Skip to main content
Download PDF

Serum exosomal small nucleolar RNA (snoRNA) signatures as a predictive biomarker for benign and malignant pulmonary nodules

Cancer Cell International volume 24, Article number: 341 (2024) Cite this article

Abstract

Low-dose CT (LDCT) is increasingly recognized as the preferred method for detecting pulmonary nodules. However, distinguishing whether a nodule is benign or malignant often necessitates repeated scans or invasive tissue sampling procedures. Therefore, there is a pressing need for non-invasive techniques to minimize unnecessary interventions. This study aim to investigate the expression profile of exosomal snoRNA in the serum of patients with benign and malignant pulmonary nodules. We identified a total of 278 snoRNAs in serum exosomes, revealing significant differences in snoRNA levels between patients with malignant and benign nodules. Specifically, the upregulated snoRNAs U78 and U37 were validated through qRT-PCR and were found significantly elevated in the serum of patients with malignant pulmonary nodules, positioning them as promising biomarkers for the early detection of lung cancer. This study underscores the potential of serum exosomal U78 and U37 as critical tools for assessing the risk of pulmonary nodules identified through CT screening.

Introduction

Lung cancer ranks among the top tumors worldwide, leading in cancer mortality in both advanced and less developed areas [1, 2]. Often presenting without symptoms and developing slowly, it’s commonly diagnosed at late stages, with non-small cell lung cancer (NSCLC) accounting for 85% of these cases [1,2,3]. Treatments include surgery, radiotherapy, chemotherapy, and immunotherapy, but the outlook for NSCLC patients is bleak, as current treatments don’t cure the disease [1,2,3]. The overall five-year survival rate for lung cancer is 22%, highlighting the critical need for more effective treatments [1, 2, 4]. Stage I NSCLC patients receiving proper treatment have a five-year survival rate between 68% and 92%, but this drops to just 14% for patients with advanced stages of the disease [1, 3,4,5,6]. Early detection via low-dose chest CT scans over the past 30 years has led to decreased incidence and improved survival rates for NSCLC patients [1, 6, 7]. The use of low-dose CT scans has increased the detection of lung nodules in routine exams, yet distinguishing benign from malignant nodules is challenging [1, 6, 7]. Decisions on further action typically rely on medical judgment or repeated low-dose CT scans at intervals to monitor nodule growth, raising concerns over radiation risk and psychological stress for patients [1, 6, 7]. Therefore, identifying new biomarkers to differentiate benign from malignant lung nodules is critically needed to enable more accurate and timely treatment options.

Small nucleolar RNAs (snoRNAs) are a subclass of small non-coding RNAs (ncRNAs) that interact with associated proteins to form small nucleolar ribonucleoproteins [8, 9]. They play a crucial role in the maturation and modification processes of various RNA types, including ribosomal RNAs (rRNAs), messenger RNAs (mRNAs), and small nuclear RNAs [9]. There are two distinct subtypes of snoRNAs: box H/ACA snoRNAs and box C/D snoRNAs, which are distinguished by their structural differences [9]. The former facilitates pseudouridylation catalysis of rRNAs, while the latter orchestrates 2’-O-ribose methylation [9]. Initially classified as housekeeping genes upon their discovery, snoRNAs have emerged as significant contributors to human disease pathogenesis, especially in malignancies [9]. It is worth noting that SNORD88C has been identified as a tumour promoter [10]. It guides 2’-O-methylation of 28 S rRNA to regulate SCD1 translation, thus suppressing autophagy and promoting the growth and metastasis of non-small cell lung cancer cells [10]. Knockdown of either SNORA65 or SNORA7A/B has been shown to inhibit the growth and colony formation of NSCLC cell lines [11]. Moreover, SNORA71A functions as an oncogene by promoting proliferation, migration, and invasion of lung cancer cells through the MAPK/ERK pathway in NSCLC, indicating its potential as a therapeutic target [12]. In addition, SNORD78 affects cancer stem-like cell activity and triggers epithelial-mesenchymal transition (EMT) in A549 cells [13, 14].

