, telomere maintenance, and DNA recombination. Our results will provide novel insight for better understanding of chromatin state-mediated gene regulation and facilitate the identification of novel diagnostic and therapeutic biomarkers of human cancers.

Genomic studies have revealed that genomic aberrations play important roles in the progression of this disease. The aim of this study was to evaluate the associations between clinical survival outcomes of the clonality and subclonality status of driver genes in breast cancer. We performed an integrated analysis to infer the clonal status of 55 driver genes in breast cancer data from TCGA. We used the chi-squared test to assess the relations between clonality of driver gene mutations and clinicopathological factors. The Kaplan–Meier method was performed for the visualization and the differences between survival curves were calculated by log-rank test. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. We identified a high proportion of clonal mutations in these driver genes. Among them, there were 17 genes showing significant associations between their clonality and multiple clinicopathologic factors. Performing survival analysis on BRCA patients with clonal or subclonal driver gene mutations, we found that clonal ERBB2, FOXA1, and KMT2C mutations and subclonal GATA3 and RB1 mutations predicted shorter overall survival compared with those with wild type. Furthermore, clonal ERBB2 and FOXA1 mutations and subclonal GATA3 and RB1 mutations independently predicted for shorter overall survival after adjusting for clinicopathological factors. By longitudinal analysis, the clonality of ERBB2, FOXA1, GATA3, and RB1 significantly predicted patients’ outcome within some specific BRCA tumor stages and histological subtypes. In summary, these clonal or subclonal mutations of driver genes have implications for diagnosis, prognosis, and treatment with BRCA patients.

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.

Background: Somatic mutations accumulate over time in cancer cells as a consequence of mutational processes. However, the role of mutational processes in carcinogenesis remains poorly understood. Methods: We infer the causal relationship between mutational processes and somatic mutations in 5,828 samples spanning 34 cancer subtypes based on the patterns of the mutational signatures and their contributions. A ranked hypergeometric test is used to identify pathways affected by a mutational process. The clinical actionability of APOBEC mutagenesis is evaluated in bladder cancer patients who accepted platinum-based therapy. Finding: Most mutational processes cause abundant recurrent mutations in cancer genes, while exceptionally ultraviolet exposure and altered activity of the error-prone polymerase bring a large number of recurrent non-driver mutations. Furthermore, some mutations are specifically induced by a certain mutational process, such as IDH1 p.R132H which is mainly caused by spontaneous deamination of 5-methylcytosine. At the pathway level, clock-like mutational processes extensively trigger mutations to dysregulate cancer signal transduction pathways. In addition, APOBEC mutational process destroys DNA double strand break repair pathway, and bladder cancer patients with high APOBEC activity, though with homologous recombination proficient, show a significantly longer overall survival with platinum regimens. Interpretation: These findings help to understand how mutational processes act on the genome to promote carcinogenesis, and further, presents novel insights for cancer prevention and treatment, as our results showing, APOBEC mutagenesis and HRD synergistically contributed to the clinical benefits of platinum-based treatment. Funding Information: National Key RD the National Natural Science Foundation of China (61873075, 32070673 and 31871336); the Heilongjiang Provincial Natural Science Foundation (YQ2019C012); the Heilongjiang Postdoctoral Foundation (LBH-Q18099); Heilongjiang Touyan Innovation Team Program and Foundation of Harbin Medical University (QMPT-1905). Declaration of Interests: The authors declare no competing financial interests.

Glioblastoma (GBM) is characterized by rapid and lethal infiltration of brain tissue, which is the primary cause of treatment failure and deaths for GBM. Therefore, understanding the molecular mechanisms of tumor cell invasion is crucial for the treatment of GBM. In this study, we dissected the single-cell RNA-seq data of 3345 cells from four patients and identified dysregulated genes including long non-coding RNAs (lncRNAs), which were involved in the development and progression of GBM. Based on co-expression network analysis, we identified a module (M1) that significantly overlapped with the largest number of dysregulated genes and was confirmed to be associated with GBM invasion by integrating EMT signature, experiment-validated invasive marker and pseudotime trajectory analysis. Further, we denoted invasion-associated lncRNAs which showed significant correlations with M1 and revealed their gradually increased expression levels along the tumor cell invasion trajectory, such as VIM-AS1, WWTR1-AS1, and NEAT1. We also observed the contribution of higher expression of these lncRNAs to poorer survival of GBM patients. These results were mostly recaptured in another validation data of 7930 single cells from 28 GBM patients. Our findings identified lncRNAs that played critical roles in regulating or controlling cell invasion and migration of GBM and provided new insights into the molecular mechanisms underlying GBM invasion as well as potential targets for the treatment of GBM.

