Detection of early relapse in multiple myeloma patients
Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
MUNI/A/1587/2023
Masarykova Univerzita
FNBr, 65269705
University Hospital Brno
AZV NU21-03-00076
Ministerstvo Zdravotnictví Ceské Republiky
Programme EXCELES, ID Project No. LX22NPO5102
European Union
PubMed
39881385
PubMed Central
PMC11776158
DOI
10.1186/s13008-025-00143-3
PII: 10.1186/s13008-025-00143-3
Knihovny.cz E-zdroje
- Klíčová slova
- Liquid biopsy, MALDI-TOF MS, Machine learning, Multiple myeloma, Relapse, Small RNA seq, microRNA,
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Multiple myeloma (MM) represents the second most common hematological malignancy characterized by the infiltration of the bone marrow by plasma cells that produce monoclonal immunoglobulin. While the quality and length of life of MM patients have significantly increased, MM remains a hard-to-treat disease; almost all patients relapse. As MM is highly heterogenous, patients relapse at different times. It is currently not possible to predict when relapse will occur; numerous studies investigating the dysregulation of non-coding RNA molecules in cancer suggest that microRNAs could be good markers of relapse. RESULTS: Using small RNA sequencing, we profiled microRNA expression in peripheral blood in three groups of MM patients who relapsed at different intervals. In total, 24 microRNAs were significantly dysregulated among analyzed subgroups. Independent validation by RT-qPCR confirmed changed levels of miR-598-3p in MM patients with different times to relapse. At the same time, differences in the mass spectra between groups were identified using matrix-assisted laser desorption/ionization time of flight mass spectrometry. All results were analyzed by machine learning. CONCLUSION: Mass spectrometry coupled with machine learning shows potential as a reliable, rapid, and cost-effective preliminary screening technique to supplement current diagnostics.
Department of Chemistry Faculty of Science Masaryk University Brno Czech Republic
Department of Clinical Hematology University Hospital Brno Brno Czech Republic
Department of Internal Medicine Hematology and Oncology University Hospital Brno Brno Czech Republic
International Clinical Research Center St Anne's University Hospital Brno Brno Czech Republic
Research Centre for Applied Molecular Oncology Masaryk Memorial Cancer Institute Brno Czech Republic
Zobrazit více v PubMed
Ludwig H, Novis Durie S, Meckl A, Hinke A, Durie B. Multiple myeloma incidence and Mortality around the Globe; interrelations between Health Access and Quality, Economic resources, and patient empowerment. Oncologist. 2020;25(9):e1406–13. PubMed PMC
Padala SA, Barsouk A, Barsouk A, Rawla P, Vakiti A, Kolhe R, et al. Epidemiology, staging, and management of multiple myeloma. Med Sci Basel Switz. 2021;9(1):3. PubMed PMC
Rajkumar SV. Multiple myeloma: 2022 update on diagnosis, risk stratification, and management. Am J Hematol. 2022;97(8):1086–107. PubMed PMC
Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538–548. PubMed
Cowan AJ, Green DJ, Kwok M, Lee S, Coffey DG, Holmberg LA et al. Diagnosis and Management of Multiple Myeloma: A Review. JAMA [Internet]. 2022 Feb 1 [cited 2024 Jul 9];327(5):464. Available from: https://jamanetwork.com/journals/jama/fullarticle/2788522 PubMed
