Sputum long non-coding RNA biomarkers for diagnosis of lung cancer

Abstract

BACKGROUND:

Analysis of molecular changes in sputum may help diagnose lung cancer. Long non-coding RNAs (lncRNAs) play vital roles in various biological processes, and their dysregulations contribute to the development and progression of lung tumorigenesis. Herein, we determine whether aberrant lncRNAs could be used as potential sputum biomarkers for lung cancer.

METHODS:

Using reverse transcription PCR, we measure expressions of lung cancer-associated lncRNAs in sputum of a discovery cohort of 67 lung cancer patients and 65 cancer-free smokers with benign diseases and a validation cohort of 59 lung cancer patients and 60 cancer-free smokers with benign diseases.

RESULTS:

In the discovery cohort, four of the lncRNAs displayed a significantly different level in sputum of lung cancer patients vs. cancer-free smokers with benign diseases (all $P<$ 0.001). From the four lncRNAs, three lncRNAs (SNHG1, H19, and HOTAIR) are identified as a biomarker panel, producing 82.09% sensitivity and 89.23% specificity for diagnosis of lung cancer. Furthermore, the biomarker panel has a higher sensitivity (82.09% vs. 52.24%, $P=$ 0.02) and a similar specificity compared with sputum cytology (89.23% vs. 90.77%, $P=$ 0.45). In addition, the lncRNA biomarker panel had a higher sensitivity (87.50% vs. 70.07%, $p=$ 0.03) for diagnosis of squamous cell carcinoma compared with adenocarcinoma of the lung, while maintaining the same specificity (89.23%). The potential of the sputum lncRNA biomarkers for lung cancer detection is confirmed in the validation cohort.

CONCLUSION:

We have for the first time shown that the analysis of lncRNAs in sputum might be a noninvasive approach for diagnosis of lung cancer.

Keywords

Diagnosis lung cancer sputum lncRNA biomarkers

1. Introduction

Approximately 155,870 Americans will die from lung cancer each year, more than the other three leading cancers combined (breast, prostate, and colorectal cancers). Over 85% lung cancers are non-small cell lung cancers (NSCLC). NSCLC mainly consists of adenocarcinoma (AC) and squamous cell carcinoma (SCC). Tobacco smoking is the major cause of NSCLC. The early detection of lung cancer in a large randomized trial using low-dose CT (LDCT) has revealed a 20% reduction in mortality as compared to chest X-rays [1]. Therefore, lung cancer early detection can increase curability and save lives. LDCT is recommended to be used for lung cancer early detection among smokers in the USA [2, 3]. However, LDCT is associated with over-diagnosis, excessive cost, and radiation exposure [2, 3]. The development of non-invasive biomarkers that can accurately and cost-effectively diagnose early stage lung cancer remains to be clinically imperative [2, 3].

It has been well accepted that lung tumors develop from a field defect characterized by molecular abnormalities resulted from repeated exposure of the entire airway to the tobacco carcinogens [4, 5]. Kadara et al. [6, 7] found that the molecular alterations in the large bronchial airways reflected those in primary lung tumors in the distal lung, regardless of the anatomic location relative to the tumors. Spira et al. [8, 9] demonstrated that molecular changes in epithelial cells collected from the normal-appearing mainstem bronchus of smokers could be developed as biomarkers for NSCLC. Sputum contains bronchial epithelial cells from the lungs and lower respiratory tract. Based on the field cancerizations, examination of the exfoliated bronchial epitheliums of airway in sputum might detect the lung tumor-related molecular alterations, and hence provide a non-invasive and specific means for diagnosis of NSCLC [6, 7, 8, 10, 11, 12].

Long non-coding RNAs (lncRNAs) are transcripts of longer than 200 nucleotides in length, and have important functions in various biological processes [13, 14]. Dysfunctions of lncRNAs play vital roles in the development of lung cancer [15]. Specifically, lncRNAs can regulate different molecular signaling pathways via changing gene expression, and therefore, are implicated in numerous mechanisms of lung carcinogenesis [16, 17, 18, 19, 20]. Since aberrant lncRNAs detected in clinical specimens are associated with lung tumorigenesis, exploration of the molecules for clinical diagnosis is a growing area of research [15, 21, 22]. For examples, Liu et al. found that an lncRNA, HOTAIR, was significantly up-regulated in NSCLC tissues and might be a prognostic biomarker for the disease [23]. Furthermore, we have found that expression levels of SNHG1 and RMRP are reliably measured in plasma, and the lncRNAs may provide cell-free circulating biomarkers for lung cancer [24]. In addition, we recently showed that integrated analysis of lncRNAs, miRNAs, and mRNAs in plasma could have a synergistic impact on lung cancer early detection [25]. However, examination of aberrant lncRNA levels in sputum, which is surrogate material for noninvasive and specific diagnosis of lung cancer has not been investigated.

