Correction: Integrating Multi-Omics for Uncovering the Architecture of Cross-Talking Pathways in Breast Cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang L
PROVIDER: S-EPMC6695152 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Correction: Integrating Multi-Omics for Uncovering the Architecture of Cross-Talking Pathways in Breast Cancer.
Wang Li L Xiao Yun Y Ping Yanyan Y Li Jing J Zhao Hongying H Li Feng F Hu Jing J Zhang Hongyi H Deng Yulan Y Tian Jiawei J Li Xia X
PloS one 20190815 8
[This corrects the article DOI: 10.1371/journal.pone.0104282.]. ...[more]
Similar Datasets
Project description:Cross-talk among abnormal pathways widely occurs in human cancer and generally leads to insensitivity to cancer treatment. Moreover, alterations in the abnormal pathways are not limited to single molecular level. Therefore, we proposed a strategy that integrates a large number of biological sources at multiple levels for systematic identification of cross-talk among risk pathways in cancer by random walk on protein interaction network. We applied the method to multi-Omics breast cancer data from The Cancer Genome Atlas (TCGA), including somatic mutation, DNA copy number, DNA methylation and gene expression profiles. We identified close cross-talk among many known cancer-related pathways with complex change patterns. Furthermore, we identified key genes (linkers) bridging these cross-talks and showed that these genes carried out consistent biological functions with the linked cross-talking pathways. Through identification of leader genes in each pathway, the architecture of cross-talking pathways was built. Notably, we observed that linkers cooperated with leaders to form the fundamentation of cross-talk of pathways which play core roles in deterioration of breast cancer. As an example, we observed that KRAS showed a direct connection to numerous cancer-related pathways, such as MAPK signaling pathway, suggesting that it may be a central communication hub. In summary, we offer an effective way to characterize complex cross-talk among disease pathways, which can be applied to other diseases and provide useful information for the treatment of cancer.
| S-EPMC4138095 | biostudies-literature
Project description:Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy of 0.82 with the test dataset. Using these subtypes, we ranked genes based on RNA expression levels. The top 25 genes were investigated, to elucidate the mechanisms that underlie patient prognosis. Bioinformatics analyses showed that the expression levels of six out of 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with LUAD patient survival (p < 0.05), and pathway analyses indicated that major cancer signaling was altered in the subtypes.
| S-EPMC7225957 | biostudies-literature
Project description:Tumor suppressor genes (TSGs) are essential genes in the development of cancer. While they have many roles in normal cells, mutation and dysregulation of the TSGs result in aberrant molecular processes in cancer cells. Therefore, understanding TSGs and their roles in the oncogenic process is crucial for prevention and treatment of cancer. In this research, multi-omics breast cancer data were used to identify molecular mechanisms of TSGs in breast cancer. Differentially expressed genes and differentially coexpressed genes were identified in four large-scale transcriptomics data from public repositories and multi-omics data analyses of copy number, methylation and gene expression were performed. The results of the analyses were integrated using enrichment analysis and meta-analysis of a p-value summation method. The integrative analysis revealed that TSGs have a significant relationship with genes of gene ontology terms that are related to cell cycle, genome stability, RNA processing and metastasis, indicating the regulatory mechanisms of TSGs on cancer cells. The analysis frame and research results will provide valuable information for the further identification of TSGs in different types of cancers.
| S-EPMC9455665 | biostudies-literature
Project description:The phenomenon of a gene expression burst has been attributed to random transitions between the active and inactive states of a gene. However, the mechanisms underlying regulation of the activation process in response to environmental changes remain unclear. Here, we model gene activation as a consequence of the competitive cross talk between a weak basal pathway and an inducible signaling pathway and reveal rich expression dynamics along with intricate dependence of noise and Fano factor on mean expression levels. These theoretical results are in good agreement with a large experimental data set in Escherichia coli, yeast, and mammalian cells. Furthermore, both theoretical analyses and supporting biological evidence converge to demonstrate the existence of a tradeoff that governs the sharp up- and downregulation of gene expression, suggesting an ordered scenario that activates a gene under varying conditions. These regulation modes, together with cross talk pathways, may provide new guidance for the analysis and interpretation of genetic data in various applications, ranging from genetic engineering to therapeutic targets of disease.