Ludwig H, Terpos E, Van De Donk N, Mateos MV, Moreau P, Dimopoulos MA et al. Prevention and management of adverse events during treatment with bispecific antibodies and CAR T cells in multiple myeloma: a consensus report of the European Myeloma Network. Lancet Oncol [Internet]. 2023 Jun [cited 2024 Oct 10];24(6):e255–69. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1470204523001596 PubMed
Rajkumar SV. Multiple myeloma: 2020 update on diagnosis, risk-stratification and management. Am J Hematol [Internet]. 2020 May [cited 2024 Jun 3];95(5):548–67. Available from: https://onlinelibrary.wiley.com/doi/10.1002/ajh.25791 PubMed
Laubach J, Garderet L, Mahindra A, Gahrton G, Caers J, Sezer O et al. Management of relapsed multiple myeloma: recommendations of the International Myeloma Working Group. Leukemia [Internet]. 2016 May [cited 2024 Nov 5];30(5):1005–17. Available from: https://www.nature.com/articles/leu2015356 PubMed
Kumar S, Paiva B, Anderson KC, Durie B, Landgren O, Moreau P et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol [Internet]. 2016 Aug [cited 2024 Oct 15];17(8):e328–46. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1470204516302066 PubMed
Lee JH, Kim SH. Treatment of relapsed and refractory multiple myeloma. BLOOD Res [Internet]. 2020 Jul 31 [cited 2024 Oct 15];55(S1):S43–53. Available from: http://www.bloodresearch.or.kr/journal/view.html?doi=10.5045/br.2020.S008 PubMed PMC
Rajkumar SV, Harousseau JL, Durie B, Anderson KC, Dimopoulos M, Kyle R et al. Consensus recommendations for the uniform reporting of clinical trials: report of the International Myeloma Workshop Consensus Panel 1. Blood [Internet]. 2011 May 5 [cited 2024 Oct 15];117(18):4691–5. Available from: https://ashpublications.org/blood/article/117/18/4691/21394/Consensus-recommendations-for-the-uniform PubMed PMC
Bhatt P, Kloock C, Comenzo R. Relapsed/Refractory Multiple Myeloma: A Review of Available Therapies and Clinical Scenarios Encountered in Myeloma Relapse. Curr Oncol [Internet]. 2023 Feb 15 [cited 2024 Oct 10];30(2):2322–47. Available from: https://www.mdpi.com/1718-7729/30/2/179 PubMed PMC
Lu Q, Yang D, Li H, Niu T, Tong A. Multiple myeloma: signaling pathways and targeted therapy. Mol Biomed [Internet]. 2024 Jul 4 [cited 2024 Jul 9];5(1):25. Available from: https://link.springer.com/10.1186/s43556-024-00188-w PubMed PMC
Saliminejad K, Khorram Khorshid HR, Soleymani Fard S, Ghaffari SH. An overview of microRNAs: Biology, functions, therapeutics, and analysis methods. J Cell Physiol. 2019;234(5):5451–65. PubMed
Gregorova J, Vychytilova-Faltejskova P, Sevcikova S. Epigenetic Regulation of MicroRNA Clusters and Families during Tumor Development. Cancers [Internet]. 2021 Mar 16 [cited 2024 Oct 10];13(6):1333. Available from: https://www.mdpi.com/2072-6694/13/6/1333 PubMed PMC
Vlachová M, Gregorová J, Vychytilová-Faltejsková P, Gabło NA, Radová L, Pospíšilová L et al. Involvement of Small Non-Coding RNA and Cell Antigens in Pathogenesis of Extramedullary Multiple Myeloma. Int J Mol Sci [Internet]. 2022 Nov 25 [cited 2024 Jun 3];23(23):14765. Available from: https://www.mdpi.com/1422-0067/23/23/14765 PubMed PMC
Wang W, Corrigan-Cummins M, Barber EA, Saleh LM, Zingone A, Ghafoor A et al. Aberrant Levels of miRNAs in Bone Marrow Microenvironment and Peripheral Blood of Myeloma Patients and Disease Progression. J Mol Diagn [Internet]. 2015 Nov [cited 2024 Oct 10];17(6):669–78. Available from: https://linkinghub.elsevier.com/retrieve/pii/S152515781500152X PubMed PMC