We recently identified six lncRNAs whose aberrant expression level in surgically resected tumor tissues was associated with lung cancer [26, 27]. The objective of this study was to investigate whether the lung cancer-associated lncRNAs could be used as sputum biomarkers for distinguishing lung cancer patients from smokers with benign diseases.

2. Materials and methods

2.1 Study population

The study protocol was approved by the Institutional Review Board of the University of Maryland Medical Center. Written informed consent forms were obtained from all participants. The participants were recruited at the point of their referral for suspected lung cancer between the ages of 55–80. Exclusion criteria included pregnancy, current pulmonary infection, surgery within 6 months, radiotherapy within 1 year, and life expectancy of $<$ 1 year. Clinical diagnosis of lung cancer was made using histopathologic examinations of specimens obtained by CT-guided transthoracic needle biopsy, transbronchial biopsy, videotape-assisted thoracoscopic surgery, or surgical resection. The surgical pathologic staging was determined according to the TNM classification of the International Union Against Cancer with the 8th American Joint Committee on Cancer and the International Staging System for Lung Cancer. Histopathological classification was determined according to the World Health Organization classification. A total of 251 subjects including 126 lung cancer patients and 125 cancer-free smokers with benign diseases were recruited. The 126 lung cancer patients were diagnosed with NSCLC consisting of 71 stage I cases, 33 stage II cases, and 22 stage III–IV cases. Seventy-six cases were AC and 50 were SCC of the lungs. All the 125 cancer-free individuals were smokers who served as control group participants. These control participants had granulomatous inflammation ( $n=$ 89), nonspecific inflammatory changes ( $n=$ 23) or lung infections ( $n=$ 13). The cancer-free individuals had been followed for at least 2 years, and none had any evidence of cancer. No difference of age, gender, and smoking status was observed in the lung cancer cases vs. cancer-free smokers (All $p>$ 0.05). The lung cases and cancer-free smokers were randomly split into a training cohort and a testing cohort by using a random number generator. The training cohort consisted of 67 lung cancer patients and 65 cancer-free smokers. The testing cohort comprised 59 lung cancer patients and 60 cancer-free smokers. The demographic and clinical characteristics of the two cohorts are presented in Tables 1 and 2.

Table 1
Characteristics of NSCLC patients and cancer-free smokers in a training cohort

	NSCLC	Cancer-free	$P$ -value
	cases	individuals
	( $n=$ 67)	( $n=$ 65)
Age	68.39	66.75	0.38
	(SD 11.36)	(SD 11.32)
Sex			0.46
Female	26	23
Male	41	42
Race			0.45
White	44	43
African American	23	22
Pack-years (median)	35.89	35.47	0.39
Stage
Stage I	40
Stage II	17
Stage III–IV	10
Histological type
Adenocarcinoma	43
Squamous cell	24
carcinoma

Abbreviations: NSCLC, non-small cell lung cancer.

Table 2

Characteristics of NSCLC patients and cancer-free smokers in a training cohort

	NSCLC	Cancer-free	$P$ -value
	cases	individuals
	( $n=$ 59)	( $n=$ 60)
Age	68.57	66.28	0.39
	(SD 12.03)	(SD 11.60)
Sex			0.43
Female	23	22
Male	36	38
Race			0.47
White	39	40
African American	20	20
Pack-years (median)	36.02	35.59	0.40
Stage
Stage I	31
Stage II	16
Stage III–IV	12
Histological type
Adenocarcinoma	33
Squamous cell	26
carcinoma

Abbreviations: NSCLC, non-small cell lung cancer.

2.2 Sputum collection, preparation, and sputum cytological study

Sputum was collected and processed from the subjects as previously described [10, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43], before they received any treatment (e.g., surgery, preoperative adjuvant chemotherapy, and radiotherapy). The sputum samples were centrifuged at 800 g for 10 min. The cell pellets were mixed with phosphate-buffered saline solution (PBS) (Sigma-Aldrich, St. Louis, MO. Cytospin slides were prepared from the cell pellets and underwent Papanicolaou staining for evaluating whether the specimens were representative of deep bronchial cells. All sputum samples were of lower respiratory origin as indicated by the presence of macrophages and bronchial epithelial cells. Cytologic diagnosis was made using the classification of Saccomanno [44]. Positive cytology included both carcinoma in situ and invasive carcinoma [28, 29]. The remaining sputum cells were stored at $-$ 80 ${}^{\circ}$ C until being tested.