Exosomes, which range in diameter from 40 to 150 nm, play a crucial role in cell-to-cell communication by transporting a diverse array of molecules, including nucleic acids, lipids, and proteins [15]. Their unique bilayer membranes provide protection against RNase degradation in bodily fluids, enhancing the stability of their cargo and suggesting their potential as biomarkers for numerous diseases, particularly tumors [16]. Recent studies suggest that snoRNAs may be among the molecules packaged into exosomes [17,18,19,20,21]. Currently, there is insufficient data to determine whether snoRNAs encapsulated in extracellular vesicles could serve as diagnostic biomarkers for lung cancer.

The aim of our research was to characterise snoRNAs in exosomes extracted from serum obtained from age-matched individuals with malignant and benign lung nodules. We identified 278 snoRNAs in serum exosomes and found significant variations in snoRNA abundance between individuals with malignant and benign lung nodules. Our study found a significant increase in U78 and U37 in serum exosomes from malignant lung nodules compared to benign ones. This suggests that they could be valuable biomarkers for early lung cancer detection.

Materials and methods

Data set source

The snoRNAs expression data of lung cancer tissues and adjacent normal tissues were downloaded from the SNORic database (http://bioinfo.life.hust.edu.cn/ SNORic) [22].

Collection of clinical samples

The study was approved by the Ethics Committee of The Fifth People’s Hospital of Wuxi Affiliated to Jiangnan University (2024-016-1). The sample consisted of 73 cases of benign nodules from individuals undergoing annual physical examinations at the Department of Respiratory, The Fifth People’s Hospital of Wuxi Affiliated to Jiangnan University. The Department of Oncology, under the jurisdiction of The Fifth People’s Hospital of Wuxi Affiliated to Jiangnan University, collected 227 cases of stage I lung adenocarcinoma (malignant lung nodules). An equal number of male and female cases were evenly distributed across the discovery and internal validation cohorts to mitigate gender bias. Participants self-reported their gender and provided informed consent voluntarily without compensation. The subjects with benign nodules had stable evaluations during follow-up CT scans spanning 2–5 years at the time of analysis, except for one case in the external validation set. This case was collected before surgery, and a diagnosis of chronic bronchitis was confirmed through histopathological examination. Cases of lung adenocarcinoma were collected before lung resection surgery and verified through pathological diagnosis. Blood samples were collected after fasting using serum separator tubes without anticoagulants. The samples were allowed to clot at room temperature for 1 h, followed by centrifugation at 1000 x g for 10 min at 4 °C to obtain the serum supernatant. The serum aliquots were frozen at -80 °C until exosomes extraction.

Serum exosomes extraction and identification

Serum exosomes were enriched and purified using the exosomes isolation kit (130-111-572, Miltenyi Biotec) and identified by the electron microscopy (TEM) analysis, Nanoparticle tracking analysis (NTA) and western blotting according to the manufacturer’s guidelines and previous report [23]. The CD63 (25682-1-AP) and GM130 (11308-1-AP) antibody used for western blotting were purchase from Proteintech (Wuhan, China).

Exosome small RNA extraction, RNA sequencing and qRT-PCR

Exosome small RNA was extracted and purified using Qiagen miRNeasy Mini Kit (Cat. No. 217004, Qiagen, Hilden, Germany) following the manufacturer’s instructions. For RNA sequencing, the Illumina NovaSeq 6000 System (Illumina, San Diego, CA) were performed to sequence the small RNA libraries following the manufacturer’s instructions. The sequencing data were analysed using the exceRpt pipeline, as implemented in Genboree [23, 24].

For qRT-PCR, Serum exosomes were enriched and purified using the exosomes isolation kit (130-111-572, Miltenyi Biotec) from 200ul serum. Subsequently, the exosome RNA was extracted and purified using Qiagen miRNeasy Mini Kit (Cat. No. 217004, Qiagen, Hilden, Germany). The extracted RNAs were reverse-transcribed into cDNA using the Mix‐X miRNA First Strand Synthesis Kit (TaKaRa Bio, Nojihigashi), following the TB‐Green Premix Ex Taq II Reagent (TaKaRa) according to the manufacturer’s instruction. U6 was used as an endogenous control as previously reports [25,26,27,28,29,30,31,32]. The qRT‐PCR reaction was evaluated by melting curve analysis. Standard curves were used to quantify snoRNA expression and data was normalized to U6. The relative expressions of snoRNAs were evaluated by the absolute quantification of snoRNA after U6 normalization, as described previously [27,28,29,30,31,32]. Each sample was analyzed in duplicate. The qRT‐PCR primers are listed in Table 1.