Papadimitriou MA, Soureas K, Papanota AM, Tsiakanikas P, Adamopoulos PG, Ntanasis-Stathopoulos I et al. miRNA-seq identification and clinical validation of CD138 + and circulating miR-25 in treatment response of multiple myeloma. J Transl Med [Internet]. 2023 Apr 6 [cited 2024 Oct 10];21(1):245. Available from: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-023-04034-5 PubMed PMC
Zhao G, Jing X, Li Z, Wu X, Gao Z, Ma R. The diagnostic and prognostic values of circulating miRNA-1246 in multiple myeloma. Hematology [Internet]. 2022 Dec 31 [cited 2024 Oct 10];27(1):778–84. Available from: https://www.tandfonline.com/doi/full/10.1080/16078454.2022.2095890 PubMed
Kupec T, Bleilevens A, Iborra S, Najjari L, Wittenborn J, Maurer J, PLOS ONE [Internet]. Stability of circulating microRNAs in serum. Roemer K, editor. 2022 Aug 31 [cited 2024 Oct 15];17(8):e0268958. Available from: https://dx.plos.org/10.1371/journal.pone.0268958 PubMed DOI PMC
Liu S, Cheng L, Fu Y, Liu BF, Liu X. Characterization of IgG N-glycome profile in colorectal cancer progression by MALDI-TOF-MS. J Proteomics [Internet]. 2018 Jun [cited 2024 Oct 15];181:225–37. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1874391918301854 PubMed
Teunissen C, Koel-Simmelink M, Pham T, Knol J, Khalil M, Trentini A et al. Identification of biomarkers for diagnosis and progression of MS by MALDI-TOF mass spectrometry. Mult Scler J [Internet]. 2011 Jul [cited 2024 Oct 15];17(7):838–50. Available from: https://journals.sagepub.com/doi/10.1177/1352458511399614 PubMed
Duncan M, DeMarco ML. MALDI-MS: Emerging roles in pathology and laboratory medicine. Clin Mass Spectrom [Internet]. 2019 Aug [cited 2024 Oct 15];13:1–4. Available from: https://linkinghub.elsevier.com/retrieve/pii/S237699981930008X PubMed PMC
Deulofeu M, Kolářová L, Salvadó V, María Peña-Méndez E, Almáši M, Štork M et al. Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma. Sci Rep [Internet]. 2019 May 28 [cited 2024 Oct 15];9(1):7975. Available from: https://www.nature.com/articles/s41598-019-44215-1 PubMed PMC
Eveillard M, Korde N, Ciardiello A, Diamond B, Lesokhin A, Mailankody S et al. Using MALDI-TOF mass spectrometry in peripheral blood for the follow up of newly diagnosed multiple myeloma patients treated with daratumumab-based combination therapy. Clin Chim Acta [Internet]. 2021 May [cited 2024 Oct 15];516:136–41. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0009898121000371 PubMed PMC
Gregorova J, Vychytilova-Faltejskova P, Kramarova T, Knechtova Z, Almasi M, Stork M et al. Proteomic analysis of the bone marrow microenvironment in extramedullary multiple myeloma patients. Neoplasma [Internet]. 2022 [cited 2024 Jun 3];69(02):412–24. Available from: http://www.elis.sk/index.php?page=shop.product_details&flypage=flypage.tpl&product_id=7523&category_id=180&option=com_virtuemart PubMed
Murray DL, Puig N, Kristinsson S, Usmani SZ, Dispenzieri A, Bianchi G, International Myeloma Working Group Mass Spectrometry Committee Report. Mass spectrometry for the evaluation of monoclonal proteins in multiple myeloma and related disorders: an. Blood Cancer J [Internet]. 2021 Feb 1 [cited 2024 Oct 15];11(2):24. Available from: https://www.nature.com/articles/s41408-021-00408-4 PubMed PMC
Pečinka L, Vlachová M, Moráň L, Gregorová J, Porokh V, Kovačovicová P et al. Improved Screening of Monoclonal Gammopathy Patients by MALDI-TOF Mass Spectrometry. J Am Soc Mass Spectrom [Internet]. 2023 Dec 6 [cited 2024 Oct 15];34(12):2646–53. Available from: https://pubs.acs.org/doi/10.1021/jasms.3c00166 PubMed DOI PMC
