Résumé
Introduction: Long non-coding RNAs (lncRNAs) have emerged as critical regulators of gene expression and cellular phenotype in hematologic malignancies, including multiple myeloma (MM). While several protein-coding gene signatures have been associated with therapeutic response, the role of lncRNAs in mediating sensitivity or resistance to key MM agents remains underexplored. In this study, we explored the lncRNA transcriptome in cell line models representing three distinct drug response phenotypes to identify candidate lncRNAs associated with resistance or sensitivity to protease inhibitors (PIs) and BCL2 inhibitors.
Methods: Six established MM cell lines were selected: RPMI-8226 and U266 (PI-resistant), LP-1 and KMS-12-BM (BCL2 inhibitor-sensitive), as well as AMO-1 and MM.1S (PI-sensitive). Cells were cultured under standard conditions, followed by total RNA extraction and poly(A)+ RNA enrichment. RNA-seq with PE100 chemistry was performed on the DNBSEQ-G400 platform, producing paired-end reads. Raw FASTQ files were processed for quality control, alignment to the GRCh38 reference genome, and transcript quantification analysis. lncRNA expression was profiled using curated GENCODE annotations. Expression levels for a panel of previously reported MM-associated lncRNAs were assessed using specialized software. Comparative analysis was conducted across cell lines to identify lncRNAs with differential expression patterns associated with drug sensitivity.
Results: Distinct lncRNA expression profiles were observed across all MM cell lines, aligned with their known drug response phenotypes. Among tumor-suppressive lncRNAs, GAS5 was markedly elevated in BCL2 inhibitor-sensitive cells. GAS5 is known to function as a decoy for glucocorticoid receptor activation and has been associated with apoptosis sensitization and downregulation of anti-apoptotic signaling in hematologic cancers. Its elevated expression in the BCL2-dependent cells may suggest a compensatory apoptotic signature, consistent with venetoclax sensitivity. Furthermore,NEAT1 and MALAT1, two well-characterized nuclear-retained lncRNAs involved in stress adaptation, splicing regulation, and cell survival, were most highly expressed in PI-sensitive cells, while showing the lowest levels at PI-resistant cells. High NEAT1 expression has been linked to PI response and DNA damage repair signaling aligning with PI sensitivity. The oncogenic lncRNA PVT1, frequently co-amplified with MYC on chromosome 8q24, showed significantly increased expression in PI-resistant and BCL2 inhibitor-sensitive versus PI-sensitive cells (P<0.050). Additionally, LINC00461 and LUCAT1, implicated in proliferation, chemoresistance, and modulation of PI3K/AKT signaling, were more highly expressed in the PI-resistant cell lines. Conversely, ZFAS1 and TUG1, often described as modulators of cell differentiation and stress response, were more highly expressed in BCL2 inhibitor-sensitive and PI-sensitive cells, possibly reflecting retained apoptotic or differentiation potential in drug-sensitive models. Several lncRNAs – including H19, HOTAIR, MEG3, and UCA1 – showed low or negligible expression among all the investigated cell lines. While previously reported in solid tumors or MM patients' subgroups, their minimal expression suggests either low basal activity in these models or context-dependent regulation.
Conclusion: Transcriptomic analysis revealed distinct lncRNA expression signatures across MM cell line models representing defined drug response phenotypes. Elevated levels of GAS5 and ZFAS1 in the BCL2 inhibitor-sensitive cells suggest potential tumor-suppressive roles in apoptosis regulation and BCL2 pathway dependence. In contrast, increased expression of LINC00461, LUCAT1, and PVT1 in the PI-resistant cells supports their potential involvement in survival signaling, proliferation, and therapy resistance. These observations highlight that these lncRNAs may serve as predictive biomarkers of therapeutic response or targets for overcoming resistance in MM. Future studies should extend these findings to primary samples of MM patients, integrating clinical response data and cytogenetic background. Ultimately, integrating lncRNA-based biomarkers into therapeutic decision-making may enhance precision medicine approaches in MM and related hematologic cancers.