Table 1 Sequence information of the primers for qRT-PCR
Full size table

Statistics

Subjects’ age was analyzed by unpaired two-sample t-test and one-way ANOVA. QRT-PCR data were analyzed by two-sample t-test and one-way ANOVA followed by Tukey’s post hoc multiple comparisons test. SnoRNAs were considered significantly differentially enriched when p < 0.05. All t-tests or ANOVAs were performed using GraphPad Prism, v8.4.2.

Results

Isolation and identification of serum exosomes

The microvesicles derived from serum of the benign nodules and lung adenocarcinoma patients were characterized by NTA, TEM, and Western blot analysis after isolation. TEM revealed that exosomes had a spherical morphology with an average size distribution of 100 nm (Fig. 1A). NTA further confirmed the average size distribution with 100 nm (Fig. 1B). The enrichment of exosomes was further confirmed by Western blot, which showed the presence of the exosome marker CD63 and the absence of GM130 (a Golgi protein) (Fig. 1C).

Fig. 1
figure 1

Identification of serum exosomes isolated by the exosomes isolation kit. (A) Structural characteristics of exosomes under TEM. (B) Exosome diameter was detected by NTA. (C) Exosome specific protein CD63 and Golgi protein GM130 was detected by Western blotting

Full size image

Identification and differential analysis of snoRNAs in serum exosomes of malignant and benign lung nodules

Exosomes were isolated from the serum of randomly selected 7 patients with malignant lung nodules and 7 patients with benign lung nodules (Table 2). The RNA-seq sequencing data were analyzed using the exceRpt pipeline for the snoRNA expression profile [23, 24]. The expression patterns of exosomal snoRNAs in the serum of malignant and benign lung nodules were found to differ significantly, as confirmed by the heatmap (Fig. 2A). Principal component analysis (PCA) was also used to determine the expression of exosomal snoRNAs in the serum, and showed the snoRNAs in the serum exosomes could distinguish between benign and malignant lung nodules with complete accuracy (Fig. 2B). The volcano plot was used to visualize the differentially expressed exosomal snoRNAs between benign and malignant lung nodules (Fig. 2C). The analysis using exceRpt revealed 255 snoRNAs across the 14 samples. To further screen the biomarkers for the malignant lung nodules, we selected genes with an average count greater than 100, a fold change greater than 2, and a p-value less than 0.05 for further study. We identified 11 snoRNAs that passed this cut-off (Table 3). All 11 snoRNAs were box C/D snoRNAs (Table 2).

Table 2 Characteristics of benign and malignant lung nodules
Full size table
Fig. 2
figure 2

Serum exosomal snoRNA expression profile in benign and malignant lung nodules. (A) The heat map of exosomal snoRNA expression profile in benign (BPN) and malignant lung nodules (T). (B) The PCA analysis of exosomal snoRNA expression profile in benign (BPN) and malignant lung nodules (T). (C) The volcano map of exosomal snoRNA expression profile in benign (BPN) and malignant lung nodules (Tumor)

Full size image
Table 3 The upregulated snoRNA in the exosomes of benign and malignant lung nodules
Full size table

Verification of differentially enriched snoRNAs in serum exosomes of an independent group of malignant and benign lung nodules

To improve the reliability of the sequencing data, we conducted a qRT-PCR assay to identify the 11 significantly overexpressed snoRNAs in 20 patients with malignant lung nodules and 20 patients with benign lung nodules in the training stage (Table 2). As illustrated in Fig. 3A, exosomal U78 and U37 exhibited significant overexpression in NSCLC, while the other 9 evaluated snoRNAs did not show any significant differences (Fig. 3A). To further investigate the variance in expression levels of U78 and U37 in lung cancer, we downloaded the data of snoRNA expression in the lung tumor tissue and adjacent tissue from SNORic database [22]. The analysis indicates a significant increase in U78 and U37 within tumour tissue (Fig. 3B), suggesting that U78 and U37 should be further investigated as potential candidates.

