miR-17-92a-1 cluster host gene: a key regulator in colorectal cancer development and progression

The miR-17-92a-1 cluster host gene (MIR17HG) is situated at 13q31.3, position 91,347,820 to 91,354,575, within the genomic sequence with accession number NC_000013.11 (GRCh38.p14 primary assembly). This gene has two linear long non-coding RNA variants called MIR17HG transcript variants 1 and 2, which have lengths of 5,018 bp (NR_027350.1) and 927 bp (NR_027349.1), respectively. MIR17HG, on the other hand, serves as a host gene for six microRNAs (hsa-miR-17, hsa-miR-18a, hsa-miR-19a, hsa-miR-19b-1, hsa-miR-20a, and hsa-miR-92a-1) (https://​www.​ncbi.​nlm.​nih.​gov/​gene/​407975). Based on the NCBI database, we have added more information regarding the MIR17HG-derived RNAs as follows:

The optimal secondary structure of transcript variants 1 and 2 for the non-coding RNA MIR17HG was analyzed using the RNAfold web server (http://​rna.​tbi.​univie.​ac.​at/​cgi-bin/​RNAWebSuite/​RNAfold.​cgi) [17, 18]. The structures were drawn in both minimum free energy (MFE) and dot-bracket notation. The MFE structure for variant 1 was calculated to be -1581.60 kcal/mol, whereas that for variant 2 was -262.50 kcal/mol. The results suggest that the secondary structure of the MIR17HG transcript variants 1 and 2 is highly stable with low MFE values, indicating their potential biological significance, the details in Fig. 1A–B.

We utilized the mfold web server (http://​unafold.​rna.​albany.​edu/​?​q=​node/​60) [19] to visualize the optimized secondary structures and the precise location of MIR17HG-related mature miRNAs (identified by green highlights) derived from their precursor (pre-miRNAs). This analysis allows us to obtain a clear and in-depth understanding of the miRNA’s location and structure (Fig. 2). Table 1 presents comprehensive attributes of six pre-miRNAs that are linked with the miR-17-92a-1 cluster host gene, namely hsa-miR-17, hsa-miR-18a, hsa-miR-19a, hsa-miR-19b-1, hsa-miR-20a, and hsa-miR-92a-1.

The clinical data of Colon Adenocarcinoma (COAD) were obtained from The cancer genome atlas (TCGA) database (https://​portal.​gdc.​cancer.​gov/​repository) [20], specifically to analyze RNA-sequencing (RNA-seq) and microRNA-sequencing (miRNA-seq) data. The inclusion criteria were as follows: (1) verification of a histopathological diagnosis of COAD; (2) confirmation of the primary tumor site as the colon; (3) categorization of the disease type as adenoma or adenocarcinoma. Cases lacking complete demographic information, including gender and vital status, as well as those lacking comprehensive RNA-seq and miRNA-seq transcriptome profiling data, were systematically excluded. Ultimately, our study encompassed 380 COAD patients for RNA-seq data analysis and 369 COAD patients for miRNA-seq data analysis. The clinical characteristics of the patients are summarized in Table 2.

The corresponding high-level data from RNA-Seq and miR-Seq were acquired using TCGA database. Raw read counts from RNA-Seq and miR-Seq datasets underwent normalization using the Trimmed Mean of M values (TMM) method. Differential expression analysis was conducted to identify differentially expressed long non-coding RNAs (DElncRNAs) and microRNAs (DEmiRNAs) between healthy solid tissues and primary malignancies. This analysis was executed using The “limma” package [21, 22] and Voom statistical analysis [23] within the R programming software (R-4.2.1, 64-bit, https://​www.​r-project.​org/​) [24], alongside RStudio Desktop version 2022.7.0.548 [25]. Subsequently, the miRNAmeConverter package [26] was employed to find the latest names of final DEmiRNAs from the miRbase version 22 database [27‐32]. Furthermore, we employed GENT2, a platform designed for the exploration of gene expression patterns across normal and tumor tissues, to validate the DElncRNAs identified using Affymetrix microarray data from the GPL570 platform [33]. To validate the DEmiRNAs, we utilized microarray data from the GSE53592, GSE35834, and GSE18392 data series, sourced from the dbDEMC version 3.0 database, a comprehensive repository of differentially expressed miRNAs in human cancers [34]. Data filtering criteria for RNA/miRNA-seq and microarray data included a requirement for |log₂ Fold Change (FC)|> 1, as well as a significance threshold based on P-value and adjusted P-value (false discovery rate (FDR)) < 0.01.

