Skip to main content

Small nucleolar RNA signatures as biomarkers for non-small-cell lung cancer



Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death. Early detection of NSCLC will improve its outcome. The current techniques for NSCLC early detection are either invasive or have low accuracy. Molecular analyses of clinical specimens present promising diagnostic approaches. Non-coding RNAs (ncRNAs) play an important role in tumorigenesis and could be developed as biomarkers for cancer. Here we aimed to develop small nucleolar RNAs (snoRNAs), a common class of ncRNAs, as biomarkers for NSCLC early detection. The study comprised three phases: (1) profiling snoRNA signatures in 22 NSCLC tissues and matched noncancerous lung tissues by GeneChip Array, (2) validating expressions of the signatures by RT-qPCR in the tissues, and (3) evaluating plasma expressions of the snoRNAs in 37 NSCLC patients, 26 patients with chronic obstructive pulmonary disease (COPD), and 22 healthy subjects.


In the surgical tissues, six snoRNAs were identified, which were overexpressed in all tumour tissues compared with their normal counterparts. The overexpressions of the genes in tumors were confirmed by RT-qPCR. The snoRNAs were stably present and reliably detectable in plasma. Of the six genes, three (SNORD33, SNORD66 and SNORD76) displayed higher plasma expressions in NSCLC patients compared with the cancer-free individuals (All < 0.01). The use of the three genes produced 81.1% sensitivity and 95.8% specificity in distinguishing NSCLC patients from both normal and COPD subjects. The plasma snoRNA expressions were not associated with stages and histological types of NSCLC (All > 0.05).


The identified snoRNAs provide potential markers for NSCLC early detection.


Non-small-cell lung cancer (NSCLC) is the number one cancer killer in the USA and worldwide [1]. The overall 5-year survival rate for stage I NSCLC patients who are typically treated with surgery remain up to 83%. In contrast, only 5-15% and less than 2% of patients with stage III and IV NSCLC are alive after five years [1]. These statistics provide the primary rationale to improve NSCLC early detection. Chest X-ray and plasma cytology have been used for detection of early NSCLC [2, 3]. However, the sensitivity was low [2, 3]. Although the bronchoscopy excels at detecting centrally occurring lung tumor, it is invasive. CT provides excellent anatomic information and can noninvasively detect lung cancer at small size; however, the improved sensitivity is associated with over-diagnosis [2, 3]. Furthermore, it is still unclear whether CT screening can ultimately reduce lung cancer mortality [4]. Therefore, the development of noninvasive approaches that can reliably detect early stage NSCLC is clinically important.

Non-coding RNAs (ncRNAs) are functional transcripts that do not code for proteins, however, play a major role in regulating almost every level of gene expression [5]. In addition to highly abundant and functionally important transfer and ribosomal RNAs, ncRNAs include other RNAs such as small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), short interfering RNAs (siRNAs), piwi-associated RNAs, small Cajal body-specific RNAs (scaRNAs), snRNAs (small nuclear RNAs), and long ncRNAs that are still partially understood [58]. Of the small ncRNAs, miRNAs and siRNAs have extensively been studied in carcinogenesis [58]. Differential expressions of miRNAs in lung cancer and their potential diagnostic values have been intensively evaluated [9, 10]. For instance, abnormal expressions of some miRNAs measured in lung tumor tissues were prognostic factors for overall survival of the patients [1113]. Furthermore, serum miRNA signatures were identified that can be used to predict outcome of the disease [14]. In addition, we demonstrated that examining altered miRNA expressions in sputum could improve early detection of lung cancer [15, 16]. Recently, new and unexpected functions of other types of small ncRNAs have been discovered, revealing that the molecules have highly diverse roles and are actively involved in the processes of carcinogenesis than previously thought [5]. Therefore, investigation of dysregulation of the small ncRNAs in the development and progression of lung tumorigenesis, and their diagnostic values is necessary.

SnoRNAs represent one of the largest groups of functionally diverse trans-acting ncRNAs currently known in mammalian cells [17]. NcRNAs range between 60-150 nucleotides in length [8, 1719]. From a structural basis, snoRNAs fall into two categories termed box C/D snoRNAs (SNORDs) and box H/ACA snoRNAs (SNORAs) [1719]. SNORDs serve as guides for the 2'-O-ribose methylation of rRNAs or snRNAs, whereas SNORAs are guides for the isomerization of uridine residues into pseudouridine [20, 21]. Accumulated evidence suggests that snoRNAs can target other RNAs including snRNAs and possibly messenger RNAs [19]. Furthermore, a link between snoRNA and carcinogenesis was first reported by Chang et al, who found that snoRNA h5sn2 was highly expressed in normal brain, but its expression was dramatically reduced in meningioma, suggesting a role for the loss of snoRNA h5sn2 in brain tumorigenesis [21]. Recently, a homozygous 2 bp (TT) deletion in snoRNA U50 was discovered in prostate cancer cell lines and localized prostate tumor tissues, while heterozygous genotype of the deletion occurred more frequently in women with breast cancer [22, 23]. Although studies are just emerging, snoRNAs may play malfunction in the development and progression of human malignancy. In this report, we first profiled snoRNA expression signatures of lung cancer tissues and then found that the identified snoRNAs were significantly upregulated in tumor tissues and plasma of NSCLC patients. The snoRNAs might provide potential markers for early detection of NSCLC.