Background Glioblastoma multiforme (GBM) is a highly, malignant tumor of the primary central nervous system. Patients diagnosed with this type of tumor have a poor prognosis. Lymphocyte activation plays important roles in the development of cancers and its therapeutic treatments. Objective We sought to identify an efficient lymphocyte activation-associated gene signature that could predict the progression and prognosis of GBM. Methods We used univariate Cox proportional hazards regression and stepwise regression algorithm to develop a lymphocyte activation-associated gene signature in the training dataset (TCGA, n = 525). Then, the signature was validated in two datasets, including GSE16011 (n = 150) and GSE13041 (n = 191) using the Kaplan Meier method. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. Results We identified a lymphocyte activation-associated gene signature (TCF3, IGFBP2, TYRO3 and NOD2) in the training dataset and classified the patients into high-risk and low-risk groups with significant differences in overall survival (median survival 15.33 months vs 12.57 months, HR = 1.55, 95% CI [1.28–1.87], log-rank test P Conclusions In summary, our results suggested that the lymphocyte activation-associated gene signature is a promising factor for the survival of patients, which is helpful for the prognosis of GBM patients.

Breast cancer (BRCA) is the most common cancer and a major cause of death in women. Long non-coding RNAs (lncRNAs) are emerging as key regulators and have been implicated in carcinogenesis and prognosis. In this study, we aimed to develop a lncRNA signature of BRCA patients to improve risk stratification. In the training cohort (GSE21653, n = 232), 17 lncRNAs were identified by univariate Cox proportional hazards regression, which were significantly associated with patients’ survival. The least absolute shrinkage and selection operator-penalized Cox proportional hazards regression analysis was used to identify a six-lncRNA signature. According to the median of the signature risk score, patients were divided into a high-risk group and a low-risk group with significant disease-free survival differences in the training cohort. A similar phenomenon was observed in validation cohorts (GSE42568, n = 101; GSE20711, n = 87). The six-lncRNA signature remained as independent prognostic factors after adjusting for clinical factors in these two cohorts. Furthermore, this signature significantly predicted the survival of grade III patients and estrogen receptor-positive patients. Furthermore, in another cohort (GSE19615, n = 115), the low-risk patients that were treated with tamoxifen therapy had longer disease-free survival than those who underwent no therapy. Overall, the six-lncRNA signature can be a potential prognostic tool used to predict disease-free survival of patients and to predict the benefits of tamoxifen treatment in BRCA, which will be helpful in guiding individualized treatments for BRCA patients.

Although much progress has been made to improve treatment, colon cancer remains a leading cause of cancer death worldwide. Metabolic reprogramming is a significant ability of cancer cells to ensure the necessary energy supply in uncontrolled proliferation. Since reprogramming energy metabolism has emerged as a new hallmark of cancer cells, accumulating evidences have suggested that metabolism-related genes may serve as key regulators of tumorigenesis and potential biomarkers. In this study, we analyzed a set of reprogramming energy metabolism-related genes by transcriptome analysis in colon cancer and revealed a five-gene signature that could significantly predict the overall survival. The reprogramming energy metabolism-related signature could distinguish patients into high-risk and low-risk groups with significantly different survival times (P = 0.0011; HR = 1.92; 95% CI [1.29–2.87]). Its prognostic value was confirmed in another two independent colon cancer cohorts (P = 5.2e–04; HR = 2.09, 95%; CI [1.37–3.2] for GSE17538 and P = 3.8e−04; HR = 2.08, 95% CI [1.37–3.16] for GSE41258). By multivariable analysis, we found that the signature was independent of clinicopathological features. Its power in promoting risk stratification of the current clinical stage was then evaluated by stratified analysis. Moreover, the signature could improve the power of the TNM stage for the prediction of overall survival and could be used in patients who received adjuvant chemotherapy. Overall, our results demonstrated the important role of the reprogramming energy metabolism-related signature in promoting stratification of high-risk patients, which could be diagnostic of adjuvant therapy benefit.

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non‐coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well‐known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan‐cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in ‘Tissue Invasion and Metastasis’ in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA‐based target therapy.

