The role of microRNA in human lung squamous cell carcinoma

Cancer Genetics and Cytogenetics 200 (2010) 127e133

The role of microRNA in human lung squamous cell carcinoma Ye Yanga, Xiaofei Lia, Qi Yangb, Xiaoping Wanga, Yongan Zhoua, Tao Jianga, Qunfeng Mac, Yun-Jie Wanga,* a

Department of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi’an 710038, Shaanxi Province, China b Department of Digestion, Tangdu Hospital, Fourth Military Medical University, Xi’an 710038, Shaanxi Province, China c Department of Cardiothoracic Surgery, Affiliated Hospital, Academy of Military Medical, Xi’an 710038, Shaanxi Province, China Received 20 August 2009; received in revised form 22 March 2010; accepted 25 March 2010

Abstract

MicroRNAs (miRNAs) are a group of small noncoding RNAs with modulator activity of gene expression. Deregulation of miRNA genes was found in several types of cancers. To explore the role of the miRNAs in Chinese lung squamous cell carcinoma (SCC), the expression profile of 711 miRNAs in SCC was analyzed. Total RNAs were used for hybridization on a commercially available array (miRCURY LNA array v.10.0), which contains 1,200 probes in tetramer, corresponding to 711 human miRNA genes. The results of miRNA microarray analysis were confirmed with quantitative real-time polymerase chain reaction. Seven human miRNAs (miR-126, miR-193a3p, miR-30d, miR-30a, miR-101, let-7i, and miR-15a) were found to be significantly downregulated in lung SCC (P ! 0.05), compared with normal lung tissues. The miRNAs miR-185* and miR125a-5p were significantly upregulated in lung SCC (P ! 0.05), compared with normal lung tissues. The miRNA let-7i was downregulated in 9 of the 20 SCC samples, and miR-126 was downregulated in 16 of 20. The deregulation of some miRNAs in lung SCC suggests their possible involvement in the development and progression of SCC. Ó 2010 Elsevier Inc. All rights reserved.

1. Introduction Lung cancer is the most common cause of cancer mortality in the world [1], and its incidence is steadily increasing. The poor prognosis of this cancer is explained mainly by the fact that the diagnosis is generally made only at advanced stages. Identification of early biomarkers is therefore needed [2]. The subtypes of lung cancer, including adenocarcinoma (AD) and squamous cell carcinoma (SCC), present unique histopathological characteristics at distinct preferential anatomic locations, but their staging and treatment are similar. More effective systemic therapy is urgently needed. The discovery of miRNAs has been a landmark event in molecular biology. miRNAs can posttranscriptionally regulate the expressions of hundreds of their target genes, thereby controlling a wide range of biological functions, including cellular proliferation [3], differentiation [4], and apoptosis [5]. Recent evidence indicates that miRNAs may function as tumor suppressors or oncogenes, and alteration in miRNA

* Corresponding author. Tel.: þ86-29-84777827. E-mail address: [email protected] (Y.-J. Wang). 0165-4608/$ e see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.cancergencyto.2010.03.014

expression may play a critical role in tumorigenesis and cancer progression [6,7]. miRNAs have been found to be involved in known oncogenic pathways, including those of Tp53 [8,9], BCL2 [10], or KRAS [11]. Finally, miRNAs seem to be very significant prognostic factors in patients with different tumors [12e15] and thus could be useful biomarkers for treatment [16]. To date, however, comprehensive data regarding a miRNA signature of SCC in Han Chinese have been limited. In this study, the miRNA profiling of three pairs of lung SCC tissues and normal lung tissues from Han Chinese subjects was analyzed, and nine miRNAs were found to express differentially in all three pairs.

2. Materials and methods 2.1. Patients and tissue specimens A total of 23 pairs of frozen samples were studied. Both tumor tissue and normal tissue were obtained from each patient who underwent surgical resection of SCC at our institution (Xijing Hospital, Fourth Military Medical University, Xi’an, China). The patients had not received

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Table 1 Sequences of reverse transcriptaseepolymerase chain reaction primers Primers

Sequence

U6 F U6 R miR-551b GSP miR-551b R miR-765 GSP miR-765 R

50 -GCTTCGGCAGCACATATACTAAAAT-30 50 -CGCTTCACGAATTTGCGTGTCAT-30 50 -GGGCGACCCATACTTG-30 50 -CAGTGCGTGTCGTGGAGT-30 50 -GGGTGGAGGAGAAGGAAG-30 50 -CAGTGCGTGTCGTGGAGT-30

Abbreviations: F, forward; R, reverse; GSP, gene-specific primer.

adjuvant chemotherapy. This study was approved by the Institutional Review Boards of the hospitals. Written informed consent was obtained from all patients.

