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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.

Keywords

cellular heterogeneity tumor plasticity cancer stem cells miRNAs

Introduction

Cancer is a significant public health problem that results in thousands of deaths each year.¹ Tumor heterogeneity is one of the reasons that complicates both prognosis and treatment efficiency. In this context, this pathology is a central focus of numerous investigations aimed at understanding the origin of tumor heterogeneity and the mechanisms of the disease, with the goal of improving both diagnostic and therapy.²

It is known that many signals in the microenvironment can lead to epigenetic changes, activation of distinct pathways, and subsequently affect phenotypic plasticity within the tumor, resulting in distinct cell subpopulations.^3,4 Among these subpopulations are differentiated tumor cells and undifferentiated tumor cells or cancer stem cells (CSCs).⁵ The latter is highly relevant in the development of tumors, as CSCs exhibit high plasticity, meaning they can change their phenotypic and functional characteristics to adapt to changing conditions. This adaptation can be either reversible or irreversible.⁶ As a result, this affects the prognosis and influences the degrees of sensitivity to therapy.^3,7 Therefore, the presence of CSCs is known to be responsible for metastasis, therapy resistance, and recurrence in distinctive types of cancer.^8,9

These cells are named as CSCs because they exhibit similar features to normal stem cells, including indefinite self-renewal capacity and the ability to differentiate.¹⁰ These capabilities are attained through the activation and repression of multiple genes, which include coding and non-coding RNAs.¹¹ Previous studies have indicated that different transcription factors are required to trigger CSC phenotype by means of epithelial-mesenchymal transition (EMT), such as TWIST, SNAIL1, and ZEB.^12,13 In contrast, there are other transcription factors such as SOX2, OCT-4, and Nanog, responsible for maintaining the undifferentiated state of tumor cells.¹³ In addition, there is a growing interest in studying other molecules such as non-coding RNAs; several studies have discovered that these RNAs have multiple functions in regulating several processes within the tumor cells.¹⁴

One of these non-coding RNAs is microRNAs (miRNAs), which are a type of small non-coding RNA molecule consisting of 18 to 25 base pairs. The canonical pathway and the most studied function of these molecules is to negatively regulate the expression of various genes through their interactions with the 3’ untranslated region (3’ UTR) of messenger RNA.¹⁵ Studies have demonstrated that miRNAs play a significant role in the regulation of pathways such as proliferation, invasion, differentiation, survival and other processes, which are also involved in stem cell function.^11,16 Furthermore, investigations have shown that dysregulation in the expression levels of miRNAs can promote pathological conditions, such as cancer.¹⁷ Dysregulation of specific miRNAs can contribute to the EMT phenotype and, as a result, impact cancer stemness and drug resistance.^18
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There are many studies which report several miRNAs that have been associated with CSC-related properties such as hsa-miR-21 in colorectal and liver cancer, hsa-miR-138 in lung cancer, hsa-miR-483-5p in gastric cancer and among others.^21,22 Nevertheless, there is not a specific group of miRNAs consistently dysregulated across several types of tumors that have been reported to be associated with the CSC phenotype. This has prompted the need to integrate evidence from various investigations, aiming to identify miRNAs commonly deregulated across tumors and specifically shared pathways.²³ This approach might support the identification of the most relevant non-coding RNAs involved in the induction of CSC phenotype, independently of tumor type, which could be useful to elucidate the potential diagnostic and prognostic targets in cancer. Therefore, we aimed to identify miRNAs commonly deregulated in SDCSCs from several tumors using experimental and bioinformatic approaches. Firstly, we generate spheroids as a model of CSCs from a lung cancer cell line and perform small RNA sequencing on both the CSC and adherent cell samples. Spheroids have been used as models for CSCs for 2 main reasons. First, they exhibit similar characteristics to CSCs in vitro, such as self-renewal, the ability to differentiate, the expression of specific CSC markers, and the capacity to form spheroids. This makes spheroids valuable systems for evaluating CSC-related properties.²⁴ Second, isolating CSCs from tumors is challenging due to their lower population within the tumor. Next, we integrate our experimental results with studies using other tumor cell lines that employ a similar experimental design to generate CSCs. Robust Rank Aggregation Approach was used to combine data from microarray and RNA sequencing. We found that hsa-miR-1246 is a commonly upregulated microRNA across all studies. Bioinformatic analysis suggested that this miRNA regulates key cell cycle components such as cyclins, CDKs, and MDM2, which could be relevant for maintaining the CSC phenotype. Previous studies have also reported a role for miR-1246 in maintaining stemness and promoting invasiveness in non-small cell lung cancer,²⁵ as well as in driving chemoresistance in pancreatic cancer.²⁶ Although miR-1246 has been extensively characterized as an oncomiR involved in tumor progression and therapy resistance, its precise cellular origin remains undefined. To date, no evidence has directly linked miR-1246 overexpression to the cancer stem cell (CSC) compartment. Therefore, this study aimed to determine whether hsa-miR-1246 is consistently overexpressed in CSC-enriched spheroid populations compared with adherent parental cells across multiple tumor types. To address this objective, we combined experimental data and publicly available small RNA-seq datasets to perform a comparative meta-analysis and bioinformatics characterization of hsa-miR-1246 targets and pathways associated with CSC maintenance. Our results demonstrate that hsa-miR-1246 expression is consistently enriched in CSC-enriched spheroid populations compared with their adherent parental counterparts, indicating that CSCs represent a key source of miR-1246 upregulation in cancer.