Identifying snoRNA signatures whose aberrant expression levels were associated with NSCLC

To define and validate snoRNA signatures whose altered expressions were associated with early stage NSCLC, we obtained surgical specimens from 22 stage I NSCLC patients who had either a lobectomy or a pneumonectomy. The cases consisted of 11 patients with squamous cell carcinoma (SCC) and 11 patients with adenocarcinoma (AC) (Table 1). The GeneChipR Arrays comprised probe sets for human snoRNAs, scaRNAs and miRNA coverage of human, mouse, rat, canine, and rhesus macaque was performed on the clinical specimens. We only analyzed and compared expressions of human mature 352 snoRNAs in the tumor and noncancerous tissue specimens. When P value < 0.01 was used as a cutoff, of the snoRNAs analyzed, 30 were overexpressed and one were underexpressed with ≥ 1.0 fold-change in lung NSCLC tissues compared with the corresponding noncancerous lung tissues (P < 0. 01) (Fig. 1 and Additional Table 1). Using a predefined criterion of a change ≥1.5-fold, we identified six snoRNAs that were statistically differently expressed between the paired tumor and noncancerous samples (all P < 0.01). These included SNORD33, SNORD66, SNORD73B, SNORD76, SNORD78, and SNORA42 (Additional Table 1). Furthermore, there was no statistical difference of expressions of the six genes between AC and SCC of the lungs, suggesting that the snoRNAs were shared in the two major histological types of NSCLC. More importantly, the six snoRNAs were overexpressed in all 22 NSCLC tissues compared with the paired noncancerous specimens, thus were proceed to the next phase of the study.

Figure 1
figure 1

SnoRNAs differentially expressed in 22 stage I non-small-cell lung cancer (NSCLC) tissues versus normal lung tissues. Hierarchical clustering of 31 snoRNA genes with a significantly different expression (p < 0.01) in tumour tissues. Rows represent individual genes; columns represent individual tissue samples. The scale stands for the intensity of gene expression (log2 scale ranges between -3.5 and 3.5).

Table 1 Characteristics of 22 NSCLC patients

Validating expressions of the six snoRNAs on surgical tissues by RT-qPCR

To determine whether the data derived from the microarray platform could be confirmed by different technique, the expressions of the six snoRNAs were assessed by using RT-qPCR assay in the same 22 NSCLC tissues and paired noncancerous lung specimens. More than 1.5-fold overexpression of the six snoRNAs was found in all NSCLC cases, respectively, as compared with that in the corresponding noncancerous tissues (All P < 0.0001). Pearson test showed that each gene's the relative expression level by RT-qPCR and log 2-transformed signal intensity value by microarray significantly correlated (Additional Table 2). The observations suggested that the identified snoRNAs could be confirmed by a different technique, and thus the differential expressions of the identified genes in the surgical specimens were based on accurate quantification. Furthermore, no significant expression difference of the six snoRNAs by RT-qPCR (P > 0.05) was observed for the stage I tumor samples with different histological types. The observation provides further evidence that the elevated snoRNA expressions in cancer tissues are not histologically specific changes.

Quantifying snoRNA expression in plasma by RT-qPCR assay

Although previous reports have documented presence of detectable quantifies of miRNA in plasma, whether snoRNAs stably exist in the cell free body fluid remain unknown. To determine if the snoRNAs were present in plasma, we first prepared two RNA pools containing equal amounts of RNA from plasma of five cancer patients and five healthy individuals, respectively. Expression of each snoRNA was then measured by RT-qPCR in the pooled RNA samples. All tested snoRNAs had ≤ 32 Ct values in both pools, indicating that the snoRNAs existed in plasma. To determine if the snoRNAs were stably and reliably detected in plasma, we first assessed the stability of endogenous ncRNAs in plasma, because it contains high levels of RNase activity. Plasma obtained from three healthy subjects was split into two parts, respectively. One part of each sample was treated with Ribonuclease A at final concentration of 10 μg/mL, whereas the second part was not added with Ribonuclease A. Expressions of the six snoRNAs and a miRNA, miR-21, were measured by using RT-qPCR in parallel. The abundance of all snoRNAs and the miR-21 in the plasma samples with the different treatments was fairly equable (P > 0.05) (Additional Fig. 1), demonstrating that the plasma snoRNAs were resistant to RNase digestion. To further verify the stability of the plasma snoRNAs, plasma was collected from another three healthy individuals. Each sample was divided into 4 parts. The first aliquot from each specimen was processed immediately for isolating RNA, while others were stored in -80°C and processed for RNA isolation on day 1, 7 and 30. Expressions of the snoRNAs were measured at the same time in these specimens that were processed from the different time points. Expression level of miR-21 was also simultaneously assayed on the specimens. Each of the snoRNAs and miR-21 displayed equal expression levels between the samples, respectively (Additional Fig. 2). Therefore, like miRNA, snoRNAs are present in a stable form and consistently measurable in archived plasma samples.

To determine specificity of snoRNA quantification by RT-qPCR assay in plasma, SNORD76 and SNORD78 that are located in the same chromosomal region (1q25.1), were synthetically generated (Integrated DNA Technologies, Inc, Coralville, IA). Each one subjected to two independent RT-qPCR reactions, where in each reaction there were present PCR primers specific to only one of the two genes. Amplification only of the appropriate gene matching the specific primer was observed, indicating that RT-qPCR assay could detect snoRNA with high specificity. Furthermore, given that the average size of snoRNAs (70 to 90 nt) was longer than that of miRNAs (22 nt), we evaluated whether the RT-qPCR assay would only detect snoRNA but not DNA sequences. RNA preparation extracted from two plasma samples was divided into two identical portions, which were then treated with or without DNase I Reaction Buffer, respectively. The expression level of the snoRNAs in each of the two groups was quantified by RT-qPCR. There was no difference in the gene expression levels between RNA extracted from the samples treated with DNase I and RNA from the samples without treatment (P < 0.05). The result suggests that the snoRNAs could be specifically detected without contaminating genomic DNA in RNA preparations.