Fig. 3
figure 3

The eleven upregulated exosomal snoRNA expression profile in benign (BPN) and malignant lung nodules (Tumor) (A) and the expression level of U78 and U37 in the lung tumor tissue and adjacent tissue from SNORic database (B). The mean differences between diagnostic groups were analyzed by Student’s t test. ***P < 0.001

Full size image

The validity of the results of the training stage was verified in an independent group of 200 patients with malignant lung nodules and 46 patients with benign lung nodules using the qRT-PCR assay (Table 2). As depicted in Fig. 4A and B, there was a significant upregulation in both U78 and U37 for patients with malignant lung nodules compared to those with benign lung nodules. To identify predictors for malignant lung nodules, Receiver Operating Characteristic (ROC) curves were calculated for U78 and U37. The AUC values for U78 and U37 were 0.9316 and 0.8434, respectively (Fig. 4C). The sensitivity and specificity were 94.93% and 84.78% for U78, and 92.01% and 76.09% for U37. The combined AUC value calculated for both U78 and U37 was 0.9678 with 100% sensitivity and 91.3% specificity (Fig. 4C).

Fig. 4
figure 4

The expression level and ROC curve analysis of exosomal U78 and U37. A-B) The expression level of exosomal U78 and U37. C) The ROC curve analysis for exosomal U78, U37 and the combination of U78 and U37. The mean differences between diagnostic groups were analyzed by Student’s t test. ***P < 0.001

Full size image

Changes of U78 and U37 in the exosomes before and after surgery

To confirm the potential downregulation of U78 and U37 in exosomes after tumour removal, we analysed exosomal snoRNAs from the serum of 40 LUAD patients before and one week after surgery. Our findings showed a significant reduction in both U78 and U37 levels in serum exosomes seven days post-surgery compared to pre-surgery levels (Fig. 5A-B). In addition, we examined the correlation between these two snoRNA levels and disease progression-free survival (PFS). Our analysis revealed a strong association between higher snoRNA levels and shorter disease progression-free survival in patients (Fig. 6A-B). These results suggest that U78 and U37 within exosomes could be promising prognostic biomarkers for LUAD.

Fig. 5
figure 5

The expression level of exosomal U78 and U37 in the serum of lung cancer before and one week after surgery

Full size image
Fig. 6
figure 6

The correlation between the expression level of exosomal U78 (A) and U37 (B) and disease progression-free survival

Full size image

Discussion

Each year, over one million people worldwide receive a diagnosis of pulmonary nodules, a significant proportion of which are malignant [33]. It is crucial to promptly identify the nature of these nodules to select the most appropriate treatment strategies, ensure timely intervention in malignant cases, and avoid unnecessary treatment for benign nodules [33]. Although current methods for detecting pulmonary nodule tissue biopsies are effective, they have limitations such as invasiveness, false negatives, and the risk of tumor spread [33]. Therefore, there is a pressing need for a detection method that is safer, simpler, and more reliable. Recent advancements in testing techniques have uncovered valuable biomarkers that aid in the diagnosis of patients with pulmonary nodules. Research has shown that combining VEGF-C expression in plasma with CT or PET scans can enhance the diagnostic process for pulmonary nodules [33]. Additionally, utilizing a combination of multiple biomarkers such as Neuron-specific enolase (NSE), cytokeratin fragment antigen 21 − 1 (CYFRA21-1), and carcinoembryonic antigen (CEA) can aid in distinguishing between benign and malignant pulmonary nodules [33]. Although these serological markers have been used clinically, their specificity is still not optimal, and their role in early identification of pulmonary nodules is still a subject of debate [33].

