hsa-miR-1246 is Consistently Overexpressed in Spheroid-Derived Cancer Stem Cells From Multiple Tumor Types.

IF 2.5 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2026-03-16 eCollection Date: 2026-01-01 DOI:10.1177/11769351251414086
Ángela Y García Fonseca, Yeimy González-Giraldo, Natalia Vargas Rondón, Andrés F Aristizábal-Pachón
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引用次数: 0

Abstract

Tumors consist of various cell types, including a small population of cancer stem cells (CSCs), which are linked to metastasis, drug resistance, and recurrence. Maintaining the CSC phenotype requires regulation of molecules, including miRNAs; they are small non-coding RNAs involved in processes such as proliferation, differentiation, invasion, and apoptosis. However, miRNAs involved in CSC generation and maintenance remain largely unidentified. In this study, we aimed to identify miRNAs associated with CSC populations by studying the miRNA profiles in Spheroids-Derived cancer stem cells (SDCSCs) from different cancer cell lines. Firstly, we used small RNA sequencing by Illumina to identify differentially expressed miRNAs from SDCSCs compared with adherent cells from lung cancer cell line. MiRNAs with P < .05 and fold change >1.0 were considered significant. Next, we conducted a meta-analysis to integrate expression data from studies performed under same conditions from several tumor cell lines such as ovarium, breast, colorectal cancer cell lines. We reanalyzed microarrays and RNA sequencing data. For integration we employed the Robust Rank Aggregation approach. We identified only one upregulated miRNA, the hsa-miR-1246 with a P-value 1.6356-5. hsa-miR-1246 showed consistent overexpression in CSC-enriched spheroids, with fold changes ranging from 2.4 to 3.8 (P < .05) across studies. Bioinformatics analysis revealed that hsa-miR-1246 interacts with cyclins, GSK3B, and other experimentally validated targets, which were related to the cell cycle (FDR 8,40E-03) and the regulation of transcription from RNA polymerase II (FDR 8,30E-03). Our results provide the first integrative study showing that hsa-miR-1246 is consistently overexpressed from cancer stem cells in different tumor cell lines, suggesting a direct link between its oncogenic activity and the CSC phenotype. Since our analysis demonstrates that miR-1246 overexpression is highly specific to CSCs rather than differentiated tumor cells, its detection in patients could serve as an indirect indicator of CSC abundance and tumor aggressiveness. This provides new biological insight into the cellular origin of this miRNA and supports its potential use as a biomarker of stemness and therapeutic target in cancer. Additional studies using in vivo models and functional knockdown experiments will be important to validate these findings and better define the role of hsa-miR-1246 in CSC regulation.

hsa-miR-1246在多种肿瘤类型的球形来源的癌症干细胞中一致过表达。
肿瘤由多种细胞类型组成,包括一小部分癌症干细胞(CSCs),它们与转移、耐药性和复发有关。维持CSC表型需要调节分子,包括mirna;它们是参与增殖、分化、侵袭和凋亡等过程的小的非编码rna。然而,参与CSC生成和维持的mirna在很大程度上仍未被识别。在这项研究中,我们旨在通过研究来自不同癌细胞系的Spheroids-Derived cancer stem cells (SDCSCs)的miRNA谱来鉴定与CSC群体相关的miRNA。首先,我们利用Illumina的小RNA测序技术,鉴定了SDCSCs与肺癌细胞系贴壁细胞的差异表达miRNAs。P值为1.0的mirna被认为具有显著性。接下来,我们进行了一项荟萃分析,以整合来自几种肿瘤细胞系(如卵巢癌、乳腺癌、结直肠癌细胞系)在相同条件下进行的研究的表达数据。我们重新分析了微阵列和RNA测序数据。对于集成,我们采用了鲁棒秩聚合方法。我们只发现了一个上调的miRNA,即hsa-miR-1246, p值为1.6356-5。hsa-miR-1246在csc富集的球体中一致过表达,倍数变化范围为2.4 ~ 3.8 (P
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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