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ALYREF correlates with tumor progression and therapy resistance in localized and advanced prostate cancer
1Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, Guangzhou Medical University, 510180 Guangzhou, Guangdong, China
2Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, 510230 Guangzhou, Guangdong, China
3Department of Urology, Meizhou People’s Hospital (Huangtang Hospital), 514000 Meizhou, Guangdong, China
4Department of Urology, Huizhou Central People’s Hospital, 516000 Huizhou, Guangdong, China
5Institute for Integrative Genome Biology, University of California, Riverside, CA 92507, USA
6Institute of Gerontology, Guangzhou Geriatric Hospital, Guangzhou Medical University, 510550 Guangzhou, Guangdong, China
*Corresponding Author(s):eyweidezhong@scut.edu.cn (Weide Zhong); Jolene@gzhmu.edu.cn (Yanru Zeng)
† These authors contributed equally.
| History | Submitted: 10 October 2025 | Accepted: 16 December 2025 | Published: 30 April 2026 |
| Copyright: | ©2026 The Author(s). Published by MRE Press. |
Background: Prostate cancer (PCa) is a leading malignancy in men, with a poor prognosis in the advanced stage. Aly/REF export factor (ALYREF), a 5-methylcytosine (m5C)-binding protein regulating RNA export, has been implicated in several cancers. This study aimed to clarify the role of ALYREF in PCa progression and prognosis. Methods: We integrated multi-omics datasets from The Cancer Genome Atlas (TCGA), Stand Up To Cancer (SU2C), Genotype-Tissue Expression (GTEx), and Cancer Dependency Map (DepMap) with immunohistochemistry and in vitro assays. Survival outcomes were analyzed using Kaplan-Meier and Cox regression. Functional enrichment assessed ALYREF-associated pathways, while ALYREF knockdown in LNCaP and enzalutamide-resistant cells evaluated its oncogenic role. Results: ALYREF was significantly upregulated in multiple cancer types and correlated with poor prognosis in PCa. High ALYREF expression predicted biochemical recurrence in localized PCa and shorter progression-free and overall survival in advanced PCa. Multi-omics profiling revealed enrichment in MYC targets, oxidative phosphorylation, E2F signaling, and mTORC1 pathways. DepMap data showed that ALYREF is essential for tumor cell proliferation, with high dependency in PCa lines. Consistently, ALYREF knockdown suppressed cell viability and clonogenic growth in both hormone-sensitive and resistant models. Conclusions: This study identifies ALYREF as an oncogenic driver and unfavorable prognostic biomarker in PCa. Integrating multi-omics evidence with functional validation, ALYREF emerges as a promising therapeutic target, particularly in advanced and therapy-resistant PCa.
Cite this article
Zhouda Cai, Yu Liu, Shengda Song, Chuanfan Zhong, Jiahong Chen, Le Zhang, et al.ALYREF correlates with tumor progression and therapy resistance in localized and advanced prostate cancer.Journal of Men's Health,2026,22(4):42-52 DOI:10.22514/jomh.2026.033
1. Introduction
Prostate cancer (PCa) ranks as the second most common malignancy among men globally and is the most prevalent malignant tumor in elderly men, with its mortality rate ranking fifth among cancers affecting men worldwide [1]. Over recent decades, advancements in diagnostic technologies and therapeutic approaches have significantly improved the overall survival rates of PCa patients [2]. PCa is an androgen-dependent disease, and current treatment options for early-stage PCa include surgical resection and radical radiotherapy [3]. However, a subset of patients inevitably progresses from hormone-sensitive prostate cancer to an advanced stage known as castration-resistant prostate cancer (CRPC). Once PCa advances to CRPC, the standard first-line treatment shifts to androgen deprivation therapy (ADT) combined with drugs targeting androgen receptor (AR) signaling pathways [4]. While significant progress has been made in the treatment of localized PCa globally, the prognosis for advanced PCa remains poor, with a substantial proportion of male patients succumbing to the disease [5]. Consequently, the identification of novel biomarkers and therapeutic targets is urgently needed to develop personalized treatment strategies for PCa patients [6].