// Jinyuan Xu 1, * , Yujia Lan 1, * , Fulong Yu 1, * , Shiwei Zhu 1, * , Jianrong Ran 1 , Jiali Zhu 1 , Hongyi Zhang 1 , Lili Li 1 , Shujun Cheng 1, 2 , Yun Xiao 1 and Xia Li 1 1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China 2 State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100021, China * These authors contributed equally to this work Correspondence to: Shujun Cheng, email: chengshj@263.net.cn Yun Xiao, email: xiaoyun@ems.hrbmu.edu.cn Xia Li, email: lixia@hrbmu.edu.cn Keywords: lncRNA signature; prostate cancer; transcriptome; prognosis; BCR-free survival Received: December 13, 2017 Accepted: February 27, 2018 Published: May 18, 2018 ABSTRACT Despite highly successful treatments for localized prostate cancer (PCa), prognostic biomarkers are needed to improve patient management and prognosis. Accumulating evidence suggests that long noncoding RNAs (lncRNAs) are key regulators with biological and clinical significance. By transcriptome analysis, we identified a set of consistently dysregulated lncRNAs in PCa across different datasets and revealed an eight-lncRNA signature that significantly associated with the biochemical recurrence (BCR)-free survival. Based on the signature, patients could be classified into high- and low-risk groups with significantly different survival (HR = 2.19; 95% CI = 1.67–2.88; P < 0.0001). Validations in the validation cohorts and another independent cohort confirmed its prognostic value for recurrence prediction. Multivariable analysis showed that the signature was independent of common clinicopathological features and stratified analysis further revealed its role in elevating risk stratification of current prognostic models. Additionally, the eight-lncRNA signature was able to improve on the CAPRA-S score for the prediction of BCR as well as to reflect the metastatic potential of PCa. Functional characterization suggested that these lncRNAs which showed PCa-specific expression patterns may involve in critical processes in tumorigenesis. Overall, our results demonstrated potential application of lncRNAs as novel independent biomarkers. The eight-lncRNA signature may have clinical potential for facilitating further stratification of more aggressive patients who would benefit from adjuvant therapy.

Long intergenic non-coding RNAs (lincRNAs) are emerging as important regulatory molecules involved in diseases including heart failure. However, little is known about how the lincRNAs work together with protein-coding genes (PCGs) contributing to the pathogenesis of heart failure. In this study, we constructed a comprehensive transcriptome profile of lincRNAs, PCGs and miRNAs using RNA-seq and miRNA-seq data of 16 heart failure patients (HFs) and 8 non-failing individuals (NFs). Through integrating lincRNA and PCG expression profiles, we identified HF-associated lincRNA modules. We identified a heart-specific lincRNA module which was significantly enriched for differentially expressed lincRNAs and PCGs. This module was associated with heart failure rather than with other clinical traits such as sex, age, smoking and diabetes mellitus. Moreover, the module was significantly correlated with certain indicators of left ventricular function like ejection fraction and left ventricular end-diastolic diameter, implying the potential of its components as crucial biomarkers. Apart from enhancer-like function, lincRNAs in this module could act as competing endogenous RNAs (ceRNAs) to regulate genes which were associated with left-ventricular systolic function. Our work provided deep insights into the critical roles of lincRNAs in the pathology of heart failure and suggested that they could be valuable biomarkers and therapeutic targets.

Background Epigenetic marks can cooperatively regulate chromatin accessibility and in turn facilitate or impede the binding of regulatory factors to various elements, suggesting their important roles in regulatory circuits. However, it remains elusive as to how epigenetic marks cooperate in the operations of regulatory network. Methods Here, we systematically characterized chromatin states of 26 epigenetic marks on different elements of protein-coding genes and miRNAs. We comprehensively analyzed, by using an integrative regulatory network, how cooperation among epigenetic, transcriptional, and post-transcriptional regulations came about. Results We observed extensive cooperation of epigenetic marks on local functional elements and complex epigenetic patterns corresponding to different biological functions. By identifying the significantly epigenetic state-modified motifs, we found that multiple combinations of epigenetic states were associated with a specific type of motif. Interestingly, miRNA-mediated motifs were linked to stable epigenetic states of downstream targets. Changes in epigenetic states of downstream targets in miRNA-mediated motifs can buffer the effects of upstream regulator on target genes, suggesting that miRNA-mediated motifs require the cooperation of epigenetic marks. Conclusions Overall, epigenetic marks are involved in the running of regulatory motifs in the way traffic lights control traffic flows and hence should be part of the architecture of complex regulatory circuits. General significance We demonstrated a detailed analysis of the cooperation of multiple epigenetic marks and how epigenetic regulation was organized into a human regulatory network. The findings form a basis for further understanding of the complicated roles of epigenetic marks on regulatory circuits.

Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.

Background DNA methylation is thought to be extensively involved in the pathogenesis of many diseases, including major psychosis. However, most studies focus on DNA methylation alteration at promoters of protein-coding genes, despite the poor correlation between DNA methylation and gene expression. Methods We analyzed differentially methylated regions and differentially expressed genes in patients with schizophrenia and bipolar disorder and normal subjects. Gene expression and DNA methylation were analyzed with RNA-seq and MeDIP-seq of post-mortem brain tissue (brain region BA9) cohort in five schizophrenia, seven bipolar disorder cases and six controls, respectively. Results Here, we performed a large-scale integrative analysis using MeDIP-seq, coupled with RNA-seq, on brain samples from major psychotic and normal subjects and observed obvious discrepancy between DNA methylation and gene expression. We found that differentially methylated regions (DMRs) were distributed across different types of genomic elements, especially introns. These intronic DMRs were significantly enriched for diverse regulatory elements, such as enhancers and binding sites of certain transcriptional factors (e.g., Pol3). Notably, we found that parts of intronic DMRs overlapped with some intragenic miRNAs, such as hsa-mir-7-3. These intronic DMR-related miRNAs were found to target many differentially expressed genes. Moreover, functional analysis demonstrated that differential target genes of intronic DMR-related miRNAs were sufficient to capture many important biological processes in major psychosis, such as neurogenesis, suggesting that miRNAs may function as important linkers mediating the relationships between DNA methylation alteration and gene expression changes. Conclusions Collectively, our study indicated that DNA methylation alteration could induce expression changes indirectly by affecting miRNAs and the exploration of DMR-related miRNAs and their targets enhanced understanding of the molecular mechanisms underlying major psychosis. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0139-4) contains supplementary material, which is available to authorized users.

Supplemental Methods. Subjects. Postmortem tissue. DNA and RNA preparation. Figure S1. Saturation analysis. Analysis was performed for each sample, with the number of reads plotted on the x-axis and correlation coefficient on the y-axis. Figure S2. The UCSC Browser screenshot showing raw RNA-seq and MeDIP-seq reads distributions around gene PLP1 in SZ patients and controls. Yellow shadows represent the regions with differential expression (up) and methylation (bottom), respectively. Figure S3. Scatter diagrams showing the correlations between promoter methylation levels (x axis) and log-transformed expression (y axis) of protein coding genes (left) and miRNAs (right). Table S1. Demographic characteristics for 18 subjects. Table S2. The number (percentage) of genes with hyper vs hypomethylated elements and gene expression (up or down) for SZ. Table S3. The number (percentage) of genes with hyper vs hypomethylated elements and gene expression (up or down) for BD. Table S4. The number of genes with hyper vs hypomethylated promoters and gene expression (up or down) for SZ. Table S5. The number of genes with hyper vs hypomethylated promoters and gene expression (up or down) for BD. (DOC 21Â kb)

Genome-wide transcriptome profiling after gene perturbation is a powerful means of elucidating gene functional mechanisms in diverse contexts. The comprehensive collection and analysis of the resulting transcriptome profiles would help to systematically characterize context-dependent gene functional mechanisms and conduct experiments in biomedical research. To this end, we collected and curated over 3000 transcriptome profiles in human and mouse from diverse gene perturbation experiments, which involved 1585 different perturbed genes (microRNAs, lncRNAs and protein-coding genes) across 1170 different cell lines/tissues. For each profile, we identified differential genes and their associated functions and pathways, constructed perturbation networks, predicted transcription regulation and cancer/drug associations and assessed cooperative perturbed genes. Based on these transcriptome analyses, the Gene Perturbation Atlas (GPA) can be used to detect (i) novel or cell-specific functions and pathways affected by perturbed genes, (ii) protein interactions and regulatory cascades affected by perturbed genes and (iii) perturbed gene-mediated cooperative effects. The GPA is a user-friendly database to support the rapid searching and exploration of gene perturbations. Particularly, we visualized functional effects of perturbed genes from multiple perspectives. In summary, the GPA is a valuable resource for characterizing gene functions and regulatory mechanisms after single-gene perturbations. The GPA is freely accessible at http://biocc.hrbmu.edu.cn/GPA/.