To determine the sensitivity of detecting snoRNA by RT-qPCR assay in plasma, the total RNA was isolated from plasma of three healthy subjects and then diluted in diethyldicarbonate water by ten orders of magnitude, respectively. The serially diluted RNAs served as experimental samples for measuring expression of the snoRNAs. The results showed excellent linearity between the RNA input and the Ct values for RT-qPCR. Furthermore, the assay had a dynamic range of at least six orders of magnitude (R2 = 0.997), and was capable of detecting al least 100 copies of the target snoRNA genes. Altogether, the snoRNAs could be accurately and reliably measured in blood plasma by the RT-qPCR platform.

Evaluating plasma expressions of the six snoRNAs in NSCLC patients, COPD patients, and healthy individuals

RT-qPCR assay was successfully performed in all plasma samples of 37 NSCLC patients and cancer-free individuals including 26 patients with chronic obstructive pulmonary disease (COPD) and 22 healthy subjects (Table 2, Fig. 2). Of the six genes, SNORA42, SNORD73B, and SNORD78 showed measurable plasma expressions, which, however, did not significantly differ between NSCLC patients, COPD patients, and healthy individuals (Table 3, Fig. 2). In contrast, SNORD33, SNORD66, and SNORD76 exhibited significantly higher expressions in NSCLC patients as compared with healthy controls, suggesting that the elevated expressions of the three genes in plasma might be cancer-associated changes (All P < 0.01). Among the three cancer-associated genes, SNORD66 was also considerably overexpressed in plasma of COPD patients as compared with healthy controls (P < 0.01), implying that the cancer-associated gene whose changes were also related to COPD. However, when plasma expression of SNORD66 was compared between NSCLC and COPD groups, it was significantly higher in the NSCLC patients as compared with the COPD patients (P < 0.01) (Table 3, Fig. 2). Therefore, although displaying higher plasma level in COPD patients as compared with healthy subjects, SNORD66 had lower plasma expression level in COPD patients as compared with NSCLC patients. On the contrary, SNORD33 and SNORD76 showed significantly higher expressions in plasma of the NSCLC group as compared with COPD group (All P < 0.01), whereas there was no statistical difference regarding expressions of the two genes between COPD and healthy groups (All P > 0.05; Table 3, Fig. 2). The finding that the increased plasma expressions of SNORD33 and SNORD76 solely occurred in NSCLC patients suggest that these two genes whose upregulations could be cancer-specific changes. Taken together, the three snoRNAs might provide potential biomarkers in distinguishing NSCLC patients from both healthy subjects and COPD patients. Therefore, the three genes, SNORD33, SNORD66, and SNORD76, proceed to the next step of the study.

Table 2 Characteristics of a cohort of NSCLC patients, healthy individuals, and COPD patients
Table 3 Plasma expression levels of snoRNAs
Figure 2
figure 2

Plasma expression levels of the six snoRNAs in 22 healthy controls, 26 patients with chronic obstructive pulmonary disease (COPD), and 37 patients with non-small-cell lung cancer (NSCLC). Horizontal lines denote mean values. *, statistical significance (< 0.01) of expression levels of snoRNA between NSCLC patients and healthy controls. , statistical significance (< 0.01) of expression levels of snoRNA between COPD patients and healthy controls. , statistical significance (< 0.01) of expression levels of snoRNA between NSCLC patients and COPD patients.

Evaluating diagnostic efficiency of the snoRNAs in plasma for NSCLC

Receiver-operator characteristic (ROC) analyses were applied to evaluate the capability of using the three snoRNAs to discriminate NSCLC patients from healthy individuals and COPD patients. As shown in Table 4, the individual snoRNAs exhibited area under the receiver-operator characteristic (AUC) values of 0.8064-0.8233 in distinguishing NSCLC patients from healthy subjects. When optimum cutoffs were selected at 0.2262, 0.9893, and 133.6453, the snoRNAs yielded 72.65-75.36% sensitivity and 85.02-87.66% specificity. Furthermore, the individual snoRNAs exhibited 0.7903-0.8186 AUCs in distinguishing NSCLC patients from COPD patients (Table 4). When optimal cutoffs were chosen at 0.2278, 0.9902, and 136.1379, the three genes produced 71.46.3-73.98% sensitivity and 84.91-87.58% specificity in indentifying NSCLC patients from COPD patients (Table 4).

Table 4 Capability of the three snoRNA genes to discriminate NSCLC patients from healthy controls and COPD patients*

We then evaluated the combination of the three snoRNAs for identification of NSCLC. The three snoRNAs in combination produced 0.8935 AUC, being considerably higher than those of each individual gene (All P < 0.05) in identification of NSCLC group from healthy group (Table 4). Accordingly, given a specificity of 95.5%, the composite panel of the snoRNAs revealed a sensitivity of 83.8% in detection of NSCLC. The parameters were significantly higher compared with those of an individual snoRNAs (all p < 0.05) in distinguishing cancer patients from normal controls. Furthermore, the three genes used in combination created 0.8827 AUC in distinguishing cancer patients from COPD individuals, yielding 81.1% sensitivity and 96.2% specificity in detection of NSCLC patients. In addition, when the optimal cutoff for each gene that was selected to discriminate NSCLC group from COPD group was used to identify cancer patients from healthy controls, ROC analyses showed that the three genes in combination revealed 83.8% sensitivity and 96.2% specificity. The results, therefore, documented that the combined analysis of the three genes in a panel had a reasonable power to differentiate NSCLC patients from both COPD patients and healthy controls.

