Ribosomal protein RPL7A modulates lung adenocarcinoma progression via circRANBP17–UPF1-mediated SIRT6 degradation

Abstract

Background

Lung cancer, primarily lung adenocarcinoma (LUAD), is the leading cause of cancer-related deaths worldwide. Despite extensive research, the mechanisms behind LUAD progression remain inadequately understood, underscoring the need for new biomarkers and therapeutic targets. Ribosomal proteins, traditionally associated with protein synthesis, are gaining recognition for their roles in tumorigenesis, though many functions remain unexplored.

Methods

This study utilized single-cell transcriptomic data and bioinformatics analyses to identify potential LUAD biomarkers. Selected biomarkers were validated using quantitative PCR (qPCR) and immunofluorescence on clinical samples. Functional roles were assessed through in vivo and in vitro assays, including migration, invasion, and proliferation studies. Mechanistic insights were gained via mRNA stability assays, RNA immunoprecipitation, fluorescence in situ hybridization, and dual luciferase reporter assays.

Results

RPL7A is a significant prognostic marker with elevated expression in metastatic LUAD tissues. Clinical validation shows that high RPL7A expression correlates with LUAD occurrence and poor overall survival (OS) (hazard ratio > 1). RPL7A knockdown inhibits LUAD cell migration, invasion, and proliferation, underscoring its key role in tumor progression. Mechanistically, RPL7A impacts lipid metabolism and the AKT pathway. Crucially, RPL7A regulates circRANBP17, a circRNA linked to LUAD metastasis and lipid metabolism. This interaction forms a complex with UPF1 to destabilize SIRT6 mRNA, a critical factor in lipogenesis. The resulting downregulation of SIRT6 highlights how RPL7A and circRANBP17 contribute to altered lipid metabolism and tumor progression in LUAD.

Conclusions

Our findings demonstrate that RPL7A promotes LUAD progression through circRANBP17-UPF1-mediated SIRT6 degradation, positioning RPL7A as a potential therapeutic target in LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11658-025-00779-7.

Introduction

Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for approximately 1.8 million fatalities annually [1]. Lung adenocarcinoma (LUAD) constitutes about 40% of these cases [2]. Despite advances in targeted therapies and immunotherapy, the 5-year survival rate for patients with LUAD remains critically low at around 15% [3]. Tumor progression severely impacts lung cancer prognosis, highlighting the urgent need to investigate the molecular mechanisms driving LUAD [4]. A deeper understanding is essential for developing more effective treatments and improving patient outcomes.

One critical aspect of these molecular mechanisms is cellular metabolism, which plays a pivotal role in tumor genesis and progression [5]. Cancer cells often undergo metabolic reprogramming to support rapid growth. While alterations in glucose and amino acid pathways are significant, lipid metabolism, particularly lipogenesis, is crucial [6]. Key molecules regulating lipogenesis such as fatty acid synthase (FASN) and acetyl-CoA carboxylase (ACC) are often upregulated in LUAD [7]. In addition, various enzymes, such as SIRT6, have been identified as influential in regulating lipogenesis, further impacting tumor aggressiveness [8].

Traditionally, ribosomes were viewed primarily as machinery for protein synthesis, translating messenger RNA (mRNA) into proteins. Recent research, however, has revealed that abnormalities in ribosomal proteins significantly impact cancer biology [9]. Over 50% of human cancers exhibit altered ribosome biogenesis, contributing to cancer by disrupting proteins involved in cell proliferation and apoptosis [10]. In lung cancer, specific ribosomal proteins, such as RPL5 and RPL11, are linked to tumor aggressiveness and patient prognosis [11, 12]. In addition, interactions between ribosomal proteins and the TP53–MDM2 pathway, crucial for regulating cell growth and DNA repair, are well documented [13, 14]. Furthermore, a few studies suggest that RPL proteins can regulate biological characteristics through the expression of non-coding RNAs (ncRNAs) [15, 16]. However, overall research in this area remains insufficient.

In this study, we identify ribosomal protein RPL7A as a significant regulator of LUAD progression. Our findings indicate that RPL7A operates independently of the RPL–MDM2–P53 pathway, suggesting alternative regulatory mechanisms. Specifically, we demonstrate that RPL7A influences the production of circRANBP17, a non-coding RNA involved in various cellular processes. Moreover, we show that RPL7A initiates the degradation of the tumor suppressor gene SIRT6 through the circRANBP17–UPF1 complex, revealing a novel interaction that contributes to LUAD progression. These insights not only deepen our understanding of ribosomal proteins in LUAD but also open new avenues for targeted therapies in this aggressive cancer type. By elucidating the intricate relationships between ribosomal proteins and ncRNAs, we aim to enhance the understanding of LUAD and its underlying mechanisms.

Materials and methods

Clinical samples

Lung tumor and liver metastasis tissue samples were collected from six patients with lung cancer (identified as L00, L01, L03, H00, H01, and H03) at the Affiliated Cancer Hospital of Zhengzhou University using ultrasound- or computed tomography (CT)-guided percutaneous biopsies. Each participant signed an informed consent form. The procedures were conducted with the approval of the Medical Ethics Committee (review number: 2022-ky-0069–001), ensuring compliance with ethical standards and patient consent protocols.

Cell culture

Human normal bronchial epithelial cells (BEAS-2B, #GNHu27, Cell Bank of the Chinese Academy of Sciences, China) and human lung adenocarcinoma cells (A549, #SCSP-503, Cell Bank of the Chinese Academy of Sciences, China; H1299, #TCHu160, Cell Bank of the Chinese Academy of Sciences, China; and PC9, #SCSP-5085, Cell Bank of the Chinese Academy of Sciences, China) were obtained and cultured in RPMI-1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (FBS; Gibco, Thermo Fisher Scientific) and 1% penicillin–streptomycin (Gibco, Thermo Fisher Scientific).

Mice models

Four-week-old male BALB/c nude mice were purchased from the Laboratory Animal Center of Zhengzhou University. Each mouse received a subcutaneous injection of 1 × 10⁶ transfected cells, which were modified to either overexpress or knock down RPL7A or circRANBP17. Tumor volumes were measured weekly using the formula (volume = length × width² × 0.5). After 5 weeks, the tumors and lungs were excised for further analysis. All experiments were conducted with the approval of the Zhengzhou University Ethics Committee.

Plasmid construction, RNAi and lentivirus production

The cDNA sequences of RPL7A and circRANBP17 were cloned into the pLC5-Puro lentiviral vector for overexpression. Mutant constructs were synthesized by GENEWIZ (Suzhou, China). Specific short hairpin RNA (shRNA) sequences targeting RPL7A and circRANBP17 were inserted into pLKO.1-Puro vectors, while siRNAs targeting their splice sites were obtained from GenePharma (Shanghai, China). Control expression vectors were constructed by GENESEED (Guangzhou, China).Transfection was carried out using X-tremeGENE HP DNA Transfection Reagent (Roche, Germany). For lentiviral particle production, LentiX-293T cells were co-transfected with pMD2.G (2.5 μg), psPAX2 (7.5 μg), and the target vector (10 μg) at 70–80% confluency. Supernatants were collected at 48 and 72 h, filtered through a 0.45 μm filter, and concentrated with Lenti-Concentin Virus Precipitation Solution (ExCell Bio). The viral particles were resuspended in PBS and either stored at −80 °C or used for immediate infections. Stable cell lines with altered expression levels of RPL7A and circRANBP17 were established through puromycin selection.