2.3 The analysis of lncRNAs in sputum by reverse transcription-PCR (RT-PCR)

RNA was extracted from cell pellets of sputum as previously described [31, 32, 33, 34, 36, 37]. The purity and concentration of RNA were determined by 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, USA). The expression levels of the six lncRNAs (SNHG1, MALAT1, HOTAIR, H19, MEG3, and RMRP) were determined in sputum by using RT-PCR with Taqman assays (Applied Biosystems, Foster City, CA, USA) as previously described [24, 25]. The cycle threshold (Ct) was defined as the number of cycles required for the fluorescent signal to cross the threshold. An internal control gene, U6, was analyzed in parallel in the specimens. Relative expression of a targeted lncRNA in a given sample was computed using the equation 2- $\Delta$ Ct, where $\Delta$ Ct $=$ Ct (targeted lncRNA) – Ct (U6). All assays were performed in triplicates. Furthermore, two interplate controls and one no-template control were carried along in each experiment. The no template control for RT was RNease free water instead of RNA sample input, and no template control for PCR was RNease free water instead of RT products input.

2.4 Statistical analysis

We used univariate analysis to identify the lncRNAs whose expression levels were associated with NSCLC. We analyzed the significantly associated lncRNAs by using logistic regression models with constrained parameters as least absolute shrinkage and selection operator (LASSO) to eliminate the less relevant variables [45, 46, 47, 47]. We estimated functions of the combined lncRNA biomarkers by logistic regression with or without adjustment for known risk factors for NSCLC. The performances of the lncRNAs were subjected to receiver-operator characteristic (ROC) curve. We also generated a 95% confidence interval for the difference in the area under the ROCs (AUCs) by using the bootstrap [48]. We established the optimal cut-off value by using the Youden index [49, 50]. The results of the training cohort were blindly validated in the testing cohort by using leave-one-out cross validation [51]. To compare different approaches for lung cancer diagnosis, we computed their AUCs to determine the sensitivity and specificity as previously described [52].

Table 3
Expression levels of sputum lncRNAs in the training cohort of lung cancer patients and cancer-free individuals

lncRNAs	Mean (SEM) of level in NSCLC patients	Mean (SEM) of level in cancer-free individuals	$P$ -value	AUC (95% CI)
SNHG1	6.65 (1.08)	5.29 (1.13)	$<$ 0.001	0.80 (0.73 to 0.87)
H19	3.39 (2.63)	2.25 (0.65)	$<$ 0.001	0.77 (0.68 to 0.83)
HOTAIR	1.08 (0.56)	0.29 (0.09)	$<$ 0.001	0.73 (0.66 to 0.83)
MEG3	1.53 (0.92)	3.02 (1.56)	$<$ 0.001	0.72 (0.63 to 0.78)

Abbreviations: SEM, the standard error of the mean; AUC, the area under receiver operating characteristic curve; CI, confidence interval.

Table 4

Diagnostic values of the sputum lncRNA biomarkers in training cohort and testing cohort

	The training cohort		The testing cohort
	Sensitivity (CI)	Specificity (CI)	Sensitivity (CI)	Specificity (CI)
NSCLC	82.09% (70.80% to 90.39%)	89.23% (79.06% to 95.56%)	81.36% (69.09% to 90.31%)	88.33% (77.43% to 95.18%)
AC	79.07% (63.96% to 89.96%)	89.23% (79.06% to 95.56%)	75.76% (57.74% to 88.91%)	88.33% (77.43% to 95.18%)
SCC	87.50% (67.64% to 97.34%)	89.23% (79.06% to 95.56%)	88.46% (69.85% to 97.55%)	88.33% (77.43% to 95.18%)

Abbreviations: NSCLC, non-small cell lung cancers; AC, adenocarcinoma; SCC, squamous cell carcinoma; CI, confidence interval.