Spearman rank correlation analysis indicated that the estimated correlations among expression levels of the three snoRNAs in plasma were low (all R2 < 0.50, P > 0.05). The data suggested that expressions of the snoRNAs were complementary to each other, and further supported that the combined analyses of the genes outperformed a single one used alone. Therefore, the panel of the snoRNAs provides a reasonable power for the early detection of NSCLC in plasma.

Moreover, the composite use of the three snoRNAs had no statistical differences of sensitivity and specificity between different stages of NSCLC (P > 0.05) (Additional Tables 3). The finding that altered expressions of the snoRNAs are found not only in advanced stage, but also in early stage NSCLC might be an important characteristic if they are to be employed for early detection. Additionally, there were no significant differences of sensitivity and specificity for the panel of snoRNAs in discriminating lung SCC and AC patients from healthy controls (P > 0.05) (Additional Tables 3). Therefore, the combined analysis of the three genes in plasma had equal diagnostic efficiency for the two major histological types of NSCLC. There was no association of the changes of the three genes with the age, gender, ethnic, and smoking packer-year of the participants (All p > 0.05) (Additional Tables 4).


In the present study, we profiled snoRNA expression signatures of early stage NSCLC by performing microarray analysis on surgical tissues. Aberrant expressions of the identified signatures were well confirmed by RT-qPCR assay. To the best of our knowledge, this is the first study to globally analyze snoRNA expression patterns in human tumor tissues. Furthermore, we demonstrated that like miRNAs, snoRNAs remained intact and were readily detectable in plasma by using RT-qPCR. More importantly, based on the discoveries, we developed a panel of plasma-based snoRNAs as potential biomarkers for early stage NSCLC. Our data might provide compelling evidence that dysregulations of the snoRNAs could play an important role in lung tumorigenesis, and measuring plasma snoRNAs might serves as a potential noninvasive approach to improve diagnosis of NSCLC.

Although rarely being reported, malfunction of some snoRNAs have recently been considered to contribute to carcinogenesis. For instance, adeno-associated viruses integrated their genome into mouse genome, causing liver cancer [24]. Interestingly, the integration sites identified in the tumors were all located within a DNA interval encoding snoRNAs [24]. Furthermore, the accumulation of gas5-generated snoRNAs was associated with an arrest of cell growth [25], and dysregulations of the snoRNAs were related to growth arrest of breast cancer cells [26, 27]. In addition, although snoRNAs and miRNAs are generated by different cellular pathways and function in different cellular compartments, some members of these two types of ncRNAs display numerous genomic similarities [2830]. Indeed, a number of human snoRNAs with miRNA-like processing signatures were recently identified [31]. The findings were consistent with those in another report [32], in which, a set of miRNAs display functional snoRNA characteristics, and the miRNAs might evolve from snoRNAs [32]. Therefore, some small ncRNAs in human cells that originate from snoRNAs were proposed to function like miRNAs [32]. Moreover, snoRNAs could play a role in posttranscriptional gene silencing. For example, HBII-52, a human SNORD gene, can regulate splicing of serotonin receptor 2C messenger RNA [33]. Like miRNAs, some snoRNAs are located at a chromosomal breakpoint involved in human carcinogenesis. For example, U50 snoRNA was originally discovered from the breakpoint of chromosomal translocation t (3,6) (q27;q15), which was involved in human B-cell lymphoma [34]. It have been suggested that the genes that are frequently located at chromosomal genomic amplification regions might have oncogenic function involved in the promotion of cancer [3537]. Notably, all the snoRNAs identified in the present study displayed up-regulation in lung tumor specimens. Interestingly, the snoRNAs are located in commonly frequent genomic amplified regions in lung cancer [38, 39]. SNORD33 is located in chromosome 19q13.3 that contain potential oncogenes in lung cancer [36, 37], while SNORD66 and SNORD76 are situated in chromosomal regions 3q27.1 and 1q25.1, respectively. 3q27.1 and 1q25.1 are two of the most frequently amplified chromosomal segments in solid tumors, particularly NSCLC [3539]. Therefore, upregulation of the snoRNAs in lung cancer might have oncogenic functions in the cacinogensis. Our primary goal of the current study is marker development. The biological relevance of the snoRNAs in tumorigenesis is currently being investigated at our laboratory.

Most of the previously identified lung cancer associated molecular genetic changes were related to the smoking status. Furthermore, some of the changes were associated with lung inflammatory diseases, especially COPD [40]. The use of such molecular alterations as biomarkers will produce false positive diagnostic rate, thus impeding their future application in clinical settings for diagnosis of lung cancer. The snoRNAs identified from the present research is fairly encouraging as biomarkers, because they highly express in plasma independently of participants' age, gender, ethnic subgroup, and smoking packer-year. In particular, high expressions of SNORD33 and SNORD76 were only observed in plasma from cancer patients. Furthermore, although SNORD66 displayed increased expression in plasma of COPD patients as compared with that in plasma of the healthy controls, it had considerably higher plasma expression level in NSCLC patients compared with COPD individuals. The observation suggests that the snoRNA panel could serves as useful biomarkers in differentiating NSCLC patients from not only healthy individuals, but also COPD subjects. Nonetheless, futures studies to comprehensively investigate biological relevance of the dysregulated SNORD66 in COPD are needed. Moreover, no significant difference regarding plasma expression of the genes was observed at different stages of NSCLC, implying that the potential markers were not stage-specific. In addition, the elevated plasma expression levels of the snoRNAs had equal frequency between AC and SCC of the lungs, suggesting that the genetic changes might be useful biomarkers for the two major histological types of lung cancer.

