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. 2026 Jul 27;53(1):1273. doi: 10.1007/s11033-026-12429-y

Transcriptome-wide profiling of cystic and solid vestibular schwannomas reveals candidate long non-coding RNA signatures

Nele Teichmann 1, Santhilal Subhash 2,3, Mario Giordano 1, Ashraqat Ahmed 1, Robert Geffers 4, Madjid Samii 1, Chandrasekhar Kanduri 3, Amir Samii 1,5, Souvik Kar 1,✉
PMCID: PMC13407589  PMID: 42507076

Abstract

Vestibular schwannoma (VS) is a benign tumor growing from Schwann cells of the vestibulocochlear nerve (cranial nerve VIII), accounting for 6–8% of brain tumors. Cystic vestibular schwannoma (cVS) represents 10% of VS cases and is characterized by unpredictable growth and worse surgical outcomes compared to solid VS (sVS). The molecular mechanisms underlying this aggressive phenotype remain poorly understood. In this context, long non-coding RNAs (lncRNAs) are gaining increased interest as biomarkers for early diagnosis and improved clinical management. lncRNAs are RNA molecules that are longer than 200 nucleotides. They are known to regulate gene transcription and participate in chromatin remodeling through various mechanisms. However, their role in cVS is largely unexplored. In this study, we identified cVS-associated lncRNAs using patient-derived samples. Whole transcriptome analysis via RNA sequencing revealed 65 differentially expressed lncRNAs and 308 protein-coding genes (PCGs) between cVS and sVS. From these, we selected the top eight lncRNAs (TENM3-AS1, ADIRF-AS1, PCA3, RP11-108K14.12, RP11-728F11.4, AC132217.4, RP11-43F13.3, and EGFLAM-AS1), each showing significant correlation with more than 20 differentially expressed PCGs. The upregulation of PCA3 and ADIRF-AS1, and the downregulation of EGFLAM-AS1 in cVS, were further validated using qRT-PCR. Here, by transcriptome-wide approach, we demonstrate that lncRNAs are prevalent in cVS disease and are likely to play critical roles in regulating important signaling pathways involved in disease pathogenesis. We believe, that detailed future investigations on this set of identified lncRNAs can provide useful insights into the biology and, ultimately, contribute to biomarker development for this neoplasm.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11033-026-12429-y.

Keywords: Vestibular schwannoma, Cystic vestibular schwannoma, Long non-coding RNAs, RNA sequencing, Differential gene expression, Biomarker development

Introduction

Vestibular schwannoma (VS) is a common intracranial tumor with an increasing incidence of up to 22.8 tumors per million people annually [19, 24]. Patients with VS manifest hearing loss, tinnitus, facial numbness, and weakness. VS are classified into cystic and solid based on their neuroradiological appearance (Fig. 1) [21, 33]. Cystic VS (cVS) are relatively rare compared to solid forms and are known to behave more aggressively than solid VS (sVS) due to the rapid tumor growth and unpredictable expansion of their cystic component (Fig. 2A) [21]. Treatment strategies for cVS are often complex. Their tendency toward rapid and unpredictable growth may limit the feasibility of prolonged observation, while both radiotherapy and surgical intervention have been associated with specific challenges, including cyst enlargement and less favorable postoperative results [30]. Therefore, alternative non-invasive therapeutic approaches are urgently needed to better understand the course of this disease and develop better treatment modalities.

Fig. 1.

Fig. 1

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A: MRI images (T1- weighted with contrast and T2-weighted) showing a cystic vestibular schwannoma as partial contrast-enhancing lesion of the right cerebellopontine angle with a cystic component (hypointense in T1 and hyperintense in T2) representing circa 50% of the tumor (red arrow), B: MRI images (T1-weighted with contrast and T2-weighted) showing an example of solid vestibular schwannoma with homogeneous contrast enhancement (red arrow)

Fig. 2.

Fig. 2

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A: Schematic representation of the anatomical relationship in a VS. B-F: Transcriptome profiles of lncRNAs and PCGs in cVS vs. sVS. B and C: Volcano plot showing 65 differentially expressed lncRNAs and B: 308 differentially expressed protein coding genes (PCGs) in CVS compared to SVS. The red and blue circles above represent up- and down-regulated lncRNAs and PCGs in CVS. The yellow dots represent the key significantly differentially expressed transcripts. D and E: Heatmap of 65 lncRNAs (left panel) and 308 protein coding genes (PCGs) (right panel) differentially expressed in cVS compared to sVS (padj < 0.05 and an absolute log-fold change (logFC) greater than 1). Heat map colors correspond to the lncRNAs and PCGs expression as indicated in the color bar: red (upregulated) and purple (downregulated). cVS: Cystic vestibular schwannoma, sVS: solid vestibular schwannoma

The development of vestibular schwannomas is mainly caused by mutations in the neurofibromatosis type 2 (NF2) gene on chromosome 22q. NF2 encodes Merlin, a tumor suppressor protein. Loss of Merlin dysregulates downstream signaling pathways, thereby contributing to the development and progression of vestibular schwannomas. In particular, the Hippo/YAP, PI3K/AKT/mTOR, and MAPK pathways are involved [12].

Recent advances in transcriptomic research have provided important insights into the molecular mechanisms and signaling networks underlying vestibular schwannoma pathogenesis. In particular, single-cell RNA sequencing has revealed that vestibular schwannomas consist of different cellular populations with distinct transcriptional profiles, including Schwann cells, immune cells, and stromal cell types [1, 7]. This raises the question of how potential interactions between these cell types influence tumor behavior and clinical outcome. In particular, direct comparisons between solid vestibular schwannomas (sVS) and cystic vestibular schwannomas (cVS) have so far been insufficiently investigated. The field of long non-coding RNAs (lncRNAs) has also received little attention to date. A better understanding of the underlying molecular mechanisms may help explain the more aggressive clinical behavior of cystic vestibular schwannomas.

Non-coding RNAs represent RNA molecules that are not translated into proteins and are known to be involved in chromatin remodelling, post-transcriptional modifications, signal transduction, and disease progression [26]. Based on their transcript size, they are classified into two major categories: small non-coding RNAs of < 200 nucleotides in length (microRNAs, snoRNAs, scaRNAs, and piRNAs) and long non-coding RNAs (lncRNAs) of > 200 nucleotides in length [8, 10]. From our previous work, using an RNA sequencing approach, we were the first to identify candidate microRNAs, snoRNAs, and lncRNAs significantly deregulated in cerebrovascular disease [9, 10]. The current study focused on transcriptome profiling of long non-coding RNA (lncRNA) molecules in cVS patients.

Using genome-wide RNA sequencing, we aimed to identify lncRNA expression profiles in cystic vestibular schwannoma patients compared with their solid component. Applying the highest level of statistical stringency resulted in candidate lncRNAs and protein-coding genes (PCGs) significantly differentially expressed in cVS. Identification of candidate lncRNAs helps to understand their mechanism of regulation during cVS development and, therefore, aids in developing novel biomarkers for improved clinical decision making. Furthermore, the expression of ADIRF-AS1 was preliminarily evaluated in plasma samples to explore its potential as a minimally invasive circulating biomarker.

Methods

Ethics statement

The study was performed in accordance with the guidelines and with permission from the Ethical Committee of the Hannover Medical School, Germany (Approval Number 6960).

Patient sample collection

Vestibular schwannoma samples with cystic components (cVS1, cVS2, and cVS3) obtained during surgery were collected on dry ice. Additionally, Vestibular schwannoma tumor samples consisting of solid components (sVS1, sVS2, and sVS3) were used as corresponding controls. The tumor samples were stored temporarily at -80 °C until total RNA extraction. VS diagnosis was based on the MRI and histopathological criteria as described previously (Table 1). Each patient had at least one clinical presentation manifested by tinnitus, facial numbness, and asymmetrical hearing loss.

Table 1.

Radiological features of tumors (samples of cVS C1, C4, C8)

Tumor stadium
(TNM classification)		 Irregular margins Associated edema Irregular contrast agent enhancement High vascularization High brainstem adhesion High facial nerve adhesion
C1 T4a No No Yes No No No
C4 T4b No Yes Yes Yes Yes Yes
C8 T4b No Yes Yes No Yes Yes
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RNA isolation and integrity

The RNA extraction is the first step in a series of experiments aimed at ultimately performing a quantitative PCR of the samples. Total RNA was extracted from frozen tumor samples. This step is accomplished using the RNeasy Micro Kit (Qiagen). The sample was transferred to RNAlater-ICE one day prior and stored at -20 °C. This ensured the stability of the RNA during the experiment. The samples were cut into 2–3 mg pieces the next day and were then transferred to a special microreaction vessel, along with 350 µl of the prepared Buffer RLT + β-mercaptoethanol. The samples then undergo several centrifugation steps, ultimately followed by centrifugation at 14,100 relative centrifugal force (RCF) for three minutes. Afterward, the solution is pipetted onto a RNeasy MinElute spin column, and following additional washing and DNase treatment, the RNA is eluted in RNA-free water. The final RNA sample of approximately 14 µl is then stored at -80 °C.

Library preparation and sequencing

In the next step, sequencing was performed using the NovaSeq 6000 S1 Reagent Kit on the Illumina NovaSeq 6000 (100 cycles, paired-end run 2 × 50 bp, with an average of 5 × 10^7 reads per RNA sample, each containing 50 ng of RNA). We evaluated the volume of data obtained. Here, initial signs indicated that there are differences in the expression of lncRNA among the subtypes of VS.

Transcriptome analysis of vestibular schwannoma

BCL files were converted into FASTQ format using bcl2fastq Conversion Software version 2.16.0.10 (Illumina). FASTQ files were mapped against an Ensembl reference human genome hg38 [20] with the splice-aware aligner HISAT2 (v2.2.1) [13] to generate BAM files. The aligned files (BAM format) were subjected to quantification using featureCounts from the Subread package (v2.0.0) [17]. The reads were quantified for GENCODE transcript annotation release v38 (corresponds to genome hg38) [6, 26]. The generated matrix file from read quantification was then used for differential expression (DE) analysis. We performed differential expression analysis using the Bioconductor package DESeq2 [18] with a contrast of solid and cVS groups. Transcripts (lncRNAs and protein coding genes) having corrected p-value or Padj < 0.05 (FDR) and absolute log-fold change greater than 1 were significantly differentially expressed. Expression patterns of DE lncRNAs were compared against DE protein-coding genes (PCGs) to look for expression correlation. Significant lncRNA-mRNA correlated pairs were calculated using the Spearman correlation coefficient by considering R value above 0.9 and with a p-value < 0.05 [26]. Functional enrichment analysis for correlated protein-coding genes was performed using GeneSCF [27], and the terms are filtered with a p-value < 0.05. Detailed pipeline and codes used for analysis are available in GitHub, https://github.com/decodebiology/Vestibular_schwannoma_2025.

Quantitative real-time PCR from tumor tissue samples

In order to validate the reliability of RNA-sequencing data, we selected PCA3, ADIRF-AS1, and EGFLAM-AS1, the three most strongly differentially expressed lncRNAs, for further analysis. Gene expression was performed using 40 ng of total RNA, which was subsequently transcribed into cDNA using mRNA-specific RT primers and TaqMan Fast Advanced Master Mix (Life Technologies, Germany). The assay numbers are as follows: PCA3 (Hs01371937_m1), ADIRF-AS1 (Hs05049794_s1), EGFLAM-AS1 (Hs05045149_s1), and GAPDH (Hs99999905_m1). Fold change was calculated using the 2ˉΔΔCt method to determine the difference in expression of PCA3, ADIRF-AS1, and EGFLAM-AS1 between cVS and the solid tissues. We determined mRNA expression changes after normalizing to the housekeeping gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Each qRT-PCR assay was repeated in triplicate.

Quantitative real-time PCR from human plasma samples

Total RNA was isolated from 2 mL of human plasma collected from patients with cVS and healthy control subjects using the Plasma/Serum Circulating and Exosomal RNA Purification Mini Kit (Norgen Biotek, Canada) according to the manufacturer’s instructions. Complementary DNA (cDNA) was synthesized from 10 ng of total RNA using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Germany). Quantitative real-time PCR (RT-qPCR) was performed using TaqMan Gene Expression Assays for ADIRF-AS1 (Assay ID: Hs05049794_s1) and the endogenous control GAPDH (Assay ID: Hs99999905_m1) with TaqMan Fast Advanced Master Mix (Cat. No. 4444963, Thermo Fisher Scientific) on a StepOne Real-Time PCR System (Thermo Fisher Scientific, Germany). Each reaction was performed in triplicate in a final reaction volume of 20 µL according to the manufacturer’s recommendations. Relative gene expression levels were calculated after normalization to GAPDH.

Statistical analysis

All experimental graphs are presented as mean ± standard error of the mean [8], and a p-value less than 0.05 was considered statistically significant. RNA-seq analysis was described in detail above.

Results

Patient cohort and RNA-seq profiles of vestibular schwannoma

cVS exhibits a more aggressive clinical behavior, are larger in volume, and tend to have an unpredictable biological behavior compared to their solid counterpart (sVS) [25, 31]. cVS are surgically challenging and have poor clinical outcomes following subtotal resection [24]. It is therefore important to elucidate alternative therapeutic approaches for the treatment of patients with cVS. By executing an RNA-sequencing approach, we provide a comprehensive analysis of cVS and sVS (Fig. 2B).

Differential regulation of lncRNAs and protein-coding genes (PCGs) in vestibular schwannoma

By comparing expression patterns between sVS (n = 3) and cVS (n = 3) from RNA-seq analysis, differentially expressed (DE) up- and down-regulated lncRNAs and protein-coding genes (PCGs) were profiled. Among them, 65 lncRNAs and 308 PCGs were significantly differentially expressed (DE) between cVS and control sVS groups. There were 20 up-regulated lncRNAs and 58 up-regulated PCGs, respectively, in cVS; 45 and 250 down-regulated lncRNAs and PCGs, respectively, in cVS. These differentially expressed (DE) transcripts were filtered using corrected p-value (p-value adj (padj) < 0.05) and an absolute log-fold change (logFC) greater than 1 between the comparison groups.

We then performed a detailed analysis of the top differentially expressed transcripts and identified that most of the lncRNAs were reported for the first time in cVS tumors (Fig. 2B). Among the novel lncRNAs, RP5-841K13.1 showed the highest fold change expression (logFC = 6.08, padj < 0.001) and EGFLAM-AS1 reported the lowest fold change expression (logFC= -3.5, padj < 0.001) in cVS, respectively. In addition, we observed that the lncRNA, TENM3-AS1, was significantly up-regulated (logFC = 1.78, padj < 0.001) in cVS compared to sVS. In a recent report, TENM3-AS1 was shown to be up-regulated and involved in osteoarthritis progression [8]. Among the PCGs, BEX1 was found to be strongly up-regulated in cVS tumors (logFC = 2.66, padj < 0.001) (Fig. 2C). BEX1 is predominantly up-regulated in gliomas and known to be involved in tumorigenesis [4, 15]. Apart from the above-discussed RNAs, we also found several functional protein-coding genes as well as lncRNAs (Fig. 2D-E).

LncRNA-protein coding genes correlation reveals cVS signatures

In the following steps, applying additional statistical stringency, we identified the top eight differentially expressed lncRNAs (four up-regulated and four down-regulated) that showed significant expression correlations with more than 20 differentially expressed PCGs. Among them, TENM3-AS1, ADIRF-AS1, PCA3, RP11-108K14.12, and EGFLAM-AS1, RP11-43F13.3, AC132217.4, RP11-728F11.4 were the top significantly up- and downregulated lncRNAs in cVS compared to sVS controls (Fig. 3A). We observed a significantly higher expression of PCA3 in cVS tumors. Recently published reports also demonstrated that PCA3, an oncogenic marker, is consistently up-regulated in prostate cancers [14]. We also observed a significant robust expression of ADIRF-AS1 lncRNA in cVS tumor samples. Consistent with our results, other studies have reported a similar higher expression of ADIRF-AS1 in osteosarcoma [30] and renal clear cell carcinoma [2]. Thus, through comparison using transcriptome sequencing, we identified the most important and functionally relevant lncRNAs and PCGs related to cVS pathogenesis.

Fig. 3.

Fig. 3

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A: Co-expression plot showing the top enriched lncRNAs with > 20 co-expressed PCGs in CVS. Red and blue dots represent top final 4 up- and top 4 downregulated lncRNAs in cVS (pdj FDR < 0.05 and an absolute logFC greater than one). Vertical dotted lines represent log-fold change cut-off ± 1.5 and values above horizontal dotted lines represent transcripts with FDR < 0.05 cut-off. cVS: Cystic vestibular schwannoma, sVS: solid vestibular schwannoma. B: qRT-PCR validation of top three lncRNAs. Bar diagrams representing the fold change mean ± SEM in PCA3, ADIRF-AS1, and EGFLAM-AS1 lncRNAs mRNA levels in human brain VS tissue samples (cVS; n = 8) vs. controls (Control; n = 5) (B), as determined by qRT-PCR gene expression assays. The housekeeping gene GAPDH was used as an internal reference control for normalizing the relative mRNA expression levels. ∗∗P < 0.001, Student’s t-test of three independent assays. C: Pathways enriched with correlated protein-coding genes and the significant pathways displayed in the bar graph are filtered using p-value < 0.05

Functional validation of PCA3, ADIRF-AS1, and EGFLAM-AS1 lncRNAs

LncRNAs PCA3 and ADIRF-AS1 were the most differentially expressed lncRNAs, with over 20 co-expressed PCGs, and were up-regulated in cVS compared to sVS groups, while EGFLAM-AS1 was down-regulated. Further validation using RT-qPCR was conducted on PCA3, ADIRF-AS1, and EGFLAM-AS1. The expression patterns observed in RNA-seq analysis were successfully confirmed with qRT-PCR. qRT-qPCR validation confirmed the upregulation of PCA3 and ADIRF-AS1. In contrast, EGFLAM-AS1 displayed a moderate increase in expression in the validation cohort, indicating a discrepancy in the direction of regulation between RNA-seq and RT-qPCR results (Fig. 3B). Their levels were normalized to the GAPDH housekeeping gene, and the normalized values were compared between control and cVS groups (Fig. 3B). We also examined protein-coding genes correlated with these top lncRNAs and found enrichment in pathways such as potassium channels, PPAR-alpha signaling, neural system, and metabolic pathways (Fig. 3C) [29].

ADIRF-AS1 expression in plasma samples

To evaluate the potential of ADIRF-AS1 as a circulating biomarker, its expression was quantified in plasma samples obtained from patients with cVS (n = 5) and healthy controls (n = 4) by RT-qPCR. Relative expression levels were normalized to GAPDH. As shown in Fig. 4, ADIRF-AS1 expression was increased by approximately 1.8-fold in plasma samples from cVS patients compared with healthy controls. However, this difference did not reach statistical significance (two -tailed Student’s t-test, p > 0.05).

Fig. 4.

Fig. 4

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qRT-PCR validation of ADIRF-AS1 expression in human plasma samples. Bar diagram representing the relative fold change (mean ± SEM) of ADIRF-AS1 lncRNA expression in plasma samples from patients with VS (n = 5) and healthy controls (n = 4) as determined by qRT-PCR. Relative expression levels were normalized to the housekeeping gene GAPDH. No statistically significant difference was observed between the groups (p > 0.05)

Discussion

Due to the problems described above in the treatment of cystic vestibular schwannomas (cVS), a deeper understanding of the differences in the pathogenesis of cVS and solid vestibular schwannomas (sVS) is potentially significant. Despite extensive molecular profiling of VS tumors in recent years, no studies to date have provided a comprehensive profiling of lncRNAs in cVSs. Herein, applying a state-of-the-art RNA sequencing strategy, we provide the first evidence of lncRNA signatures in patients with cVS tumors. Applying a comprehensive bioinformatics approach, we identified several lncRNAs and protein-coding genes (PCGs) differentially expressed in cVS pathogenesis. Our extensive analysis revealed PCA3 and ADIRF-AS1 as top candidate lncRNAs significantly up-regulated, while EGFLAM-AS1 was down-regulated and had correlative expression patterns with a significant number of differentially expressed PCGs in cVS diseases. Our transcriptomics-wide profiling may reveal previously unrecognized functional roles of PCA3, ADIRF-AS1, and EGFLAM-AS1 and their co-expressed PCGs in the pathogenesis of cVSs.