Nonmutational epigenetic reprogramming represents a critical hallmark of tumor heterogeneity [7]. Among various RNA modifications, RNA 5-methylcytosine (m5C), characterized by the methylation of the fifth carbon of cytosine in RNA, plays a pivotal role in this process [7]. The regulation of RNA m5C is orchestrated by a suite of proteins, including methyltransferases (“writers”), demethylases (“erasers”), and RNA-binding proteins (“readers”), which collectively modulate the dynamic m5C modification landscape [8]. Prior studies have demonstrated that aberrant m5C modifications across multiple genes are associated with cancer progression [9, 10, 11]. Notably, the RNA methyltransferase Aly/REF export factor (ALYREF), a nuclear protein that specifically binds to m5C-modified RNA sites, facilitates the nuclear-to-cytoplasmic export of these modified RNAs, thereby influencing gene expression dynamics [12]. Our preliminary investigations have suggested that ALYREF holds potential as a biomarker in localized PCa [9]. However, a comprehensive understanding of its role across the spectrum from early- to late-stage PCa remains elusive. This study aims to address this knowledge gap by elucidating the functional significance of ALYREF in PCa progression.
2. Materials and methods
2.1 Public datasets processing
To investigate gene expression patterns, mRNA expression profiles from healthy human tissues were acquired from the Genotype-Tissue Expression (GTEx) database. RNA-sequencing (RNA-seq) data, alongside corresponding clinical annotations for 32 tumor types, were sourced from the UCSC Xena platform. PCa datasets, specifically The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) and SU2C, were obtained from the PCaDB repository, with detailed data processing methodologies described in our previously published work [13]. Additionally, mutational data for the PCa datasets were retrieved from the cBioPortal platform (www.cbioportal.org). Detailed baseline data for all patients are provided in Supplementary Tables 1,2.
2.2 Survival analysis
To assess the prognostic relevance of ALYREF in PCa patients, we performed Kaplan-Meier (KM) survival analysis alongside Cox proportional hazards regression analyses using the R package survival (version 3.7-0). The optimal threshold for ALYREF expression was established with the R package survminer (version 0.5.0), enabling stratification of each cohort into high- and low-expression groups based on this cut-off value.
2.3 In vitro validation of ALYREF gene dependency
To investigate the dependency of various tumor cell lines on ALYREF, we leveraged Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based gene editing data obtained from the Broad Institute’s Dependency Map (DepMap) portal (https://depmap.org/portal/). The specific scores for cell lines are summarized in Supplementary Table 3. Specifically, we analyzed the Perturbation Effects module within the DepMap framework, which enabled a comprehensive evaluation of ALYREF’s role in mediating tumor cell viability and proliferation.
2.4 Mutational profiling
Mutational landscapes in PCa were characterized using the R package Maftools (version 2.22.0) [14]. Variations in mutation frequencies across groups were assessed through the Wilcoxon rank-sum test to identify significant differences. Visualization of the mutational data was performed using the R package ComplexHeatmap (version 2.22.0) to generate comprehensive graphical representations [15].
2.5 Functional enrichment
To explore the associations between ALYREF and other mRNAs, we conducted Spearman correlation analysis using the TCGA-PRAD and SU2C datasets, ranking genes by their correlation coefficients in descending order. To identify significantly enriched biological pathways, Gene Set Enrichment Analysis (GSEA) was performed utilizing the R package clusterProfiler (version 4.15.2), focusing on HALLMARK gene sets [16]. The resulting GSEA outputs were visualized using the R package GseaVis (version 0.1.1) to generate clear and informative graphical representations [17].
