Bioinformatics Facility, Centralized Core Research Facility, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.
ICMR-AIIMS Computational Genomics Centre, Division of Biomedical Informatics, Indian Council of Medical Research, Ansari Nagar, New Delhi, 110029, India.
Sci Rep. 2021 May 26;11(1):10957. doi: 10.1038/s41598-021-90424-y.
Multiple myeloma (MM) is a plasma cell malignancy with diverse clinical phenotypes and molecular heterogeneity not completely understood. Differentially expressed genes (DEGs) and miRNAs (DEMs) in MM may influence disease pathogenesis, clinical presentation / drug sensitivities. But these signatures overlap meagrely plausibly due to complexity of myeloma genome, diversity in primary cells studied, molecular technologies/ analytical tools utilized. This warrants further investigations since DEGs/DEMs can impact clinical outcomes and guide personalized therapy. We have conducted genome-wide meta-analysis of DEGs/DEMs in MM versus Normal Plasma Cells (NPCs) and derived unified putative signatures for MM. 100 DEMs and 1,362 DEGs were found deranged between MM and NPCs. Signatures of 37 DEMs ('Union 37') and 154 DEGs ('Union 154') were deduced that shared 17 DEMs and 22 DEGs with published prognostic signatures, respectively. Two miRs (miR-16-2-3p, 30d-2-3p) correlated with survival outcomes. PPI analysis identified 5 topmost functionally connected hub genes (UBC, ITGA4, HSP90AB1, VCAM1, VCP). Transcription factor regulatory networks were determined for five seed DEGs with ≥ 4 biomarker applications (CDKN1A, CDKN2A, MMP9, IGF1, MKI67) and three topmost up/ down regulated DEMs (miR-23b, 195, let7b/ miR-20a, 155, 92a). Further studies are warranted to establish and translate prognostic potential of these signatures for MM.
多发性骨髓瘤（MM）是一种浆细胞恶性肿瘤，其临床表现和分子异质性多种多样，尚未完全了解。MM 中的差异表达基因（DEGs）和 microRNAs（DEMs）可能影响疾病发病机制、临床表现/药物敏感性。但是，由于骨髓瘤基因组的复杂性、研究的原代细胞多样性、所使用的分子技术/分析工具的多样性，这些特征重叠甚少。这需要进一步的研究，因为 DEGs/DEMs 可以影响临床结果并指导个性化治疗。我们对 MM 与正常浆细胞（NPCs）中的 DEGs/DEMs 进行了全基因组荟萃分析，并得出了 MM 的统一假定特征。在 MM 和 NPCs 之间发现了 100 个 DEMs 和 1362 个 DEGs 失调。推断出 37 个 DEMs（“Union 37”）和 154 个 DEGs（“Union 154”）的特征，分别与已发表的预后特征共享 17 个 DEMs 和 22 个 DEGs。两个 miR（miR-16-2-3p，30d-2-3p）与生存结果相关。PPI 分析确定了 5 个功能连接度最高的 hub 基因（UBC、ITGA4、HSP90AB1、VCAM1、VCP）。确定了五个具有≥4 种生物标志物应用（CDKN1A、CDKN2A、MMP9、IGF1、MKI67）和三个上调/下调最明显的 DEMs（miR-23b、195、let7b/miR-20a、155、92a）的种子 DEGs 的转录因子调控网络。需要进一步的研究来确定和转化这些 MM 特征的预后潜力。