RNA extraction and actinomycin D treatment

Total RNA was extracted from cultured cells using the TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s protocol. The integrity and concentration of the RNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). For the evaluation of RNA stability, cells were treated with actinomycin D (ActD; Sigma-Aldrich, St. Louis, MO, USA) at a final concentration of 5 μg/mL. Cells were incubated with ActD for 0, 4, 8, and 24 h to monitor the degradation of RNA transcripts. Following treatment, RNA was extracted as described above and used for downstream analyses, including quantitative real-time PCR (qRT-PCR) and RNA sequencing, to assess the impact of ActD on gene expression profiles.

Cytoplasmic and nuclear RNA isolation

Cells were collected and washed with PBS before being resuspended in a mixture of Thermo Fisher’s NE-PER reagents. The cytoplasmic extraction reagent was added to the cell suspension, and the mixture was incubated to allow for cell lysis and separation of the cytoplasmic fraction. The lysate was centrifuged to separate the cytoplasmic supernatant from the nuclear pellet. The nuclear pellet was then resuspended in the nuclear extraction reagent and incubated to release nuclear RNA.

Both fractions were processed for RNA isolation using a commercial kit, including a step to remove genomic DNA. The RNA was eluted and its quality and quantity were assessed. The isolated RNA was suitable for various downstream applications to study the roles of cytoplasmic and nuclear RNA.

Cell proliferation assay and Transwell assay

Cell proliferation was measured using a commercial cell proliferation assay kit. Cells were seeded in 96-well plates and treated with varying concentrations of the test compound. After the treatment period, a reagent was added and the plate was incubated. Absorbance was then measured at a specific wavelength to determine cell viability relative to control wells. Cell migration and invasion were assessed using Transwell chambers with 8 μm pore size membranes. For migration, cells were seeded in serum-free medium in the upper chamber, with medium containing serum in the lower chamber. For invasion, the membranes were coated with Matrigel before cell seeding. After incubation, cells that migrated or invaded to the lower surface were fixed, stained, and counted under a microscope. The average number of cells per field was used to quantify migration and invasion.

RNA sequencing

Total RNA was isolated from cells using a standard extraction kit, with quality and quantity assessed via spectrophotometry and agarose gel electrophoresis. High-quality RNA samples were utilized for library construction using a commercial RNA library preparation kit. mRNA was enriched through poly-A selection or ribosomal RNA depletion, then fragmented and reverse transcribed into cDNA. The cDNA was end-repaired, adenylated at the 3′ ends, and ligated with sequencing adapters, followed by PCR amplification.The libraries were quantified and quality-checked using a bioanalyzer before sequencing on an Illumina platform, generating paired-end reads. Raw data underwent processing to remove adapter sequences and low-quality reads. Clean reads were aligned to a reference genome using an appropriate aligner, and gene expression levels were quantified and normalized. Differential expression analysis was performed using relevant bioinformatics tools.

RNA immunoprecipitation (RIP) and MeRIP

RIP and methylated RNA immunoprecipitation (MeRIP) were conducted using the Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore, USA) per the manufacturer’s guidelines. For RIP, cells were harvested and lysed in a buffer containing protease and RNase inhibitors. The lysate was incubated with magnetic beads coated with antibodies against the target RNA-binding protein, with control IgG as a negative control, overnight at 4 °C with gentle rotation. After washing the beads to eliminate nonspecific binding, co-precipitated RNAs were eluted using RIP wash buffer. Eluted RNA was isolated by adding proteinase K, followed by phenol–chloroform extraction and ethanol precipitation. Purified RNA was quantified and assessed for quality via spectrophotometry and agarose gel electrophoresis. For MeRIP, the procedure mirrored that of RIP, utilizing antibodies specific to m6A-modified RNA. Following similar lysis and immunoprecipitation steps, m6A-modified RNAs were eluted and purified. The isolated RNA from both methods was subsequently analyzed by qRT-PCR or sequencing to identify and quantify enriched RNA species.

RNA pull‑down assay and mass spectrometry analysis

RNA pull-down assays were conducted using the BioSin RNA Pull-Down Kit (Guangzhou) per the manufacturer’s protocol. Biotinylated RNA probes were synthesized in vitro with T7 RNA polymerase and biotin-16-UTP (Roche). Approximately 1 mg of whole-cell lysate from HEK293T cells was incubated with the biotinylated RNA for 1 h at 4 °C to form RNA–protein complexes. These complexes were captured using 50 μL of streptavidin-coated magnetic beads, pre-washed to reduce nonspecific binding, and incubated for an additional hour at 4 °C. After five washes with wash buffer, bound proteins were eluted with 1 × Laemmli buffer and heated at 95 °C for 5 min. Eluted proteins were resolved by SDS-PAGE on a 10% polyacrylamide gel and visualized with the Pierce Silver Stain Kit (Thermo Scientific). Selected bands underwent in-gel trypsin digestion to produce peptides, which were extracted, desalted using C18 ZipTips, and analyzed via high-resolution mass spectrometry on an Orbitrap mass spectrometer. Peptide identification was performed with MaxQuant, utilizing a false discovery rate (FDR) threshold of < 1%.

Luciferase reporter assay

Luciferase reporter assays were performed using the Luciferase Assay System (Promega) according to the manufacturer’s instructions. A549 and PC9 cells were seeded in 24-well plates and allowed to adhere overnight. The next day, the cells were transfected with a firefly luciferase reporter plasmid containing the promoter region of interest using Lipofectamine 3000 (Invitrogen). After 24 h of transfection, the cells were lysed in reporter lysis buffer. The lysates were then transferred to a 96-well plate for luminescence measurements. Luciferase activity was measured using a plate reader with automatic injection of the luciferase substrate. Luciferase activity was quantified and normalized to cell number or protein concentration to account for variations in transfection efficiency.

Western blotting, immunohistochemical analysis, and quantitative RT-PCR

Protein expression was evaluated by western blotting, immunohistochemical analysis (IHC), and gene transcription quantified via qRT-PCR, all performed according to previously established protocols [12, 13]. In addition, hematoxylin and eosin (H&E) staining, conventional RT‑PCR, and gel electrophoresis were conducted. Primers were custom synthesized by SHENGGONG (Shanghai, China) with sequences listed in Supplementary Table S1, and the primary antibodies utilized detailed in Supplementary Table S2. Immunostaining outcomes were documented and analyzed using an optical microscope (Nikon, Eclipse Ni-U).

Molecular docking analysis and molecular dynamics simulation

Molecular docking was conducted using AutoDock Vina (1.1.2). The UPF1 structure was predicted via AlphaFold. Docking simulations centered on the UPF1 active site were performed, selecting poses based on the lowest binding energy. Interactions were visualized with PyMOL (2.3) and Chimera software.