Methodology

Generation of Spheroids-Derived Cancer Stem Cells

In this study, we worked with NCI H1975 cells, which were obtained from ATCC in 2022. It is a non-small cell lung cancer adenocarcinoma which was isolated in 1988 from the lungs of a nonsmoking woman.²⁵ The cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) with 10% FBS until they reached confluence. Subsequently, we seeded 50 000 cells per well in an ultra-low attachment 24-well plate with DMEM/F12 medium free of fetal bovine serum (FBS), supplemented with 2 mM glutamine, 20 ng/ml Epidermal Growth Factor (EGF), 10 ng/ml β-Fibroblast Growth Factor (bFGF), 1X Insulin-Transferrin-Selenium (ITS), and 1X B27. All of these components were used to stimulate the generation of CSCs.^26
-28 We replaced the medium every 3-4 days for 1 month until spheroid formation occurred. To confirm the presence of the CSC phenotype, we isolated the cells using the CD133 MicroBead Kit (human; Catalog #130-097-049 by Miltenyi Biotec), as CD133 is a marker of CSCs.²⁹ Briefly, cells are labeled with CD133 MicroBeads, incubated, and then separated using a MACS column under a magnetic field. The CD133+ cells are retained in the column, while unlabeled cells are washed away, allowing for purified cell collection. Finally, the CD133+ cells are eluted by removing the column from the magnetic field, allowing for purified cell collection in a new tube.

Small-RNA Sequencing

Once the SDCSCs were obtained, RNA was isolated using the Quick-DNA/RNA™ Miniprep Kit (Catalog #D7001, Zymo Research). RNA concentration for each sample was determined using a Nanodrop, with a minimum of 2 µg required. The quality and integrity of the RNA were assessed using the Qubit™ RNA IQ Assay Kit (Catalog #Q33221, Thermo Fisher), with a minimum score of 8.0 necessary. We used Novogene’s sequencing services to perform high-depth sequencing with 10 million reads. The samples were sequenced using small RNA sequencing (sRNA-seq) on the Illumina platform, with the following parameters accordance with the manufacturer’s protocol: sample preparation, library construction, add 3’ and 5’ adapter, reverse transcription, a single-end 50 bp (SE50) sequencing strategy was used to sequence, and QC report.³⁰ In our analysis, 3 biological replicates of SDCSCs and adherent cells were sequenced.

Differential Expression Analysis of miRNAs

From our small RNA-seq dataset, we determined the differential expression of miRNAs in spheroid-derived cancer stem cells compared to adherent cells in the NCI-H1975 cell line. Data processing was performed using GALAXY.ORG, specifically the Genomic File Manipulation tool (usegalaxy.org).³¹ This platform allows running code in interactive environments such as RStudio and Jupyter, among others. Briefly, the raw data were evaluated in FASTQ format to ensure that all samples met specific requirements for inclusion in the analysis. These included a quality score of Q30 or above for each base, meaning that, on average, 99.9% of the bases are correctly identified, with only 0.1% of the bases likely being incorrect. The data also had to meet the overall quality score for the entire sequencing run and contain no per-base ‘N’ content. The Cutadapt tool was then used to remove adapter sequences and nonspecific dimers. Next, we used MiRDeep2 Mapper for read alignment against the human genome, followed by the identification of miRNAs based on precursors and mature miRNAs showed in miRbase.³² Consequently, the MiRDeep2 Quantifier tool was used to determine the expression of these miRNAs in each sample. The DESeq2 tool was used to assess the differential expression of miRNAs between spheroid-derived cancer stem cells and adherent cells. Finally, we used the Filter tool to generate a list containing only the miRNAs with a P-value <.05.³³

After that, we created a volcano plot using SRplot, an online tool for data visualization.³⁴ The volcano plot helps to identify the most relevant miRNAs based on P-values and fold change. Briefly, we input the list of dysregulated miRNAs along with their adjusted P value and fold changes, selected the miRNAs we wanted to label, and then ran the tool to generate the plot.

Pathway Interactions of miRNAs Dysregulated in Spheroid-Derived Cancer Stem Cells From the NCI-H1975 Lung Cancer Cell Line

To identify the possible pathways associated to upregulated and downregulated miRNAs from SDCSCs, we used a DIANA tool called miRPath v4.0.³⁵ DIANA-miRPath is a bioinformatics platform that utilizes predicted or experimentally validated miRNA interactions to investigate the combined effects of miRNAs. This tool helps to understand the biological significance of differentially expressed miRNAs by mapping them to various biological pathways. Briefly, we input the list of upregulated or downregulated miRNAs, and then selected the following conditions: Targets: TarBase v8.0 (which includes only experimentally validated miRNA-target interactions), TarBase targets: Direct, Species: Homo sapiens, miRNA Annotation: miRBase v22.1, Terms/Pathways: KEGG, Merging Method: Pathways union, Testing Method: Classic analysis, P-value threshold: .05, FDR Correction: Applied

Searching Data

First, we browsed PubMed and Google Scholar, free search engines that provide access to academic literature, primarily in the biomedical and life sciences fields. Then, we conducted extensive research through the Gene Expression Omnibus (GEO),^36,37 a public genomics data repository where array- and sequence-based data are deposited. We used the following keywords: ‘CANCER AND CSC’, ‘microRNAs AND CSC’, ‘microRNAs AND CSC AND Cancer’, ‘miRNAs AND Cancer stem cell’, ‘miRNAs AND Cancer stem cell’ AND patients, ‘miRNAs AND CSC AND patients’ to perform the search to identify relevant studies. The last search for all the above sources was conducted in August 2024.