Although the results look promising, the sensitivity (81.1%) and specificity (95.5%) of the snoRNAs are still not yet efficient for routine clinical application. To surmount the problem, we need to identify additional cancer-associated ncRNAs that can be added to the current ones so that the diagnostic efficacy of the plasma-based approach could be improved. The fundamental mechanism supports this premise is that although only about 352 snoRNAs were analyzed, more than 500 snoRNAs might exist in the human genomic sequences [41]. Furthermore, we expect to improve such result through combing lung cancer-associated miRNAs with the identified snoRNAs [11, 13, 14]. In addition, other types of ncRNAs, such as piwi-associated RNAs may also play important role in carcinogenesis [5, 42]. Including other classes of small ncRNAs with high associated with NSCLC would also improve diagnostic accuracy of the noninvasive approach. To that end, we are analyzing tumor specimens by applying microarray platform to target various types of ncRNAs to develop additional markers for NSCLC.


We have defined and developed a panel of snoRNAs, whose altered expressions were associated with early stage NSCLC. We demonstrated that the snoRNAs existed in a stable form and were reliably measurable in plasma. Detection of the class of ncRNAs in plasma could potentially be used as a noninvasive diagnostic tool for early NSCLC. Nonetheless, a large multi-center clinical project to further validate the full utility is required before it could be adopted in routine clinical setting.


Patients and clinical specimens

To define and validate snoRNA signatures whose altered expressions were associated with early stage NSCLC, surgical specimens were obtained from 22 lung cancer patients who had either a lobectomy or a pneumonectomy between March 6, 2000 and June 23, 2003 at the University of Maryland Medical Center. All cases were diagnosed with histologically confirmed stage I NSCLC, comprising 11 patients with SCC and 11 patients with AC (Table 1). Tumor tissues were intraoperatively dissected from the surrounding lung parenchyma; paired noncancerous lung tissues were obtained from the same patients at an area distant from their tumors. Serial cryostat sections from the specimens were stained with hematoxylin and eosin to confirm the diagnosis based on the most recent WHO classification of tumors of the lungs [43, 44]. None of the patients had received preoperative adjuvant chemotherapy or radiotherapy. To evaluate expressions of the snoRNAs in plasma, 37 NSCLC patients, 22 healthy subjects, and 26 patients with COPD were recruited (Table 2). Peripheral blood was drawn in EDTA tubes (Greiner Bio-One GmbH, Monroe, NC) from the participants and processed within 1 hour of collection by centrifugation at 1,500 × g for 15 min at 4°C. Blood plasma was then collected as previously described [14], and immediately aliquoted and stored in a dedicated -80°C freezer. The study was approved by Institutional Review Board.

RNA isolation

Total RNA containing small RNA was extracted from the tissue and plasma specimens by using a mirVana ncRNA Isolation Kit (Ambion, Austin, TX) as previously described [14, 16]. The purity and concentration of RNA were determined from OD260/280 readings using a dual beam UV spectrophotometer (Eppendorf AG, Hamburg, Germany). RNA integrity was determined by capillary electrophoresis using the RNA 6000 Nano Lab-on-a-Chip kit and the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA). Only RNA extracts with integrity number values > 6 underwent in further analysis.

SnoRNA profiling of surgical resected lung tissues

SnoRNA profiling was performed by using GeneChipR Array (Affymetrix, Inc, Santa Clara, CA). The array comprised 7,815 probe sets that were designed to analyze small non-coding RNAs. Microarray experiments were done with all 22 matched malignant and noncancerous sample pairs according to the manufacturer's instructions as described in our previous report [45]. Briefly, 3 μg total RNA was labeled with Biotin FlashTag Biotin Labeling Kit (Affymetrix, Inc). The labeling reaction was hybridized on the arrays in Affymetrix Hybridization Oven 640 (Affymetrix, Inc) at 48°C for 16 hours. The arrays were stained with Fluidics Station 450 using fluidics script FS450_0003 (Affymetrix, Inc), and then scanned on a microarray scanner (Axon Instruments Inc, Foster City, CA). SnoRNA probe outliers were defined as per the manufacturer's instructions (Affymetrix, Inc) and further analyzed for data summarization, normalization, and quality control by using the web-based QC Tool software ( The normalized microarray data underwent further analysis as described in statistical section.

Quantification of snoRNA expression by real-time RT-qPCR

Expressions of the identified snoRNAs were first validated in the surgically resected tissues and then tested in plasma by using real-time SYBR green RT-qPCR assay. Briefly, 10 ng of plasma RNA was polyadenylated by poly(A) polymerase and reverse transcribed to cDNA using miScript Reverse Transcription kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. RT-qPCR was performed using miScript SYBR Green PCR kit (Qiagen) with the manufacturer provided miScript Universal primer and the snoRNA-specific forward primers in ABI PRISM 7900 Real-time PCR system (Applied Biosystems, Foster City, CA). The primers were designed based on the snoRNA sequences obtained from the gene database of The National Center for Biotechnology Information. The primer sequences for the snoRNAs and their amplification profile are available upon request. Each PCR reaction was carried out in a volume of 25 μl containing 2 μl of the cDNA, 0.1 μ mol/l of each primer and 2× SYBR Green PCR Master mix (Qiagen). At the end of the PCR cycles, melting curve analyses were performed. All assays were performed in triplicates, and one no-template control and two interplate controls were carried along in each experiment. Expression levels of the snoRNAs were calculated using comparative cycle threshold (Ct) method as previously described [15, 16]. Ct values of the target snoRNAs were normalized in relation to that of small nuclear U6 RNA. U6 RNA was proven as an internal control for ncRNA quantification [15, 16, 45, 46]. ΔCt was calculated by subtracting the Ct values of U6 from those of the snoRNA tested, and fold-change of each snoRNA was determined by the equation 2-ΔΔCt.

