Identification of Potential Prognostic Biomarker for Predicting Survival in Multiple Myeloma Using Bioinformatics Analysis and Experiments.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhou J
PROVIDER: S-EPMC8461066 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundDysregulated long non-coding RNAs (lncRNAs) have been found to have oncogenic and/or tumor suppressive roles in the development and progression of cancer, implying their potentials as novel independent biomarkers for cancer diagnosis and prognosis. However, the prognostic significance of expression profile-based lncRNA signature for outcome prediction in patients with multiple myeloma (MM) has not yet been investigated.MethodsLncRNA expression profiles of a large cohort of patients with MM were obtained and analyzed by repurposing the publically available microarray data. An lncRNA-focus risk score model was developed from the training dataset, and then validated in the testing and another two independent external datasets. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance for survival prediction. The biological function of prognostic lncRNAs was predicted using bioinformatics analysis.ResultsFour lncRNAs were identified to be significantly associated with overall survival (OS) of patients with MM in the training dataset, and were combined to develop a four-lncRNA prognostic signature to stratify patients into high-risk and low-risk groups. Patients of training dataset in the high-risk group exhibited shorter OS than those in the low-risk group (HR = 2.718, 95 % CI = 1.937-3.815, p <0.001). The similar prognostic values of four-lncRNA signature were observed in the testing dataset, entire GSE24080 dataset and another two independent external datasets. Multivariate Cox regression and stratified analysis showed that the prognostic power of four-lncRNA signature was independent of clinical features, including serum beta 2-microglobulin (Sβ2M), serum albumin (ALB) and lactate dehydrogenase (LDH). ROC analysis also demonstrated the better performance for predicting 3-year OS. Functional enrichment analysis suggested that these four lncRNAs may be involved in known genetic and epigenetic events linked to MM.ConclusionsOur results demonstrated potential application of lncRNAs as novel independent biomarkers for diagnosis and prognosis in MM. These lncRNA biomarkers may contribute to the understanding of underlying molecular basis of MM.
| S-EPMC4567800 | biostudies-literature
Project description:BackgroundAn increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma.MethodsGene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA.ResultsIn this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8).ConclusionIn summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.
| S-EPMC7661128 | biostudies-literature
Project description:PURPOSE:The wide heterogeneity in multiple myeloma (MM) outcome is driven mainly by cytogenetic abnormalities. The current definition of high-risk profile is restrictive and oversimplified. To adapt MM treatment to risk, we need to better define a cytogenetic risk classification. To address this issue, we simultaneously examined the prognostic impact of del(17p); t(4;14); del(1p32); 1q21 gain; and trisomies 3, 5, and 21 in a cohort of newly diagnosed patients with MM. METHODS:Data were obtained from 1,635 patients enrolled in four trials implemented by the Intergroupe Francophone du Myélome. The oldest collection of data were used for model development and internal validation. For external validation, one of the two independent data sets was used to assess the performance of the model in patients treated with more current regimens. Six cytogenetic abnormalities were identified as clinically relevant, and a prognostic index (PI) that was based on the parameter estimates of the multivariable Cox model was computed for all patients. RESULTS:In all data sets, a higher PI was consistently associated with a poor survival outcome. Dependent on the validation cohorts used, hazard ratios for patients in the high-risk category for death were between six and 15 times higher than those of patients in the low-risk category. Among patients with t(4;14) or del(17p), we observed a worse survival in those classified in the high-risk category than in those in the intermediate-risk category. The PI showed good performance for discriminating between patients who died and those who survived (Harrell's concordance index greater than 70%). CONCLUSION:The cytogenetic PI improves the classification of newly diagnosed patients with MM in the high-risk group compared with current classifications. These findings may facilitate the development of risk-adapted treatment strategies.
| S-EPMC6804890 | biostudies-literature
Project description:BackgroundMultiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles.MethodsGene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan-Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters.ResultsGSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients.ConclusionIn summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.