Abstracts retrieved from these searches were screened for relevance to the topic of interest. Only those that included insights relevant to our research question were shortlisted for further evaluation. The datasets linked to the shortlisted abstracts were then assessed against a set of predefined eligibility criteria, which included study design, sample size, and outcome measures.

To ensure a systematic and transparent process, we also employed filters where applicable:

Language: English only

Study Type: Experimental research

Study Selection

Two independent reviewers (Reviewer 1 and Reviewer 2) screened all titles and abstracts retrieved from the searches. Each reviewer independently assessed the relevance of the records in line with data quality (eg, raw data, 3 replicates), and any discrepancies between the reviewers were resolved through discussion. After the initial screening, full-text articles of potentially eligible studies were retrieved and reviewed. Both reviewers independently assessed the full texts against the inclusion criteria. In total, 61 records were screened, 9 underwent full review, and 5 were included in the final analysis

The inclusion criteria were as follows: (I) datasets in cancer cell lines, (II) Datasets of cancer stem cells generated from adherent cells using DMEM/F12 serum-free medium and complemented with growth factors, such as fibroblastic growth factor, epidermal growth factor, and supplements as Glutamine, B27 among others. (III) Datasets including 3 replicates, (IV) datasets that determined the differential miRNA profiles by microarray or RNA sequencing, and the investigation published raw data, (V) Datasets from SDCSCs that were confirmed to express at least one marker of CSCs, such as CD133, CD44, and/or ALDH²⁹ In contrast, the studies were excluded considering the following criteria: (I) Works including data derived from non-human model, (II) datasets lacked triplicates, (III) datasets using qPCR, (IV) studies where the cell line had a treatment with some drug or extract, and (V) the investigation did not report raw data.

Meta-Analysis

Once the datasets were selected, we performed data reprocessing on the raw data from each study, including our experimental dataset to get a list of differentially regulated miRNAs and ensure reproducibility and transparency of results. For microarray experiments, we utilized the Gene Expression Omnibus 2 R (GEO2R) tool. This tool was employed to compare 2 groups of samples to identify differentially expressed genes or miRNAs through LIMMA. In this case, we evaluated miRNAs under 2 experimental conditions (SDCSC vs adherent cells). Therefore, the LIMMA was applied, followed by the Benjamini & Hochberg false discovery rate method for P-value adjustment, along with a log2 fold change threshold, to identify differential expression.^38
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For raw sequence data which were available on the Sequence Read Archive (SRA) platform provider for NCBI servers,⁴¹ we performed data reprocessing using GALAXY.ORG, (https://usegalaxy.org/).³¹

The results of reprocessing were presented in a table where we selected those differentially expressed miRNAs with an adjustment P-value <.05. Subsequently, we ranked the differentially regulated miRNAs according to log2 fold change threshold. As a result, we obtained 2 separate lists: 1 for up-regulated miRNAs and another for down-regulated. MiRNAs with the largest fold change relative to the control (Adherent cells) were placed at the beginning, while those with a smaller fold change were listed at the end. We excluded miRNAs with ambiguous nomenclature or those named used immature conventions based on miRbase version 22.1.³²

Finally, to carry out the meta-analysis, we used Robust Rank Aggregation (RRA) method in Rstudio to identify common or consistent miRNAs across different datasets in a robust manner.⁴² The package containing functions for RRA analysis is downloaded from CRAN website and installed into our system. The next steps were performed in accordance with the author’s protocol. ⁴² RRA assigns a score to each miRNA based on its frequency and position in individual rankings, thereby prioritizing miRNAs that consistently rank high across multiple datasets, and considering as significant those with a P-value adjustment <.05

Targets Identification

To identify the targets of the miRNAs, we employed the TarBase V.9 database.^43,44 This database encompasses miRNA–target interactions confirmed through biological experiments. it contains ∼6 million experimentally validated miRNA–target interactions. Strong evidence is supported by 15 techniques such as reporter assays, CLIP-seq, CLASH, AGO-CLIP, HITS CLIP among other, and 22 throughput experimental techniques derived from methods including microarray, western blot, qPCR, and others.⁴³ In our research, we set the next parameters: Homo sapiens, high Experimental throughput and just direct experimental type.

Enrichment Analysis

To carry out the enrichment analysis, we employed the Database for Annotation, Visualization and Integrated Discovery (DAVID), a bioinformatics resource system which was updated in 2021.⁴⁵ We uploaded a query list of ensemble IDs for genes found to be regulated for the miRNAs from the Diana tool Tarbase V.9 and specified the following parameters: Organism = H. sapiens (human), Select identifier = ENSEMBL_GENE_ID, List type = Gene list. For functional enrichment analysis, DAVID utilizes an algorithm based on empirical sampling and is supported by several databases. In our study, we focused only on data published by KEGG. Additionally, for biological processes, cellular components, and molecular functions, we utilized Gene Ontology, also provided by DAVID. Subsequently, we selected the enrichment terms with adjusted P-value less than or equal to .05 that were relevant to the maintenance of CSC.