Exosomes are the smallest type of extracellular vesicles, ranging from 50 to 100 nm in diameter [15]. They are composed of a phospholipid bilayer and are secreted by various cell types, including cancer cells, into bodily fluids such as blood, plasma, urine, cerebrospinal fluid (CSF), amniotic fluid, and saliva [15, 16]. Exosomal vesicles transport a variety of molecules, such as DNA, mRNA, non-coding RNA, proteins, and lipids [15]. They serve as stable molecular signatures of their cell of origin, making them suitable candidates for cancer biomarkers. Exosomal vesicles play pivotal roles in intracellular communication, cell signaling, immune response modulation, cancer development, and organ-specific metastasis [15]. Exosomal microRNAs and proteins are potential biomarkers for distinguishing between benign and malignant pulmonary nodules [34,35,36]. Recent studies have identified FGB and FGG as potential novel biomarkers through significant differences in plasma exosomal proteomics [36]. Additionally, the serum exosomal miR-21/Let-7a ratio shows promise in distinguishing non-small cell lung cancer from benign pulmonary diseases [34]. However, research on whether small nucleolar RNAs (snoRNAs) in extracellular vesicles can differentiate between benign and malignant pulmonary nodules is limited. This study investigates the variance of snoRNA content in serum exosomes between benign and malignant lung nodules using RNA sequencing and quantitative PCR for the first time. The study found significant differences in the levels of snoRNAs, specifically U78 and U37, in exosomes. This suggests that these snoRNAs could be useful biomarkers for distinguishing between benign and malignant lung nodules.

Numerous studies have demonstrated the association between snoRNA dysregulation and cancer progression, including lung cancer [8, 37]. In this investigation, we observed a significant elevation of U78 and U37 in exosomes present in the serum of patients with malignant pulmonary nodules. To further investigate the variance in expression levels of U78 and U37 between tumor tissue and adjacent tissue, we analyzed the SNORic database. The analysis, shown in Fig. 3B, indicates a significant increase in U78 and U37 within tumour tissue, suggesting their potential importance in the initiation and progression of lung cancer. Di Zheng et al. also confirmed the upregulation of SNORD78 in cancer tissues compared to adjacent normal tissues, highlighting its role as an oncogene that promotes cancer cell proliferation, invasion, and self-renewal [13]. Tumour cells communicate with their microenvironment, including immune cells and fibroblasts, through exosome-mediated RNA secretion [15]. Several studies have revealed small nucleolar RNAs could be secreted via exosomes and direct the 2’-O-methylation in the target cells [8, 18]. Therefore, we hypothesise that U78 and U37 within exosomes may contribute to the progression of malignant pulmonary nodules. However, further investigations are required to validate this hypothesis.

This study has several limitations that require consideration. Firstly, the sample size is relatively small due to various reasons. To confirm whether U78 and U37 in exosomes can serve as biomarkers for distinguishing benign and malignant pulmonary nodules, a larger sample size is necessary for further validation. Secondly, prior investigations have explored the variance between snoRNA in plasma and normal lung cancer [10,11,12,13,14, 27, 31, 38,39,40,41]. Wang et al. reported a significant increase in plasma levels of SNARD42B and SNARD111 in patients with non-small cell lung cancer and early-stage non-small cell cancer [27]. This contradicts the differential expression of snoRNAs in serum exosomes as identified in our study. The authors noted that SNORD42B and SNORD111 are present not only within exosomes but also freely in plasma, indicating diversity in snoRNA distribution within serum or plasma [27]. Further investigation is required to determine whether U78 and U37 exhibit distribution patterns similar to those of SNORD42B and SNORD111.

The study demonstrates that snoRNAs, a major class of ncRNAs, specifically U78 and U37, are enriched in exosomes isolated from serum of malignant lung nodules compared to benign lung nodules. Although, the outcomes of much larger independent validation studies are difficult to predict, the results presented here point, at least, serum exosomal snoRNA U78 and U37 could serve as a predictive biomarker for malignant lung nodules.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  2. Leiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol. 2023;20(9):624–39.

    Article  PubMed  Google Scholar 

  3. Nooreldeen R, Bach H. Current and Future Development in Lung Cancer diagnosis. Int J Mol Sci 2021;22(16).

  4. Yang CY, Lin YT, Lin LJ, Chang YH, Chen HY, Wang YP, Shih JY, Yu CJ, Yang PC. Stage Shift improves Lung Cancer Survival: real-world evidence. J Thorac Oncol. 2023;18(1):47–56.