2.6 Cell assays
The LNCaP and enzalutamide-resistant LNCaP (LNCaP_ENZR) cell lines were sourced and established as previously described [18]. Both cell lines were maintained in Roswell Park Memorial Institute 1640 (RPMI-1640) medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin, cultured at 37 °C in a humidified atmosphere with 5% CO2. Small interfering RNAs (siRNAs) and short hairpin RNAs (shRNAs) targeting ALYREF were designed and synthesized by Tsingke Company, with sequences detailed in Supplementary Table 4. Transfection protocols and quantitative Polymerase Chain Reaction (qPCR) analyses were conducted following the methodologies outlined in our prior work [19]. To evaluate cell viability and proliferative capacity, Cell Counting Kit-8 (CCK-8) and plate colony formation assays were performed on LNCaP and LNCaP_ENZR cells, with experimental procedures detailed in our previous study [18]. Briefly, cell viability was evaluated using the CCK-8 assay. Transfected cells (1 × 103 per well) were seeded into 96-well plates, and absorbance at 450 nm was measured at 24–72 hours after 2-hour incubation with CCK-8 (MA0218, Meilunbio, Dalian, China). For colony formation, 5 × 103 transfected cells were plated in 6-well plates and cultured for 14 days, after which colonies were fixed, crystal violet-stained, imaged, and counted.
2.7 Statistical analysis
Data processing and graphical representations were conducted using R software (version 4.4.1) alongside GraphPad Prism (version 9.0; GraphPad Software, San Diego, CA, USA). To evaluate associations between variables, Spearman’s rank correlation coefficient was computed. Comparisons of continuous variables were performed using either the Wilcoxon rank-sum test or Student’s t-test, depending on data distribution. Statistical significance was determined using a two-tailed p-value threshold of less than 0.05.
3. Results
3.1 Pan-cancer analysis of ALYREF expression and prognostic value
To investigate the expression patterns of ALYREF, we initially analyzed datasets from the Genotype-Tissue Expression (GTEx) and Human Protein Atlas (HPA) repositories, which provide comprehensive profiles of normal human tissues. As depicted in Fig. 1A, the highest levels of ALYREF expression were observed in skeletal muscle, testis, and bladder, while the lowest expression was found in kidney, cervix, and breast tissues, indicating ubiquitous yet variable expression across different organs. By integrating GTEx normal tissue data with TCGA pan-cancer dataset, we observed significantly elevated ALYREF expression in cancerous tissues compared with their normal counterparts across most cancer types, with the exception of kidney chromophobe carcinoma (KICH), as shown in Fig. 1B. These differences were statistically significant in the majority of cases. Similarly, when comparing tumor tissues with adjacent non-cancerous tissues in the TCGA pan-cancer cohort, ALYREF expression was consistently higher in tumors, except in thyroid carcinoma (THCA) and KICH (Fig. 1C). Furthermore, an analysis of four distinct prognostic endpoints in the TCGA pan-cancer cohort revealed that elevated ALYREF expression was associated with unfavorable prognosis in most cancer types, with the notable exception of ovarian cancer (OV), where it appeared to confer a protective effect (Fig. 1D,E). These findings underscore the potential of ALYREF as a biomarker in oncology, particularly given its statistically significant differential expression in PCa, highlighting its prognostic relevance across diverse malignancies.
Fig. 1.Pan-cancer analysis of ALYREF expression and prognostic significance. (A) Circular heatmap illustrating the mRNA expression levels of ALYREF across various normal human tissues. (B) Boxplot comparing ALYREF expression between tumor and normal tissues. (C) Boxplot depicting ALYREF expression in tumor versus adjacent non-cancerous tissues. (D) Dot plot representing hazard ratios (HR) with 95% confidence intervals (CI) for ALYREF expression across four prognostic endpoints. (E) Forest plot summarizing survival analysis results, including hazard ratios (HR) with 95% CI and p-values for each cancer type, with red dots indicating statistical significance (p < 0.05). ACC: Adrenocortical carcinoma; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; DLBC: Diffuse large B-cell lymphoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and neck squamous cell carcinoma; KICH: Kidney chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LGG: Lower grade glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SKCM: Skin cutaneous melanoma; STAD: Stomach adenocarcinoma; TGCT: Testicular germ cell tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine corpus endometrial carcinoma; UCS: Uterine carcinosarcoma; UVM: Uveal melanoma; OS: Overall survival; DSS: Disease-specific survival; DFI: Disease-free interval; PFS: Progression-free survival; TCGA: The Cancer Genome Atlas; GTEx: Genotype-Tissue Expression.