The RNA-seq and microarray analysis results revealed that MIR17HG exhibits significant overexpression in tumor tissues compared to their normal (Table 3, Fig. 3A). Alternatively, while the expression profiles of hsa-miR-18a-3p, hsa-miR-20a-3p, and hsa-miR-92a-3p appeared inconsistent between miRNA-seq and microarray analyses, the other MIR17HG-derived miRNAs, including hsa-miR-17-5p, hsa-miR-17-3p, hsa-miR-18a-5p, hsa-miR-19a-3p, hsa-miR-19b-1-5p, and hsa-miR-20a-5p, collectively demonstrated an upregulation pattern in patients diagnosed with COAD (Table 4, Fig. 3B).

Our analysis revealed an impressive AUC value of 0.93 for MIR17HG (P-value < 0.0001, Fig. 4A). Furthermore, the AUC values of other microRNAs, including hsa-miR-17-5p, hsa-miR-17-3p, hsa-miR-18a-5p, hsa-miR-19a-3p, hsa-miR-19b-1-5p, and hsa-miR-20a-5p, were also evaluated. Remarkably, these microRNAs exhibited high AUC values of 1, 0.97, 0.99, 0.99, 0.99, and 0.99, respectively (P-values < 0.0001, Figs. 4B–G). This robust performance underscores the strong discriminatory power of MIR17HG and its associated miRNAs to distinguish COAD from normal tissues. Additionally, Fig. 4 serves as a pivotal resource by delineating the optimal cutoff values for distinguishing between tumor and normal tissue for each of these non-coding RNAs. Such delineation further enhances the chance of clinical utility of these ncRNAs by providing precise guidelines for their application in COAD. Further studies and validation are warranted to affirm their clinical utility.

To elucidate the regulatory role of MIR17HG, we established a Competing Endogenous RNA (CeRNA) axis. The MIR17HG-associated miRNAs were identified by applying a stringent criterion, setting the CLIP region P-value to ≤ 0.01 utilizing the starBase v2.0 database (https://​rnasysu.​com/​encori/​index.​php) [35, 36]. To harmonize the nomenclature with the latest miRNA standards, the miRNAs retrieved from starBase were subjected to the miRNAmeConverter package [26] aligning with miRbase version 22 [27‐32]. Subsequently, we got an intersection between the down-regulated miRNAs from our TCGA miRNA-seq data analysis (LogFC < -1 and FDR < 0.01) and starBase miRNAs with the Venny 2.1 web tool (https://​bioinfogp.​cnb.​csic.​es/​tools/​venny/​) [37]. Validated target mRNAs were identified using the multiMIR package, referencing data from miRecords, miRTarBase, and Tarbase databases [38]. Similarly, an intersection analysis was performed to align up-regulated mRNAs derived from our TCGA RNA-seq data analysis (with LogFC > 1 and FDR < 0.01) with the mRNAs from the multiMIR package, again utilizing the Venny 2.1 web tool. Data analysis was carried out using the R programming software (version R-4.2.1, 64-bit, https://​www.​r-project.​org/​) [24] in conjunction with RStudio Desktop (version 2022.7.0.548) [25]. Ultimately, the Cytoscape software (version 3.9.1) [39] was employed to construct the MIR17HG-miRNAs-mRNAs CeRNA axis, with the aid of the CytoHubba app [40] to identify central nodes within the network.

Consequently, we have identified the top ten molecules according to the maximal clique centrality (MCC) ranking approach within the regulatory network of MIR17HG. Specifically, hsa-miR-214-3p, hsa-miR-140-3p, hsa-miR-501-3p, hsa-miR-1287-5p, hsa-miR-28-3p, hsa-miR-323a-3p, hsa-miR-664b-3p, CDCA4, NCAPG2, and MIR17HG were ranked with the highest MCC scores (Fig. 5A and Table 5). Collectively, these findings introduce the MIR17HG network as a central regulatory axis in the regulation of MIR17HG-mediated cellular processes in the context of COAD. However, it is crucial to emphasize that further experimental investigations are warranted to validate the intricate interactions among these nodes and MIR17HG, thereby enhancing our comprehension of the underlying molecular mechanisms governing COAD pathobiology.