    Article  PubMed  Google Scholar 

  5. Luo YH, Luo L, Wampfler JA, Wang Y, Liu D, Chen YM, Adjei AA, Midthun DE, Yang P. 5-year overall survival in patients with lung cancer eligible or ineligible for screening according to US Preventive Services Task Force criteria: a prospective, observational cohort study. Lancet Oncol. 2019;20(8):1098–108.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Balata H, Quaife SL, Craig C, Ryan DJ, Bradley P, Crosbie PAJ, Murray RL, Evison M. Early diagnosis and Lung Cancer Screening. Clin Oncol-Uk. 2022;34(11):708–15.

    Article  CAS  Google Scholar 

  7. Jonas DE, Reuland DS, Reddy SM, Nagle M, Clark SD, Weber RP, Enyioha C, Malo TL, Brenner AT, Armstrong C, et al. Screening for Lung Cancer with Low-Dose computed Tomography updated evidence report and systematic review for the US Preventive Services Task Force. Jama-J Am Med Assoc. 2021;325(10):971–87.

    Article  Google Scholar 

  8. Huang ZH, Du YP, Wen JT, Lu BF, Zhao Y. snoRNAs: functions and mechanisms in biological processes, and roles in tumor pathophysiology. Cell Death Discov. 2022;8(1):259.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kiss T. Small nucleolar RNAs: an abundant group of noncoding RNAs with diverse cellular functions. Cell. 2002;109(2):145–8.

    Article  CAS  PubMed  Google Scholar 

  10. Wang K, Wang S, Zhang Y, Xie L, Song X, Song X. SNORD88C guided 2’-O-methylation of 28S rRNA regulates SCD1 translation to inhibit autophagy and promote growth and metastasis in non-small cell lung cancer. Cell Death Differ. 2023;30(2):341–55.

    Article  CAS  PubMed  Google Scholar 

  11. Cui C, Liu Y, Gerloff D, Rohde C, Pauli C, Kohn M, Misiak D, Oellerich T, Schwartz S, Schmidt LH, et al. NOP10 predicts lung cancer prognosis and its associated small nucleolar RNAs drive proliferation and migration. Oncogene. 2021;40(5):909–21.

    Article  CAS  PubMed  Google Scholar 

  12. Tang G, Zeng Z, Sun W, Li S, You C, Tang F, Peng S, Ma S, Luo Y, Xu J, et al. Small nucleolar RNA 71A promotes Lung Cancer Cell Proliferation, Migration and Invasion via MAPK/ERK Pathway. J Cancer. 2019;10(10):2261–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zheng D, Zhang J, Ni J, Luo J, Wang J, Tang L, Zhang L, Wang L, Xu J, Su B, et al. Small nucleolar RNA 78 promotes the tumorigenesis in non-small cell lung cancer. J Exp Clin Cancer Res. 2015;34(1):49.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Su J, Liao J, Gao L, Shen J, Guarnera MA, Zhan M, Fang H, Stass SA, Jiang F. Analysis of small nucleolar RNAs in sputum for lung cancer diagnosis. Oncotarget. 2016;7(5):5131–42.

    Article  PubMed  Google Scholar 

  15. Kalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. Science 2020;367(6478).

  16. Wang X, Tian L, Lu J, Ng IO. Exosomes and cancer - diagnostic and prognostic biomarkers and therapeutic vehicle. Oncogenesis. 2022;11(1):54.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Rai AK, Rajan KS, Bisserier M, Brojakowska A, Sebastian A, Evans AC, Coleman MA, Mills PJ, Arakelyan A, Uchida S, et al. Spaceflight-Associated Changes of snoRNAs in Peripheral Blood mononuclear cells and plasma Exosomes-A pilot study. Front Cardiovasc Med. 2022;9:886689.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Rimer JM, Lee J, Holley CL, Crowder RJ, Chen DL, Hanson PI, Ory DS, Schaffer JE. Long-range function of secreted small nucleolar RNAs that direct 2’-O-methylation. J Biol Chem. 2018;293(34):13284–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang K, Chen J, Li C, Yuan Y, Fang S, Liu W, Qian Y, Ma J, Chang L, Chen F, et al. Exosome-mediated transfer of SNHG7 enhances docetaxel resistance in lung adenocarcinoma. Cancer Lett. 2022;526:142–54.