Subsequently, molecular dynamics simulations of the circRANBP17–UPF1–SIRT6 mRNA complex were executed in GROMACS (version 2021.5). Trajectory analyses included root mean square deviation (RMSD) and root mean square fluctuation (RMSF) assessments to evaluate stability and flexibility. Free energy landscapes (FEL) illustrated energy distributions, while hydrogen bond formation was analyzed to characterize interaction stability.

Statistical analysis

Statistical analyses were performed using SPSS version 27.0 and GraphPad Prism version 9.0. Data are expressed as the mean ± standard deviation (SD) from at least three independent experiments. For two-group comparisons, a Student’s t-test was used, while one-way analysis of variance (ANOVA) was utilized for comparisons among multiple groups. Chi-squared (χ²) tests and Fisher’s exact test were applied for clinicopathologic characterizations. Correlations between variables, such as RPL7A, circRANBP17, and SIRT6 expression, were assessed using linear regression and the Pearson correlation test. For non-normally distributed data, the Wilcoxon rank-sum test was used. Survival data were analyzed using the Kaplan–Meier method and the Log-rank test. Receiver operating characteristic (ROC) curves evaluated diagnostic performance, and a Cox regression model was employed for multivariate analysis. Statistical significance was defined as *P < 0.05, **P < 0.01, or ***P < 0.001. Further details can be found in the Supplementary Materials and Methods.

Results

Identification and clinical characterization of RPL7A in LUAD

To investigate key factors mediating metastasis and progression in lung adenocarcinoma (LUAD), we performed single-cell transcriptome sequencing on primary and liver metastatic tissues from three LUAD cases, analyzing 32,460 cells. This included paired samples from three primary lung lesions (L-01, L-02, and L-03) and their respective liver metastases (H-01, H-02, and H-03) (Fig. 1B). Using unsupervised clustering analysis (Fig. 1C) and marker gene identification (Fig. 1D), we classified several cell types, including lymphocytes, endothelial cells, monocytes/macrophages, hepatocytes, cholangiocytes, and tumor cells. Further clustering of tumor cells identified 16 subgroups (Fig. 1E), with subgroups 1, 5, and 9 significantly increased in liver metastases, suggesting their involvement in LUAD metastasis (Fig. 1F). We identified 107 differential genes correlated with liver metastasis (Fig. 1G; Fig. S1A).

Fig. 1.

Identification and clinical characterization of RPL7A in LUAD. A Flowchart outlining the study design and methodology. B Collection of clinical samples from three paired LUAD tissues for single-cell transcriptional sequencing. C Unsupervised clustering analysis of single-cell transcriptomes and identification of different histological cell types. D Cell clustering biomarkers corresponding to different histological cell types. E Division of tumor cells into subgroups based on transcriptional profiles. F Percentage distribution of the various cancer cell subgroups. G Venn diagram illustrating the differentially expressed genes (DEGs) identified in liver metastasis sites compared with metastatic cancer cell subgroups. H Venn analysis of DEGs from single-cell transcriptome analysis and LUAD samples derived from the TCGA-LUAD and GSE229705 datasets. I Venn analysis of DEGs from panel (H) intersecting with prognosis-related genes from the TCGA-LUAD database. J High expression levels of RPL7A in LUAD tissues within the TCGA-LUAD dataset. K Elevated RPL7A expression observed in paired LUAD tissues from the TCGA-LUAD dataset. L Reduced methylation levels of the RPL7A promoter in LUAD tissues compared with normal tissues, as indicated in the TCGA-LUAD dataset. M Kaplan–Meier analysis demonstrating that high RPL7A expression correlates with poorer clinical outcomes in patients with LUAD, based on data from the Kaplan–Meier Plotter database. N–O Comprehensive pan-cancer analyses of RPL7A expression and prognostic patterns utilizing data from the TCGA database. Data in the text are expressed as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Furthermore, we integrated our results with differential expressed genes and poor prognosis related genes from TCGA-LUAD and GSE229705 datasets, revealing 37 overlapping genes associated with poor prognosis (Fig. 1H, I). Among these, RPL7A was notably highlighted owing to its limited mention in cancer literature (Fig. 1I). Analysis of TCGA-LUAD dataset indicated that RPL7A mRNA was significantly elevated in LUAD tissues (Fig. 1J, K), and RPL7A overexpression was consistently observed across cohorts, such as GSE43458 and GSE159857 datasets (Fig. S1I). Further analysis of TCGA-LUAD via UALCAN confirmed that RPL7A dysregulation is independent of ethnicity (Fig. S1J).

Meanwhile, by analysing TCGA-LUAD datasets, we found promoter methylation levels of RPL7A were reduced in tumors (Fig. 1L). Thus, in order to identify whether RPL7A level was regulated by its promoter methylation, we treated A549/PC9 cells with the DNMT inhibitor DNMT-IN-5 and found that RPL7A expression was significantly upregulated, directly linking promoter hypomethylation to its transcriptional activation (Fig. S1K).

Simultaneously, analysis of the CPTAC dataset via the UALCAN database revealed a significant elevation of RPL7A protein levels in LUAD tissues [17], with a correlation to pathological staging (Fig. S1B, S1C). Immunohistochemical analysis based on clinical LUAD samples also corroborated these findings (Fig. S1D, S1E). Furthermore, Kaplan–Meier analysis showed that high RPL7A expression correlated with poor prognosis (HR = 1.47, P < 0.05) through analyzing the Kaplan–Meier plotter database [18] (Fig. 1M).

Also, the expression and prognosis pattern of RPL7A underwent pan-cancer analysis (Fig. 1N, O). Collectively, these findings suggest that RPL7A plays a significant role in the progression of LUAD and may have important clinical relevance.

RPL7A promotes LUAD metastasis and proliferation

At the cellular level, we confirmed that RPL7A levels were elevated in LUAD cell lines (A549, H1299, and PC9) compared with normal bronchial epithelial cells (BEAS-2B) (Fig. 2A). We further assessed the efficiency of transient transfections for both silencing and overexpressing RPL7A in these LUAD cell lines (Fig. 2B, C). Following RPL7A silencing and overexpression, Transwell migration and invasion assays demonstrated that RPL7A significantly regulates the migratory and invasive capabilities of A549 and PC9 cells (Fig. 2D, E). Concurrently, CCK8 proliferation assays indicated a marked decrease in cell proliferation upon RPL7A silencing (Fig. 2F). Similarly, colony formation assays following RPL7A silencing also corroborated this observation (Fig. S1F, S1G). In contrast, overexpression of RPL7A significantly enhanced the proliferation of lung cancer cells (Fig. 2G). Together, these findings establish RPL7A as a critical regulator of migration, invasion, and proliferation in LUAD cells.

Fig. 2.

