KDM1A Identified as a Potential Oncogenic Driver and Prognostic Biomarker via Multi-Omics Analysis.
Ontology highlight
ABSTRACT: Background
Lysine-specific demethylase 1A (KDM1A) is a histone demethylation enzyme and a crucial epigenetic factor for multiple pathological pathways that mediate carcinogenesis and immunogenicity. Although increasing evidence supposes the association between KDM1A and cancers, no systematic multi-omics analysis of KDM1A is available.Methods
We systematically evaluated the KDM1A expression of various cancer and normal tissues and the unique relationship between KDM1A expression and prognosis of cancer cases based on The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. The genetic variations, phosphorylation, and DNA methylation of KDM1A were analyzed via various tools. We further analyzed the correlation of KDM1A expression and fibroblasts and immune cell infiltration score of TCGA samples via TIMER2.0.Results
KDM1A was highly expressed in 17 types of total 33 cancers, while it expressed low levels in only 4 cancers. High KDM1A expression was associated with worse survival status in various cancers. KDM1A expression was positively correlated with the cancer-associated fibroblasts and myeloid-derived suppressor cells infiltration levels in most cancer types. Additionally, KDM1A in most cancer types was negatively correlated with Th1 cell infiltration and positively correlated with Th2 cells. Moreover, spliceosome, cell cycle, and RNA transport pathways were involved in the functional mechanisms of KDM1A via enrichment analysis.Conclusions
Our study describes the epigenetic factor KDM1A as an oncogene and prognostic biomarker. Our findings provide valuable guidance for further analysis of KDM1A function in pathogenesis and potential clinical treatment.
SUBMITTER: Li L
PROVIDER: S-EPMC8677413 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectivesEndometrial carcinoma (EC) is one of the three major gynecological malignancies, in which 15% - 20% patients will have recurrence and metastasis. Though there are many studies on the prognosis on this cancer, the performances of existing models evaluating the risk of its recurrence and metastasis are yet to be improved. In addition, a comprehensive multi-omics analyses on the prognostic signatures of EC are on demand. In this study, we aimed to construct a relatively stable and reliable model for predicting recurrence and metastasis of EC. This will help determine the risk level of patients and choose appropriate adjuvant therapy, thereby avoiding improper treatment, and improving the prognosis of patients.MethodsThe mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), copy number variation (CNV) data and clinical information of patients with EC were downloaded from The Cancer Genome Atlas (TCGA). Differential expression analyses were performed between the recurrence or metastasis group and the non-recurrence/metastasis group. Then, we screened potential prognostic markers from the four kinds of omics data respectively and established prediction models using three classifiers.ResultsWe achieved differential expressed mRNAs, lncRNAs, miRNAs and CNVs between the two groups. According to feature selection scores by the random forest algorithm, 275 CNV features, 50 lncRNA features, 150 miRNA features and 150 mRNA features were selected, respectively. And the prediction model constructed by the features of lncRNA data using random forest method showed the best performance, with an area under the curve of 0.763, and an accuracy of 0.819 under 10-fold cross-validation.ConclusionWe developed a computational model using omics information, which is able to predicting recurrence and metastasis risk of EC accurately.
| S-EPMC9438970 | biostudies-literature
Project description:PurposeEndometrial carcinoma (EC) is a clinically heterogeneous disease characterized by a number of different histological subtypes, and its heterogeneity may be involved in the accumulation of multiple genetic alterations. The aim of this work was to investigate the comprehensive mutational profile of EC tumors, and examine the associations between somatic mutations and clinicopathological features or survival in EC patients.MethodsA total of 100 surgical tumors were obtained from EC patients who had previously undergone surgery. Genomic DNA samples extracted from fresh-frozen tissues were analyzed using the Ion AmpliSeq Cancer Hotspot Panel v2 Kit, covering 50 tumor-related genes.ResultsValidated mutations were detected in 91 of the 100 tumors (91%) and identified in eight of the most frequently mutated genes, namely PTEN (57%), PIK3CA (51%), TP53 (30%), KRAS (23%), CTNNB1 (21%), FBFR2 (13%), FBXW7(10%) and RB1 (9%). PTEN mutations were found to associated with young age (< 60), early-stage, endometrioid histology, non-recurrence and better overall survival (OS). CTNNB1 mutations were associated with young age, endometrioid histology and better OS. On the other hands, TP53 mutations were associated with late-stage, non-endometrioid histology, high-grade, recurrence and worse OS. FBWX7 mutations were associated with late-stage, vascular invasion and lymph node metastasis. FGFR2 mutations correlated with deep (≥ 1/2) myometrial invasion.ConclusionOur comprehensive mutational profile will be useful for understanding and evaluating the molecular characteristics of EC tumors, and may lead to the establishment of novel treatment strategies that improve the survival of patients with EC in the future.