| S-EPMC7499120 | biostudies-literature
Project description:DNA repair genes and pathways that are transcriptionally dysregulated in cancer provide the first line of evidence for the altered DNA repair status in tumours, and hence have been explored intensively as a source for biomarker discovery. The molecular mechanisms underlying DNA repair dysregulation, however, have not been systematically investigated in any cancer type. In this study, we performed a statistical analysis to dissect the roles of DNA copy number alteration (CNA), DNA methylation (DM) at gene promoter regions and the expression changes of transcription factors (TFs) in the differential expression of individual DNA repair genes in normal versus tumour breast samples. These gene-level results were summarised at pathway level to assess whether different DNA repair pathways are affected in distinct manners. Our results suggest that CNA and expression changes of TFs are major causes of DNA repair dysregulation in breast cancer, and that a subset of the identified TFs may exert global impacts on the dysregulation of multiple repair pathways. Our work hence provides novel insights into DNA repair dysregulation in breast cancer. These insights improve our understanding of the molecular basis of the DNA repair biomarkers identified thus far, and have potential to inform future biomarker discovery.
| S-EPMC5036051 | biostudies-literature
Project description:Juvenile idiopathic arthritis (JIA) is highly heterogeneous in terms of etiology and clinical presentation with ambiguity in JIA classification. The advance of high-throughput omics technologies in recent years has gained us significant knowledge about the molecular mechanisms of JIA. Besides a minor proportion of JIA cases as monogenic, most JIA cases are polygenic disease caused by autoimmune mechanisms. A number of HLA alleles (including both HLA class I and class II genes), and 23 non-HLA genetic loci have been identified of association with different JIA subtypes. Omics technologies, i.e., transcriptome profiling and epigenomic analysis, contributed significant knowledge on the molecular mechanisms of JIA in addition to the genetic approach. New molecular knowledge on different JIA subtypes enables us to reconsider the JIA classification, but also highlights novel therapeutic targets to develop a cure for the devastating JIA.
| S-EPMC7602566 | biostudies-literature
Project description:Breast cancer (BC) remains a major disease posing a threat to women's health, but the underlying biological interpretation remains largely unknown. Here, we aimed to identify genes associated with breast cancer and analyze their pathophysiological mechanisms based on multi-omics Mendelian randomization (MR). Summary-data-based MR (SMR) was performed to estimate the causal effects of blood and breast mammary tissue expression quantitative trait loci (eQTLs) on BC. External validation analysis was used to validate the identified genes. Integration analyses BC GWAS summaries with eQTLs and DNA methylation QTLs (mQTLs) from the blood were conducted using SMR to prioritize putative blood genes and their regulatory elements associated with BC risk. Finally, two prior genes (ATG10 and RCCD1) from blood tissue reached significant levels in both BCAC (ATG10: ORBRCR = 0.91, PBRCR = 1.29 × 10-11; RCCD1: ORBRCR = 0.90, PBRCR = 3.72 × 10-15) and FinnGen cohorts (ATG10: ORFinnGen = 0.89, PFinnGen = 8.55 × 10-5; RCCD1: ORFinnGen = 0.89, PFinnGen = 2.38 × 10-8). Additionally, those two genes from breast tissues also replicated in both BCAC (ATG10: ORBRCR = 0.95, PBRCR = 1.02 × 10-9; RCCD1: ORBRCR = 0.87, PBRCR = 4.70 × 10-10) and FinnGen cohorts (ATG10: ORFinnGen = 0.93, PFinnGen = 2.38 × 10-4; RCCD1: ORFinnGen = 0.85, PFinnGen = 3.81 × 10-6). Sensitive analysis and external validation analysis validated those two identified genes. Multi-omics MR analysis showed that the SNP signals associated with ATG10 and RCCD1 were significant across the data from BC Genome-wide association study (GWAS), eQTL, and mQTL studies. In conclusion, we identified two priority genes that are potentially associated with BC. These findings improve our limited understanding of the mechanism of BC and shed light on the development of therapeutic agents for treating BC.