RPL7A promotes LUAD metastasis and proliferation in vivo and in vitro. A The relative expression of RPL7A in normal bronchial epithelial cell lines and LUAD cell lines by qRT-PCR. B, C Gene silencing and overexpression efficiency verification in A549 and PC9 cell lines after transfection. D, E Tranwell invasion and migration assays were performed to determine the metastatic ability of LUAD cells transfected with siRNAs and the overexpression plasmid of RPL7A. F, G CCK8 assays were performed to determine the proliferation ability of LUAD cells transfected with the overexpression plasmid or siRNAs. H Mean intensity of immunofluorescence (IF) staining of a tissue microarray from patients with clinical LUAD I Representative images of immunofluorescence (IF) staining of a tissue microarray from patients with clinical LUAD of different stages. J Representative images of xenograft tumors (five mice per group) in nude mice transplanted with stably overexpressed RPL7A in PC9 cells. K Tumor volume was monitored every 5 days for 25 days. L The tumor weight in different groups was examined at day 25 post-inoculation. Data in the text are expressed as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

To further investigate the clinical relevance of RPL7A, we utilized a tissue microarray from patients with LUAD to conduct immunofluorescence experiments examining the expression of RPL7A and Ki67. The results revealed a significant increase in RPL7A protein expression in patients with advanced lung adenocarcinoma (Fig. 2H; Table 1). Notably, high RPL7A expression was associated with clinical grading of more advanced tumors and elevated Ki67 levels (Fig. 2I), and correlation analysis revealed a significant positive association between RPL7A and Ki-67 expression (Fig. S1H).

Table 1.

Correlations between the RPL7A expression level and clinicopathological parameters of patients with LUAD

Characteristics	RPL7A expression		P-value
	Low	High
n	48	32
T stage, n (%)
T1	12 (15%)	4 (5%)	0.584
T2	25 (31.2%)	19 (23.8%)
T3	2 (2.5%)	2 (2.5%)
T4	9 (11.2%)	7 (8.8%)
N stage, n (%)
N0	23 (28.7%)	8 (10%)	0.022
N1	13 (16.2%)	9 (11.2%)
N2	9 (11.2%)	15 (18.8%)
N3	3 (3.8%)	0 (0%)
M stage, n (%)
M0	43 (53.8%)	24 (30%)	0.083
M1	5 (6.2%)	8 (10%)
Pathologic stage, n (%)
Stage I	15 (18.8%)	2 (2.5%)	0.015
Stage II	16 (20%)	8 (10%)
Stage III	12 (15%)	15 (18.8%)
Stage IV	5 (6.2%)	7 (8.8%)
Sex, n (%)
Female	21 (26.2%)	13 (16.2%)	0.782
Male	27 (33.8%)	19 (23.8%)

To validate the role of RPL7A in vivo, we performed subcutaneous tumor implantation experiments in nude mice (Fig. 2J). The results indicated that tumors with RPL7A overexpression grew substantially larger than those in the control group (Fig. 2K). Post-sacrifice measurements of tumor weight further confirmed a significant increase in tumor mass in the RPL7A overexpression group compared with the wild-type group (Fig. 2L). These findings collectively underscore the importance of RPL7A in promoting tumor growth and malignancy in LUAD.

Regulation role of RPL7A is independent of the RP–P53 pathway in LUAD

While the role of RPL7A in LUAD remains underexplored, the regulatory effects of ribosomal proteins (RPs) on tumors are increasingly acknowledged, particularly in their interaction with P53. To investigate whether RPL7A influences LUAD via P53, we conducted experiments using the P53-inactivated H1299 cell line. Silencing RPL7A resulted in a significant inhibition of tumor cell migration and proliferation, even in the absence of functional P53 (Fig. 3A).

Fig. 3.

RPL7A modulates LUAD progression through lipid metabolism regulation and AKT pathway activation independent of the classic P53 pathway. A Transwell invasion and migration assays were conducted in the TP53 loss-of-function mutant LUAD cell line H1299 to evaluate the dependency of RPL7A’s pro-metastatic function on TP53, with prior treatment using cycloheximide to minimize interference from ribosomal protein synthesis inhibition. B Transcriptional RNA-seq analysis identified differentially expressed genes following RPL7A knockdown in A549 cells. C KEGG enrichment analysis of differentially expressed genes (DEGs) following RPL7A RNAi. D GO enrichment analysis of DEGs, categorizing findings into biological processes, cellular components, and molecular functions. E Identification of significantly enriched lipid metabolism pathways based on GO enrichment analysis of DEGs. F Gene set enrichment analysis (GSEA) graphs depicting genes involved in fatty acid metabolism processes, based on the Reactome database. G Heatmap showing differential expression of genes related to de novo lipogenesis and lipid metabolism between the negative control (NC) and RPL7A RNAi groups in A549 cell lines. H Western blot analyses of proteins associated with de novo lipogenesis and lipid metabolism following RPL7A-targeting siRNA transfection in A549 cells and RPL7A overexpression in PC9 cells. I Western blot analyses of AKT pathway and epithelial–mesenchymal transition (EMT) markers in A549 cells post-siRNA transfection. Data in the text are expressed as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

To validate these findings, we repeated the experiments in H1299 cells that were pretreated with cycloheximide to minimize the interference from ribosomal protein synthesis inhibition. The results indicated that even with this inhibition, silencing RPL7A still led to a substantial reduction in invasive and migratory capabilities (Fig. 3A). This suggests that RPL7A may regulate LUAD through mechanisms that extend beyond the classical TP53 pathway and ribosomal translation modulation. To further elucidate the molecular mechanisms underlying RPL7A’s regulation of LUAD, we performed next generation sequencing (NGS) on triplicate RPL7A-silenced and control A549 cells, identifying differentially expressed genes (P < 0.05) (Fig. 3B). Enrichment analyses of differentially expressed genes using the KEGG database identified potential associations with the PI3K–AKT pathway (Fig. 3C). Gene Ontology (GO) enrichment analysis revealed that RPL7A was associated with signaling, gene transcription, and cell differentiation in the biological process (BP) category, as well as protein binding and DNA binding in the molecular function (MF) category (Fig. 3D). In addition, the functional enrichment associated with the lipid metabolic process drew our attention, as numerous studies have highlighted that the reprogramming of lipid metabolism is crucial for tumor metastasis to the liver. To explore this further, we undertook a detailed analysis of specific lipid metabolic pathways. Our findings revealed that silencing RPL7A significantly inhibited several lipid metabolism-related pathways, including lipid catabolism, lipid storage, and lipid transport (Fig. 3E). This suggests that RPL7A may play a pivotal role in the regulation of lipid metabolism in the context of tumor progression.

Subsequently, we performed gene set enrichment analysis (GSEA) to evaluate functional alterations in LUAD cells following RPL7A knockdown. The results indicated that RPL7A is closely associated with changes in various biological processes, including cellular metabolism, epithelial–mesenchymal transition (EMT), and RNA metabolism (Fig. S2A, S2B). Notably, GSEA results demonstrated downregulation of key stages of fatty acid and cholesterol metabolism, providing avenues for further mechanistic exploration (Fig. 3F). Specifically, key gene markers of de novo lipogenesis and oxidation also showed significant inhibition in RNAi-RPL7A group (Fig. 3G).

Furthermore, the protein level of de novo lipogenesis and oxidation markers was found to positively relate to RPL7A levels, as seen from western blots after transfection with siRNAs targeting RPL7A in A549 cells and overexpression plasmid of RPL7A in PC9 cells (Fig. 3H). Meanwhile, NADPH (a cofactor for lipid biosynthesis) and triglyceride levels in LUAD cell lines following knockdown of RPL7A were decreased upon silencing (Fig. S3F, S3H). Also, significant inhibition of the AKT pathway and regulation of EMT were seen following RPL7A silencing (Fig. 3I; Fig. S2C, S2D).

