Abstract Myeloma is plasma cell disorder, mostly effects adults over 60 years. Non-coding RNAs are emerging field and play vital role in development of disease. Major non-coding RNAs are miRNAs, lncRNAs, circRNAs and sn/snoRNAs. We analyzed restricted and publically available RNA-seq and small RNA-seq data sets for biomarkers identification and their involvement in myeloma. We obtained restricted data from “BluePrint”, which contains 11 myeloma plasma cells and 4 normal tonsil plasma cells (EGAS00001001110). We identified 1534 genes are differentially regulated (2-fold cut-off, >10-FC 218 genes). Top 10 upregulated genes were: EDNRB (1246-FC), SCUBE1 (737-FC), MC4R (691-FC), NDNF (601-FC), PTGS2 (567-FC), GPRC5D (531-FC), MFAP3L (467-FC), CCND1 (381-FC), CXCL12 (344-FC) and BTBD3 (326-FC) ; top 10 downregulated were: EBF1 (-111-FC), HLA-DRB1(-133-FC), CPXM1 (-171-FC), LOC642131 (-171-FC), IGHV1OR15-3 (-171-FC), HLA-DRB5 (-204-FC), PRAMENP (-210-FC), DTX1 (-220-FC), CD22 (-337-FC) and RFTN1 (-356-FC). Publically available small RNA-seq data downloaded and analyzed for miRNAs, lncRNAs, circRNAs and sn/snoRNAs which contains 3 healthy donor’s plasma cells and 3 newly diagnosed myeloma patient plasma cells (PRJNA377345). We used mirDIP portal to analyze miRNA and mRNA differentially expressed data, predicated from more than 13 databases showed major role of miR-152-3p (targets 28 mRNAs), miR-93-5p (targets 19 mRNAs), miR-301a-3p (targets 13 mRNAs), miR-29c-3p (targets 12 mRNAs), and miR-144-3p (targets 9 mRNAs). Integrated analysis can provide valuable information from the transcriptomics data and effect of miRNAs on mRNAs. Citation Format: Srinivas V. Koduru. microRNA/mRNA integrated analysis of multiple myeloma transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2291.