| S-EPMC8247704 | biostudies-literature
Project description:New approaches to stratify multiple myeloma patients based on prognosis and therapeutic decision-making, or prediction, are needed since patients are currently managed in a similar manner regardless of individual risk factors or disease characteristics. However, despite new and improved biomarkers for determining the prognosis of patients, there is currently insufficient information to utilise biomarkers to intensify, reduce or altogether change treatment, nor to target patient-specific biology in a so-called predictive manner. The ever-increasing number and complexity of drug classes to treat multiple myeloma have improved response rates and so clinically useful biomarkers will need to be relevant in the era of such novel therapies. Therefore, the field of multiple myeloma biomarker development is rapidly progressing, spurred on by new technologies and therapeutic approaches, and underpinned by a deeper understanding of tumour biology with individualised patient management the goal. In this review, we describe the main biomarker categories in multiple myeloma and relate these to diagnostic, prognostic and predictive applications.
| S-EPMC8461914 | biostudies-literature
Project description:BackgroundMultiple myeloma is a cancer which has a high occurrence rate and causes great injury to people worldwide. In recent years, many studies reported the effects of miRNA on the appearance of multiple myeloma. However, due to the differences of samples and sequencing platforms, a large number of inconsistent results have been generated among these studies, which limited the cure of multiple myeloma at the miRNA level.MethodsWe performed meta-analyses to identify the key miRNA biomarkers which could be applied on the treatment of multiple myeloma. The key miRNAs were determined by overlap comparisons of seven datasets in multiple myeloma. Then, the target genes for key miRNAs were predicted by the software TargetScan. Additionally, functional enrichments and binding TFs were investigated by DAVID database and Tfacts database, respectively.ResultsFirstly, comparing the normal tissues, 13 miRNAs were differently expressed miRNAs (DEMs) for at least three datasets. They were considered as key miRNAs, with 12 up-regulated (hsa-miR-106b, hsa-miR-125b, hsa-miR-130b, hsa-miR-138, hsa-miR-15b, hsa-miR-181a, hsa-miR-183, hsa-miR-191, hsa-miR-19a, hsa-miR-20a, hsa-miR-221 and hsa-miR-25) and one down-regulated (hsa-miR-223). Secondly, functional enrichment analyses indicated that target genes of the upregulated miRNAs were mainly transcript factors and enriched in transcription regulation. Besides, these genes were enriched in multiple pathways: the cancer signal pathway, insulin signal metabolic pathway, cell binding molecules, melanin generation, long-term regression and P53 signaling pathway. However, no significant enrichment was found for target genes of the down-regulated genes. Due to the distinct regulation function, four miRNAs (hsa-miR-19a has-miR-221 has-miR25 and has-miR223) were ascertained as the potential prognostic and diagnostic markers in MM. Thirdly, transcript factors analysis unveiled that there were 148 TFs and 60 TFs which bind target genes of the up-regulated miRNAs and target genes of the down-regulated miRNAs, respectively. They respectively generated 652 and 139 reactions of TFs and target genes. Additionally, 50 (31.6%) TFs were shared, while higher specificity was found in TFs of target genes for the upregulated miRNAs.DiscussionsTogether, our findings provided the key miRNAs which affected occurrence of multiple myeloma and regulation function of these miRNAs. It is valuable for the prognosis and diagnosis of multiple myeloma.
| S-EPMC5289111 | biostudies-literature
Project description:IntroductionGlioma is the most common primary tumor in the central nervous system, and prognostic biomarkers are still lacking. HIC ZBTB transcriptional repressor 2 (HIC2) is a hypermethylated gene that plays an important functional role in cardiac development. However, the actual role of HIC2 in glioma progression remains unclear. This study aimed to investigate the function of HIC2 and whether it could be a prognostic biomarker in glioma.MethodsThe DNA methylation and mRNA expression profiles of HIC2 were downloaded from public databases. The prognostic prediction ability and mechanism research of HIC2 were evaluated.ResultsWe found that HIC2 was hypermethylated and expressed at low levels in glioma samples. Hypermethylation and low expression of HIC2 predicted poor prognosis. Multivariate Cox regression analysis suggested that HIC2 was an independent prognostic factor for gliomas. Co-IP assays demonstrated that HIC2 interacts with RNF44, and dual-luciferase reporter assays and ChIP assays revealed that HIC2 transcriptionally inhibits PTPRN2 expression.ConclusionsOur findings suggest that HIC2 represents a tumor suppressor gene and prognostic biomarker for glioma progression and that overexpression of HIC2 inhibits the proliferation of glioma in vitro and in vivo by interacting with RNF44 and PTPRN2.