Considering the previous reports of ribosomal incidents and cellular stress, we evaluated key DNA damage response markers and found marked activation of DNA repair pathways after RPL7A knockdown (Fig. S2E), suggesting RPL7A suppression elicits genotoxic stress.

In summary, our results indicate that RPL7A regulates LUAD progression not solely through the traditional RP–P53 pathway but rather through the regulation of lipid synthesis and AKT signaling pathways.

RPL7A promotion of LUAD metastasis is dependent on circRANBP17

To investigate how RPL7A influences the AKT pathway in LUAD, we analyzed high-throughput sequencing data, revealing significant alterations in non-coding RNAs, particularly circRNAs due to their stability and tissue-specific expression. Our RNAi-RPL7A A549 model identified various differentially expressed circRNAs, including some previously unannotated (Fig. 4A). Initially, we validated circRNA expression using divergent primers by qRT-PCR in the PC9 cell line post-RPL7A silencing, finding that several circRNAs, including circRNA12063 (henceforth termed circRANBP17), were significantly downregulated (Fig. 4B), while the host gene RANBP17 remained unaffected (Fig. S3A). Conversely, overexpression of RPL7A in PC9 cells resulted in a notable increase in circRANBP17 levels (Fig. 4C).

Fig. 4.

RPL7A promotion of LUAD metastasis is dependent on circRANBP17 in vivo and in vitro. A Heatmap illustrating differentially expressed circRNAs following RPL7A knockdown in A549 cells. B qRT-PCR analysis of novel circRNAs after RPL7A knockdown in PC9 cells, utilizing divergent primers. C qRT-PCR assessment of circRANBP17 expression after RPL7A overexpression in PC9 cells. D qRT-PCR assessment of circRANBP17 expression in normal bronchial epithelial cell lines versus LUAD cell lines. E Validation of antisense oligonucleotides (ASO) targeting circRANBP17 in LUAD cell lines. F CCK8 assays determining the proliferation ability of LUAD cells transfected with ASOs targeting circRANBP17. G Transwell invasion and migration assays assessing the metastatic potential of LUAD cells transfected with ASOs targeting circRANBP17. H Representative images of xenograft tumors in nude mice implanted with PC9 cells stably overexpressing RPL7A. I Tumor weights were measured at day 25 post-inoculation across different groups. J Tumor volume was monitored every 5 days over a 25-day period. K Western blot analyses of proteins involved in de novo lipogenesis and lipid metabolism following transfection with ASO-circRANBP17 in A549 cells. L Rescue experiment assessing the migration ability of LUAD cells following transfection with the negative control (NC), RPL7A overexpression plasmid, and ASO-circRANBP17 plus RPL7A overexpression plasmid, evaluated via Transwell migration assays. M Rescue experiment assessing the expression of lipogenesis-related markers following transfection with the negative control (NC), RPL7A overexpression plasmid, and ASO-circRANBP17 plus RPL7A overexpression plasmid, evaluated via western blots. Data in the text are expressed as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Furthermore, circRANBP17 expression was significantly elevated in LUAD cell lines compared with the normal bronchial epithelial cell line BEAS-2B (Fig. 4D), highlighting a potential regulatory loop between RPL7A and circRANBP17 in LUAD. To explore the functional significance of circRANBP17, we utilized antisense oligonucleotides (ASOs) targeting its back-splicing junction, effectively silencing its expression (Fig. 4E). In addition, we verified that RPL7A knockdown had no impact on the mRNA levels of the host gene RANBP17, thereby ensuring the targeting specificity (Fig. S3B). Functional assays demonstrated a marked reduction in migratory and proliferative capacities of A549 and PC9 cells upon circRANBP17 knockdown (Fig. 4F, G), indicating its crucial role in LUAD regulation.

Further, we assessed circRANBP17’s in vivo impact on LUAD. Following the establishment of stable A549 cells overexpressing circRANBP17, subcutaneous xenograft experiments in nude mice revealed that tumors with circRANBP17 overexpression were significantly larger than controls (Fig. 4H–J).

Furthermore, qRT-PCR analysis showed lipid-metabolism-related molecular markers were significantly decreased in A549 cells with silenced circRANBP17 (Fig. S3C). Western blot analyses of the de novo lipogenesis and lipid metabolism related proteins also demonstrated circRANBP17’s significant regulatory role in lipid metabolism in A549 and PC9 cell lines (Fig. 4K; Fig. S3D). Downregulation of NADPH and triglyceride levels in LUAD cell lines following knockdown of circRANBP17 was also detected (Fig.S3I, S3J). To establish a causal link between RPL7A and circRANBP17 in LUAD metastasis, rescue experiments showed that silencing circRANBP17 counteracted the pro-migratory effects induced by RPL7A overexpression in A549 cells (Fig. 4L). Moreover, western blots showed that ASO-mediated circRANBP17 knockdown rescues RPL7A-induced perturbations in lipid metabolism (Fig. 4M). This strongly suggests that RPL7A promotes LUAD metastasis through circRANBP17 upregulation, highlighting a novel regulatory axis in LUAD pathogenesis.

Characterization of the novel circular RNA circRANBP17 regulated by RPL7A

Given that circRANBP17 was identified through sequencing as a novel circular RNA regulated by RPL7A and has been shown in previous studies to mediate RPL7A’s biological functions in lung adenocarcinoma, we sought to further elucidate its molecular structure and biological properties.

We designed specific primers targeting its back-splicing junction. The circular DNA fragment obtained was amplified and sequenced using Sanger sequencing, which confirmed alignment with previous NGS results (Fig. 5A). Next, we utilized divergent primers for circRANBP17, alongside both convergent and divergent primers for GAPDH, to amplify cDNA and gDNA from PC9 cells, followed by gel electrophoresis. The divergent primers successfully amplified circRANBP17 from cDNA, while convergent primers did not amplify it from gDNA, contrasting with the amplification of linear mRNA GAPDH (Fig. 5B). This strongly supports the circular structure of circRANBP17.

Fig. 5.