To investigate the potential utility of aberrant MIR17HG gene expression as a clinical response indicator and its suitability as a candidate predictive biomarker for drug screening, we conducted a drug sensitivity analysis utilizing the Gene Set Cancer Analysis (GSCA) database (http://​bioinfo.​life.​hust.​edu.​cn/​GSCA/​#/) [41]. This database compiles the IC50 of several small molecules in human pan-cancer cell lines and its corresponding gene expression from two primary sources, the Cancer therapeutics response portal (CTRP) and Genomics of Drug Sensitivity in Cancer (GDSC) databases [42‐47]. The analysis involves employing Pearson correlation to assess the relationship between gene expression and drug sensitivity, and the derived P-values are subjected to FDR adjustment. A negative correlation signifies that increased expression of the gene of interest corresponds to reduced drug sensitivity or potential resistance, and vice versa. While the outcomes from the GDSC database have suggested that cancer patients exhibiting high expression levels of the MIR17HG gene tend to display resistance to I-BET-762, NPK76-II-72–1, and Vorinostat, the CTRP results have further extended our understanding. The CTRP data have illuminated that elevated MIR17HG gene expression is likely associated with resistance to additional compounds, namely Belinostat, Tivantinib, and CR-1-31B. For a comprehensive overview of these correlations between MIR17HG expression and drug sensitivity, we refer to Fig. 5B, which visually encapsulates the intricate relationship between MIR17HG and drug responses. These findings collectively highlight the potential clinical significance of MIR17HG expression as an indicator of drug resistance in the context of cancer treatment. Given the absence of dedicated databases or datasets exclusively tailored for CRC, we have leveraged this comprehensive database to investigate the potential correlation between MIR17HG expression and drug sensitivity at the pan-cancer level. Nevertheless, it is essential to acknowledge the need for additional validation specifically within the context of CRC, as a means to address this particular limitation and enhance the robustness of our findings.

The “Similar Gene Detection” module of GEPIA2, a gene expression profiling interactive analysis database (http://​gepia2.​cancer-pku.​cn/​#index) [48] was employed to identify a set of 500 genes exhibiting transcription profiles most closely resembling that of MIR17HG, drawing from the TCGA-COAD project. Only genes displaying a Pearson’s correlation coefficient ≥ 0.4 were considered for inclusion in this analysis. Subsequently, we assessed the expression patterns of the identified genes using our RNA-seq data and specifically selected those protein-coding genes that displayed significant differential expression (FDR < 0.01, LogFC > 1). Consequently, our focus was directed solely toward those protein-coding genes exhibiting a positive correlation with MIR17HG and concurrently displaying an upregulated expression pattern in COAD for subsequent analyses (63 protein-coding genes that are shown in supplementary material 1).

We conducted an enrichment analysis on the set of 63 mentioned mRNAs to elucidate the potential functional roles of protein-coding genes that exhibit co-expression with MIR17HG. This analysis encompassed gene ontology (GO) enrichment assessments across cellular component, molecular function, and biological process categories. Additionally, pathway enrichment analysis was performed, encompassing the Kyoto Encyclopedia of Genes and Genomes (KEGG), Wikipathway, and Reactome databases. The Enrichr tool (https://​maayanlab.​cloud/​Enrichr/​) was employed for these enrichment analyses, utilizing a significance threshold set at a P-value below 0.05 [49‐51]. Furthermore, to visually represent the results of the GO and pathway enrichment analysis, dot plots were created. This was accomplished using the ggplot2 package [52] within the R programming software (version R-4.2.1, 64 bit, https://​www.​r-project.​org/​) [24] and RStudio Desktop (version 2022.7.0.548) [25]).