Vlachová M, Pečinka L, Gregorová J, Moráň L, Růžičková T, Kovačovicová P et al. Liquid biopsy of peripheral blood using mass spectrometry detects primary extramedullary disease in multiple myeloma patients. Sci Rep [Internet]. 2024 Aug 13 [cited 2024 Oct 15];14(1):18777. Available from: https://www.nature.com/articles/s41598-024-69408-1 PubMed PMC
Dimopoulos MA, Moreau P, Terpos E, Mateos MV, Zweegman S, Cook G et al. Multiple myeloma: EHA-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Ann Oncol [Internet]. 2021 Mar [cited 2024 Oct 30];32(3):309–22. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0923753420431692 PubMed
Kumar SK, Rajkumar V, Kyle RA, Van Duin M, Sonneveld P, Mateos MV et al. Multiple myeloma. Nat Rev Dis Primer [Internet]. 2017 Jul 20 [cited 2024 Jun 3];3(1):17046. Available from: https://www.nature.com/articles/nrdp201746 PubMed
Cliff ERS, Mohyuddin GR. Overall survival as a primary end point in multiple myeloma trials. Nat Rev Clin Oncol [Internet]. 2022 Sep [cited 2024 Oct 30];19(9):565–6. Available from: https://www.nature.com/articles/s41571-022-00665-7 PubMed
Marinac CR, Ghobrial IM, Birmann BM, Soiffer J, Rebbeck TR. Dissecting racial disparities in multiple myeloma. Blood Cancer J [Internet]. 2020 Feb 17 [cited 2024 Jul 15];10(2):19. Available from: https://www.nature.com/articles/s41408-020-0284-7 PubMed PMC
Puła A, Robak P, Jarych D, Mikulski D, Misiewicz M, Drozdz I et al. The Relationship between Serum miRNAs and Early Mortality in Multiple Myeloma Patients Treated with Bortezomib-Based Regimens. Int J Mol Sci [Internet]. 2023 Feb 2 [cited 2024 Oct 30];24(3):2938. Available from: https://www.mdpi.com/1422-0067/24/3/2938 PubMed PMC
Sonneveld P, Broijl A. Treatment of relapsed and refractory multiple myeloma. Haematologica [Internet]. 2016 Apr [cited 2024 Oct 30];101(4):396–406. Available from: http://www.haematologica.org/lookup/doi/10.3324/haematol.2015.129189 PubMed PMC
Chim CS, Kumar SK, Orlowski RZ, Cook G, Richardson PG, Gertz MA et al. Management of relapsed and refractory multiple myeloma: novel agents, antibodies, immunotherapies and beyond. Leukemia [Internet]. 2018 Feb [cited 2024 Oct 30];32(2):252–62. Available from: https://www.nature.com/articles/leu2017329 PubMed PMC
Insenser M, Quintero A, De Lope S, Álvarez-Blasco F, Martínez-García MÁ, Luque-Ramírez M et al. Validation of circulating microRNAs miR-142-3p and miR-598-3p in women with polycystic ovary syndrome as potential diagnostic markers. Hum Reprod [Internet]. 2023 May 2 [cited 2024 Oct 30];38(5):951–60. Available from: https://academic.oup.com/humrep/article/38/5/951/7080093 PubMed
Lin X, Huang X, Wang L, Liu W. The long noncoding RNA MALAT1/microRNA-598-3p axis regulates the proliferation and apoptosis of retinoblastoma cells through the PI3K/AKT pathway. Mol Vis. 2022;28:269–79. PubMed PMC
Fu L, Li Z, Zhu J, Wang P, Fan G, Dai Y et al. Serum expression levels of microRNA-382-3p, – 598-3p, – 1246 and – 184 in breast cancer patients. Oncol Lett [Internet]. 2016 Jul [cited 2024 Oct 30];12(1):269–74. Available from: https://www.spandidos-publications.com/10.3892/ol.2016.4582 PubMed PMC
Li K, Fang Y, Liao J, Duan J, Feng L, Luo X et al. Upregulation of miR–598 promotes cell proliferation and cell cycle progression in human colorectal carcinoma by suppressing INPP5E expression. Mol Med Rep [Internet]. 2017 Dec 6 [cited 2024 Oct 30]; Available from: http://www.spandidos-publications.com/10.3892/mmr.2017.8207 PubMed PMC
