Macauda, AngelicaPiredda, ChiaraClay-Gilmour, Alyssa ISainz, JuanBuda, GabrieleMarkiewicz, MiroslawBarington, TorbenZiv, EladHildebrandt, Michelle A TBelachew, Alem AVarkonyi, JuditPrejzner, WitoldDruzd-Sitek, AgnieszkaSpinelli, JohnAndersen, Niels FrostHofmann, Jonathan NDudziński, MarekMartinez-Lopez, JoaquinIskierka-Jazdzewska, ElzbietaMilne, Roger LMazur, GrzegorzGiles, Graham GEbbesen, Lene HyldahlRymko, MarcinJamroziak, KrzysztofSubocz, EdytaReis, Rui ManuelGarcia-Sanz, RamonSuska, AnnaHaastrup, Eva KannikZawirska, DariaGrzasko, NorbertVangsted, Annette JuulDumontet, CharlesKruszewski, MarcinDutka, MagdalenaCamp, Nicola JWaller, Rosalie GTomczak, WaldemarPelosini, MatteoRaźny, MałgorzataMarques, HerlanderAbildgaard, NielsWątek, MarzenaJurczyszyn, ArturBrown, Elizabeth EBerndt, SonjaButrym, AleksandraVachon, Celine MNorman, Aaron DSlager, Susan LGemignani, FedericaCanzian, FedericoCampa, Daniele2023-02-092023-02-092021-03-30http://hdl.handle.net/10668/17318Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/eQTLgenetic polymorphismsmultiple myelomaoverall survivalprogression-free survivalAgedApyraseFemaleGene Expression ProfilingGenetic Association StudiesGerm-Line MutationHumansMaleMiddle AgedMitochondrial ProteinsMultiple MyelomaPolymorphism, Single NucleotideQuantitative Trait LociRNA-Binding ProteinsSurvival Analysisresearch article33675538open access10.1002/ijc.335471097-0215PMC8770990https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ijc.33547https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770990/pdf