| S-EPMC10998764 | biostudies-literature
Project description:Multi-sample single-cell multi-omics datasets, which simultaneously measure multiple data modalities in the same cells and in multiple samples, facilitate the study of gene expression and gene regulatory activities on a population scale. Existing integration methods can integrate either multiple samples or multiple modalities, but not both simultaneously. To address this limitation, we developed Smmit, a computational pipeline that leverages existing integration methods to simultaneously integrate both samples and modalities and produces a unified representation of reduced dimensions. We demonstrate Smmit's capability to integrate information across samples and modalities while preserving cell type differences in two real datasets. Smmit is an R software package that is freely available at Github: https://github.com/zji90/Smmit.
| S-EPMC10104121 | biostudies-literature
Project description:Inferring in humans biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine and cannot be easily studied in humans. Thus, biomedical research has continuously relied on animal models for studying the impact of these compounds and attempted to M-^StranslateM-^T the results to humans. In this context, the Systems Biology Verification for Industrial Methodology for Process Verification in Research (SBV IMPROVER) initiative had run a Species Translation Challenge for the scientific community to explore and understand the limit of translatability from rodent to human using systems biology. Therefore, a multi-layer omics dataset was generated that comprised of phosphoproteomics, transcriptomics and cytokine data derived from normal human (NHBE) and rat (NRBE) bronchial epithelial cells exposed in parallel to more than 50 different stimuli under identical conditions. The present manuscript describes in detail the experimental settings, the generation, processing and quality control analysis of the multi-layer omics dataset. The datasets are accessible in public repositories could be leveraged for further translation studies.
2014-06-10 | E-MTAB-2091 | biostudies-arrayexpress
Project description:Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to the current pandemic. Many factors, including age and comorbidities, influence the severity and mortality of COVID-19. SARS-CoV-2 infection can cause pulmonary vascular dysfunction. The COVID-19 case-fatality rate in patients with pulmonary arterial hypertension (PAH) is higher in comparison with the general population. In this study, we aimed to identify pathobiological processes common to COVID-19 and PAH by utilizing the human protein-protein interactome and whole-genome transcription data from peripheral blood mononuclear cells (PBMCs) and from lung tissue. We found that there are significantly more interactions between SARS-CoV-2 targets and PAH disease proteins than expected by chance, suggesting that the PAH disease module is in the neighborhood of SARS-CoV-2 targets in the human interactome. In addition, SARS-CoV-2 infection-induced changes in gene expression significantly overlap with PAH-induced gene expression changes in both tissues, indicating SARS-CoV-2 and PAH may share common transcriptional regulators. We identified many upregulated genes and downregulated genes common to COVID-19 and PAH. Interestingly, we observed different co-regulation patterns and dysfunctional signaling pathways in PBMCs versus lung tissue. Endophenotype enrichment analysis revealed that genes regulating fibrosis, inflammation, hypoxia, oxidative stress, immune response, and thromboembolism are significantly enriched in the COVID-19-PAH co-expression modules. We examined the network proximity of the targets of repositioned drugs for COVID-19 to the co-expression modules in PBMCs and lung tissue, and identified 42 drugs that can be potentially used for COVID-19 patients with PAH as a comorbidity. The uncovered common pathobiological pathways are crucial for discovering therapeutic targets and designing tailored treatments for COVID-19 patients who also have PAH.
| S-EPMC9880519 | biostudies-literature