    Article  CAS  PubMed  Google Scholar 

  20. Kitagawa T, Taniuchi K, Tsuboi M, Sakaguchi M, Kohsaki T, Okabayashi T, Saibara T. Circulating pancreatic cancer exosomal RNAs for detection of pancreatic cancer. Mol Oncol. 2019;13(2):212–27.

    Article  CAS  PubMed  Google Scholar 

  21. Li S, Qi Y, Huang Y, Guo Y, Huang T, Jia L. Exosome-derived SNHG16 sponging miR-4500 activates HUVEC angiogenesis by targeting GALNT1 via PI3K/Akt/mTOR pathway in hepatocellular carcinoma. J Physiol Biochem. 2021;77(4):667–82.

    Article  CAS  PubMed  Google Scholar 

  22. Gong J, Li Y, Liu CJ, Xiang Y, Li C, Ye Y, Zhang Z, Hawke DH, Park PK, Diao L, et al. A pan-cancer analysis of the expression and clinical relevance of small nucleolar RNAs in Human Cancer. Cell Rep. 2017;21(7):1968–81.

    Article  CAS  PubMed  Google Scholar 

  23. Murillo OD, Thistlethwaite W, Rozowsky J, Subramanian SL, Lucero R, Shah N, Jackson AR, Srinivasan S, Chung A, Laurent CD, et al. exRNA Atlas Analysis reveals distinct extracellular RNA Cargo types and their carriers present across human biofluids. Cell. 2019;177(2):463–77. e415.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Rozowsky J, Kitchen RR, Park JJ, Galeev TR, Diao J, Warrell J, Thistlethwaite W, Subramanian SL, Milosavljevic A, Gerstein M. exceRpt: a Comprehensive Analytic platform for extracellular RNA profiling. Cell Syst. 2019;8(4):352–e357353.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lv CY, Ding WJ, Wang YL, Zhao ZY, Li JH, Chen Y, Lv J. A PEG-based method for the isolation of urinary exosomes and its application in renal fibrosis diagnostics using cargo miR-29c and miR-21 analysis. Int Urol Nephrol. 2018;50(5):973–82.

    Article  CAS  PubMed  Google Scholar 

  26. Crossland RE, Norden J, Bibby LA, Davis J, Dickinson AM. Evaluation of optimal extracellular vesicle small RNA isolation and qRT-PCR normalisation for serum and urine. J Immunol Methods. 2016;429:39–49.

    Article  CAS  PubMed  Google Scholar 

  27. Wang K, Song X, Wang S, Li X, Zhang Z, Xie L, Song X. Plasma SNORD42B and SNORD111 as potential biomarkers for early diagnosis of non-small cell lung cancer. J Clin Lab Anal. 2022;36(11):e24740.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Crossland RE, Norden J, Bibby L, Davis J, Dickinson AM. Validation of isolation methodology and endogenous control selection for qRT-PCR Assessment of Microrna expression in serum and urine exosomes. Blood 2014;124(21).

  29. Li X, Zhao X, Xie L, Song XG, Song XR. Identification of four snoRNAs (SNORD16, SNORA73B, SCARNA4, and SNORD49B) as novel non-invasive biomarkers for diagnosis of breast cancer. Cancer Cell Int 2024;24(1).

  30. Steinbusch MMF, Fang YX, Milner PI, Clegg PD, Young DA, Welting TJM, Peffers MJ. Serum snoRNAs as biomarkers for joint ageing and post traumatic osteoarthritis. Sci Rep-Uk 2017, 7.

  31. Liao J, Yu L, Mei Y, Guarnera M, Shen J, Li R, Liu Z, Jiang F. Small nucleolar RNA signatures as biomarkers for non-small-cell lung cancer. Mol Cancer. 2010;9:198.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Peffers MJ, Chabronova A, Balaskas P, Fang YX, Dyer P, Cremers A, Emans PJ, Feczko PZ, Caron MM, Welting TJM. SnoRNA signatures in cartilage ageing and osteoarthritis. Sci Rep-Uk 2020, 10(1).