Statistical analysis

To find snoRNAs that were differentially expressed between paired NSCLC specimens and corresponding noncancerous tissues, we first analyzed the normalized microarray data by using GenePattern (, BRB-ArrayTools version 3.6 (, and microarray software suite 4 (TM4) ( We then performed tree visualization by using Java Treeview 1.0 (Stanford University School of Medicine, Stanford, CA). Pearson's correlations were used for the comparison of RT-qPCR and microarray data. ROC curve analysis was done using plasma expression level of each gene for the NSCLC patients, COPD patients, and normal controls by Analyse-it software (Analyse-it Software Ltd, Leeds, UK). Using this approach, AUC identified optimal sensitivity and specificity levels at which to distinguish NSCLC patients from healthy individuals or COPD patients, and corresponding thresholds were calculated for each snoRNA. In addition, Spearman rank correlation was carried out to analyze the correlation between the expressions of the identified snoRNAs. Moreover, the associations between the expression levels of the snoRNAs and both clinicopathologic and demographic characteristics of the cases and controls were evaluated by using univariate and multivariate logistic regression models. All P values shown were two sided, and a P value of < 0.05 was considered statistically significant.


  1. Higgins MJ, Ettinger DS: Chemotherapy for lung cancer: the state of the art in 2009. Expert Rev Anticancer Ther. 2009, 9: 1365-1378. 10.1586/era.09.115

    Article  PubMed  Google Scholar 

  2. Frost JK, Ball WC, Levin ML, Tockman MS, Baker RR, Carter D, Eggleston JC, Erozan YS, Gupta PK, Khouri NF: Early lung cancer detection: results of the initial (prevalence) radiologic and cytologic screening in the Johns Hopkins study. Am Rev Respir Dis. 1984, 130: 549-554.

    CAS  PubMed  Google Scholar 

  3. Hirsch FR, Franklin WA, Gazdar AF, Bunn PA: Early detection of lung cancer: clinical perspectives of recent advances in biology and radiology. Clin Cancer Res. 2001, 7: 5-22.

    CAS  PubMed  Google Scholar 

  4. Toloza EM, Harpole L, McCrory DC: Noninvasive staging of non-small cell lung cancer: a review of the current evidence. Chest. 2003, 123: 137-146. 10.1378/chest.123.1_suppl.137S.

    Article  Google Scholar 

  5. Galasso M, Elena Sana M, Volinia S: Non-coding RNAs: a key to future personalized molecular therapy?. Genome Med. 2010, 2: 12- 10.1186/gm133

    Article  PubMed Central  PubMed  Google Scholar 

  6. Ambros V: MicroRNA pathways in flies and worms: growth, death, fat, stress, and timing. Cell. 2003, 6: 673-676. 10.1016/S0092-8674(03)00428-8.

    Article  Google Scholar 

  7. Gillet R, Felden B: Emerging views on tmRNA-mediated protein tagging and ribosome rescue. Mol Microbiol. 2001, 4: 879-885. 10.1046/j.1365-2958.2001.02701.x.

    Article  Google Scholar 

  8. Jády BE, Kiss T: A small nucleolar guide RNA functions both in 2'-O-ribose methylation and pseudouridylation of the U5 spliceosomal RNA. EMBO J. 2001, 20: 541-551. 10.1093/emboj/20.3.541

    Article  PubMed Central  PubMed  Google Scholar 

  9. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR: MicroRNA expression profiles classify human cancers. Nature. 2005, 435: 834-838. 10.1038/nature03702

    Article  CAS  PubMed  Google Scholar 

  10. Foekens JA, Sieuwerts AM, Smid M, Look MP, de Weerd V, Boersma AW, Klijn JG, Wiemer EA, Martens JW: Four miRNAs associated with aggressiveness of lymph node-negative, estrogen receptor-positive human breast cancer. Proc Natl Acad Sci USA. 2008, 105: 13021-13026. 10.1073/pnas.0803304105

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T, Calin GA, Liu CG, Croce CM, Harris CC: Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 2006, 9: 189-198. 10.1016/j.ccr.2006.01.025

    Article  CAS  PubMed  Google Scholar 

  12. Patnaik SK, Kannisto E, Knudsen S, Yendamuri S: Evaluation of microRNA expression profiles that may predict recurrence of localized stage I non-small cell lung cancer after surgical resection. Cancer Res. 2010, 70: 36-45. 10.1158/0008-5472.CAN-09-3153

    Article  CAS  PubMed  Google Scholar 

  13. Lebanony D, Benjamin H, Gilad S, Ezagouri M, Dov A, Ashkenazi K, Gefen N, Izraeli S, Rechavi G, Pass H, Nonaka D, Li J, Spector Y, Rosenfeld N, Chajut A, Cohen D, Aharonov R, Mansukhani M: Diagnostic assay based on hsa-miR-205 expression distinguishes squamous from nonsquamous non-small-cell lung carcinoma. J Clin Oncol. 2009, 27: 2030-2037. 10.1200/JCO.2008.19.4134