3.2 Prognostic value of ALYREF in localized and advanced PCa
To elucidate the prognostic significance of ALYREF across different stages of PCa, we analyzed two representative datasets: TCGA-PRAD for localized PCa and SU2C for advanced PCa. As illustrated in Fig. 2A–C, using biochemical recurrence (BCR) in TCGA-PRAD and progression-free survival (PFS) and overall survival (OS) in SU2C as prognostic endpoints, patients with high ALYREF expression exhibited significantly worse outcomes. Specifically, in localized PCa, elevated ALYREF expression was associated with a higher incidence of biochemical recurrence, while in advanced PCa, it correlated with increased resistance to androgen receptor signaling inhibitors (ARSI) and reduced survival time. Subsequent univariate and multivariate Cox regression analyses further showed that ALYREF serves as an independent prognostic risk factor in both localized and advanced PCa (Fig. 2D,E). The results above highlight the prognostic value of ALYREF in different stages of PCa.
Fig. 2.Prognostic implications of ALYREF in localized and advanced prostate cancer. (A) Kaplan-Meier survival analysis illustrating biochemical recurrence (BCR)-free survival in the TCGA-PRAD cohort. (B) Kaplan-Meier curve depicting progression-free survival (PFS) in the SU2C advanced prostate cancer cohort, stratified by high and low ALYREF expression. (C) Kaplan-Meier analysis of overall survival (OS) in the SU2C cohort. (D) Forest plot of univariate Cox proportional hazards regression models evaluating the prognostic impact of clinical characteristics. (E) Forest plot of multivariate Cox proportional hazards regression. ARSI: Androgen Receptor Signaling Inhibitors; PSA: Prostate-Specific Antigen; PRAD: Prostate adenocarcinoma; TCGA: The Cancer Genome Atlas; HR: hazard ratios; CI: confidence intervals; AR: Androgen receptor; NEPC: Neuroendocrine prostate cancer; TMB: Tumor mutational burden; SU2C: Stand Up To Cancer.
3.3 ALYREF promotes proliferation in multiple tumor cell lines
To investigate the functional role of ALYREF following its established prognostic significance, we examined its impact in various cell lines using the DepMap database. By categorizing cell lines according to organ-specific tissue types, we observed that ALYREF dependency scores were consistently below 0, with the majority falling below −1 (Fig. 3A). This finding suggests that ALYREF is essential for the proliferation of diverse cell lines, particularly cancer cells. Additionally, we ranked cell lines based on their dependency scores from lowest to highest, identifying the top 10 cell lines with the strongest dependency on ALYREF. Notably, the breast cancer cell line MDAMB361 exhibited the highest dependency (Fig. 3B). Similarly, among PCa cell lines, the 22RV1 cell line ranked first in terms of dependency score (Fig. 3C). These results underscore the potential of ALYREF as a therapy target in cancers.
Fig. 3.Functional dependency of ALYREF across tumor cell lines. (A) Boxplot illustrating the distribution of ALYREF gene effect scores (Chronos) across various tissue types from the CRISPR (DepMap Public 24Q4+Score, Chronos) dataset, with the vertical dashed line indicating the median gene effect score, highlighting tissue-specific dependency patterns. (B) Bar chart ranking the top 10 cell lines with the strongest ALYREF dependency based on gene effect scores across diverse lineages. (C) Bar chart depicting ALYREF dependency scores for prostate cancer cell lines. DepMap: Cancer Dependency Map.
3.4 Wet-lab validation of ALYREF in PCa
To validate the expression dynamics of ALYREF across different stages of PCa, we analyzed the TCGA-PRAD, Taylor dataset and GSE35988. These datasets demonstrated that ALYREF expression was lowest in benign prostate tissue, elevated in primary PCa, and highest in advanced stages (Fig. 4A). Immunohistochemistry (IHC) results from the Human Protein Atlas (HPA) further revealed that ALYREF is predominantly localized in the cytoplasm, cell membrane, and nucleus of PCa tissues (Fig. 4B). To investigate the oncogenic role of ALYREF in PCa, we conducted in vitro loss-of-function experiments using wild-type LNCaP (LNCaP_WT) and enzalutamide-resistant LNCaP (LNCaP_ENZR) cell lines. We designed two siRNA sequences targeting ALYREF, which effectively reduced ALYREF expression (Fig. 4C). CCK-8 assays demonstrated that ALYREF knockdown significantly decreased cell viability in both LNCaP_WT and LNCaP_ENZR (Fig. 4D). We further applied stable ALYREF knockdown using ALYREF-targeting shRNA, and the knockdown efficiency of shRNA was validated (Supplementary Fig. 1). Stable ALYREF depletion markedly reduced the colony-forming capacity of both LNCaP_WT and LNCaP_ENZR cells (Fig. 4E). These findings indicate that ALYREF expression increases with PCa progression and exerts an oncogenic effect.