Convergence Analysis

To identify the common genes between cancer research and target genes of miRNA identified in this meta-analysis, we created a Venn diagram using the Bioinformatics & Evolutionary Genomics tool (refer to Bioinformatics & Evolutionary Genomics Venn Tool). We provided 2 input files: Input file 1 contained the list of genes regulated by has-miR-1246, as reported by Diana Tools TarBase V.9, and Input file 2 contained the list of differentially expressed genes from between breast cancer stem cells and breast cancer cells isolated from tumors of 2 patients, as described in the article by Lei et al⁴⁶ which met the criteria of including data from patients who had not received any treatment before sample collection and the analysis was compared both CSC populations and differentiated cancer cells from the same patient. To determine the functional of common genes, we performed an enrichment analysis as we explained above.

Statistical Analysis

All statistical analyses were carried out using R software (v4.3.1), GraphPad Prism (v10.0), and tools integrated into the Galaxy.org platform. Differential expression of miRNAs between spheroid-derived cancer stem cells (SDCSCs) and adherent parental cells was evaluated with DESeq2 for RNAs seq or LIMMA for microarrays. miRNAs were considered significantly deregulated when the adjusted P-value was below .05 and the |log₂(fold change)| >1.

Pathway enrichment was explored using KEGG and Gene Ontology databases through the DAVID and DIANA-miRPath platforms,^43,47 applying the Benjamini–Hochberg correction for multiple testing (adjusted P < .05). These statistical thresholds were used to ensure the robustness of the results. Visualizations, such as volcano plots and enrichment bar charts, were produced with SRPlot and GraphPad Prism.

Results

The miRNAs Dysregulated in Spheroids- Derived Cancer Stem Cells Are Involved in Pathways in Cancer

First, we characterized the morphology of cultured NCI-H1975 adherent cells and spheroid-derived cancer stem cells (SDCSCs). The latter were obtained under spheroid culture conditions using DMEM/F12 medium supplemented with B27, EGF, and bFGF, which were enriched for CD133⁺ cells using magnetic-activated cell sorting (MACS) as described in the Methodology section. As shown in Figure 1, CSCs displayed their typical oval morphology and the ability to form compact spheroids, a hallmark feature of stem-like cells. These observations confirm the successful enrichment and maintenance of the CSC population under these specific culture conditions (Figure 1(B)).

Figure 1.

Enrichment and morphology of CD133⁺ CSCs derived from NCI-H1975 cell lines. Representative images illustrate the morphology of NCI-H1975 adherent cells using a 10× objective (A), and the formation of spheroid-derived cancer stem cells using 40× and 10× objectives, respectively (B and C).

Small RNA sequencing of the NCI-H1975 cell line was performed, and data processing was carried out using Galaxy.org. A total of 223 miRNAs were found to be differentially expressed in SDCSCs relative to adherent cells. 98 of these miRNAs were discovered upregulated and 125 were downregulated with a P-value adjustment (Supplemental Table 1). Among these miRNAs, we identified the most relevant ones, in line with significant P-value adjustment <.05 and fold change >1.0, including hsa-miR-1247-5p, hsa-miR-200c-3p, and hsa-miR-205 as downregulated in CSCs, and hsa-miR-1246, hsa-miR-574-5p, and hsa-miR-5684 as upregulated in CSCs (Figure 2).

Figure 2.

Volcano plot illustrating the differentially expressed miRNAs identified from small RNA sequencing of the lung cancer cell line NCI-H1975.

Consequently, the enrichment analysis using DIANA-miRPath revealed that both upregulated and downregulated miRNAs are involved in pathways such as the cell cycle, proteoglycans in cancer, p53 signaling pathway, Hippo signaling pathway, among others (Figure 3 and Supplemental Tables 2 and 3).

Figure 3.

Enrichment analysis of the main pathways associated with differentially expressed miRNAs in spheroid-derived cancer stem cells (SDCSCs) compared with adherent parental cells from the NCI-H1975 lung cancer line.

The hsa-miR-1246 Is Differentially Expressed in Spheroids-Derived Cancer Stem From Several Tumor Models

Since there are no miRNAs consistently documented as dysregulated in CSCs across tumors, we decided to integrate our results with data from other tumor models reporting dysregulated miRNAs in SDCSCs, to determine whether the same molecules are affected. This approach could provide insights into potential non-coding RNA targets involved in the induction of the CSC phenotype, regardless of tumor type.

In this context, we conducted a meta-analysis with a dataset search that yielded 61 studies, of which 9 underwent a full review, and 5 were included in the final analysis, along with our dataset. (Figure 4).

Figure 4.

Flow diagram of screening studies.