Characterization of the novel circular RNA circRANBP17 regulated by RPL7A. A Sanger sequencing confirmed the back-splicing sequence of circRANBP17. B PCR and northern blot analyses demonstrated that divergent primers detected circRPPH1 in cDNA but not in gDNA, with GAPDH serving as a negative control. C FISH assays were conducted to determine the localization of circRANBP17 in LUAD cell lines. D Nucleoplasmic separation experiments revealed that circRANBP17 is predominantly localized in the cytoplasm of LUAD cells, with GAPDH and U6 used as internal controls for cytoplasmic and nuclear transcripts, respectively. E N6-methyladenosine (m6A) modification sites in circRANBP17 were predicted using the SRAMP database. F m6A levels of circRANBP17 were quantified by MeRIP-qPCR in LUAD cell lines. G Left: qRT-PCR analysis investigated the relationships between circRANBP17 and classic m6A readers and erasers. Right: RNA immunoprecipitation (RIP) assays confirmed the interaction of YTHDC1 with circRANBP17. H MeRIP-qPCR in RPL7A-depleted cells. Data in the text are expressed as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001

We further examined circRANBP17’s subcellular localization using specific probes in fluorescence in situ hybridization (FISH) experiments on PC9 cells, which showed that circRANBP17 is primarily localized in the cytoplasm with a minor nuclear presence (Fig. 5C). Nuclear–cytoplasmic fractionation in A549 and PC9 cells corroborated these findings (Fig. 5D). Considering the role of post-transcriptional modifications, particularly methylation, in regulating non-coding RNA, we assessed whether circRANBP17 undergoes m6A modification. Bioinformatic predictions indicated potential m6A sites on circRANBP17 (Fig. 5E) [19]. RNA immunoprecipitation with an anti-m6A antibody revealed significant enrichment of circRANBP17, confirming it as a target of m6A modification (Fig. 5F). In addition, silencing the m6A reader YTHDC1 resulted in a significant increase in circRANBP17 levels, and RIP experiments with YTHDC1 antibodies confirmed its ability to enrich circRANBP17 molecules (Fig. 5G). Furthermore, MeRIP-qPCR was performed in RPL7A-depleted A549 cells. The results showed that circRANBP17 m6A levels decreased by approximately 50% (Fig. 5H), indicating RPL7A-dependent regulation of circRNA methylation. Taken together, our results confirmed the molecular characteristics of RPL7A downstream of circRANBP17, while its m6A modification potential implies its complex regulatory mechanisms and biological roles.

circRANBP17 mediates RPL7A regulatory functions by modulating SIRT6 mRNA stability

Despite the identification of circRANBP17 as a downstream molecule mediating the tumor-promoting effects of RPL7A in lung adenocarcinoma (LUAD), the precise molecular mechanisms by which circRANBP17 achieves this regulation remain largely unknown. To elucidate how circRANBP17 influences LUAD, we conducted next generation sequencing (NGS) on A549 cells transfected with ASO-scramble and ASO-circRANBP17 (Fig. 6A). The analysis revealed 1367 significantly upregulated and 385 downregulated genes (Log2|FC| > 2, P < 0.05) (Fig. 6B). GO enrichment analysis indicated that circRANBP17 is associated with mRNA precursors and mRNA binding (Fig. 6C). GSEA, on the basis of the Reactome database, suggested a significant correlation between circRANBP17 levels and nonsense-mediated decay (Fig. 6D), as well as a potential link to the AKT pathway (Fig. 6E).

Fig. 6.

circRANBP17 mediates RPL7A regulatory functions by modulating SIRT6 mRNA stability. A Volcano plots illustrating differentially expressed genes (DEGs) identified by transcriptomic RNA-seq in RNAi-circRANBP17 A549 cell lines. B Bar chart depicting the numbers of upregulated and downregulated DEGs. C Gene Ontology (GO) enrichment analysis of DEGs. D Gene set enrichment analysis (GSEA) of genes involved in mRNA metabolism processes, including nonsense-mediated RNA decay (NMD), based on the Reactome database. E Enrichment analysis of DEGs utilizing Reactome datasets. F Venn diagram analyzing the overlap of NMD targets, DEGs, and the GO biological process dataset related to lipid metabolic regulation. G Heatmap displaying the expression levels of five genes implicated in panel F. H qRT-PCR analysis of circRANBP17 expression following RPL7A overexpression in A549 and PC9 cell lines. I Transwell invasion and migration assays were conducted to assess the role of SIRT6 in the metastatic potential of the PC9 cell line. J A mutated beta-globin luciferase reporter plasmid was constructed and transfected into A549 cell lines to evaluate effects of circRANBP17 silencing and overexpression. K Analysis of SIRT6 mRNA stability in LUAD cells with or without circRANBP17 silencing. L Dot plot illustrating a negative correlation between RPL7A and SIRT6 mRNA expression in the TCGA-LUAD dataset. M Triglycerides were significantly increased upon silencing SIRT6 in the PC9 cell line. Data in the text are expressed as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001

To investigate the specific mechanisms by which circRNABP17 regulates lipid metabolism and the nonsense-mediated decay (NMD) pathway, we performed a Venn analysis integrating the GO database’s “regulation of lipid metabolic process” with NMD-predicted target molecules and circRNABP17-related differentially expressed genes (DEGs) (Fig. 6F). This analysis identified five key genes, and their expression levels are illustrated in the accompanying heatmap (Fig. 6G).

Through a thorough literature search, we pinpointed SIRT6 as a critical molecule involved in lipid synthesis. To further explore this relationship, we overexpressed circRNABP17 in LUAD cell lines and confirmed a significant negative correlation between the levels of SIRT6 and circRNABP17 (Fig. 6H). In addition, Transwell migration and invasion assays revealed that the overexpression of SIRT6 markedly inhibited the migratory and invasive capabilities of PC9 LUAD cells (Fig. 6I). Moreover, triglyceride levels was elevated after SIRT6 silencing, which indicated the inhibitory role of SIRT6 in lipogenesis (Fig. 6M). Collectively, these findings suggest that circRNABP17 may play a pivotal role in the regulation of lipid metabolism via its interaction with SIRT6, impacting tumor progression. Moreover, SIRT6 was significantly downregulated in hepatic metastases compared with primary tumors using clinical single-cell transcriptomic analyses (Fig. S3E). Furthermore, to validate circRANBP17’s role in regulating NMD, we constructed a luciferase reporter gene using a known NMD substrate—beta-globin with a PTC mutation point. The results indicated a positive correlation between circRANBP17 and the NMD process (Fig. 6J). mRNA stability experiments demonstrated that silencing circRANBP17 in LUAD cell lines significantly increased SIRT6 mRNA stability (Fig. 6K), suggesting that circRANBP17 reduces SIRT6 mRNA stability.

In addition, correlation analysis of TCGA data revealed a significant negative correlation between RPL7A and SIRT6 mRNA expression in LUAD samples (Fig. 6L). Collectively, these findings suggest that circRANBP17 mediates RPL7A’s regulation of LUAD through the modulation of SIRT6 mRNA stability.

The circRANBP17–UPF1 complex facilitates the degradation of SIRT6 mRNA

To explore the molecular mechanisms by which circRANBP17 regulates SIRT6, we investigated the interactions between these molecules. Smith–Waterman local alignment revealed a reverse complement tendency between circRANBP17 and SIRT6 mRNA, with a homology complementarity ratio exceeding 40% (Fig. 7A). In addition, we analyzed proteins binding to circRANBP17 and SIRT6 mRNA, identifying UPF1, a key player in nonsense-mediated decay (NMD), as an interaction partner (Fig. 7B). Meanwhile, UPF1 level was verified independently of RPL7A and circRANBP17 level (Fig. S3J, S3K), indicating that RPL7A/circRANBP17 regulate SIRT6 stability post-transcriptionally without affecting UPF1 abundance. After validating the efficiency of circRANBP17 complementary probes, we found that SIRT6 mRNA can also be enriched by a circRANBP17 antisense probe (Fig. 7C). An RNA antisense purification assay using a circRANBP17 antisense probe was conducted, followed by silver staining, western blotting, and mass spectrometry (Fig. 7D, E). Mass spectrometry identified the protein constituents, highlighting UPF1 as a significant component (Fig. 7F). Western blot analyses following the antisense purification assay also verified the interaction of UPF1 with circRNABP17 (Fig. 7G).