The findings from our analysis reveal significant involvement of the identified genes in a variety of crucial cellular processes. Notably, these genes play key roles in non-coding RNA (ncRNA) processing, the regulation of exit from the mitotic phase, and the control of centriole replication, highlighting their importance in fundamental biological mechanisms. Furthermore, our investigation unveiled that a substantial proportion of these genes exhibit enrichment in various molecular activities, including single-stranded DNA helicase activity, tRNA (Guanine) methyltransferase activity, as well as DNA and RNA binding. Additionally, some of these genes were associated with histone acetyltransferase activity, underscoring their potential influence on chromatin modification. In terms of cellular localization, our results indicate that these genes are predominantly situated within the cellular nucleus, with a noteworthy presence in the nucleolus. This subcellular distribution further emphasizes their essential roles in nuclear processes and gene regulation. Moreover, our analysis extends beyond functional annotations and encompasses insights into pathway involvement. We have identified prominent pathways based on comprehensive databases such as KEGG, Wikipathway, and Reactome. These pathways include Homologous recombination, basal transcription factors, DNA double-strand break repair and cellular responses via ATM (ataxia-telangiectasia mutated), and gastric cancer network pathways. These pathway associations shed light on the potential implications of the identified genes in various biological contexts (Fig. 6).

The MIR17HG exerts multifaceted functions that are pivotal in normal developmental biology as well as in the pathogenesis of cancer. In the context of carcinogenesis, MIR17HG demonstrates a shift towards oncogenic activities. It actively promotes cellular proliferation, fosters a milieu conducive to angiogenesis, and inhibits cellular differentiation—all hallmarks of cancer progression. Furthermore, it contributes to the sustenance of cell viability, thereby potentially aiding in the persistent survival of malignant cells. These actions position MIR17HG as a crucial regulator within the cellular microenvironment [53]. Therefore, the specific mechanisms of MIR17HG and its associated miRNAs are delineated, elucidating their roles in the intricate network of gene regulation within oncogenic outcomes (summarized in Table 6 and Fig. 7).

The long non-coding RNA MIR17HG enhances the metastatic and invasive capabilities of SW620 and HCT116 CRC cell lines. It functions as a CeRNA and modulates the expression of NF-κB/RELA by downregulation of miR-375 [15]. It enhances glycolysis activity in CRC cells through the regulation of the miR-138-5p/HK1 axis. Alternatively, overexpression of MIR17HG significantly increases the invasive potential of SW480 and RKO cell lines. Transplantation of these modified cell lines into nude mice increases the incidence of liver metastasis lesions [54].

Studies conducted in the HCT116 cell line have provided insights into the regulatory role of miR-17-5p in colorectal cancer. This microRNA suppresses the expression of MFN2, leading to reduced mitochondrial fusion, increased mitochondrial fission, and enhanced mitophagy. These dynamic changes in mitochondrial behavior contribute to the development of resistance to 5-FU chemotherapy. Notably, these findings have been consistently replicated in mouse models, emphasizing their significance [55]. Furthermore, miR-17-5p directly targets BLINK mRNA and reduces its expression in SW620 and HCT116 cell lines, resulting in an enhanced capacity for invasion and migration in CRC cells [15]. Elevated expression of miR-17-5p in SW480 and COLO205 cell lines is associated with enhanced metastatic behavior and drug resistance. This outcome is attributed to the direct targeting of PTEN by miR-17-5p, with the down-regulation of PTEN playing a pivotal role in the promotion of metastasis and the acquisition of multiple drug resistance [56]. Additionally, using SW480 and HCT-116 cell lines, it was observed that miR-17-5p, by targeting RUNX3, activates the TGF-β signaling pathway and enhances colorectal cancer metastasis [57]. Pan et al. have indicated that miR-17 inhibits CYLD expression, thereby contributing to 5-FU resistance in the T84 cell line [58]. In primary colorectal cancer tissues, miR-17-5p is downregulated in the presence of metastasis compared to non-metastasis CRC, and it directly interacts with VIM mRNA. Increasing miR-17-5p expression reduces vimentin levels, thereby mitigating cell migration and invasion in LoVo and HT29 cells. In summary, miR-17-5p exerts a regulatory influence on vimentin expression, thereby impacting CRC metastasis [59]. Another study involving SW480 and LoVo cell lines revealed that miR-17-5p specifically targets P130, leading to the activation of the Wnt/β-catenin pathway and driving the progression of CRC by increasing the proliferation, invasion and migration, and decreasing apoptosis of CRC cells [60]. Alternatively, experiments carried out on CRC cell lines, specifically LoVo and HCT116, have revealed that the upregulation of miR-17-3p results in a decrease of apoptosis and a simultaneous enhancement of invasion, migration, and proliferation in CRC cells. These functional changes are attributed to the regulatory interaction between miR-17-3p and PLCD1 [61]. Zhang et al. demonstrated that exosomes derived from cancer-associated fibroblasts exhibit high expression of miR-17-5p compared to normal fibroblasts. miR-17 also exhibits a targeted binding to the 3'UTR of GABBR1 mRNA, resulting in heightened invasion and proliferation in HCT116 and HT-29 cell lines [62]. Generally, the oncogenic potential of hsa-miR-17 in CRC is summarized in Fig. 8.