    Article  CAS  PubMed  Google Scholar 

  14. Hu Z, Chen X, Zhao Y, Tian T, Jin G, Shu Y, Chen Y, Xu L, Zen K, Zhang C, Shen H: Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J Clin Oncol. 2010, 28: 1721-1726. 10.1200/JCO.2009.24.9342

    Article  PubMed  Google Scholar 

  15. Yu L, Todd NW, Xing L, Xie Y, Zhang H, Liu Z, Fang H, Zhang J, Katz RL, Jiang F: Early detection of lung adenocarcinoma in sputum by a panel of microRNA markers. Int J Cancer. 2010, ,

    Google Scholar 

  16. Xie Y, Todd NW, Liu Z, Zhan M, Fang H, Peng H, Alattar M, Deepak J, Stass SA, Jiang F: Altered miRNA expression in sputum for diagnosis of non-small cell lung cancer. Lung Cancer. 2010, 67: 170-176. 10.1016/j.lungcan.2009.04.004

    Article  PubMed Central  PubMed  Google Scholar 

  17. Kiss T: Small nucleolar RNAs: an abundant group of noncoding RNAs with diverse cellular functions. Cell. 2002, 109: 145-148. 10.1016/S0092-8674(02)00718-3

    Article  CAS  PubMed  Google Scholar 

  18. Mattick JS, Makunin IV: Small regulatory RNAs in mammals. Hum Mol Genet. 2005, 14: 121-132. 10.1093/hmg/ddi101.

    Article  Google Scholar 

  19. Lestrade L, Weber MJ: snoRNA-LBME-db, a comprehensive database of human H/ACA and C/D box snoRNAs. Nucleic Acids Res. 2006, 34: 158-162. 10.1093/nar/gkj002.

    Article  Google Scholar 

  20. Cavaillé J, Buiting K, Kiefmann M, Lalande M, Brannan CI, Horsthemke B, Bachellerie JP, Brosius J, Hüttenhofer A: Identification of brain-specific and imprinted small nucleolar RNA genes exhibiting an unusual genomic organization. Proc Natl Acad Sci USA. 2000, 26: 14311-14316. 10.1073/pnas.250426397.

    Article  Google Scholar 

  21. Chang LS, Lin SY, Lieu AS, Wu TL: Differential expression of human 5S snoRNA genes. Biochem Biophys Res Commun. 2002, 299: 196-200. 10.1016/S0006-291X(02)02623-2

    Article  CAS  PubMed  Google Scholar 

  22. Dong XY, Guo P, Boyd J, Sun X, Li Q, Zhou W, Dong JT: Implication of snoRNA U50 in human breast cancer. J Genet Genomics. 2009, 36: 447-454. 10.1016/S1673-8527(08)60134-4

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Dong XY, Rodriguez C, Guo P, Sun X, Talbot JT, Zhou W, Petros J, Li Q, Vessella RL, Kibel AS, Stevens VL, Calle EE, Dong JT: SnoRNA U50 is a candidate tumor-suppressor gene at 6q14.3 with a mutation associated with clinically significant prostate cancer. Hum Mol Genet. 2008, 17: 1031-1042. 10.1093/hmg/ddm375

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Donsante A, Miller DG, Li Y, Vogler C, Brunt EM, Russell DW, Sands MS: AAV vector integration sites in mouse hepatocellular carcinoma. Science. 2007, 317: 477- 10.1126/science.1142658

    Article  CAS  PubMed  Google Scholar 

  25. Smith CM, Steitz JA: Classification of gas5 as a multi-small-nucleolar-RNA (snoRNA) host gene and a member of the 5'-terminal oligopyrimidine gene family reveals common features of snoRNA host genes. Mol Cell Biol. 1998, 18: 6897-6909.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Schneider C, King RM, Philipson L: Genes specifically expressed at growth arrest of mammalian cells. Cell. 1988, 54: 787-793. 10.1016/S0092-8674(88)91065-3

    Article  CAS  PubMed  Google Scholar 

  27. Mourtada-Maarabouni M, Pickard MR, Hedge VL, Farzaneh F, Williams GT: GAS5, a non-protein-coding RNA, controls apoptosis and is downregulated in breast cancer. Oncogene. 2009, 28: 195-208. 10.1038/onc.2008.373

    Article  CAS  PubMed  Google Scholar 

  28. Niwa R, Slack FJ: The evolution of animal microRNA function. Curr Opin Genet Dev. 2007, 17: 145-150. 10.1016/j.gde.2007.02.004

    Article  CAS  PubMed  Google Scholar 

  29. Luo Y, Li S: Genome-wide analyses of retrogenes derived from the human box H/ACA snoRNAs. Nucleic Acids Res. 2007, 35: 559-571.

    CAS  PubMed  Google Scholar 

  30. Weber MJ: Mammalian Small Nucleolar RNAs Are Mobile Genetic Elements. PLoS Genet. 2006, 2: 1984-1997. 10.1371/journal.pgen.0020205.