Fig. 7.

The circRANBP17–UPF1 complex facilitates the degradation of SIRT6 mRNA. A Smith–Waterman local alignment revealed a complementary reverse sequence between circRANBP17 and SIRT6 mRNA. B Venn diagram illustrating the overlap of predicted binding proteins for circRANBP17 and SIRT6. C Validation of circRANBP17 binding to SIRT6 mRNA using antisense probes, followed by RNA antisense purification and qRT-PCR analysis. D, E RNA antisense purification experiments were performed using scrambled and circRANBP17 antisense probes in A549 cell lysates, followed by silver staining, western blotting, and mass spectrometry analysis. F Identification of UPF1 protein via mass spectrometry. G Western blot analysis confirming the interaction between UPF1 and circRANBP17 following RNA antisense purification. H RNA immunoprecipitation (RIP)-qPCR analysis of circRANBP17 in A549 cells using an UPF1 antibody. I, J RIP-qPCR analysis of SIRT6 mRNA in A549 cells, utilizing an UPF1 antibody, complemented by northern blot verification. K, L Rescue experiments conducted post-transfection with negative control (NC), ASO-circRANBP17, and ASO-circRANBP17 + siSIRT6, with Transwell migration and invasion assays, along with CCK8 assays, assessing the migratory and proliferative capacities of LUAD cells. M qRT-PCR analysis of SIRT6 levels across three transfected A549 cell lines (NC, siUPF1, and siUPF1 + ASO-circRANBP17), demonstrating the potentiating effect of circRANBP17 on UPF1’s regulation of SIRT6. N qRT-PCR analysis of classical NMD targets in four transfected A549 cell lines (NC, ASO-circRANBP17, siUPF1, and siUPF1 + ASO-circRANBP17), highlighting the synergistic effect of circRANBP17 on UPF1 and the NMD pathway. O RIP assays using UPF1 antibody in A549 cell lines before and after circRANBP17 knockdown. P Relative mRNA levels of SIRT6 in cells with circRANBP17 overexpression, OE-circRANBP17 + siUPF1, and OE-circRANBP17 + NMDI14 (UPF1 inhibitor). Data in the text are expressed as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Furthermore, RIP experiments confirmed UPF1’s binding to circRANBP17 and demonstrated significant enrichment of SIRT6 mRNA by UPF1 antibodies in A549 cells (Fig. 7H, I). Northern blots of SIRT6 mRNA immunoprecipated in RIP assays are shown in Fig. 7J.

To establish an axis from circRANBP17 to the downstream SIRT6 in LUAD metastasis, rescue experiments showed that silencing SIRT6 counteracted the migratory and proliferation inhibition effects induced by circRANBP17 silencing in PC9 cells (Fig. 7K, L). This strongly suggests that circRANBP17 promotes LUAD metastasis through SIRT6 downregulation.

In addition, to further investigate the regulatory characteristics of circRNABP17 and UPF1 on the NMD degradation of SIRT6 mRNA, we compared the effects of silencing UPF1 alone to the simultaneous silencing of both circRNABP17 and UPF1. The results revealed that the simultaneous silencing of circRNABP17 and UPF1 led to significantly elevated SIRT6 mRNA levels compared with UPF1 silencing alone (Fig. 7M), indicating a potentiated effect of circRNABP17 on UPF1’s function regarding SIRT6 regulation. Moreover, using circRANBP17 overexpression PC9 cells, we found both siRNA-mediated knockdown of UPF1 (a core NMD factor) and targeted inhibition of UPF1’s functional site significantly abrogated circRANBP17’s regulatory effect on SIRT6 (Fig. 7P).

Furthermore, qPCR experiments on classical NMD targets demonstrated the synergistic effect of circRNABP17 on UPF1 and the NMD process (Fig. 7N). Meanwhile, as shown in Fig. 7O, circRANBP17 depletion reduced UPF1–SIRT6 mRNA interaction by 60%, indicating that circRANBP17 facilitates recruitment of SIRT6 mRNA to this complex (Fig. 7O). In summary, our results further reveal that circRANBP17 can bind with UPF1 and SIRT6 mRNA to facilitate the NMD-pathway-mediated degradation of SIRT6 mRNA.

Molecular docking analysis and molecular dynamics simulation of the circRANBP17–UPF1–SIRT6 complex

After establishing that circRANBP17 enhances the degradation of SIRT6 mRNA by UPF1, we undertook a deeper investigation into the stability of this molecular complex through molecular docking and modeling. Initially, we employed AlphaFold to predict the molecular structure of the UPF1 protein (Fig. 8A) and subsequently performed molecular docking and modeling of the circRANBP17–UPF1 protein–SIRT6 mRNA complex (Fig. 8B), followed by molecular dynamics simulations.

Fig. 8.

Molecular docking analysis and molecular dynamics simulation of the circRANBP17–UPF1–SIRT6 complex. A Structural model of the UPF1 protein predicted by AlphaFold. B Binding conformation of the circRANBP17–UPF1 protein–SIRT6 mRNA complex. C, D Free energy landscape (FEL) representations for the UPF1 protein–SIRT6 mRNA complex and the circRANBP17–UPF1–SIRT6 complex. E, F Analysis of hydrogen bond interactions in the UPF1 protein–SIRT6 mRNA complex and the circRANBP17–UPF1–SIRT6 complex. G, H Assessment of root mean square deviation (RMSD) for the UPF1 protein–SIRT6 mRNA complex and the circRANBP17–UPF1–SIRT6 complex. I, J Evaluation of root mean square fluctuation (RMSF) for the UPF1 protein–SIRT6 mRNA complex and the circRANBP17–UPF1–SIRT6 complex

We computed the Gibbs free energy and constructed free energy landscapes (FEL) for both complexes using Gromacs. The results revealed that the circRANBP17–UPF1 protein–SIRT6 mRNA complex exhibits a more concentrated energy cluster compared with the UPF1–SIRT6 mRNA system, indicating enhanced stability (Fig. 8C, D). In addition, hydrogen bond analysis indicated a higher number of hydrogen bonds in the ternary complex (Fig. 8E, F), further supporting its enhanced stability.

To quantitatively assess the stability, we analyzed the root mean square deviation (RMSD), a key metric for binding stability, and found that the RMSD for the circRANBP17–UPF1 protein–SIRT6 mRNA complex was significantly lower than that of the UPF1–SIRT6 mRNA complex (Fig. 8G, H). This reduction in RMSD underscores the enhanced stability of the ternary complex. In addition, root mean square fluctuation (RMSF) analysis, which indicates the flexibility of amino acid residues, revealed lower RMSF values for the circRANBP17–UPF1–SIRT6 mRNA complex (Fig. 8I, J), suggesting reduced fluctuations and greater rigidity within this complex.

Overall, the findings from our molecular docking and dynamics simulations indicate that the interaction between circRANBP17 and the UPF1 protein–SIRT6 mRNA complex significantly enhances structural stability, providing a coherent explanation for the observed facilitation of SIRT6 mRNA degradation by UPF1 in the presence of circRANBP17.