Some studies analyzed different cell lines for a cancer type, such as the study performed by Srivastava in 2019 of ovarian cancer,⁴⁹ and the work carried out by Degrauwe (2016) of Glioma.⁵⁰ In contrast, the other studies used a cell line per cancer type, which included Breast, and Colorectal cancer (Table 1). In total, 42 samples were obtained from these datasets, which were distributed as follow: 21 samples of adherent cells and the other 21 of CSCs. It is important to highlight that all Spheroid-derived cancer stem cells samples from different datasets were acquired through their corresponding phenotype of adherent cell culture, using the appropriate media to stimulate and generate the CSC phenotype as was described in inclusion criteria.

Table 1.

Datasets Selected to Performance De Meta-Analysis Based on the Differential Expression of miRNAs, Between CSC Versus Adherent Cell in Various Types of Cancer.

Author, year	GEO accession	Type of cancer	Cell line	Platform	Number of miRNAs detected	DE miRNAs (post-analysis) CSC versus adherent cells
García Fonseca et al (2024)⁴⁸	SRA: PRJNA1225305	Lung cancer	NCI H1975	Small RNA sequencing (sRNA-seq) SE50	2885	98 (up)125 (down)
Srivastava and Wang (2019)⁴⁹	GSE107155	Ovarian cancer	Kuramochi	NanoString nCounter Human v3 microRNA expression system	828	137 (up)1 (down)
Srivastava and Wang (2019)⁴⁹	GSE107155	Ovarian cancer	SKOV3	NanoString nCounter Human v3 microRNA expression system	828	46 (up)45 (down)
Boo et al (2017)⁵¹	GSE68031	Breast cancer	MCF-7	(miRNA-4) Affymetrix Multispecies miRNA-4 Array	6094	63 (up)82 (down)
Degrauwe et al (2016)⁵⁰	GSE73846	Glioma	MGG4MGG8MGG11	Agilent-046064 unrestricted-human_miRNA_V19.0_Micrroarray	2030	2 (down)
Boo et al (2020)⁵²	GSE68246	Breast cancer	MCF-7	Illumina HiSeq 2000 (Homo sapiens)	2885	135(up)131 (down)
Toden et al (2019)⁵³	GSE125904	Colorectal cancer	HCT116	Illumina HiSeq 2500 (Homo sapiens)	2885	107 (up)106(down)

Abbreviations: CSC, cancer stem cells; DE, differentially expressed.

The RRA method consistently indicated the upregulation of hsa-miR-1246 in SDCSCs across different cancer types, compared to adherent cells, with adjusted P-value 1.6356⁻⁵ (Figure 5 and Supplemental Table 4). It should be noted that for the upregulated list, we included only 6 datasets, as the study GSE73846⁴⁵ provided no evidence of upregulated miRNAs.

Figure 5.

Expression levels of hsa-miR-1246 in spheroid-derived cancer stem cells (SDCSCs) compared with adherent parental cells.

As illustrate in Figure 5, the study in the breast cancer cell line (GSE68246) showed a higher expression level of hsa-miR-1246, with a log2 fold change of around 6. In contrast, lung cancer, colorectal cancer, and ovarian cancer (SKOV-3) had similar log2 fold changes of approximately 4, whereas the breast cancer cell line (GSE68031) reveled a lower log2 fold change of about 2 (Figure 5).

The Targets of hsa-miR-1246 Are Involved in the Cell Cycle

To identify the pathways associated with hsa-miR-1246, we conducted a search for potential targets of this microRNA that could be implicated in maintaining CSC phenotype. A total of 452 targets experimentally validated for hsa-miR-1246 were identified using TarBase (Supplemental Table 6).⁴³

In relation to enrichment analysis identified by KEGG for targets of hsa-miR-1246, we observed that some pathways involving hsa-miR-1246 are related to the cell cycle, the p53 signaling pathway, cellular senescence, gap junctions, the Polycomb repressive complex, among others (Figure 6 and Supplemental Table 7). However, the cell cycle was the only pathway statistically significant with an FDR <0.05.

Figure 6.

Pathway enrichment analysis.

In line with the results from DAVID, the genes regulated by hsa-miR-1246 include Cyclin-dependent kinase, MDM2 proto-oncogene, and BUB3 mitotic checkpoint, all of which are relevant to the proliferation process, a key factor in cancer development. Other genes regulated by this miRNA and involved in the cell cycle are listed in Table 2 below.

Table 2.

Experimentally Validated Target Genes of hsa-miR-1246 Associated with Cell Cycle .

hsa-miR-1246
Cell cycle	• Glycogen Synthase Kinase 3 Beta (GSK3B) • BUB3 mitotic checkpoint protein • MDM2 proto-oncogene(MDM2) • NIPBL cohesin loading factor(NIPBL) • RB transcriptional corepressor like 1(RBL1) • STAG2 cohesin complex component(STAG2) • WEE1 G2 checkpoint kinase(WEE1) • Cyclin D2(CCND2) • Cyclin E2(CCNE2) • Cyclin dependent kinase 1(CDK1) • Cyclin dependent kinase 6(CDK6) • Mitotic arrest deficient 2 like 1(MAD2L1) • Proliferating cell nuclear antigen(PCNA) • Protein phosphatase 2 catalytic subunit beta(PPP2CB) • Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon(YWHAE) • Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein theta(YWHAQ)

hsa-miR-1246

Cell cycle

• Glycogen Synthase Kinase 3 Beta (GSK3B) • BUB3 mitotic checkpoint protein • MDM2 proto-oncogene(MDM2) • NIPBL cohesin loading factor(NIPBL) • RB transcriptional corepressor like 1(RBL1) • STAG2 cohesin complex component(STAG2) • WEE1 G2 checkpoint kinase(WEE1) • Cyclin D2(CCND2) • Cyclin E2(CCNE2) • Cyclin dependent kinase 1(CDK1) • Cyclin dependent kinase 6(CDK6) • Mitotic arrest deficient 2 like 1(MAD2L1) • Proliferating cell nuclear antigen(PCNA) • Protein phosphatase 2 catalytic subunit beta(PPP2CB) • Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon(YWHAE) • Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein theta(YWHAQ)