miR-19a, via its interaction with TIA1, facilitates the promotion of migration and proliferation in SW480 CRC cells. Consistently, xenograft mouse models corroborated this observed effect [63]. The studies on CaCo2 and SW480 CRC cells established that miR-19a overexpression significantly increased the proliferation, invasion, and migration of CRC cells by under-expressing CLCA4. Furthermore, it was demonstrated that the miR-19a/CLCA4 axis plays a regulatory role in the PI3K/AKT pathway in these cell lines [64]. Alternatively, miR-19a-3p fosters proliferation and migration in HCT116 CRC cells by inhibiting the expression of FOXF2 [65]. Inhibiting the expression of miR-19a-3p can result in the elevation of the FOXF2-associated Wnt/β-catenin signaling pathway. This, in turn, impacts the epithelial-mesenchymal transition (EMT), cell proliferation, invasion, and cellular migration in both HT29 and HCT116 cells [80]. In their study, Yin and colleagues investigated the regulatory role of miR-19a in SW480 CRC cell lines. miR-19a was found to exert control over the viability, invasiveness, and migratory properties of CRC cells by directly interacting with THBS1. Notably, the application of a miR-19a inhibitor effectively mitigated the malignant characteristics of CRC cells, concomitantly leading to the down-regulation of matrix metallopeptidase 9 (MMP-9) and vascular endothelial growth factor C (VEGFC) expression [66]. In hypoxic conditions, miR-19a initiates the activation of the PI3K/AKT pathway, leading to an enhancement in cell migration and invasion within HCT116 and SW480 CRC cells. This functional modulation is primarily attributed to the targeting of PTEN mRNA by miR-19a [67]. In contrast to the prevailing consensus that regards miR-19a as an oncogenic factor, the findings by Chen et al. indicated that elevated miR-19a expression led to the inhibition of KRAS expression and subsequently curtailed angiogenesis in HCT116 cells. Importantly, this effect was consistently evident in a mouse model as well [68]. Figure 9 delineates a compendium of the regulatory mechanisms pertinent to hsa-miR-19a.

miR-19b-1 assumes a significant role as a central regulator of genes associated with lipid metabolism, specifically focusing on ACSL/SCD. miR-19b-1 directly oversees the pro-tumorigenic axis involving ACSL/SCD and exhibits the capability to impede invasion in SW620 and LoVo CRC cells. Importantly, it should be highlighted that diminished expression of miR-19b-1 is associated with a reduced survival rate among CRC patients, implying the potential involvement of ACSL/SCD in patient relapse [69].

Mir-18a exhibits direct binding to the 3’ UTR of CDC42, a pivotal mediator within the PI3K pathway. Research conducted on HCT116 and LIM1215 CRC cell lines unveiled that miR-18a significantly reduces cell proliferation and migration while simultaneously enhancing apoptosis and the efficacy of pro-apoptotic agents [70]. Furthermore, it has been revealed that miR-18a exerts inhibitory effects on the growth, invasiveness, and migratory capabilities of HCT116 and SW620 colorectal cancer (CRC) cell lines. This regulatory influence is achieved through the specific targeting of TBPL1 by miR-18a [71]. Vu et al. brought to light the potential role of miR-18a in Fusobacterium nucleatum-mediated chemoresistance among CRC patients. Their research elucidated that miR-18a functions in the regulation of autophagy within HCT116 cells by suppressing ULK1 mRNA. Furthermore, their findings indicated that individuals with recurrent CRC exhibited elevated Fusobacterium nucleatum levels and diminished miR-18a expression when compared to patients who did not experience recurrence [72]. Moreover, in another study, it was demonstrated that miR-18a establishes a direct binding interaction with ATM mRNA. This interaction results in a reduction of DNA double-strand break repair within HCT116 CRC cells [73]. The different functions of hsa-miR-18a in CRC are delineated in Fig. 10.

