bioRxivCancer BiologyPreprint
Integrative multi-modal analysis reveals the contribution of noncoding RNAs to post-treatment progression of IDH-mutant astrocytomas
Anja HartewigiD, Serafiina JaatineniD, Matti AnnalaiD, Joonas A. HaapasaloiD, Wei ZhangiD, Hannu HaapasaloiD, Matti NykteriD, Xin LaiiD, Kirsi J. RautajokiiD
Abstract
IDH-mutant (IDHmut) astrocytomas typically arise as grade 2-3 tumors, but a subset of them progress to grade 4 after treatment, significantly worsening the prognosis. It is unresolved how noncoding RNAs (ncRNAs) contribute to this tumor progression. To characterize ncRNAs regulatory roles, we profiled and analyzed the coding and noncoding transcriptomes of matched tumor samples before and after progression to grade 4 in IDHmut astrocytoma patients. By integrating our data with public primary and matched tumor cohorts, we found that upregulated protein-coding genes in progressed tumors overlapped with those in primary grade 4 tumors and were linked to cell proliferation. In contrast, downregulated genes differed between primary and post-treatment grade 4 tumors. A large fraction of genes that were downregulated only in the post-treatment setting were associated with decreased cell differentiation. We identified 53 progression-related ncRNAs predicted to regulate 125 differentially expressed genes. Gene regulatory network analysis revealed their involvement in cell cycle control, extracellular matrix (ECM) organization, and neural differentiation. Notably, hsa-let-7b-3p, a tumor suppressor microRNA, showed recurrent hemizygous deletions and downregulation after post-therapy progression. The long noncoding RNA (lncRNA) PVT1 was recurrently gained and upregulated in grade 4 tumors. The lncRNA NEAT1, previously linked to treatment resistance, was especially upregulated post-treatment and had the highest number of predicted targets (n=38), many related to ECM organization. Overall, our findings highlight the role of ncRNAs in the post-treatment progression of IDHmut astrocytomas, offering new insights into mechanisms of their malignancy.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: deep learning
- Citations
- 0
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