Qiang Z, Feng J, Wang C, Zheng M, Wen Z. MiR-598-3p functions as a tumor suppressor in pediatric T-cell acute lymphoblastic leukemia. Trop J Pharm Res [Internet]. 2022 Jan 17 [cited 2024 Oct 30];20(3):497–503. Available from: https://www.ajol.info/index.php/tjpr/article/view/220126
Chen X, Xiong X, Cui D, Yang F, Wei D, Li H et al. DEPTOR is an in vivo tumor suppressor that inhibits prostate tumorigenesis via the inactivation of mTORC1/2 signals. Oncogene [Internet]. 2020 Feb 13 [cited 2024 Oct 30];39(7):1557–71. Available from: https://www.nature.com/articles/s41388-019-1085-y PubMed PMC
Quwaider D, Corchete LA, Misiewicz-Krzeminska I, Sarasquete ME, Pérez JJ, Krzeminski P et al. DEPTOR maintains plasma cell differentiation and favorably affects prognosis in multiple myeloma. J Hematol OncolJ Hematol Oncol [Internet]. 2017 Dec [cited 2024 Oct 30];10(1):92. Available from: http://jhoonline.biomedcentral.com/articles/10.1186/s13045-017-0461-8 PubMed PMC
Kallingal A, Thankachan S, Venkatesh T, Kabbekodu SP, Suresh PS. Role of miR-15b/16–2 cluster network in endometrial cancer: An in silico pathway and prognostic analysis. Meta Gene [Internet]. 2022 Feb [cited 2024 Oct 30];31:101018. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2214540022000093
Lovat F, Fassan M, Gasparini P, Rizzotto L, Cascione L, Pizzi M et al. miR-15b/16 – 2 deletion promotes B-cell malignancies. Proc Natl Acad Sci [Internet]. 2015 Sep 15 [cited 2024 Oct 30];112(37):11636–41. Available from: https://pnas.doi/full/10.1073/pnas.1514954112 PubMed DOI PMC
Duan L, Zhao H, Xiong Y, Tang X, Yang Y, Hu Z et al. miR-16-2* Interferes with WNT5A to Regulate Osteogenesis of Mesenchymal Stem Cells. Cell Physiol Biochem [Internet]. 2018 [cited 2024 Oct 30];51(3):1087–102. Available from: https://karger.com/CPB/article/doi/10.1159/000495489 PubMed
Frenquelli M, Caridi N, Antonini E, Storti F, Viganò V, Gaviraghi M et al. The WNT receptor ROR2 drives the interaction of multiple myeloma cells with the microenvironment through AKT activation. Leukemia [Internet]. 2020 Jan [cited 2024 Oct 30];34(1):257–70. Available from: https://www.nature.com/articles/s41375-019-0486-9 PubMed PMC
Qiang YW, Chen Y, Brown N, Hu B, Epstein J, Barlogie B, et al. Characterization of Wnt/beta-catenin signalling in osteoclasts in multiple myeloma. Br J Haematol. 2010;148(5):726–38. PubMed PMC
Robak P, Dróżdż I, Jarych D, Mikulski D, Węgłowska E, Siemieniuk-Ryś M et al. The Value of Serum MicroRNA Expression Signature in Predicting Refractoriness to Bortezomib-Based Therapy in Multiple Myeloma Patients. Cancers [Internet]. 2020 Sep 9 [cited 2024 Oct 30];12(9):2569. Available from: https://www.mdpi.com/2072-6694/12/9/2569 PubMed PMC
Katiyar A, Kaur G, Rani L, Jena L, Singh H, Kumar L et al. Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data. Sci Rep [Internet]. 2021 May 26 [cited 2024 Oct 30];11(1):10957. Available from: https://www.nature.com/articles/s41598-021-90424-y PubMed PMC
Du Y, Miao Z, Wang K, Lv Y, Qiu L, Guo L. Expression levels and clinical values of miR-92b-3p in breast cancer. World J Surg Oncol [Internet]. 2021 Dec [cited 2024 Oct 30];19(1):239. Available from: https://wjso.biomedcentral.com/articles/10.1186/s12957-021-02347-7 PubMed PMC