Due to the cellular heterogeneity and complex intratumoral interactions demonstrated in the aforementioned single-cell studies, the genomic alterations observed today cannot be explained solely by mutations in NF2 and the associated loss of Merlin function [1, 7]. Future integration of single-cell transcriptomic approaches with the bulk RNA sequencing performed in the present study may help identify cell type-specific lncRNAs and their regulatory mechanisms acting downstream of Merlin-associated signaling pathways [5]. This could ultimately lead to a better understanding of the progression of cystic vestibular schwannomas (cVS).

ADIRF-AS1, also known as adipogenesis regulatory factor-antisense RNA1, belongs to the class of intronic non-coding RNA and is located on chromosome 10. Although not well characterized in humans, recent emerging studies have associated its role with various diseases, such as osteosarcoma, renal clear cell carcinoma, colorectal cancer, and intrauterine adhesion disease (IUA) [2, 30, 32]. Consistent with our results, ADIRF-AS1 was found to be overexpressed in osteosarcoma, renal clear cell carcinoma, and IDD. Xu et al. [30] identified that ADIRF-AS1 was overexpressed, and higher expression levels were linked to poorer overall survival rates in patients with osteosarcoma. Knockdown of the ADIRF-AS1 gene in osteosarcoma cells resulted in reduced proliferation, migration, and invasiveness, and enhanced apoptosis. In vivo experiments demonstrated that knockdown of ADIRF-AS1 effectively inhibited tumor growth. Additionally, they showed that ADIRF-AS1 behaves as an endogenous competitor for the microRNA-761 that led to insulin receptor substrate 1 (IRS1) overexpression. The above findings from Xu et al. [30] suggest that ADIRF-AS1 acts as an oncogenic marker by targeting the miR-761/IRS1 axis, suggesting it as a future therapeutic candidate for osteosarcoma. In another study led by Brooks et al. [2], ADIRF-AS1 was identified as a circadian lncRNA, which interacts with the polybromo-associated BAF, PBAF (PBRM1/BRG1) complex. Deletion of ADIRF-AS1 inhibited the tumorigenesis of clear cell renal carcinoma (ccRCC) xenografts, and higher expression indicated poor prognosis and drives tumorigenesis in ccRCC cases, suggesting its plausible role as an oncogenic lncRNA. Moreover, recently Zhang et al. [32] further confirmed that ADIRF-AS1, a crucial lncRNA, was involved in the development of intrauterine adhesion disease (IUA). Consistent with the above studies, our findings confirmed that the ADIRF-AS1 transcript was strongly up-regulated through the RNA-Seq approach, and its expression change was further confirmed by qRT-PCR analysis. These data suggest that ADIRF-AS1 might also be involved in cVS tumor formation and progression, and further studies should be conducted to uncover its potential functions. To further explore the translational potential of ADIRF-AS1 as a circulating biomarker, we additionally performed a preliminary RT-qPCR analysis in plasma samples from patients with AKN. Although ADIRF-AS1 expression showed an approximately 1.6-fold increase compared with healthy controls, this difference did not reach statistical significance. Given the small sample size of this exploratory analysis, larger independent cohorts will be required to determine whether circulating ADIRF-AS1 may represent a reliable non-invasive biomarker for cVS.

Prostate cancer antigen 3 (PCA3) belongs to the class of antisense intronic lncRNA and is located on chromosome 9 [16]. The authors demonstrated that a knockdown of PCA3 resulted in a significant increase in the expression of epithelial markers such as E-cadherin, claudin-3, and CK18. PCA3 has been identified as a specific diagnostic marker for prostate cancer and modulates prostate cancer cell survival through androgen receptor signaling [14]. Additionally, it is known to regulate epithelial-mesenchymal transition (EMT) markers, including those controlling gene expression and cell signaling [16]. PCA3 expression in urine serves as a clinically approved, FDA-endorsed biomarker for prostate cancer diagnosis [23]. In line with the above findings, our results revealed a significant upregulation in mRNA expression of PCA3 lncRNA in cVS disease, which was further validated and confirmed through qRT-PCR analysis. Future studies in larger patient cohorts will discern the potential role of PCA3 in cVS pathogenesis.

The EGF-like, fibronectin type-III, and laminin G-like domain-containing protein-antisense RNA (EGFLAM-AS1), also known as lncRNA-CTD-210809.1, belongs to the class of antisense lncRNAs and is located on chromosome 5. In our RNA sequencing analysis, EGFLAM-AS1 was identified as one of the most strongly downregulated lncRNAs in cVS. However, qRT-PCR validation revealed moderate upregulation. However, qRT-PCR validation demonstrated a moderate upregulation of EGFLAM-AS1 expression. This discrepancy may be explained by the different analytical characteristics of RNA sequencing and qRT-PCR. While RNA sequencing provides a genome-wide assessment of transcript abundance, qRT-PCR offers higher sensitivity and specificity for the quantification of individual transcripts, particularly those expressed at low levels. In addition, the limited sample size and the biological heterogeneity of cVS tissue, including differences in cellular composition, may have contributed to the observed differences between the two methods.

Interestingly, although previous studies reported reduced EGFLAM-AS1 expression in breast cancer metastasis and gastric cancer [28, 34], a recent study by Chuang et al. [3] demonstrated significantly increased EGFLAM-AS1 expression in uterine leiomyomas. This observation is consistent with our qRT-PCR findings and suggests that the biological role and expression pattern of EGFLAM-AS1 may be tissue- and disease-specific rather than universally regulated across tumor entities. Therefore, the discrepant findings between our RNA-seq and qRT-PCR analyses should be interpreted cautiously and warrant further validation in larger independent cohorts. Additional functional studies with larger sample sizes and functional analyses are therefore required to clarify the role of EGFLAM-AS1 in cystic vestibular schwannoma.

The above observations in particular warrant further studies to unravel the functional role of ADIRF-AS1, PCA3, and EGFLAM-AS1 in cystic vestibular schwannoma pathogenesis.

In the pathway enrichment analysis, the correlation between the candidate lncRNAs and protein-coding genes (PCGs) showed a strong association with PPAR-alpha signaling, potassium channel activity, and other neuronal and metabolic pathways. In particular, the PPAR-alpha signaling pathway appears to be of special interest in the context of cVS biology. It is involved in the regulation of cellular metabolism as well as inflammatory responses, angiogenesis, and vascular homeostasis [22, 29]. Therefore, alterations in these processes may lead to increased vascular permeability and intratumoral fluid accumulation. These mechanisms have previously been discussed as potential factors contributing to the development of cVS [29]. Although our study cannot demonstrate a direct causal relationship, the enrichment of PPAR-alpha-associated genes suggests that this pathway may contribute to the distinct biological characteristics of cVS and should therefore be the subject of further investigation.

A significant strength of this study is that we provide evidence for the first time that there are candidate lncRNAs and protein-coding genes differentially expressed in cVS patients. However, a considerable limitation of this study pertains to the small sample size (n = 3) and the co-expression analysis is exploratory and hypothesis-generating and does not constitute evidence of direct regulatory interactions. The identified lncRNA–mRNA co-expression relationships should be interpreted as candidate associations requiring validation in larger independent cohorts. Nevertheless, future studies in larger patient samples will overcome such limitations and will therefore aid in biomarker discovery.

Conclusion

Our study provides a comprehensive transcriptomic profile identifying lncRNAs and their co-expressed PCGs in human resected vestibular schwannomas. To the best of our knowledge, this is the first high-throughput study demonstrating differential expression patterns in cystic vestibular schwannomas (cVS). Our results identified lncRNAs, ADIRF-AS1, PCA3, and EGFLAM-AS1 as strong candidates in human cVS disease. Future studies understanding their functional role in cVS biology will open new avenues for better therapeutic targets and help identify biomarkers in patients with untreatable tumors.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (267.1KB, xlsx)

Acknowledgements

We would like to express our sincere gratitude to Internationale Stiftung Neurobionik for their financial support of our research, and to International Neuroscience Institute (INI) Hannover for providing us with access to their laboratories, and for the collaboration with the Helmholtz Institute Braunschweig. All the computations were performed on HPC resource ELZAR provided by Cold Spring Harbor Laboratory (CSHL).

Author contributions

Study conception and planning: S.K., C.K., A.S.; Experimental design: S.K.; Experiments conducted on human tissue samples: N.T., A.A.; Data curation and interpretation: S.K., N.T.; Writing original draft: S.K., S.S., N.T.; Bioinformatics analysis: S.S.; Providing clinical data: M.G; Transcriptome data: R.G., Access to surgically resected tumor samples: A.S., M.S.; Supervision: S.K., C.K., A.S. All authors approved the manuscript in its current form.

Funding

This work was supported by the Neurobionik Foundation Hannover, Germany (funding granted to S.K.).

Data availability

The RNA-seq data used in this study has been deposited at the GEO repository with the accession number: GSE278713 and can be accessed by the reviewers using this security token apwzwkmitbudtkz.

Code availability

All the computational codes and pipeline used for RNA-seq analysis is deposited in github repository, https://github.com/decodebiology/Vestibular_schwannoma_2025.

Declarations

Competing interests

The authors declare no competing interests.

Ethics declaration

VS Patients: The study protocol was strictly performed in accordance with the Declaration of Helsinki and with permission by the local ethical committee of the Hannover Medical School, Germany (Approval Number 6960). Written informed consent for the publication of the medical images and any potentially identifiable data was obtained from the patients prior to publication.

Footnotes

The original online version of this article was revised: Figure 2 has been corrected.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Nele Teichmann and Santhilal Subhash tThese authors contributed equally to this work.

Change history

8/13/2026

The original online version of this article was revised: Figure 2 has been corrected.