Research conducted on SW480 cells revealed that miR-20a plays a role in the regulation of apoptosis by specifically targeting BID mRNA, a member of the pro-apoptotic gene family within BCL-2 [74]. miR-20a also influences the responsiveness of SW480 and HCT116 CRC cells to NK cells through its targeting of MICA [75]. miR-20a serves as a direct regulator of Foxj2, with experimental findings indicating that heightened miR-20a expression leads to a notable escalation in the proliferation, invasion, and migration of HCT116 CRC cells. This functional modulation is primarily attributed to the suppressive action of miR-20a on Foxj2 mRNA, underscoring its pivotal role in these cellular responses [76]. Zhang et al. indicated that miR-20a induced EMT and took part in the regulation of migration and invasion in SW480 cells, primarily by suppressing the expression of SMAD4 [77]. Moreover, It also directly binds to 3' UTR of POCD4 mRNA, resulting in the promotion of cell proliferation and resistance to 5-FU in SW480 and HCT116 CRC cell lines [78]. In another study, miR-20a displayed substantial downregulation compared to non-hypoxic conditions in colon cancer cells when subjected to hypoxic conditions. This reduction in miR-20a expression was found to impede the autophagic response triggered by hypoxia. Notably, miR-20a achieves this inhibitory effect by directly targeting several key regulators of autophagy, including ATG5 and FIP200, within LoVo and SW480 cell lines [79]. Figure 11 encapsulates the regulatory roles of miR-20a within CRC.

Inflammatory bowel diseases (IBDs) represent persistent intestinal disorders, commonly classified into two principal subtypes: Crohn’s disease (CD) and ulcerative colitis (UC) [81]. Recent investigations have brought to light the intricate role of MIR17HG-derived ncRNAs in the pathogenesis and control of essential molecular pathways associated with IBDs, several of which are elucidated below:

Wang et al. revealed that miR-20a-5p exhibited the most expression reduction in patients with CD compared to healthy controls. Their intervention, utilizing microspheres composed of poly (lactic-co-glycolic acid) and loaded with miR-20a-5p, led to remarkable enhancements in colitis, a decrease in mucosal inflammation, and an enhancement in the function of the epithelial barrier in mouse models of CD [82]. In a separate study, it was demonstrated that within the unaffected mucosal tissues of patients with CD, the expression of miR-18a and mir-20a was elevated in comparison to the control group [83]. Chen et al. identified a significant reduction in the expression of miR-19a and a remarkable increase in TNF-α within human colon tissues afflicted with UC [84]. However, findings from a study by Schaefer et al. indicated a significant elevation in miR-19a levels in both UC colon biopsies and blood samples compared to those from normal controls [85]. The serum levels of miR-19a-3p and miR-19b-3p were found to be lower in CD patients exhibiting a stricture phenotype compared to control CD patients. This analysis in patients with a 4-year follow-up period provided support for the hypothesis that diminished levels of miR-19a-3p and miR-19b-3p precede the development of strictures phenotype [86]. An examination conducted on IL-10 knockout mice revealed that the expression levels of ten miRNAs, including miR-19a, exhibited elevation in both the colon tissues and peripheral blood leukocytes compared to healthy control mice [87]. Additionally, there is supporting evidence indicating a significant upregulation of circulating miR-19b in individuals with IBD when compared to the control group [88].

Patients diagnosed with UC and CD encounter a substantial long-term concern, notably an elevated susceptibility to the development of CRC. In the context of CD, a noteworthy pattern emerges during the transition from non-neoplastic tissue to dysplasia, with miR-17 showing an upregulation. However, as the disease progression advances from dysplasia to full-blown cancer, a subsequent downregulation of miR-17 is observed [89]. This dynamic miR-17 expression profile signifies its intricate involvement in the multi-step process of CRC development within the context of CD, emphasizing the importance of further investigations into its regulatory mechanisms and potential implications for disease management.