The Targets of hsa-miR-1246 Are Involved in Both the Positive and Negative Regulation of Transcription by RNA Polymerase

In reference to analysis from DAVID, which employs Gene Ontology databases, we noted that the significant biological processes involving genes regulated by hsa-miR-1246 are related to the regulation of transcription, as presented in Figure 7 and Supplemental Table 8. In terms of cellular components, most genes are involved in the cytosol, nucleus, and nucleoplasm (Figure 7 and Supplemental Table 9). Concerning molecular function, protein binding and RNA binding were the most relevant functions (Figure 7 and Supplemental Table 10).

Figure 7.

Gene ontology analysis for hsa-miR-1246 target genes. Gene Ontology (GO) enrichment analysis was performed for the predicted hsa-miR-1246 target genes, including biological process (BP), cellular component (CC), and molecular function (MF) categories.

Convergence Analysis Between hsa-miR-1246 Target Genes and Differentially Expressed Genes in Cancer Stem Cells From Patients

To determine which of the targets regulated by hsa-miR-1246 might be involved in SDCSCs, we conducted a convergence analysis, to assess which of these genes have been detected in samples from cancer patients. We compared the genes regulated by hsa-miR-1246, as documented by Diana Tool Tarbase V.9 (Supplemental Table 6), with dysregulated genes detected in cancer stem cells derived from patients (Supplemental Table 11).

The data were from a study published by Lei and collaborators in 2016.⁴⁶ They performed transcriptome sequencing on HER2-positive breast, isolating two cell populations: breast cancer stem cells (BCSCs) and breast cancer cells (BCCs) from tumors of 2 patients.

Lei and collaborators identified, in their transcriptome sequencing, 404 genes that were differentially expressed with an FDR ⩽ 0.001 by comparing CSC versus cancer non stem cells.⁴⁶ Nine of these genes overlap with those identified in our study that are regulated by hsa-miR-1246, as evidenced in the Venn diagram. (Figure 8 and Supplemental Table 12). Among these 9 genes, 8 are upregulated and 1 is downregulated in BCSCs compared to BCCs (Figure 9 and Supplemental Table 12).

Figure 8.

Convergent analysis of hsa-miR-1246 target genes and genes expressed in breast cancer stem cells (CSCs).

Figure 9.

Expression levels of the 9 hsa-miR-1246 target genes identified in breast cancer stem cells (CSCs).

In relation to enrichment analysis of these 9 commonly identified genes, we did not find any significant pathways. However, we noted that the biological processes associated with these genes are related to the regulation of DNA-templated transcription, as indicated in Supplemental Table 13. In terms of cellular components, these genes are associated with the Golgi apparatus (Supplemental Table 14). For molecular functions, no common functions were identified.

Discussion

Investigations focusing on cancer stem cells have increased in recent years, as their crucial role in metastasis development and therapy resistance has become more apparent.^6,54 Hence, there is a growing interest into elucidating molecular and cellular mechanisms related to induction of this phenotype, specifically, in identifying those molecules, such as microRNAs, that may be implicated in these processes. Here, we identified that hsa-miR-1246 is upregulated in SDCSCs induced in in vitro models from different types of tumors, performing a miRome meta-analysis.

Previous reports have identified miR-1246 as an oncogenic microRNA (oncomiR) associated with potential biomarker of early diagnosis in cancer.^55,56 Also it has been identifying as promoted proliferation, migration and drug resistance from assay in vitro.^57
-59 However, these studies largely examined only one type of tumor cell line or bulk cell populations from the tumor, leaving the cellular origin of miR-1246 overexpression unresolved. Our analysis reveals, for the first time, that miR-1246 enrichment occurs specifically within CSC-like spheroid populations across distinct tumor cell lines cultured under identical conditions. This finding suggests that CSCs are a major source of miR-1246 within tumors, thereby linking stemness to miR-1246-mediated oncogenic signaling.

According to our pathway enrichment analysis, hsa-miR-1246 is involved in the regulation of genes associated with cell cycle (Figure 6). This pathway may play a crucial role in maintaining of CSCs phenotype. Interestingly, Kim et al (2016) demonstrated the significant role of this miRNA in CSCs.⁶⁰ They isolated CSCs from a lung cancer cell line and identified that hsa-miR-1246 was overexpressed in this population through microarray analysis, which is consistent with our sequencing results from the NCI-H1975 lung cancer cell line. Subsequently, they inhibited hsa-miR-1246 in lung cancer cell lines and observed a decrease in stemness markers and epithelial-mesenchymal transition. This inhibition led to reduced proliferation, sphere formation, colony formation, and invasion, emphasizing its crucial role in CSC development.⁶⁰ Our results align with these observations, confirming the consistent upregulation of hsa-miR-1246 in CSC-like populations. However, unlike Kim et al., who focused solely on a lung cancer model, our meta-analysis demonstrates that this pattern is conserved across multiple tumor types, suggesting that hsa-miR-1246 may act as a common molecular determinant of stemness rather than a tumor-specific marker.