Gong L, Ren M, Lv Z, Yang Y, Wang Z. miR-92b-3p Promotes Colorectal Carcinoma Cell Proliferation, Invasion, and Migration by Inhibiting FBXW7 In Vitro and In Vivo. DNA Cell Biol [Internet]. 2018 May [cited 2024 Oct 30];37(5):501–11. Available from: http://www.liebertpub.com/doi/10.1089/dna.2017.4080 PubMed
Long M, Zhan M, Xu S, Yang R, Chen W, Zhang S et al. miR-92b-3p acts as a tumor suppressor by targeting Gabra3 in pancreatic cancer. Mol Cancer [Internet]. 2017 Dec [cited 2024 Oct 30];16(1):167. Available from: http://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-017-0723-7 PubMed PMC
Sun Y, Feng Y, Zhang G, Xu Y. The endonuclease APE1 processes miR-92b formation, thereby regulating expression of the tumor suppressor LDLR in cervical cancer cells. Ther Adv Med Oncol [Internet]. 2019 Jan [cited 2024 Oct 30];11:1758835919855859. Available from: https://journals.sagepub.com/doi/10.1177/1758835919855859 PubMed PMC
Wang C, Uemura M, Tomiyama E, Matsushita M, Koh Y, Nakano K et al. MicroRNA-92b‐3p is a prognostic oncomiR that targets TSC1 in clear cell renal cell carcinoma. Cancer Sci [Internet]. 2020 Apr [cited 2024 Oct 30];111(4):1146–55. Available from: https://onlinelibrary.wiley.com/doi/10.1111/cas.14325 PubMed PMC
Wang G, Cheng B, Jia R, Tan B, Liu W. Altered expression of microRNA–92b–3p predicts survival outcomes of patients with prostate cancer and functions as an oncogene in tumor progression. Oncol Lett [Internet]. 2020 Nov 3 [cited 2024 Oct 30];21(1):1–1. Available from: http://www.spandidos-publications.com/10.3892/ol.2020.12265 PubMed PMC
Chen X, Zhang Y, Li X, Yang Z, Liu A, Yu X. Diagnosis and staging of multiple myeloma using serum-based laser-induced breakdown spectroscopy combined with machine learning methods. Biomed Opt Express. 2021;12(6):3584–96. PubMed PMC
Mosquera Orgueira A, González Pérez MS, Díaz Arias JÁ, Antelo Rodríguez B, Alonso Vence N, Bendaña López Á, et al. Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data. Leukemia. 2021;35(10):2924–35. PubMed
Xiong X, Wang J, Hu S, Dai Y, Zhang Y, Hu C. Differentiating between multiple myeloma and metastasis subtypes of lumbar vertebra lesions using machine learning-based Radiomics. Front Oncol. 2021;11:601699. PubMed PMC
Yan W, Shi H, He T, Chen J, Wang C, Liao A, et al. Employment of Artificial Intelligence Based on Routine Laboratory Results for the early diagnosis of multiple myeloma. Front Oncol. 2021;11:608191. PubMed PMC
Han X, Li D, Wang S, Lin Y, Liu Y, Lin L et al. Serum amino acids quantification by plasmonic colloidosome-coupled MALDI-TOF MS for triple-negative breast cancer diagnosis. Mater Today Bio [Internet]. 2022 Dec [cited 2024 Dec 4];17:100486. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2590006422002848 PubMed PMC
Lai X, Guo K, Huang W, Su Y, Chen S, Li Q et al. Combining MALDI-MS with machine learning for metabolomic characterization of lung cancer patient sera. Anal Methods [Internet]. 2022 [cited 2024 Dec 4];14(5):499–507. Available from: https://xlink.rsc.org/?DOI=D1AY01940F PubMed
Lee JW, Lee K, Ahn SH, Son BH, Ko BS, Kim HJ et al. Potential of MALDI-TOF-based serum N-glycan analysis for the diagnosis and surveillance of breast cancer. Sci Rep [Internet]. 2020 Nov 5 [cited 2024 Dec 4];10(1):19136. Available from: https://www.nature.com/articles/s41598-020-76195-y PubMed PMC
Bhattacharyya S, Epstein J, Suva LJ. Biomarkers that Discriminate Multiple Myeloma Patients with or without Skeletal Involvement Detected Using SELDI-TOF Mass Spectrometry and Statistical and Machine Learning Tools. Dis Markers [Internet]. 2006 Jan [cited 2024 Oct 30];22(4):245–55. Available from: https://onlinelibrary.wiley.com/doi/10.1155/2006/728296 PubMed DOI PMC
He A, Bai J, Huang C, Yang J, Zhang W, Wang J et al. Detection of serum tumor markers in multiple myeloma using the CLINPROT system. Int J Hematol [Internet]. 2012 Jun [cited 2024 Oct 30];95(6):668–74. Available from: http://link.springer.com/10.1007/s12185-012-1080-3 PubMed
Wang Q, Li Y, Liang Y, Hu C, Zhai Y, Zhao G et al. Construction of A Multiple Myeloma Diagnostic Model by Magnetic Bead-Based MALDI‐TOF Mass Spectrometry of Serum and Pattern Recognition Software. Anat Rec [Internet]. 2009 Apr [cited 2024 Oct 30];292(4):604–10. Available from: https://anatomypubs.onlinelibrary.wiley.com/doi/10.1002/ar.20871 PubMed
Kubiczková L, Kryukov F, Slabý O, Dementyeva E, Jarkovský J, Nekvindová J, et al. Circulating serum microRNAs as novel diagnostic and prognostic biomarkers for multiple myeloma and monoclonal gammopathy of undetermined significance. Haematologica. 2014;99(3):511–8. PubMed PMC
Sedlarikova L, Bollova B, Radova L, Brozova L, Jarkovsky J, Almasi M et al. Circulating exosomal long noncoding RNA PRINS—First findings in monoclonal gammopathies. Hematol Oncol [Internet]. 2018 Dec [cited 2024 Oct 29];36(5):786–91. Available from: https://onlinelibrary.wiley.com/doi/10.1002/hon.2554 PubMed PMC
Patil AH, Halushka MK. miRge3.0: a comprehensive microRNA and tRF sequencing analysis pipeline. NAR Genomics Bioinforma [Internet]. 2021 Jun 23 [cited 2024 Oct 21];3(3):lqab068. Available from: https://academic.oup.com/nargab/article/doi/10.1093/nargab/lqab068/6325159 PubMed PMC
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing [Internet]. Vienna, Austria. 2020. Available from: https://www.R-project.org/
McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res [Internet]. 2012 May 1 [cited 2024 Oct 21];40(10):4288–97. Available from: https://academic.oup.com/nar/article/40/10/4288/2411520 PubMed PMC
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics [Internet]. 2010 Jan 1 [cited 2024 Oct 21];26(1):139–40. Available from: https://academic.oup.com/bioinformatics/article/26/1/139/182458 PubMed PMC
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res [Internet]. 2015 Apr 20 [cited 2024 Oct 21];43(7):e47–e47. Available from: http://academic.oup.com/nar/article/43/7/e47/2414268/limma-powers-differential-expression-analyses-for PubMed PMC
Kuhn M. Building Predictive Models in R Using the caret Package. J Stat Softw [Internet]. 2008 [cited 2024 Dec 9];28(5). Available from: http://www.jstatsoft.org/v28/i05/
Gibb S, Strimmer K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics [Internet]. 2012 Sep 1 [cited 2024 Dec 9];28(17):2270–1. Available from: https://academic.oup.com/bioinformatics/article/28/17/2270/246552 PubMed
Pečinka L, Moráň L, Kovačovicová P, Meloni F, Havel J, Pivetta T et al. Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing. Heliyon [Internet]. 2024 May [cited 2024 Dec 4];10(9):e29936. Available from: https://linkinghub.elsevier.com/retrieve/pii/S240584402405967X PubMed PMC
Thévenot EA, Roux A, Xu Y, Ezan E, Junot C. Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. J Proteome Res [Internet]. 2015 Aug 7 [cited 2024 Dec 9];14(8):3322–35. Available from: https://pubs.acs.doi/10.1021/acs.jproteome.5b00354 PubMed DOI