Change history

8/20/2026

A Correction to this paper has been published: 10.1007/s11033-026-12606-z

References

  • 1.Barrett TF, Patel B, Khan SM, Mullins RDZ, Yim AKY, Pugazenthi S, Mahlokozera T, Zipfel GJ, Herzog JA, Chicoine MR, Wick CC, Durakovic N, Osbun JW, Shew M, Sweeney AD, Patel AJ, Buchman CA, Petti AA, Puram SV, Kim AH (2024) Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state. Nat Commun 15(1):478. 10.1038/s41467-023-42762-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Brooks R, Monzy J, Aaron B, Zhang X, Kossenkov A, Hayden J, Keeney F, Speicher DW, Zhang L, Dang CV (2022) Circadian lncRNA ADIRF-AS1 binds PBAF and regulates renal clear cell tumorigenesis. Cell Rep 41(3):111514. 10.1016/j.celrep.2022.111514 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Chuang TD, Quintanilla D, Boos D, Khorram O (2022) Differential expression of super-enhancer-associated long non-coding RNAs in uterine leiomyomas. Reproductive sciences (Thousand Oaks, Calif. 29(10):2960–29766. 10.1007/s43032-022-00981-4
  • 4.Doi T, Ogawa H, Tanaka Y, Hayashi Y, Maniwa Y (2020) Bex1 significantly contributes to the proliferation and invasiveness of malignant tumor cells. Oncol Lett 20(6):362. 10.3892/ol.2020.12226 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Dong W, Zhang Y, Wang Z, Wu H (2025) Integrative single-cell and spatial transcriptomic analyses uncover microenvironmental programs in vestibular schwannoma. NPJ Precision Oncol 9:45. 10.1038/s41698-025-01028-y [Google Scholar]
  • 6.Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I, Berry A, Bignell A, Boix C, Sala C, Cunningham S, Di Domenico F, Donaldson T, Fiddes S, Girón ITGarcía, Gonzalez C, Flicek JM, P (2021) GENCODE 2021. Nucleic Acids Res 49(D1):D916–D923. 10.1093/nar/gkaa1087 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Huo Z, Wang Z, Liu Y, Wang C, Wang Y, Zhang Y, Wang Q, Yang T, Wu H (2023) Single-cell transcriptomes reveal the heterogeneity and microenvironment of vestibular schwannoma. Neurooncology 26(3):444–457. 10.1093/neuonc/noad201 [Google Scholar]
  • 8.Kapranov P, Cheng J, Dike S, Nix DA, Duttagupta R, Willingham AT, Stadler PF, Hertel J, Hackermüller J, Hofacker IL, Bell I, Cheung E, Drenkow J, Dumais E, Patel S, Helt G, Ganesh M, Ghosh S, Piccolboni A, Sementchenko V, Gingeras TR (2007) RNA maps reveal new RNA classes and a possible function for pervasive transcription, vol 316. Science, New York, N.Y.), pp 1484–1488. 583010.1126/science.1138341 [Google Scholar]
  • 9.Kar S, Bali KK, Baisantry A, Geffers R, Hartmann C, Samii A, Bertalanffy H (2018) Genome-wide sequencing reveals small nucleolar RNAs downregulated in cerebral cavernous malformations. Cell Mol Neurobiol 38(7):1369–1382. 10.1007/s10571-018-0602-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kar S, Bali KK, Baisantry A, Geffers R, Samii A, Bertalanffy H (2017) Genome-wide sequencing reveals microRNAs downregulated in cerebral cavernous malformations. J Mol neuroscience: MN 61(2):178–188. 10.1007/s12031-017-0880-6 [DOI] [PubMed] [Google Scholar]
  • 11.Katsoula G, Steinberg J, Tuerlings M, Coutinho de Almeida R, Southam L, Swift D, Meulenbelt I, Wilkinson JM, Zeggini E (2022) A molecular map of long non-coding RNA expression, isoform switching and alternative splicing in osteoarthritis. Hum Mol Genet 31(12):2090–2105. 10.1093/hmg/ddac017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Kim BH, Chung YH, Woo TG, Kang SM, Park S, Kim M, Park BJ (2024) NF2-related schwannomatosis (NF2): Molecular insights and therapeutic avenues. Int J Mol Sci 25(12):6558. 10.3390/ijms25126558 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Kim D, Paggi JM, Park C, Bennett C, Salzberg SL (2019) Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37(8):907–915. 10.1038/s41587-019-0201-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Lauer RC, Barry M, Smith TL, Thomas AM, Wu J, Du R, Lee JH, Rao A, Dobroff AS, Arap MA, Nunes DN, Silva IT, Dias-Neto E, Chen I, McCance DJ, Cavenee WK, Pasqualini R, Arap W (2023) Dysregulation of the PRUNE2/PCA3 genetic axis in human prostate cancer: from experimental discovery to validation in two independent patient cohorts. eLife 12:e81929. 10.7554/eLife.81929 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Lee S, Kang H, Shin E, Jeon J, Youn H, Youn B (2021) BEX1 and BEX4 induce GBM progression through regulation of actin polymerization and activation of YAP/TAZ signaling. Int J Mol Sci 22(18):9845. 10.3390/ijms22189845 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Lemos AEG, Matos ADR, Ferreira LB, Gimba ERP (2019) The long non-coding RNA PCA3: an update of its functions and clinical applications as a biomarker in prostate cancer. Oncotarget 10(61):6589–6603. 10.18632/oncotarget.27284 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinf (Oxford England) 30(7):923–930. 10.1093/bioinformatics/btt656 [Google Scholar]