Discussion

In this study, we established that ribosomal protein RPL7A promotes the degradation of SIRT6 through the circRANBP17–UPF1 complex. This discovery uncovers a mechanism for lung adenocarcinoma (LUAD) progression that functions independently of the P53 pathway. Our findings highlight the critical role of RPL7A in shaping oncogenic processes through circular RNAs (circRNAs), revealing that RPL7A is not only involved in protein synthesis but also influences essential regulatory networks that govern tumor behavior. This new insight adds a layer of complexity to our understanding of LUAD biology and suggests that targeting the RPL7A–circRANBP17 axis may offer a promising approach for developing therapeutic strategies against this aggressive cancer.

Prior studies have shown limited evidence that ribosomal proteins can modulate the expression of various non-coding RNAs (ncRNAs), linking them to oncogenic traits. However, our research reveals two significant distinctions. Firstly, we demonstrate RPL7A’s specific regulatory effect on circular RNAs (circRNAs), particularly circRANBP17, enhancing the understanding of ribosomal proteins’ interactions with the unique structures and functions of circRNAs. Secondly, while conventional mechanisms typically involve circRNAs acting as sponges for microRNAs via interactions with Argonaute proteins [20], we unveil that circRANBP17 directly interacts with the UPF1 protein to mediate nonsense-mediated decay (NMD). This alternative pathway not only expands the functional capabilities of circRANBP17 but also suggests a novel mechanism by which ribosomal proteins and circRNAs jointly influence RNA stability and gene expression in LUAD.

Despite the contributions of this study, certain limitations should be acknowledged. A primary challenge is the incomplete understanding of the specific regulatory mechanisms by which RPL7A affects circRANBP17. While we have established a foundational framework, the precise interactions and post-translational modifications involved in this pathway require further investigation. In addition, the multifaceted role of SIRT6 in cancer biology complicates our understanding, as SIRT6 is implicated in DNA repair, metabolism, and inflammation, each contributing differently to tumor progression [21]. Thus, further studies are necessary to clarify how SIRT6 interacts with RPL7A and circRANBP17 within the context of LUAD.

To address these limitations, future research should focus on elucidating the complex molecular mechanisms that govern the RPL7A–circRANBP17–SIRT6 axis. Furthermore, investigating the impact of post-translational modifications on RPL7A may provide deeper insights into its regulatory role. Given SIRT6’s diverse functions, it would also be valuable to explore how RPL7A and circRANBP17 may influence SIRT6’s roles in various cellular contexts, including stress response and metabolic regulation.

Our findings raise several important questions regarding the specificity and broader relevance of the RPL7A–circRANBP17 pathway in lung adenocarcinoma. For instance, it remains to be determined whether this mechanism is unique to LUAD or if it extends to other cancer types. Additional studies are needed to investigate whether RPL7A’s regulation of circRNAs represents a common pathway in oncogenesis. Finally, the potential for developing targeted circRNA-based therapies [21], such as circRNA vaccines, warrants exploration as a novel therapeutic strategy in clinical settings. Addressing these inquiries could not only deepen our understanding of LUAD but also pave the way for innovative cancer treatment approaches.

Conclusions

Our findings underscore RPL7A as a significant prognostic biomarker for LUAD, with its upregulation associated with poor patient outcomes. Further experiments revealed that RPL7A plays a crucial role in the progression of lung adenocarcinoma by enhancing cell migration, invasion, and proliferation, while its downregulation notably inhibited these aggressive behaviors. Moreover, we demonstrated that RPL7A influences the stability of the tumor suppressor gene SIRT6 mRNA through circRANBP17 and UPF1, affecting cellular metabolism and key signaling pathways such as the AKT pathway (Fig. 9). Overall, our study highlights the pivotal role of RPL7A in LUAD progression, suggesting it may serve as a promising diagnostic marker and potential therapeutic target for improving the clinical management of this disease.

Fig. 9.

Schematic representation of the regulatory mechanism by which ribosomal protein RPL7A mediates SIRT6 mRNA degradation through the circRANBP17–UPF1 complex, influencing lung adenocarcinoma progression and lipid metabolism

Abbreviations

ActD

Actinomycin D

ASOs

Antisense oligonucleotides

BP

Biological process

CPTAC

Clinical Proteomic Tumor Analysis Consortium

EMT

Epithelial–mesenchymal transition

FISH

Fluorescence in situ hybridization

GSEA

Gene set enrichment analysis

HR

Hazard ratio

KEGG

Kyoto Encyclopedia of Genes and Genomes

LUAD

Lung adenocarcinoma

m6A

N6-methyladenosine

MeRIP

Methylated RNA immunoprecipitation

NMD

Nonsense-mediated decay

OS

Overall survival

PBS

Phosphate-buffered saline

PI3K

Phosphoinositide 3-kinase

RIP

RNA immunoprecipitation

RMSD

Root mean square deviation

RMSF

Root mean square fluctuation

ROC

Receiver operating characteristic

RPs

Ribosomal proteins

TCGA

The Cancer Genome Atlas

Author contributions

Y.Z. conducted the main experiments. J.H. and Y.P. assisted in data collection. Y.L. and Z.Z. performed data analysis. H.C. and S.Z. prepared the figures and contributed to result interpretation. Z.S., Y.L., and Q.W. supervised the project. Y.Z. and J.H. wrote the main manuscript text. Q.W. and Y.L. provided critical revisions. All authors reviewed and approved the final manuscript.

Funding

This work was supported by a project cosponsored by Key Research and Development Projects of Henan Province in 2023—Key technologies of novel precision immunotherapy for refractory malignant tumors (no. 231111313300), Henan Medical Key Laboratory of Refractory lung cancer (no. [2020]27), Key project of medical science and technology in Henan Province (SBGJ202101009), Leading Talent Cultivation Project of Henan Health Science and Technology Innovation Talents (YXKC2020009), Henan Refractory Lung Cancer Drug Treatment Engineering Technology Research Center (no. [2020]4), Henan Province Young and Middle-aged Health Science and Technology Innovation Leading Talent Project (YXKC2022004), The Provincial and Ministry Co-constructed Key Projects of Henan Medical Science and Technology (SBGJ202401004), and Henan Province Health Young and Middle-Aged Discipline Leader Project (HNSWJW-2022011).

Data availability

All data utilized in this study can be obtained from the corresponding authors upon reasonable request.

Declarations

Ethics approval and consent to participate

Approval for the acquisition of patient tissues and clinicopathological data was granted by the Medical Ethics Committee of the Affiliated Cancer Hospital of Zhengzhou University (review number: 2022-ky-0069–002; approval date: July 2022) in accordance with the ethical principles outlined in the Declaration of Helsinki. The animal experiments were authorized by the Ethics Committee for Animal Experiments at Zhengzhou University (review number: ZZU-LAC2024090322; approval date: September 2024), which operates in compliance with the International Council for Laboratory Animal Science (ICLAS) guidelines. Both committees ensured that all procedures adhered to rigorous ethical standards, safeguarding the rights and welfare of human participants and laboratory animals throughout the study.

Competing interests

The authors declare no competing interests.