    Article  CAS  Google Scholar 

  31. Ender C, Krek A, Friedländer MR, Beitzinger M, Weinmann L, Chen W, Pfeffer S, Rajewsky N, Meister G: A human snoRNA with microRNA-like functions. Mol Cell. 2008, 32: 519-528. 10.1016/j.molcel.2008.10.017

    Article  CAS  PubMed  Google Scholar 

  32. Scott MS, Avolio F, Ono M, Lamond AI, Barton GJ: Human miRNA precursors with box H/ACA snoRNA features. PLoS Comput Biol. 2009, 5: 1-13. 10.1371/journal.pcbi.1000507.

    Article  Google Scholar 

  33. Kishore S, Stamm S: The snoRNA HBII-52 regulates alternative splicing of the serotonin receptor 2C. Science. 2006, 311: 230-232. 10.1126/science.1118265

    Article  CAS  PubMed  Google Scholar 

  34. Tanaka R, Satoh H, Moriyama M, Satoh K, Morishita Y, Yoshida S, Watanabe T, Nakamura Y, Mori S: Intronic U50 small-nucleolar-RNA (snoRNA) host gene of no protein-coding potential is mapped at the chromosome breakpoint t(3;6)(q27;q15) of human B-cell lymphoma. Genes Cells. 2000, 5: 277-287. 10.1046/j.1365-2443.2000.00325.x

    Article  CAS  PubMed  Google Scholar 

  35. Bell DW: Our changing view of the genomic landscape of cancer. J Pathol. 2010, 220: 231-243.

    PubMed Central  CAS  PubMed  Google Scholar 

  36. Gebhart E: Double minutes, cytogenetic equivalents of gene amplification, in human neoplasia - a review. Clin Transl Oncol. 2005, 7: 477-485. 10.1007/BF02717000

    Article  PubMed  Google Scholar 

  37. Schwab M: Oncogene amplification in solid tumors. Semin Cancer Biol. 1999, 9: 319-325. 10.1006/scbi.1999.0126

    Article  CAS  PubMed  Google Scholar 

  38. Li R, Wang H, Bekele BN, Yin Z, Caraway NP, Katz RL, Stass SA, Jiang F: Identification of putative oncogenes in lung adenocarcinoma by a comprehensive functional genomic approach. Oncogene. 2006, 18: 2628-2635. 10.1038/sj.onc.1209289.

    Article  Google Scholar 

  39. Jiang F, Yin Z, Caraway NP, Li R, Katz RL: Genomic profiles in stage I primary non small cell lung cancer using comparative genomic hybridization analysis of cDNA microarrays. Neoplasia. 2004, 6: 623-635. 10.1593/neo.04142

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Brody JS, Spira A: State of the art. Chronic obstructive pulmonary disease, inflammation, and lung cancer. Proc Am Thorac Soc. 2006, 6: 535-537. 10.1513/pats.200603-089MS.

    Article  Google Scholar 

  41. Gardner PP, Bateman A, Poole AM: SnoPatrol: how many snoRNA genes are there?. J Biol. 2010, ,

    Google Scholar 

  42. Riedmann LT, Schwentner R: miRNA, siRNA, piRNA and argonautes: News in small matters. RNA Biol. 2010, 7: 133-139. 10.4161/rna.7.2.11288

    Article  CAS  PubMed  Google Scholar 

  43. Hammar SP, Brambilla C, Pugatch B: Pathology and Genetics, Tumours of the Lung, Pleura, Thymus and Heart. Squamous cell carcinoma. Edited by: Travis WD, Brambilla E, Muller-Hermelink HK. 2005, 6-30. Lyon, France, IARC Press,

    Google Scholar 

  44. Colby TV, Noguchi M, Henschke C: Pathology and Genetics, Tumours of the Lung, Pleura, Thymus and Heart. Adenocarcinoma. Edited by: Travis WD, Brambilla E, Muller-Hermelink HK. 2005, 35-44. Lyon, France: IARC Press,

    Google Scholar 

  45. Xing L, Todd N, Yu L, Fang HB, Jiang F: Early detection of squamous cell lung cancer in sputum by a panel of microRNA markers. Modern Pathology. 2010, ,

    Google Scholar 

  46. Park NJ, Zhou H, Elashoff D, Henson BS, Kastratovic DA, Abemayor E, Wong DT: Salivary microRNA: discovery, characterization, and clinical utility for oral cancer detection. Clin Cancer Res. 2009, 15: 5473-5477. 10.1158/1078-0432.CCR-09-0736

    Article  PubMed Central  CAS  PubMed  Google Scholar 

Download references


This work was supported in part by American Cancer Society Research Scholar Grant, National Cancer Institute (NCI) Grants CA-135382, CA-137742, and CA-133956, Associate Member Award from NCI-The Early Detection Research Network, Clinical Innovator Award from Flight Attendant Medical Research Institute, a scholar career development award from NIH K12RR023250-University of Maryland Multidisciplinary Research Career Development Program, and an exploratory research grant from Maryland Stem Cell Fund (F. J.).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Feng Jiang.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

FJ designed the study, performed research, analyzed data and wrote the paper. JL, YM, JS, and LY performed microarray and RT-qPCR analyses. RL performed analyzed data and wrote the paper. ZL performed data mining analyses. MG recruited patients, obtained written consents, and collected clinical data. All authors have read and approved the final manuscript.

Electronic supplementary material


Additional file 1:SnoRNAs that show changes in clinical specimens of lung cancer patients. SnoRNAs differentially expressed in non-small cell lung cancer tissues versus normal lung tissues and plasma of cancer patients and control subjects. (DOC 170 KB)

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Liao, J., Yu, L., Mei, Y. et al. Small nucleolar RNA signatures as biomarkers for non-small-cell lung cancer. Mol Cancer 9, 198 (2010).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: