Rajkumar, S.V., Dimopoulos, M.A., Palumbo, A., Blade, J., Merlini, G., Mateos, M.-V., … and Miguel, J.F.S., International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma, Lancet Oncol., 2014, vol. 15, no. 12, pp. e538–e548. https://doi.org/10.1016/S1470-2045(14)70442-5

Article  PubMed  Google Scholar 

Qasaimeh, M.A., Wu, Y.C., Bose, S., Menachery, A., Talluri, S., Gonzalez, G., … and Karnik, R., Isolation of circulating plasma cells in multiple myeloma using CD138 antibody-based capture in a microfluidic device, Sci. Rep., 2017, vol. 7, p. 45681. https://doi.org/10.1038/srep45681

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gantana, E.J., Musekwa, E., and Chapanduka, Z.C., Advances in estimating plasma cells in bone marrow: A comprehensive method review, Afr. J. Lab. Med., 2024, vol. 13, no. 1, p. 2381. https://doi.org/10.4102/ajlm.v13i1.2381

Article  CAS  PubMed  PubMed Central  Google Scholar 

Alaggio, R., Amador, C., Anagnostopoulos, I., Attygalle, A.D., Araujo, I.B.D.O., Berti, E., … and Xiao, W., The 5th edition of the World Health Organization classification of haematolymphoid tumours: Lymphoid neoplasms, Leukemia, 2022, vol. 36, no. 7, pp. 1720–1748. https://doi.org/10.1038/s41375-022-01620-2

Abdallah, N., Rajkumar, S.V., Greipp, P., Kapoor, P., Gertz, M.A., Dispenzieri, A., … and Kumar, S.K., Cytogenetic abnormalities in multiple myeloma: Association with disease characteristics and treatment response, Blood Cancer J., 2020, vol. 10, no. 8, p. 82. https://doi.org/10.1038/s41408-020-00348-5

Article  PubMed  PubMed Central  Google Scholar 

Barilà, G., Bonaldi, L., Grassi, A., Martines, A., Liço, A., Macrì, N., … and Zambello, R., Identification of the true hyperdiploid multiple myeloma subset by combining conventional karyotyping and FISH analysis, Blood Cancer J., 2020, vol. 10, no. 2, p. 18. https://doi.org/10.1038/s41408-020-0285-6

Article  PubMed  PubMed Central  Google Scholar 

Bergsagel, P.L., Kuehl, W.M., Zhan, F., Sawyer, J., Barlogie, B., and Shaughnessy, J., Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma, Blood, 2005, vol. 106, no. 1, pp. 296–303. https://doi.org/10.1182/blood-2005-01-0034

Article  CAS  PubMed  PubMed Central  Google Scholar 

Alvarez-Benayas, J., Trasanidis, N., Katsarou, A., Ponnusamy, K., Chaidos, A., May, P.C., … and Karadimitris, A., Chromatin-based, in cis and in trans regulatory rewiring underpins distinct oncogenic transcriptomes in multiple myeloma, Nat. Commun., 2021, vol. 12, no. 1, p. 5450. https://doi.org/10.1038/s41467-021-25704-2

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhan, F., Colla, S., Wu, X., Chen, B., Stewart, J.P., … and Shaughnessy, J.D., CKS1B, overexpressed in aggressive disease, regulates multiple myeloma growth and survival through SKP2- and p27Kip1-dependent and -independent mechanisms, Blood, 2007, vol. 109, no. 11, pp. 4995–5001. https://doi.org/10.1182/blood-2006-07-038703

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhan, F., Huang, Y., Colla, S., Stewart, J.P., Hanamura, I., Gupta, S., … and Shaughnessy, J.D., The molecular classification of multiple myeloma, Blood, 2006, vol. 108, no. 6, pp. 2020–2028. https://doi.org/10.1182/blood-2005-11-013458

Article  CAS  PubMed  PubMed Central  Google Scholar 

Broyl, A., Hose, D., Lokhorst, H., De Knegt, Y., Peeters, J., Jauch, A., … and Sonneveld, P., Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients, Blood, 2010, vol. 116, no. 14, pp. 2543–2553. https://doi.org/10.1182/blood-2009-12-261032

Article  CAS  PubMed  Google Scholar 

Shaughnessy, J.D., Zhan, F., Burington, B.E., Huang, Y., Colla, S., Hanamura, I., … and Barlogie, B., A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1, Blood, 2007, vol. 109, no. 6, pp. 2276–2284. https://doi.org/10.1182/blood

Article  CAS  PubMed  Google Scholar 

Kuiper, R., Broyl, A., De Knegt, Y., Van Vliet, M.H., Van Beers, E.H., Van Der Holt, B., … and Sonneveld, P., A gene expression signature for high-risk multiple myeloma, Leukemia, 2012, vol. 26, no. 11, pp. 2406–2413. https://doi.org/10.1038/leu.2012.127

Article  CAS  PubMed  Google Scholar 

Bhutani, M., Zhang, Q., Friend, R., Voorhees, P.M., Druhan, L.J., Barlogie, B., … and Usmani, S.Z., Investigation of a gene signature to predict response to immunomodulatory derivatives for patients with multiple myeloma: An exploratory, retrospective study using microarray datasets from prospective clinical trials, Lancet Haematol., 2017, vol. 4, no. 9, pp. e443–e451. https://doi.org/10.1016/S2352-3026(17)30143-6

Article  PubMed  Google Scholar 

Decaux, O., Lodé, L., Magrangeas, F., Charbonnel, C., Gouraud, W., Jézéquel, P., … Minvielle, S., Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: A study of the Intergroupe Francophone du Myélome, J. Clin. Oncol., 2008, vol. 26, no. 29, pp. 4798–4805. https://doi.org/10.1200/JCO.2007.13.8545

Article  CAS  PubMed  Google Scholar 

Perrot, A., Corre, J., and Avet-Loiseau, H., Risk stratification and targets in multiple myeloma: From genomics to the bedside, in American Society of Clinical Oncology Educational Book, 2018, vol. 38, pp. 675–680. https://doi.org/10.1200/EDBK_200879

Article  PubMed  Google Scholar 

Mitra, A.K., Harding, T., Mukherjee, U.K., Jang, J.S., Li, Y., Hongzheng, R., … and Van Ness, B., A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma, Blood Cancer J., 2017, vol. 7, no. 6, p. e581. https://doi.org/10.1038/BCJ.2017.56

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhan, F., Hardin, J., Kordsmeier, B., Bumm, K., Zheng, M., Tian, E., … and Shaughnessy, J., Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells, Blood, 2002, vol. 99, no. 5, pp. 1745–1757. https://doi.org/10.1182/blood.V99.5.1745

Article  CAS  PubMed  Google Scholar 

De Vos, J., Thykjær, T., Tarte, K., Ensslen, M., Raynaud, P., Requirand, G., … and Klein, B., Comparison of gene expression profiling between malignant and normal plasma cells with oligonucleotide arrays, Oncogene, 2002, vol. 21, no. 44, pp. 6848–6857. https://doi.org/10.1038/sj.onc.1205868

Article  CAS  PubMed  Google Scholar 

Zhan, F., Hardin, J., Kordsmeier, B., Bumm, K., Zheng, M., Tian, E., … and Shaughnessy, J., Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells, Blood, 2002, vol. 99, no. 5, pp. 1745–1757. https://doi.org/10.1182/blood.V99.5.1745

Article  CAS  PubMed  Google Scholar 

Rao, M.S., Van Vleet, T.R., Ciurlionis, R., Buck, W.R., Mittelstadt, S.W., Blomme, E.A. G., and Liguori, M.J., Comparison of RNA-Seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies, Front. Genet., 2019, vol. 9, p. 636. https://doi.org/10.3389/fgene.2018.00636

Article  CAS  PubMed  PubMed Central  Google Scholar 

Stefano, G.B., Comparing bioinformatic gene expression profiling methods: Microarray and RNA-Seq, Med. Sci. Monit. Basic Res., 2014, vol. 20, pp. 138–142. https://doi.org/10.12659/MSMBR.892101

Article  PubMed  PubMed Central  Google Scholar 

Suntsova, M., Gaifullin, N., Allina, D., Reshetun, A., Li, X., Mendeleeva, L., … and Buzdin, A., Atlas of RNA sequencing profiles for normal human tissues, Sci. Data, 2019, vol. 6, no. 1. https://doi.org/10.1038/s41597-019-0043-4

Borisov, N., Sergeeva, A., Suntsova, M., Raevskiy, M., Gaifullin, N., Mendeleeva, L., … and Buzdin, A., Machine learning applicability for classification of PAD/VCD chemotherapy response using 53 multiple myeloma RNA sequencing profiles, Front. Oncol., 2021, vol. 11, p. 652063. https://doi.org/10.3389/fonc.2021.652063

Bittencourt, S.A., FastQC: A quality control tool for high throughput sequence data, in Babraham Bioinformatics, 2010.

Patro, R., Duggal, G., Love, M.I., Irizarry, R.A., and Kingsford, C., Salmon provides fast and bias-aware quantification of transcript expression, Nat. Methods, 2017, vol. 14, no. 4, pp. 417–419. https://doi.org/10.1038/nmeth.4197

Article  CAS  PubMed  PubMed Central  Google Scholar 

Love, M.I., Soneson, C., Hickey, P.F., Johnson, L.K., Tessa Pierce, N., Shepherd, L., … and Patro, R., Tximeta: Reference sequence checksums for provenance identification in RNA-seq, PLoS Comput. Biol., 2020, vol. 16, no. 2, p. e1007664. https://doi.org/10.1371/JOURNAL.PCBI.1007664

Article  CAS  PubMed  PubMed Central  Google Scholar 

Durinck, S., Spellman, P.T., Birney, E., and Huber, W., Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt, Nat. Protoc., 2009, vol. 4, no. 8, pp. 1184–1191. https://doi.org/10.1038/nprot.2009.97

Article  CAS  PubMed  PubMed Central