| S-EPMC7810788 | biostudies-literature
Project description:MotivationDrug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data.ResultsThis tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans.Availability and implementationDrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6761967 | biostudies-literature
Project description:Minichromosome Maintenance Complex Component 4 (MCM4) is a vital component of the mini-chromosome maintenance complex family, crucial for initiating the replication of eukaryotic genomes. Recently, there has been a growing interest in investigating the significance of MCM4 in different types of cancer. Despite the existing research on this topic, a comprehensive analysis of MCM4 across various cancer types has been lacking. This study aims to bridge this knowledge gap by presenting a thorough pan-cancer analysis of MCM4, shedding light on its functional implications and potential clinical applications. The study utilized multi-omics samples from various databases. Bioinformatic tools were employed to explore the expression profiles, genetic alterations, phosphorylation states, immune cell infiltration patterns, immune subtypes, functional enrichment, disease prognosis, as well as the diagnostic potential of MCM4 and its responsiveness to drugs in a range of cancers. Our research demonstrates that MCM4 is closely associated with the oncogenesis, prognosis and diagnosis of various tumors and proposes that MCM4 may function as a potential biomarker in pan-cancer, providing a deeper understanding of its potential role in cancer development and treatment.
| S-EPMC10948783 | biostudies-literature
Project description:BackgroundThe tumor microenvironment (TME) plays a crucial role in the initiation and progression of cancer. Bladder cancer (BLCA) is a malignant tumor of the genitourinary system. Its heterogeneity results in significant differences in the prognosis of patients. To date, this is still a huge challenge for clinical treatment. In recent years, more and more evidence showed that dysregulation of transcription factors (TFs) plays an important role in tumor progression, invasion, and metastasis. Unfortunately, the role of TFs on the tumor microenvironment in bladder cancer is unclear.MethodsThe original data of BLCA and corresponding adjacent tissues were obtained from The Cancer Genome Atlas (TCGA) database. TFs were downloaded from the Animal Transcription Factor DataBase (Animal TFDB). Intersection analysis was used to obtain TFs that were differentially expressed between tumor and adjacent tissues. Gene Set Cancer Analysis (GSCALite) and CIBERSORT software were used to reveal the key differentially expressed TFs (DE-TFs). Subsequently, UALCAN and Human Protein Atlas (HPA) databases were used to disclose the expression of key DE-TFs in BLCA. The K-M curve divulged the relationship between the key DE-TFs and the patient's overall survival (OS), and the univariate and multivariate Cox regression analyses were conducted to explore independent prognostic factors. The cluster profiler package and Gene Set Enrichment Analysis (GSEA) were used for functional enrichment of genes related to the key DE-TFs. Finally, CIBERSORT software analyzed the immune landscape of BLCA.ResultsWe obtained a total of 117 BLCA-related DE-TFs. Among them, ETV7 was identified as the key DE-TFs due to its association with the autophagy activation pathway and various immune cells in cancer. Online databases of UALCAN and HPA indicated that ETV7 was overexpressed in tumors and negatively correlated with tumor severity. The K-M curve showed that the OS of patients with high expression of ETV7 was poor, which indicated that it was an independent prognostic factor. Functional enrichment of 87 DEGs between ETV7-high and -low expression groups indicated that it was closely related to the immune response and the functions of a variety of immune cells. Finally, CIBERSORT results proved that the high and low expression of ETV7 also caused significant differences in the tumor immune microenvironment of patients.ConclusionOverall, we proved that the transcription factor ETV7 was a novel prognostic factor, which may improve the individualized outcome prediction in BLCA by regulating the tumor immune microenvironment.
| S-EPMC8678556 | biostudies-literature
Project description:An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics' sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the 'Cancer' and 'Gene' sections. The 'Survival' panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, 'Survival Analysis' in 'Customized-analysis,' allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics' sophisticated information, and also constructed a Summary panel in the 'Cancer' and 'Gene' sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.
| S-EPMC7145679 | biostudies-literature
Project description:Laminin Subunit Beta 3 (LAMB3) is a transcription factor and participates in the coding of laminin. It plays an important role in cell proliferation, adhesion, and transfer by regulating various target genes and signaling pathways. However, the role of LAMB3 in human pan-cancer immunology and prognosis is still poorly understood. The TCGA, GTEx, CCLE, and HPA databases were utilized for the analysis of LAMB mRNA and protein expression. The expression of LAMB3 in various immune and molecular subtypes of human cancer was examined using the TISIDB database. The prognostic significance of LAMB3 in various cancers and clinical subtypes was investigated using Kaplan-Meier and Cox regression analysis. The relationship between LAMB3 expression, various immune cell infiltration, immune checkpoints, tumor mutational load, microsatellite instability, and DNA methylation was examined using the TCGA database. Clinical samples of four lung cancer cell lines and eight lung cancer cases were collected to confirm the expression of mRNA in lung cancer. In 17 cancers, the mRNA for LAMB3 is expressed differently and has good diagnostic and prognostic value in 22 cancers. Cox regression and Nomogram analysis show that LAMB3 is an independent risk factor for 15 cancers. LAMB3 is implicated in a variety of tumorigenesis and immune-related signaling pathways, according to GO, KEGG, and GSEA results. LAMB3 expression was associated with TMB in 33 cancer types and MSI in 32 cancer types, while in lung cancer LAMB3 expression was strongly associated with immune cell infiltration and negatively correlated with all seven methylated CpG islands. Cellular experiments demonstrated that LAMB3 promotes malignant behavior of tumor cells. Preliminary mechanistic exploration revealed its close association with PD-L1, CTLA4, cell stemness gene CD133 and β-catenin-related signaling pathways. Based on these findings, it appears that LAMB3 could be a potential therapeutic target for immunotherapy and tumor prognosis. Our findings reveal an important role for LAMB3 in different cancer types.