3. Results

3.1 Developing a panel of sputum lncRNA biomarkers for the early detection of lung cancer

All targeted six lncRNAs had $\leqslant$ 32 Ct values in each sputum sample of the training cohort, and therefore were reliably detectable in the specimens. No product was synthesized in the negative control samples. As shown in Table 3, four (SNHG1, H19, and HOTAIR, and MEG3) of the six lncRNAs displayed a significantly different level in sputum of patients with lung cancer vs. cancer-free individuals, producing 0.72–0.80 AUCs (all $P<$ 0.01). Of the four lncRNAs, three (SNHG1, H19, and HOTAIR) had a higher, whereas one (MEG3) had a lower expression level in sputum of lung cancer patents vs. cancer-free individuals (All $P<$ 0.01) (Table 3). We used logistic regression models with constrained parameters as in LASSO to develop a panel of lncRNA biomarkers for lung cancer. SNHG1, H19, and HOTAIR were selected as the best biomarkers and incorporated into an algorithm, which produced 0.90 AUC for distinguishing lung cancer cases from cancer-free subjects (Supplementary Fig. 1). Furthermore, the estimated correlations among expression levels of the three lncRNAs were low (All $P>$ 0.05), implying that the diagnostic values of the lncRNAs were complementary to each other. Using Youden’s index, we set up optimal cut-off at 0.36. Based on the cut-off, combined analysis of the three lncRNAs generated 82.03% sensitivity and 89.23% specificity for diagnosis of lung cancer (Table 4). Sputum cytology has 52.24% sensitivity and 90.77% specificity. Therefore, the sputum biomarkers had a higher sensitivity (82.03%) compared with sputum cytology (52.24%) ( $P=$ 0.02), while maintaining a similar specificity (89.23% vs. 90.77%, $p=$ 0.45) (Fig. 1). Interestingly, the panel of the lncRNA biomarkers had a higher sensitivity (87.50%) for SCC compared with AC of the lungs (79.07%), while maintaining the same specificity (89.23%) (Table 4). Therefore, the sputum biomarker panel could better differentiate SCC cases from cancer-free individuals compared with AC cases. In addition, the three lncRNAs did not display statistical differences of sensitivity and specificity between early and advanced stages (stage I–II vs. stage III–IV) ( $P>$ 0.05). Moreover, the sputum biomarkers had no special association with the age, gender and ethnicity of the participants (All $p>$ 0.05). The expression levels of the sputum lncRNAs were associated with smoking history of lung cancer patients at the edge of significance ( $P=$ 0.05).

Figure 1.

Sensitivity and specificity of sputum lncRNA biomarkers and cytology for diagnosis of lung cancer. The sputum lncRNA biomarker panel has a higher sensitivity and a similar specificity compared with sputum cytology ( $P=$ 0.02). ${}^{*}$ , $P<$ 0.05.

3.2 Validating the sputum lncRNA markers in an independent cohort of lung cancer patients and cancer-free individuals

To evaluate the diagnostic performance of the sputum biomarkers, the three lncRNAs (SNHG1, H19, and HOTAIR) were assessed in sputum of additional 59 NSCLC patients and 60 cancer-free individuals. The three genes used in combination could differentiate the NSCLC patients from cancer-free individuals with 81.36% sensitivity and 88.33% specificity (Table 4). Furthermore, the panel of the lncRNA biomarkers had a higher sensitivity for SCC (88.46%) compared with AC (75.76%) of the lungs ( $P=$ 0.03) with the same specificity (88.33%). In addition, the three sputum lncRNA biomarkers didn’t show association with the age, gender and ethnicity of the participants (All $p>$ 0.05). Taken together, the results generated from the validation cohort confirmed the potential of combined use of the three lncRNAs as a sputum biomarker panel for the early detection of lung cancer.

4. Discussion

As a mirror to lung diseases, sputum is noninvasively obtained and contains bronchial epithelial cells from the lungs and lower respiratory tract, and thus has the advantages as surrogate material for specifically diagnosing lung cancer. Herein, we for the first time report the feasibility of detection of lncRNA in sputum and develop a panel of sputum lncRNA biomarkers for lung cancer. Furthermore, the biomarker panel has a higher sensitivity and a similar specificity compared with sputum cytology, a standard clinical model. Like other sputum molecular biomarkers, the sputum lncRNA biomarkers display a higher diagnostic performance for SCC than AC of the lungs. Interestingly, the sensitivity and specificity of the biomarkers are independent of stage of lung cancer. Therefore, the discovery might be an important characteristic if they are employed for the diagnosis of NSCLC, particularly SCC, at the early stage. Nevertheless, the potential of the sputum lncRNA biomarkers for the early detection of lung cancer need to be prospectively validated in a large cohort study.