  33. Redberg RF, O’Malley PG. Important questions about Lung Cancer Screening Programs when Incidental findings Exceed Lung Cancer nodules by 40 to 1. JAMA Intern Med. 2017;177(3):311–2.

    Article  PubMed  Google Scholar 

  34. Yang G, Wang T, Qu X, Chen S, Han Z, Chen S, Chen M, Lin J, Yu S, Gao L, et al. Exosomal miR-21/Let-7a ratio distinguishes non-small cell lung cancer from benign pulmonary diseases. Asia Pac J Clin Oncol. 2020;16(4):280–6.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kuang M, Tao X, Peng Y, Zhang W, Pan Y, Cheng L, Yuan C, Zhao Y, Mao H, Zhuge L, et al. Proteomic analysis of plasma exosomes to differentiate malignant from benign pulmonary nodules. Clin Proteom. 2019;16:5.

    Article  Google Scholar 

  36. Kuang M, Peng Y, Tao X, Zhou Z, Mao H, Zhuge L, Sun Y, Zhang H. FGB and FGG derived from plasma exosomes as potential biomarkers to distinguish benign from malignant pulmonary nodules. Clin Exp Med. 2019;19(4):557–64.

    Article  CAS  PubMed  Google Scholar 

  37. Mourksi NE, Morin C, Fenouil T, Diaz JJ, Marcel V. snoRNAs offer Novel Insight and Promising perspectives for Lung Cancer understanding and management. Cells 2020;9(3).

  38. Zhou H, Yao Y, Li Y, Guo N, Zhang H, Wang Z, Chen Y, Dai G. Identification of Small Nucleolar RNA SNORD60 as a Potential Biomarker and Its Clinical Significance in Lung Adenocarcinoma. Biomed Res Int 2022;2022:5501171.

  39. Dong X, Song X, Ding S, Yu M, Shang X, Wang K, Chang M, Xie L, Song X. Tumor-educated platelet SNORD55 as a potential biomarker for the early diagnosis of non-small cell lung cancer. Thorac Cancer. 2021;12(5):659–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gao L, Ma J, Mannoor K, Guarnera MA, Shetty A, Zhan M, Xing L, Stass SA, Jiang F. Genome-wide small nucleolar RNA expression analysis of lung cancer by next-generation deep sequencing. Int J Cancer. 2015;136(6):E623–629.

    Article  CAS  PubMed  Google Scholar 

  41. Mannoor K, Shen J, Liao J, Liu Z, Jiang F. Small nucleolar RNA signatures of lung tumor-initiating cells. Mol Cancer. 2014;13:104.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by grants from General Project of Wuxi Municipal Health Commission (No. M202009) and Youbo Mass Entrepreneurship and Innovation doctoral innovation project (YB202104).

Author information

Author notes

  1. Fei Cao and You contributed equally to this work.

Authors and Affiliations

  1. Department of Oncology, The Fifth People’s Hospital of Wuxi Affiliated to Jiangnan University, Wuxi, China

    Fei Cao

  2. Department of Respiratory, The Fifth People’s Hospital of Wuxi Affiliated to Jiangnan University, Wuxi, China

    Qian You & Feng Zhu

  3. Department of Respiratory, Wuxi 9th Affiliated Hospital of Soochow University, Wuxi, China

    Yu Zhang

Authors
  1. Fei Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qian You
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YZ and FZ designed the experiments. FC and QY performed the experiments and analyzed the results. YZ and FZ wrote the manuscript.

Corresponding authors

Correspondence to Feng Zhu or Yu Zhang.

Ethics declarations

Ethics approval and consent to participate

The studies involving animal experiments were reviewed and approved by the Ethics Committee of Soochow University.

Declaration of AI-assisted technologies in the writing process

During the preparation of this work the authors used chatGPT in order to improve language and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, F., You, Q., Zhu, F. et al. Serum exosomal small nucleolar RNA (snoRNA) signatures as a predictive biomarker for benign and malignant pulmonary nodules. Cancer Cell Int 24, 341 (2024). https://doi.org/10.1186/s12935-024-03522-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12935-024-03522-y

Keywords

Cancer Cell International

ISSN: 1475-2867

Contact us