| S-EPMC10415034 | biostudies-literature
Project description:MicroRNAs (miRNAs) and their target genes are aberrantly expressed in many cancers and are linked to carcinogenesis and metastasis, especially among hepatocellular carcinoma (HCC) patients. This study sought to identify new biomarkers related to HCC prognosis using small RNA sequencing from the tumor and matched normal adjacent tissue of 32 patients with HCC. Eight miRNAs were downregulated and 61 were upregulated more than twofold. Of these, five miRNAs, hsa-miR-3180, hsa-miR-5589-5p, hsa-miR-490-5p, hsa-miR-137, and hsa-miR-378i, were significantly associated with 5-year overall survival (OS) rates. Differential upregulation of hsa-miR-3180 and downregulation of hsa-miR-378i in tumor samples supported the finding that low and high concentrations of hsa-miR-3180 (p = 0.029) and hsa-miR-378i (p = 0.047), respectively, were associated with higher 5-year OS. Cox regression analyses indicated that hsa-miR‑3180 (HR = 0.08; p = 0.013) and hsa-miR‑378i (HR = 18.34; p = 0.045) were independent prognostic factors of poor survival. However, high hsa-miR‑3180 expression obtained larger AUCs for OS and progression-free survival (PFS) and had better nomogram prediction than hsa-miR‑378i. These findings indicate that hsa-miR‑3180 may be associated with HCC progression and could serve as a potential biomarker for this disease.
2023-03-20 | GSE227378 | GEO
Project description:Protein regulator of cytokinesis 1 (PRC1) is involved in cytokinesis. Growing evidence suggests the association of PRC1 with multiple cancers. Here, we unveil that, in 28 cancer types, PRC1 is higher expressed in tumor tissues than in non-malignant tissues. Overexpression of PRC1 indicates unfavorable prognostic value, especially in ACC, LGG, KIRP, LICH, LUAD, MESO, PAAD, SARC and UCEC, while methylation of the PRC1 gene at sites associated with its inactivation has a favorable prognostic value in ACC, KIRP, LUAD, MESO, KIRP and LGG. Differentially expressed genes (DEGs) associated with high (> median) PRC1 expression contribute to key signaling pathways related with cell cycle, DNA damage and repair, EMT, cell migration, invasion and cell proliferation in most cancer types. More specifically, the DEGs involved in RAS/RAF/MAPK, PI3K/AKT, WNT, NOTCH, TGF-β, integrin, EMT process, focal adhesion, RHO GTPase-related pathway or microtubule cytoskeleton regulation are upregulated when PRC1 expression is above median, as confirmed for most cancers. Most importantly, high expression of PRC1 appears to be associated with an overabundance of poor-prognosis TH2 cells. Furthermore, positive correlations of PRC1 and some immune checkpoint genes (CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, and CD86) were observed in several cancers, especially BLCA, BRCA, KIRC, LUAD, LIHC, PRAD and THCA. These findings plead in favor of further studies validating the diagnostic and prognostic impact of PRC1 as well as the elaboration of pharmacological strategies for targeting PRC1.
| S-EPMC10413716 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is the most common and among the deadliest of pancreatic cancers. Its 5-year survival is only ∼8%. Pancreatic cancers are a heterogeneous group of diseases, of which PDAC is particularly aggressive. Like many other cancers, PDAC also starts as a pre-invasive precursor lesion (known as pancreatic intraepithelial neoplasia, PanIN), which offers an opportunity for both early detection and early treatment. Even advanced PDAC can benefit from prognostic biomarkers. However, reliable biomarkers for early diagnosis or those for prognosis of therapy remain an unfulfilled goal for PDAC. In this study, we selected 153 PDAC patients from the TCGA database and used their clinical, DNA methylation, gene expression, and micro-RNA (miRNA) and long non-coding RNA (lncRNA) expression data for multi-omics analysis. Differential methylations at about 12,000 CpG sites were observed in PDAC tumor genomes, with about 61% of them hypermethylated, predominantly in the promoter regions and in CpG-islands. We correlated promoter methylation and gene expression for mRNAs and identified 17 genes that were previously recognized as PDAC biomarkers. Similarly, several genes (B3GNT3, DMBT1, DEPDC1B) and lncRNAs (PVT1, and GATA6-AS) are strongly correlated with survival, which have not been reported in PDAC before. Other genes such as EFR3B, whose biological roles are not well known in mammals are also found to strongly associated with survival. We further identified 406 promoter methylation target loci associated with patients survival, including known esophageal squamous cell carcinoma biomarkers, cg03234186 (ZNF154), and cg02587316, cg18630667, and cg05020604 (ZNF382). Overall, this is one of the first studies that identified survival associated genes using multi-omics data from PDAC patients.
| S-EPMC6659773 | biostudies-literature