guest ::
| Home > Publications database > Group-specific cellular metabolism in Medulloblastoma. > print |
| 001 | 276420 | ||
| 005 | 20240229155002.0 | ||
| 024 | 7 | _ | |a 10.1186/s12967-023-04211-6 |2 doi |
| 024 | 7 | _ | |a pmid:37277823 |2 pmid |
| 024 | 7 | _ | |a altmetric:149635394 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2023-01103 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Funke, Viktoria L E |b 0 |
| 245 | _ | _ | |a Group-specific cellular metabolism in Medulloblastoma. |
| 260 | _ | _ | |a London |c 2023 |b BioMed Central |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1686063160_3284 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Cancer metabolism influences multiple aspects of tumorigenesis and causes diversity across malignancies. Although comprehensive research has extended our knowledge of molecular subgroups in medulloblastoma (MB), discrete analysis of metabolic heterogeneity is currently lacking. This study seeks to improve our understanding of metabolic phenotypes in MB and their impact on patients' outcomes.Data from four independent MB cohorts encompassing 1,288 patients were analysed. We explored metabolic characteristics of 902 patients (ICGC and MAGIC cohorts) on bulk RNA level. Moreover, data from 491 patients (ICGC cohort) were searched for DNA alterations in genes regulating cell metabolism. To determine the role of intratumoral metabolic differences, we examined single-cell RNA-sequencing (scRNA-seq) data from 34 additional patients. Findings on metabolic heterogeneity were correlated to clinical data.Established MB groups exhibit substantial differences in metabolic gene expression. By employing unsupervised analyses, we identified three clusters of group 3 and 4 samples with distinct metabolic features in ICGC and MAGIC cohorts. Analysis of scRNA-seq data confirmed our results of intertumoral heterogeneity underlying the according differences in metabolic gene expression. On DNA level, we discovered clear associations between altered regulatory genes involved in MB development and lipid metabolism. Additionally, we determined the prognostic value of metabolic gene expression in MB and showed that expression of genes involved in metabolism of inositol phosphates and nucleotides correlates with patient survival.Our research underlines the biological and clinical relevance of metabolic alterations in MB. Thus, distinct metabolic signatures presented here might be the first step towards future metabolism-targeted therapeutic options. |
| 536 | _ | _ | |a 312 - Funktionelle und strukturelle Genomforschung (POF4-312) |0 G:(DE-HGF)POF4-312 |c POF4-312 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |a Inositol phosphates |2 Other |
| 650 | _ | 7 | |a Medulloblastoma |2 Other |
| 650 | _ | 7 | |a Metabolism |2 Other |
| 650 | _ | 7 | |a Nucleotides |2 Other |
| 650 | _ | 7 | |a RNA-Seq |2 Other |
| 700 | 1 | _ | |a Walter, Carolin |b 1 |
| 700 | 1 | _ | |a Melcher, Viktoria |b 2 |
| 700 | 1 | _ | |a Wei, Lanying |b 3 |
| 700 | 1 | _ | |a Sandmann, Sarah |b 4 |
| 700 | 1 | _ | |a Hotfilder, Marc |b 5 |
| 700 | 1 | _ | |a Varghese, Julian |b 6 |
| 700 | 1 | _ | |a Jäger, Natalie |0 P:(DE-He78)bff9e3e3d86865d2b0836bb8f3ce98f3 |b 7 |u dkfz |
| 700 | 1 | _ | |a Kool, Marcel |0 P:(DE-He78)4c28e2aade5f44d8eca9dd8e97638ec8 |b 8 |u dkfz |
| 700 | 1 | _ | |a Jones, David T W |0 P:(DE-He78)551bb92841f634070997aa168d818492 |b 9 |u dkfz |
| 700 | 1 | _ | |a Pfister, Stefan M |0 P:(DE-He78)f746aa965c4e1af518b016de3aaff5d9 |b 10 |u dkfz |
| 700 | 1 | _ | |a Milde, Till |0 P:(DE-He78)0be2f86573954f87e97f8a4dbb05cb0f |b 11 |u dkfz |
| 700 | 1 | _ | |a Mynarek, Martin |b 12 |
| 700 | 1 | _ | |a Rutkowski, Stefan |b 13 |
| 700 | 1 | _ | |a Seggewiss, Jochen |b 14 |
| 700 | 1 | _ | |a Jeising, Daniela |b 15 |
| 700 | 1 | _ | |a de Faria, Flavia W |b 16 |
| 700 | 1 | _ | |a Marquardt, Thorsten |b 17 |
| 700 | 1 | _ | |a Albert, Thomas K |b 18 |
| 700 | 1 | _ | |a Schüller, Ulrich |b 19 |
| 700 | 1 | _ | |a Kerl, Kornelius |0 0000-0002-5676-8102 |b 20 |
| 773 | _ | _ | |a 10.1186/s12967-023-04211-6 |g Vol. 21, no. 1, p. 363 |0 PERI:(DE-600)2118570-0 |n 1 |p 363 |t Journal of translational medicine |v 21 |y 2023 |x 1479-5876 |
| 909 | C | O | |o oai:inrepo02.dkfz.de:276420 |p VDB |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 7 |6 P:(DE-He78)bff9e3e3d86865d2b0836bb8f3ce98f3 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 8 |6 P:(DE-He78)4c28e2aade5f44d8eca9dd8e97638ec8 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 9 |6 P:(DE-He78)551bb92841f634070997aa168d818492 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 10 |6 P:(DE-He78)f746aa965c4e1af518b016de3aaff5d9 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 11 |6 P:(DE-He78)0be2f86573954f87e97f8a4dbb05cb0f |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF4-310 |0 G:(DE-HGF)POF4-312 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Funktionelle und strukturelle Genomforschung |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2022-11-22 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2022-11-22 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2022-11-22 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2022-11-22 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b J TRANSL MED : 2022 |d 2023-08-20 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-08-20 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-08-20 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2023-08-20 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2023-04-12T15:07:28Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2023-04-12T15:07:28Z |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2023-04-12T15:07:28Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2023-08-20 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2023-08-20 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-08-20 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-08-20 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1110 |2 StatID |b Current Contents - Clinical Medicine |d 2023-08-20 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b J TRANSL MED : 2022 |d 2023-08-20 |
| 920 | 1 | _ | |0 I:(DE-He78)B062-20160331 |k B062 |l B062 Pädiatrische Neuroonkologie |x 0 |
| 920 | 1 | _ | |0 I:(DE-He78)HD01-20160331 |k HD01 |l DKTK HD zentral |x 1 |
| 920 | 1 | _ | |0 I:(DE-He78)B360-20160331 |k B360 |l Pediatric Glioma |x 2 |
| 920 | 1 | _ | |0 I:(DE-He78)B310-20160331 |k B310 |l KKE Pädiatrische Onkologie |x 3 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)B062-20160331 |
| 980 | _ | _ | |a I:(DE-He78)HD01-20160331 |
| 980 | _ | _ | |a I:(DE-He78)B360-20160331 |
| 980 | _ | _ | |a I:(DE-He78)B310-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|