On the other hand, a study of oral carcinomas identified that hsa-miR-1246 had increased expression in tumor tissues and putative CSCs. The miRNA enhanced cancer stemness and chemoresistance when spheroid cells from patients with oral squamous carcinoma were cultured in vitro. The authors determinate through luciferase assay that its direct target was cyclin G2 (CCNG2), which was shown to be a tumor suppressor acting in cell cycle arrest.⁶¹ Moreover, it has been evidenced the association between this miRNA and CCNG2, as well as its role in cancer progression in various models, including pancreatic, breast, and colorectal cancer.^53,62
-65 In addition, we identified that hsa-miR-1246 interacts with other types of cyclins, such as cyclin D2 (CCND2) and cyclin E2 (CCNE2; Table 2).

Furthermore, other pathways have been associated with the role of hsa-miR-1246 in CSC phenotype. For example, in liver cancer cell line, it was confirmed that hsa-miR-1246 is overexpressed in sorted CD133+ liver CSC subsets. In that work, using luciferase assays, the researchers identified that this miRNA interacts with AXIN2 and GSK3β, last one also demonstrated in our analysis (Table 2). These are negative regulators of canonical Wnt signaling and they act as tumor suppressors.⁶⁶ Therefore, the downregulation of these genes leads to the activation of β-catenin, promoting stem cell phenotypes in liver cancer cells.⁶⁶

Based on our results related to biological processes, we have found that the targets of hsa-miR-1246 are involved in the regulation of transcription by RNA polymerase II. This is one of the most important steps to determines cell identity and function.⁶⁷ The regulation of transcription from RNA polymerase is a crucial process in gene expression, controlling how genes are turned on or off.^67,68 The activation of RNA polymerase could be related to different cyclins and cyclin-dependent kinase (CDKs) that are regulated by hsa-miR-1246 (Table 2). Due to, cyclins work with CDKs and function as regulators of transcription and cell cycle progression.^69,70 This was demonstrated by Martin et al (2020), who identified that CDK7 activity is critical for driving the transition from initiation to elongation of transcription of DNA transcription by RNA polymerase II (RNA Pol II). Similarly, CDK9 and the cyclin CCNT allow that RNA pol II can be released of pause state and continuous the transcription process.⁷¹ These observations imply that changes in expression of those genes is key to triggering a complex disease such as cancer.⁷¹ Furthermore, Aoi and Shilatifard (2023) also emphasized the importance of RNA Pol II. They highlighted that elongation factors play a crucial role in regulating promoter-proximal RNA Pol II pause release, RNA Pol II stability, and RNA processing. Disruptions in these processes are associated with progressive disorders such as cancer.⁷² Altogether, these findings suggest that miR-1246 may reinforce CSC properties not only by promoting cell cycle progression but also by maintaining an active transcriptional landscape that supports self-renewal and plasticity, 2 hallmarks of stemness.

These outcomes allow us to underline the importance of understanding the mechanisms that maintain and develop the CSC population, providing a great opportunity to contribute to knowledge of cancer in advanced stages and give tools for futures therapies for those stages. Nevertheless, studying CSCs in patients remains challenging due to the difficulty in isolating this population, which is typically present in very low abundance (0.05%-1% of all tumor cells).⁷³ Additionally, the absence of a specific marker that reliably distinguishes these cells from normal stem cells, which share similar behaviors and plasticity of CSC-related phenotypes, complicates the process to work just with CSC from patients.^6,73 This is one of the reasons why most investigations use spheroids as models of CSCs derived from dedifferentiated adherent cell lines.^49,52,53,60

Although our analyses were performed using cell line-derived spheroid models, which may not fully replicate tumor heterogeneity, these models are well established as robust systems for studying CSC biology. The consistency of our observations with patient-derived data further supports the translational relevance of our findings. For instance, data from human studies have demonstrated in 2022, Levati et al published that the hsa-miR-1246 was expressed in the plasma of non-responders to therapy compared to responders in melanoma patients. Additionally, the expression of this miRNA was associated with poor progression-free survival.⁷⁴ On the other hand, Huynh and coworkers did a meta-analysis in 2023, where indicated that expression of hsa-miR-1246 might serve as a reliable circulating biomarker in screening cancers such as breast, colorectal, pancreatic, hepatocellular carcinoma among others.⁵⁶ Other meta-analysis also demonstrated that hsa-miR-1246 could be a biomarker for gastrointestinal cancer from body fluids.⁵⁵ All these outcomes suggest that hsa-miR-1246 is likely functioning as an oncomir. However, its mechanism remains unclear, and no study in patients has yet identified the specific cancer cell population from which this miRNA is expressed, a gap that our investigation helps to address through our CSC-based model. Given the challenges of studying miRNAs in the context of the CSC phenotype in patients, our work provides a starting point for elucidating the mechanisms by which hsa-miR-1246 contributes to the generation and maintenance of CSCs. Our results obtained from tumor cell lines are consistent with findings reported in patients and further complement them by revealing the likely cellular source of hsa-miR-1246 overexpression. Specifically, our study raises the possibility that the high levels of hsa-miR-1246 detected in patient fluids may originate from the CSC population. However, this needs to be studied in the future.