Fig. 4.Validation of ALYREF expression and functional role in prostate cancer. (A) Boxplots comparing ALYREF mRNA expression across benign, primary, and advanced prostate cancer stages. (B) Immunohistochemistry (IHC) images from the Human Protein Atlas (HPA) showing ALYREF protein expression in prostate tissue. (C) Bar graph depicting relative ALYREF mRNA expression in LNCaP wild-type (LNCaP_WT) and enzalutamide-resistant (LNCaP_ENZR) cell lines following transfection with negative control (NC) siRNA or two ALYREF-targeting siRNAs (si#1, si#2), with statistical significance indicated. (D) Growth curves from CCK-8 assays showing cell viability over 72 hours in LNCaP_WT and LNCaP_ENZR cells transfected with NC siRNA or ALYREF-targeting siRNAs, demonstrating reduced proliferation upon knockdown. (E) Representative images of colony formation assays in LNCaP_WT and LNCaP_ENZR cells transfected with NC shRNA or two ALYREF-targeting shRNAs (sh#1, sh#2). NC: negative control. (*p < 0.05, **p < 0.01, ****p < 0.0001).
3.5 Potential biological functions of ALYREF in PCa
To elucidate the potential biological functions of ALYREF across different stages of PCa, we performed functional enrichment analysis using the TCGA-PRAD and SU2C datasets, prioritizing the top 10 statistically significant pathways (Supplementary Table 5). In the TCGA-PRAD dataset (Fig. 5A), activated pathways included Oxidative Phosphorylation, MYC Targets V1, DNA Repair, and MYC Targets V2. These findings suggest that ALYREF may promote tumor cell proliferation and survival by enhancing oxidative phosphorylation metabolism, MYC-mediated transcriptional activation, and DNA damage repair mechanisms. Conversely, suppressed pathways encompassed UV Response DN, Mitotic Spindle, Protein Secretion, Epithelial-Mesenchymal Transition, Androgen Response, and KRAS Signaling UP.
In the SU2C advanced PCa dataset (Fig. 5B), the ALYREF-associated pathways exhibited similarities to those in TCGA-PRAD. Activated pathways included E2F Targets, G2M Checkpoint, MYC Targets V1, Mitotic Spindle, Unfolded Protein Response, Oxidative Phosphorylation, UV Response UP, and MTORC1 Signaling. These results indicate that ALYREF may drive tumor progression by activating the E2F transcriptional network, regulating the G2M checkpoint, facilitating mitotic processes, modulating the unfolded protein response, and enhancing MTORC1 signaling, thereby supporting cell cycle progression, protein homeostasis, and metastatic tumor resistance. The DNA Repair pathway showed a weaker association. Notably, only one pathway, KRAS Signaling DN, was suppressed in the SU2C dataset. These findings suggest that ALYREF is associated with partially overlapping activated pathways across different stages of PCa, with some distinct differences potentially linked to the previously observed increase in ALYREF expression during PCa progression.
Fig. 5.Functional enrichment analysis of ALYREF-associated pathways in prostate cancer. (A) Gene Set Enrichment Analysis (GSEA) plot for the TCGA-PRAD cohort, displaying the top 10 significantly enriched pathways ranked by p value. (B) GSEA plot for the SU2C cohort, highlighting the top 10 enriched pathways ranked by p value. TCGA-PRAD: The Cancer Genome Atlas Prostate Adenocarcinoma; SU2C: Stand Up To Cancer.