The three lncRNAs (SNHG1, H19, and HOTAIR) have diverse and important functions in lung tumorigenesis through regulating different molecular pathways. For instance, elevated expression of SNHG1 was frequently observed in lung cancer tissues [53]. Furthermore, SNHG1 could promote NSCLC progression of lung cancer via miR-101-3p/SOX9/Wnt/ $\beta$ -catenin regulatory network and miR-145-5p/MTDH axis [54, 53]. In addition, SNHG1 plays an oncogenic role in lung cancer through ZEB1 signaling pathway by inhibiting TAp63 [55]. H19 is an imprinted oncofetal lncRNA, which is expressed in the embryo, down-regulated at birth and then reappears in tumors [56]. H19 plays important function in cell proliferation and differentiation, epithelial to mesenchymal transition, and mesenchymal to epithelial transition [57]. H19 has also the versatile and functions depending on its context within the process of tumor progression [58]. Interestingly, lung tumor-released H19 could promote gefitinib resistance via packaging into exosomes in lung cancer cells [59]. HOTAIR represses gene expression through recruitment of chromatin modifiers. The increased HOTAIR expression has been founded in lung cancer, and the expression level correlates with metastasis and poor prognosis. Moreover, HOTAIR promotes proliferation, survival, invasion, metastasis, and drug resistance in lung cancer cells through miR-217/DACH1 signaling pathway [60, 61]. Our current study extends the previous findings by developing the lung cancer-associated lncRNA as sputum biomarkers that might be useful in the early detection of lung cancer.

There are some limitations in this present study. 1), the size of the cohorts is small. We will perform a large and prospective study to validate the diagnostic value of the sputum lncRNA biomarkers for the early detection of lung cancer. 2), the panel of the three biomarkers is developed from six lung cancer-associated lncRNAs. Although showing promise, the three biomarkers’ diagnostic performance is not high enough to be used in clinical settings. By searching published data, we recently found 21 lncRNAs whose malfunction was characterized in lung tumorigenesis [19, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]. Our ongoing study is evaluating if the additional lncRNAs might be added into the biomarker panel to improve the diagnosis of lung cancer. 3), the objective of this present study is to develop sputum biomarkers for differentiating lung cancer from cancer-free smokers who have benign diseases. Toward that end, we collect sputum from lung cancer patients and cancer-free smokers with benign diseases who serve as control individuals. Sputum of healthy controls is not available in this study. Therefore, we are not able to detect differences of lncRNAs in sputum between smokers with benign diseases and healthy controls. In future, it might be important to collect sputum from nonsmokers who don’t have any benign disease to determine if there is different lncRNA changes in sputum between the individuals with the smokes with benign diseases and the healthy controls.

In sum, sputum lncRNA biomarkers are developed that could potentially be used as a noninvasive approach for differentiating early stage NSCLC patients from cancer-free smokers. Nevertheless, undertaking a prospective study to further validate the sputum biomarkers in a large cohort is required.

Footnotes

Acknowledgments

This work was supported in part by VA Merit Award I01 CX000512, the Geaton and JoAnn DeCesaris Family Foundation, NIH/NCI-1R21CA240556, University of Maryland Marlene & Stewart Greenebaum Comprehensive Cancer Center Pilot Grant Program, and DoD-Idea Development Award (F.J.), and NIH/NCI-Early Detection Research Network-5U24CA11509 (S.S.).

Conflict of interest

The authors declare no conflict of interest.

Supplementary

Supplementary Fig. 1.

The receiver-operator characteristic (ROC) curve of sputum lncRNA biomarkers for lung cancer diagnosis. Three lncRNAs (SNHG1, H19, and HOTAIR) were selected as the best ones and incorporated into an algorithm: Probability $P=\exp(U)/[1+\exp(U)]$ , where $U=2.93+2.12*\log(\text{SNHG1})-1.26*\log(\text{H19})-1.1795*\log(\text{HOTAIR})$ . The three-lncRNA-algorithm produced the area under the ROC (AUC) of 0.90 for distinguishing lung cancer cases from cancer-free individuals.

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Sputum long non-coding RNA biomarkers for diagnosis of lung cancer

Abstract

BACKGROUND:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 Study population

Table 1 Characteristics of NSCLC patients and cancer-free smokers in a training cohort

2.3 The analysis of lncRNAs in sputum by reverse transcription-PCR (RT-PCR)

2.4 Statistical analysis

Table 3 Expression levels of sputum lncRNAs in the training cohort of lung cancer patients and cancer-free individuals

3.1 Developing a panel of sputum lncRNA biomarkers for the early detection of lung cancer

4. Discussion

Footnotes

Acknowledgments

Conflict of interest

Supplementary

References

Table 1
Characteristics of NSCLC patients and cancer-free smokers in a training cohort

Table 3
Expression levels of sputum lncRNAs in the training cohort of lung cancer patients and cancer-free individuals