| S-EPMC10018090 | biostudies-literature
Project description:Endometrial cancer (EC) is one of the most common gynecologic malignancies. Identification of potential EC biomarkers is essential to improve the prognosis and development of therapies against EC. Synaptotagmin-like protein 1 (SYTL1), as a small GTPase Rab27 effector, mainly plays a role in vesicle trafficking and cytotoxic granule exocytosis in lymphocytes. However the role of SYTL1 in EC remains uncertain. We performed a comprehensive assessment of the relationship between SYTL1 and patient diagnosis and prognosis by analysis of EC patients' data from TCGA. We employed the LinkedOmics and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to analyze the biological function of SYTL1 in EC. In addition, the correlation between SYTL1 expression and its DNA methylation was performed by using cBioportal, UALCAN, TCGA Wanderer and MethSurv databases. We further assessed the link between SYTL1 and tumor-infiltrating immune cells by using gene set variation analysis (GSVA).Results We found that SYTL1 was highly expressed in EC patients and cell lines. And increased expression of SYTL1 was associated with age, clinical stage, histological type, histological grade and good overall survival (OS).SYTL1 DNA methylation is negatively associated with SYTL1 expression and UCEC patients' OS. SYTL1 expression is closely correlated with immune infiltration. Furthermore, we carried out in vitro experiments to verify the results of bioinformatic analysis.Conclusion Our results demonstrated that the elevation of SYTL1 expression is associated with good OS and SYTL1 might be a potential diagnostic and prognostic marker in EC.
| S-EPMC9850549 | biostudies-literature
Project description:Lung cancer is the leading cause of mortalities among all types of cancer. Therefore, the screening of biomarkers that are related with the progression of lung cancer is crucial for early diagnosis and efficient therapy of lung cancer. In the present study, bioinformatic analysis identified replication factor C 5 (RFC5) as a potential novel oncogene in lung cancer. RFC5 functions as a clamp loader and is involved in DNA replication and repair. Analysis of public databases and reverse transcription-quantitative polymerase chain reaction indicated that RFC5 was significantly increased in tumor tissues compared with adjacent normal tissues. A high RFC5 expression was observed to be associated with more aggressive malignant clinicopathological features, including higher T stage, more advanced regional lymph node metastasis and a higher probability of relapse. Notably, there were notable differences in overall survival (OS), first progression and post-progression survival between the high RFC5 expression group and low RFC5 expression group. Univariate and multivariate Cox regression analyses indicated that RFC5 was an independent risk factor that was associated with poorer OS and disease-free survival. According to GSEA, several gene sets that are associated with cell cycle and DNA damage were enriched in the RFC5 overexpression group, which indicated that RFC5 might promote the proliferation of lung cancer cells. Our finding indicated that RFC5 might be a novel prognostic biomarker of lung cancer, and it might be serve as a potential diagnosis and therapy target for lung cancer in the future.
| S-EPMC6126192 | biostudies-literature
Project description:The pathophysiology of ovarian cancer (OV) is complex and depends on multiple biological processes and pathways. Therefore, there is an urgent need to identify reliable prognostic biomarkers for predicting clinical outcomes and helping personalize treatment of OV. A long non-coding RNA (lncRNA)-based risk score model was constructed to infer the prognostic efficacy of transcription factors (TFs) based on the OV dataset from The Cancer Genome Atlas. The risk score model was further validated in other independent cohorts from Gene Expression Omnibus. Time-dependent receiver operating characteristic curves were used to evaluate the survival prediction performance in comparison with other clinical and molecular variables. Our results revealed that the top-ranked TF-associating lncRNAs were significantly associated with overall survival, progression-free survival and disease-free survival. Stratification analysis according to clinical variables indicated the prognostic independence of POLR2A-associating lncRNAs. In comparison, the signature of POLR2A-associating lncRNAs was more sensitive and specific than existing clinical and molecular signatures. Functional and experimental analysis suggested that POLR2A-associating lncRNAs may be involved in known biological processes and pathways of OV. Our findings revealed that the lncRNA-based risk score model can provide helpful information on OV prognosis stratification and discovery of therapeutic biomarkers.
| S-EPMC6378234 | biostudies-literature