3.6 ALYREF and genetic mutations in PCa
To comprehensively explore the association between ALYREF and genetic mutations in PCa, we conducted a multi-omics analysis. In localized PCa, the mutational burden was relatively low, with genes such as Tumor Protein p53 (TP53), Speckle-Type POZ Protein (SPOP), and Titin (TTN) exhibiting mutation frequencies ≥10%, as shown in Fig. 6A. When stratifying by ALYREF expression levels, we observed significantly higher mutation frequencies for TP53 and LRP1B in the high ALYREF expression group (Fig. 6B). Conversely, when grouping by mutation status, only LRP1B mutations were associated with a statistically significant increase in ALYREF expression (Fig. 6C). In advanced PCa cohorts, the mutational burden was markedly elevated, with TP53 and TTN mutations occurring at frequencies ≥30%, and AR mutations at 19% (Fig. 6D). Upon further analysis of ALYREF expression subgroups, only SYNE1 displayed a significantly higher mutation frequency in the high ALYREF expression group, while TP53 mutations showed a modestly elevated frequency (Fig. 6E). Intriguingly, when stratified by the mutation status of high-frequency mutated genes, ALYREF expression levels remained largely unchanged (Fig. 6F). These findings suggest that ALYREF expression subgroups exhibit distinct patterns of genetic mutation heterogeneity. However, ALYREF expression appears relatively stable in both localized and advanced PCa, with minimal variation driven by genetic mutations in most cases.
Fig. 6.Mutational landscape and association with ALYREF expression in prostate cancer. (A) Oncoplot displaying the mutation frequency of the top 10 most frequently mutated genes in the TCGA-PRAD cohort. (B) Boxplot comparing mutation frequencies of selected genes between high and low ALYREF expression groups in the TCGA-PRAD cohort, with statistical significance denoted by asterisks (*p < 0.05, **p < 0.01). (C) Boxplot illustrating ALYREF expression levels stratified by mutation status (mutated vs. non-mutated) for key genes in the TCGA-PRAD cohort. (D) Oncoplot showing the mutation frequency of the top 10 most frequently mutated genes in the SU2C cohort. (E) Boxplot comparing mutation frequencies of selected genes between high and low ALYREF expression groups in the SU2C cohort, with significant differences marked. (F) Boxplot depicting ALYREF expression levels stratified by mutation status for key genes in the SU2C cohort. TCGA-PRAD: The Cancer Genome Atlas Prostate Adenocarcinoma; SU2C: Stand Up To Cancer.
4. Discussion
Localized PCa is typically characterized by an indolent clinical course, with many patients exhibiting no noticeable symptoms or only mild manifestations that may resemble benign prostatic hyperplasia [20]. In some instances, the disease remains undetected during a patient’s lifetime and is identified only posthumously during autopsy. However, once PCa progresses to an advanced stage, the prognosis becomes significantly worse, often accompanied by resistance to multiple therapeutic interventions [21]. For patients with advanced PCa, particularly those with metastatic disease, ARSI therapy remains the standard first-line treatment. However, the development of resistance is almost inevitable [22]. At present, treatment response is primarily assessed through rising Prostate-Specific Antigen (PSA) levels and imaging evaluations, such as Computed Tomography (CT) and bone scans, yet these approaches still lack sufficient specificity [23]. Consequently, the development of novel biomarkers and therapy targets is critical for improving the management and outcomes of PCa across its continuum.
ALYREF, a heat-stable nuclear protein, exhibits a distinctive capacity to recognize m5C sequences and function as a molecular chaperone [24]. This protein plays a critical role in facilitating the dimerization of unfolded leucine zipper motifs, thereby promoting transcriptional activation [25]. Additionally, ALYREF interacts with specific sequences in the translational regions of mRNAs, modulating gene expression, nuclear mRNA export, and genomic stability [24, 26]. Emerging evidence has implicated ALYREF in tumorigenesis across various cancer types. For instance, Nan et al. [27] demonstrated that ALYREF, through its interaction with modified circular RNA ras-responsive element-binding protein 1 (circRREB1), enhances lung cancer progression by inducing mitophagy. Furthermore, ALYREF has been shown to stabilize Cell-cycle related and expression-elevated protein (CREPT) mRNA, thereby promoting nasopharyngeal carcinoma progression [28], and to modify Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), contributing to sorafenib resistance in hepatocellular carcinoma [29]. The advent of multi-omics technologies has accelerated the discovery of novel cancer biomarkers [30, 31, 32]. Leveraging multi-omics data, this study elucidates the pivotal role of ALYREF in the progression of PCa from early to advanced stages.