  • 18.Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. 10.1186/s13059-014-0550-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Marinelli JP, Lohse CM, Carlson ML (2019) Incidence of vestibular schwannoma over the past half-century: a population-based study of Olmsted County, Minnesota. Otology Neurotology 40(10):1303–1310. 10.1097/MAO.0000000000002385 [Google Scholar]
  • 20.Martin FJ, Amode MR, Aneja A, Austine-Orimoloye O, Azov AG, Barnes I, Becker A, Bennett R, Berry A, Bhai J, Bhurji SK, Bignell A, Boddu S, Lins B, Brooks PR, Ramaraju L, Charkhchi SB, Cockburn M, Da Fiorretto A, Davidson L, Flicek C, P (2023) Ensembl 2023. Nucleic Acids Res 51(D1):D933–D941. 10.1093/nar/gkac958 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Metwali H, Samii M, Samii A, Gerganov V (2014) The peculiar cystic vestibular schwannoma: a single-center experience. World Neurosurg 82(6):1271–1275. 10.1016/j.wneu.2014.07.011 [DOI] [PubMed] [Google Scholar]
  • 22.Montaigne D, Butruille L, Staels B, Duez H (2021) PPAR control of metabolism and cardiovascular functions. Nat Reviews Cardiol 18(12):809–823. 10.1038/s41569-021-00569-6 [Google Scholar]
  • 23.Mugoni V, Ciani Y, Nardella C, Demichelis F (2022) Circulating RNAs in prostate cancer patients. Cancer Lett 524:57–69. 10.1016/j.canlet.2021.10.011 [DOI] [PubMed] [Google Scholar]
  • 24.Stangerup SE, Tos M, Thomsen J, Caye-Thomasen P (2010) True incidence of vestibular schwannoma? Neurosurgery 67(5):1335–1340. 10.1227/NEU.0b013e3181f22660 [DOI] [PubMed] [Google Scholar]
  • 25.Stastna D, Urgosik D, Chytka T, Liscak R (2021) Large vestibular schwannomas: long-term outcomes after stereotactic radiosurgery. Neuroendocrinol Lett 41(6):329–338 [PubMed] [Google Scholar]
  • 26.Subhash S, Kalmbach N, Wegner F, Petri S, Glomb T, Dittrich-Breiholz O, Huang C, Bali KK, Kunz WS, Samii A, Bertalanffy H, Kanduri C, Kar S (2019) Transcriptome-wide Profiling of Cerebral Cavernous Malformations Patients Reveal Important Long noncoding RNA molecular signatures. Sci Rep 9(1):18203. 10.1038/s41598-019-54845-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Subhash S, Kanduri C (2016) GeneSCF: a real-time based functional enrichment tool with support for multiple organisms. BMC Bioinformatics 17(1):365. 10.1186/s12859-016-1250-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Wang M, Wang M, Wang Z, Yu X, Song Y, Wang C, Xu Y, Wei F, Zhao Y, Xu Y (2018) Long non-coding RNA-CTD-2108O9.1 represses breast cancer metastasis by influencing leukemia inhibitory factor receptor. Cancer Sci 109(6):1764–1774. 10.1111/cas.13592 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Warden A, Truitt J, Merriman M, Ponomareva O, Jameson K, Ferguson LB, Mayfield RD, Harris RA (2016) Localization of PPAR isotypes in the adult mouse and human brain. Sci Rep 6:27618. 10.1038/srep27618 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Xu L, Tan Y, Xu F, Zhang Y (2022) Long noncoding RNA ADIRF antisense RNA 1 upregulates insulin receptor substrate 1 to decrease the aggressiveness of osteosarcoma by sponging microRNA-761. Bioengineered 13(2):2028–2043. 10.1080/21655979.2021.2019872 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Yan S, Wang Q, Huo Z, Yang T, Yin X, Wang Z, Zhang Z, Wu H (2019) Gene expression profiles between cystic and solid vestibular schwannoma indicate susceptible molecules and pathways in the cystic formation of vestibular schwannoma. Funct Integr Genom 19(4):673–684. 10.1007/s10142-019-00672-5 [Google Scholar]
  • 32.Zhang J, Jiang P, Tu Y, Li N, Huang Y, Jiang S, Kong W, Yuan R (2022) Identification and validation of long non-coding RNA associated ceRNAs in intrauterine adhesion. Bioengineered 13(1):1039–1048. 10.1080/21655979.2021.2017578 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Zhang Z, Wang Z, Sun L, Li X, Huang Q, Yang T, Wu H (2014) Mutation spectrum and differential gene expression in cystic and solid vestibular schwannoma. Genet medicine: official J Am Coll Med Genet 16(3):264–270. 10.1038/gim.2013.114 [Google Scholar]
  • 34.Zhao JH, Sun JX, Song YX, Chen XW, Yang YC, Ma B, Wang J, Gao P, Wang ZN (2016) A novel long noncoding RNA-LOWEG is low expressed in gastric cancer and acts as a tumor suppressor by inhibiting cell invasion. J Cancer Res Clin Oncol 142(3):601–609. 10.1007/s00432-015-2071-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (267.1KB, xlsx)

Data Availability Statement

The RNA-seq data used in this study has been deposited at the GEO repository with the accession number: GSE278713 and can be accessed by the reviewers using this security token apwzwkmitbudtkz.

All the computational codes and pipeline used for RNA-seq analysis is deposited in github repository, https://github.com/decodebiology/Vestibular_schwannoma_2025.

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