Consequently, to approximate our model based on patient results, we performed a convergent analysis between the target genes of hsa-miR-1246 and the dysregulated genes identified in patients. This analysis aimed to identify which of all genes reported as regulated by hsa-miR-1246 could be involved in the CSC phenotype. We found 9 genes that are shared between those regulated by hsa-miR-1246 and those dysregulated in CSCs from patient transcriptome sequencing from investigation by Lei and collaborators in 2016⁴⁶ (Figures 8 and 9). Of these 9 genes, all are associated with the regulation of DNA templated transcription processes crucial for cancer development.^75,76 This finding underscores the relevance of cell line studies as a valuable approach for understanding CSC mechanisms, especially given the complexities of CSC population from patients. This evidence provides insights into the tumor processes in patients and can guide further studies aiming to find therapeutic targets.

In summary, our results highlight the importance of research using cell lines, particularly when direct investigation of patient samples is challenging. This study demonstrates that hsa-miR-1246 is consistently overexpressed in spheroid-derived cancer stem cells (SDCSCs) compartment rather than from differentiated tumor cells across different tumor types. Hsa-miR-1246 detection in patients may therefore serve as an indirect indicator of CSC abundance and tumor aggressiveness, underscoring its potential as a biomarker of stemness and as a target for CSC-directed therapies.

Future studies should aim to validate these findings using patient-derived xenograft models to confirm the cellular origin of circulating hsa-miR-1246. In addition, exploring its downstream targets and regulatory networks may reveal new therapeutic strategies for selectively eradicating CSCs. In this context, hsa-miR-1246 emerges as a particularly promising candidate for future investigations into CSC biology, given its consistent overexpression across multiple tumor models and its regulatory influence on genes such as Cyclins and GSK3β, key components of pathways involved in cell cycle control, proliferation, and stemness maintenance.

Therefore, additional in vivo validation and functional assays, including knockdown and overexpression experiments, will be required to confirm its specific role in CSC regulation. This study represents the first stage of a broader research project; based on the complete sequencing dataset obtained, follow-up experiments are underway to validate not only hsa-miR-1246 but also other microRNAs differentially expressed between CSCs and adherent cells.

Ultimately, elucidating these regulatory networks will deepen our understanding of CSC biology and may elucidated the way for miRNA-based therapeutic approaches. Targeting hsa-miR-1246—through inhibition or modulation—could represent a novel strategy for overcoming therapy resistance by eliminating CSC populations that drive tumor recurrence and progression.

Supplemental Material

sj-xlsx-1-cix-10.1177_11769351251414086 – Supplemental material for hsa-miR-1246 is Consistently Overexpressed in Spheroid-Derived Cancer Stem Cells From Multiple Tumor Types

Supplemental material, sj-xlsx-1-cix-10.1177_11769351251414086 for hsa-miR-1246 is Consistently Overexpressed in Spheroid-Derived Cancer Stem Cells From Multiple Tumor Types by Ángela Y. García Fonseca, Yeimy GonzÁlez-Giraldo, Natalia Vargas Rondón and Andrés F. AristizÁbal-Pachón in Cancer Informatics

Footnotes

Abbreviations

• cancer stem cells (CSCs)

• spheroid-derived cancer stem cells (SDCSCs)

• microRNAs (miRNAs)

• robust rank aggregation (RRA)

• epithelial-mesenchymal transition (EMT)

• Dulbecco’s modified eagle medium (DMEM)

• fetal bovine serum (FBS)

• epidermal growth factor (EGF)

• β-fibroblast growth factor (bFGF)

• insulin-transferrin-selenium (ITS)

• gene expression omnibus (GEO)

• gene expression omnibus 2 R (GEO2R)

• sequence read archive (SRA)

• breast cancer stem cells (BCSCs) and

• breast cancer cells (BCCs)

• cyclin G2 (CCNG2)

• cyclin D2 (CCND2)

• cyclin E2 (CCNE2)

• cyclins and cyclin-dependent kinase (CDKs)

• RNA polymerase II (RNA Pol II)

ORCID iDs

Ángela Y. García Fonseca

Andrés F. Aristizábal-Pachón

Ethical Considerations

Authors must declare all information about ethics in this section including followings as appropriate: Approval of the research protocol by an Institutional Reviewer Board: N/A.

Author Contributions

Angela Garcia Fonseca: Conceptualization, methodology, data analysis, interpretation of results and writing original draft preparation. Yeimy Gonzalez: Methodology, data analysis, interpretation of results and review and editing. Natalia Vargas: Interpretation of results and review and editing. Andres Aristizabal: Writing-review and editing and funding acquisition. All authors read and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: AGF was supported by a PhD fellowship (Programa de Becas de Excelencia Doctoral Bicentenario) and by the internal call of Pontificia Universidad Javeriana for supporting doctoral research projects, with budget code: 12013930401200

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

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