Dataset Information

Identification of Epigenetic-Dysregulated lncRNAs Signature in Osteosarcoma by Multi-Omics Data Analysis.

ABSTRACT:

Background

Alterations of epigenetic modification patterns are potential markers of cancer. The current study characterized six histone modifications in osteosarcoma and identified epigenetically dysregulated long non-coding RNAs (epi-lncRNAs).

Methods

Multi-omics data were obtained from osteosarcoma cell line SJSA1 and a normal cell line. Differentially expressed lncRNAs (DElncRNAs) between osteosarcoma and normal skeletal muscle were analyzed using Limma. MACS2 was applied to identify the "peaks" modified by each histone in the cell. Promoters or enhancers of DElncRNA were overlapped with differential histone-modified regions (DHMR) to screen epi-lncRNAs. Univariate and multivariate Cox regression analysis were performed to detect the genes closely related to the prognosis of osteosarcoma and to construct risk models.

Results

A total of 17 symbolic epi-lncRNA in osteosarcoma were screened, and 13 of them were differentially expressed between osteosarcoma and normal samples. Eight epi-lncRNAs were retained by Univariate Cox regression analysis. Four of these epi-lncRNAs were used to construct an epi-lncRNA signature. The risk score of each osteosarcoma sample in the high- or low-risk group was estimated according to the epi-lncRNA signature. The overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The area under the receiver operating characteristic (ROC) curve of the model was 0.79 and 0.82 for 1-, 3-, and 5-year OS, respectively.

Conclusion

Our results revealed the histone modification pattern in osteosarcoma and developed 4-epi-lncRNA signature to predict the prognosis of osteosarcoma, laying a foundation for the identification of highly specific epigenetic biomarkers for osteosarcoma.

SUBMITTER: Huang J

PROVIDER: S-EPMC9243510 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Globally, pancreatic adenocarcinoma is a recognized cause of pancreatic death (PAAD) associated with high mortality. Long non-coding RNAs (lncRNAs) play an important role in several biological processes in pancreatic cancer. Methods: The gene expression profile of PAAD patients were obtained from The Cancer Genome Atlas (TCGA) database. The limma package was used to identify epigenetic disorders of lncRNAs and PCG. Subsequently, the genomic characteristics and landscape of lncRNAs were explored. The pancreatic cancer-related lncRNAs gene set from Lnc2Cancer v3.0 were collected and the difference between cancer samples and normal samples were analysed. A prognostic model consisting of five epigenetic lncRNA (epi-lncRNAs) was established by univariate and multivariate Cox proportional hazards regression analyses and was verified across different data sets. Finally, the expression of core epi-lncRNAs was identified by PCR experiment. Results: A total of 2237 epi-lncRNAs, 11855 non-epi-lncRNAs, 13518 epi-PCGs, and 6097 non-epi-PCGs, were identified. The abnormal frequency of lncRNAs in pancreatic cancer was much lower than that in PCG, and 138 epi-lncRNAs were enriched in human cancer-related lncRNAs. Epi-lncRNAs had a higher number with longer lengths and a greater number of transcripts. Epi-lncRNAs associated with epigenetic disorders had a higher number of exons, gene length, and isomers as compared to non-epi-lncRNAs. Further, the five pancreatic cancer-specific epi-lncRNA genes (AL161431.1, LINC00663, LINC00941, SNHG10, and TM4SF1-AS1) were identified. Based on these five pancreatic cancer-specific epis-lncRNAs, a prognostic model for pancreatic cancer was established. The RT-PCR result confirmed that AL161431.1, LINC00663, LINC00941, and SNHG10 expressions in pancreatic cancer samples were higher as compared to normal pancreatic samples; the expression of TM4SF1-AS1 in pancreatic cancer cells was significantly lower than that in normal pancreatic samples. Conclusions: Epigenetic abnormalities could promote abnormal lncRNA expression in pancreatic cancer and may play an important role in its progression.

Project description:Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes (P < 0.01), 336 copies of amplified genes (P < 0.05), and 36 copies of deleted genes (P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases' surviving state.

Project description:BackgroundLncRNAs (Long non-coding RNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides. LncRNA has been novel candidate biomarkers in cancer diagnosis and prognosis. However, it is difficult to discover the true association mechanism between lncRNAs and complex diseases. The unprecedented enrichment of multi-omics data and the rapid development of machine learning technology provide us with the opportunity to design a machine learning framework to study the relationship between lncRNAs and complex diseases.ResultsIn this article, we proposed a new machine learning approach, namely LGDLDA (LncRNA-Gene-Disease association networks based LncRNA-Disease Association prediction), for disease-related lncRNAs association prediction based multi-omics data, machine learning methods and neural network neighborhood information aggregation. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively, and it calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. We obtain gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix, and we obtain disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs.ConclusionsCompared with lncRNA-disease prediction methods, our proposed method takes into account more critical information and obtains the performance improvement cancer-related lncRNA predictions. Randomly split data experiment results show that the stability of LGDLDA is better than IDHI-MIRW, NCPLDA, LncDisAP and NCPHLDA. The results on different simulation data sets show that LGDLDA can accurately and effectively predict the disease-related lncRNAs. Furthermore, we applied the method to three real cancer data including gastric cancer, colorectal cancer and breast cancer to predict potential cancer-related lncRNAs.

Project description:PurposeTo identify a gene signature for the prognosis of breast cancer using high-throughput analysis.MethodsRNASeq, single nucleotide polymorphism (SNP), copy number variation (CNV) data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into training set or verification set. Genes related to breast cancer prognosis and differentially expressed genes (DEGs) with CNV or SNP were screened from training set, then integrated together for feature selection of identify robust biomarkers using RandomForest. Finally, a gene-related prognostic model was established and its performance was verified in TCGA test set, Gene Expression Omnibus (GEO) validation set and breast cancer subtypes.ResultsA total of 2287 prognosis-related genes, 131 genes with amplified copy numbers, 724 gens with copy number deletions, and 280 genes with significant mutations screened from Genomic Variants were closely correlated with the development of breast cancer. A total of 120 candidate genes were obtained by integrating genes from Genomic Variants and those related to prognosis, then 6 characteristic genes (CD24, PRRG1, IQSEC3, MRGPRX, RCC2, and CASP8) were top-ranked by RandomForest for feature selection, noticeably, several of these have been previously reported to be associated with the progression of breast cancer. Cox regression analysis was performed to establish a 6-gene signature, which can stratify the risk of samples from training set, test set and external validation set, moreover, the five-year survival AUC of the model in the training set and validation set was both higher than 0.65. Thus, the 6-gene signature developed in the current study could serve as an independent prognostic factor for breast cancer patients.ConclusionThis study constructed a 6-gene signature as a novel prognostic marker for predicting the survival of breast cancer patients, providing new diagnostic/prognostic biomarkers and therapeutic targets for breast cancer patients.