Our pan-cancer analysis aligns with previous studies, confirming that ALYREF is overexpressed in most tumor types and serves as an unfavorable prognostic factor. This pattern is particularly pronounced in PCa, where elevated ALYREF expression correlates with adverse outcomes across early-stage BCR and late-stage PFS and OS. Additionally, pan-cancer CRISPR screening data indicate that ALYREF is essential for the survival of most tumor cells. Consistent with these findings, in vitro experiments using PCa cell models, specifically LNCaP_WT and LNCaP_ENZR, demonstrate that ALYREF knockdown significantly attenuates PCa progression.
While our study has established that ALYREF promotes the progression of PCa across different stages, the mechanisms underlying its role remain elusive. Recent research has revealed that ALYREF interacts with m5C-modified Acetyl-CoA carboxylase 1 (ACC1) mRNA, enhancing its stability and nuclear export, which in turn upregulates ACC1 expression and lipid accumulation, thereby driving PCa progression [33]. However, similar to other studies on ALYREF, this work focuses on ALYREF’s auxiliary role in tumor progression, such as stabilizing RNA and facilitating nuclear export, without positioning ALYREF as the central focus of investigation, particularly in the context of PCa. Our functional enrichment analysis demonstrates that ALYREF is associated with several consistently activated pathways in both early- and late-stage PCa, including Oxidative Phosphorylation, MYC Targets V1, and DNA Repair. These findings suggest a multifaceted role for ALYREF in PCa biology. Meanwhile, with the rapid advancement of urine-based biomarkers as a non-invasive diagnostic approach, the relevance of ALYREF in this context warrants attention. Garcia-Martin et al. [34] reported that ALYREF is a key RNA-binding protein recognizing exosomal EXOmotifs and regulating the selective loading of specific molecules into small extracellular vesicles. This mechanism suggests that ALYREF may influence the release of cellular components into urine. Future studies are warranted to elucidate the mechanistic details of how ALYREF drives PCa progression across its various stages, building on these preliminary insights.
Several limitations should be acknowledged. This study relied mainly on publicly available datasets, which may introduce data heterogeneity. Moreover, investigations of ALYREF were limited to in silico and in vitro analyses, and further in vivo and mechanistic studies are needed to fully elucidate its biological functions.
5. Conclusions
ALYREF is overexpressed in PCa tissues, correlating with adverse outcomes. Besides, following the knockdown of ALYREF, the progression of PCa and its resistance to treatment are attenuated. In summary, ALYREF emerges as a promising biomarker and therapeutic target, necessitating further mechanistic studies for personalized PCa strategies.
Availability of data and materials
All data used in this study were obtained from publicly available databases. Detailed information on the datasets is provided in the Materials and Methods section.
Author contributions
ZDC, YL, LZ, SDS and JML—were responsible for data collection and analysis. ZDC, YL and CFZ—conducted the cell experiments. ZDC, YL and SDS—drafted the initial manuscript. JHC, CC, YRZ and WDZ—contributed to the revision of the manuscript. JHC, YRZ and WDZ—provided funding for the project. YRZ and WDZ—supervised and designed the study. All authors contributed to the revision of the manuscript and approved the final version.
Ethics approval and consent to participate
Not applicable.
Acknowledgment
We express our gratitude to the creators of the publicly accessible datasets and the developers of the R-based bioinformatics tools utilized in this research.
Funding
This work was supported by the Guangzhou Health Science and Technology Project (20251A011001), Science and Technology Project of Guangzhou (2024A03J1108), National Natural Science Foundation of China (82573325), the Regional Joint Fund—Regional Cultivation Project (Grant number 2023A1515140040) and Guangdong S&T Program (2023B1111030006).
Conflict of interest
The authors declare no conflict of interest.
Supplementary material
Supplementary material associated with this article can be found, in the online version, at https://oss.jomh.org/files/article/2049382999391125504/attachment/ Supplementary%20material.xls.