2.2. Analysis of miRNA microarray Three pairs of specimens were analyzed by miRNA microarray. Total RNAs were harvested with TRIzol (Invitrogen, Carlsbad, CA) and RNeasy mini kit (Qiagen, Valencia, CA) according to the manufacturer’s instructions. After RNA measurement on a NanoDrop instrument (Thermo Scientific, Wilmington, DE), the samples were labeled using a miRCURY Hy3/Hy5 Power labeling kit and were hybridized on a miRCURY LNA array (v.10.0; Exiqon, Vedbaek, Denmark). The samples were hybridized on a hybridization station. Scanning was performed with an Axon GenePix 4000B microarray scanner (Axon InstrumentseMolecular Devices, Union City, CA). GenePix pro v.6.0 software was used to read the raw intensity of the image. The intensity of green signal was calculated after background subtraction; four replicated spots of each probe on the same slide were averaged. Median normalization

was calculated as Normalized Data 5 (Foreground  Background)/Median, where Median is the 50 percent quantile of miRNA intensity O50 in all samples after background correction. The statistical significance of differentially expressed miRNA was analyzed by Student’s t-test. 2.3. Quantitative real-time polymerase chain reaction Quantitative real-time polymerase chain reaction (qRTPCR) was performed in duplicate, including negative reverse transcription (RT) controls to assess genomic DNA and nontemplate controls to ensure lack of signal in the assay background. The RT reaction for miR-551b and miRNA-765 consisted of 2 mL 10 RT buffer (Epicentre Biotechnologies, Madison, WI), 2 mL dNTPs 0.25 mmol/L each (HyTest, Turku, Finland), 1 mL RT Primer 1 mmol/L each (Applied Biosystems, Foster City, CA), 0.3 mL RNase inhibitor protein 40 U/mL (Epicentre), 2 mL MMLV-RT 10 U/mL (Epicentre), and 2 mg total RNA, in a final volume of 20 mL. Reactions were incubated at 16 C for 30 minutes, 42 C for 42 minutes, then 85 C for 5 minutes. Following the RT step, 1 mL of the RT product was transferred into a 25 mL PCR consisting of 2.5 mL 10 PCR buffer (Epicentre), 1.5 mL 25 mmol/L MgCl2 (Promega, Madison, WI), 2.5 mL dNTPs 2.5 mmol/L each (Ambion, Austin, TX), 0.25 each 10,000 SYBR Green I (Invitrogen), 1 mL forward primer 10 mmol/L, 1mL reverse primer 10 mmol/L, and 1 unit Taq polymerase (Promega) (Table 1). The PCR cycling began with template denaturation at 95 C for 5 minutes, then 40 cycles at 95 C for 10 seconds, 60 C for 20 seconds, 72 C for 20 seconds, and 78 C for 20 seconds, performed on a Corbett Rotor-Gene 3000 real-time PCR system (Qiagen). Thresholds and baselines were manually determined, with

Table 2 Statistical results, chromosomal location, and putative targets of miRNAs upregulated in lung SCC, compared with normal lung tissue miRNAa

Fold (SCC/Normal), mean 6 SD

P-value

Chromosomal localization

hsa-miR-34a hsa-miR-637 hsa-miR-412 ebv-miR-BART17-5p kshv-miR-K12-6-3p hsa-miR-185* hsa-miR-338-5p kshv-miR-K12-8 hsa-miR-22* hsa-miR-509-5p sv40-miR-S1-5p hsa-miR-125a-5p hsa-miR-221*

1.580 3.106 3.622 1.506 2.057 1.543 4.998 1.759 1.836 1.517 1.907 1.555 2.715

0.412 2.526 2.412 0.521 1.034 0.014 2.835 .811 1.031 0.612 1.256 0.008 1.439

0.281 0.059 0.090 0.289 0.104 0.042 0.068 0.212 0.266 0.107 0.153 0.006 0.104

1p36.23 19p13.3 14q32.31 d d 22q11.2 17q25.3 d 17p13.3 Xq27.3 d 19q13.3 Xp11.3

hsa-miR-551b hsa-miR-645

1.765 6 1.191 3.098 6 1.561

0.171 0.129

3q26.2 20q13.13

6 6 6 6 6 6 6 6 6 6 6 6 6

Putative targetsb BCL2, MET (alias c-Met), SIRT1, MYCN, CCND1 RBM9, MNT, DAGLA, SGTA, GLP1R FREM2, PRKRIR, RALGPS1, SOX6, GNB4 d d IKZF4 (previously ZNFN1A4), AQP5, ESRRA, RAC3, RGS14 PHC3, FAM84A, NETO1, ST8SIA3, USP25 d LCE5A, SPATA19, LCE2C, ABHD12, FKBP3 FIGN, FOXP1, TET1, AFF3, SFRS11 d STARD13, ZNF792, GCNT1, FUT4, NAIF1 KIT (alias c-KIT ), CDKN1B (alias P27KIP1), CDKN1C (alias P57), RXFP4, FBXW2 HTR3B, BST2, ABCC3, TRIM41, CACNG6 RAB22A, PHYHIPL, B3GALNT1, TNKS, SOX30

Abbreviations: ebv, EpsteineBarr virus; hsa, Homo sapiens; kshv, Kaposi’s sarcoma-associated herpesvirus; miRNA, micro-RNA; SCC, squamous cell carcinoma; sv40, simian virus 40; SD, standard deviation. a An asterisk is part of the miRNA nomenclature system and is not linked to any footnote specific to this table. b For each row, the top five putative targets identified with TargetScan are reported.

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Table 3 Statistical results, chromosomal location, and putative targets of miRNAs downregulated in lung SCC, compared with normal lung tissue miRNAa

Fold (SCC/Normal), mean 6 SD

hsa-miR-126 hsa-miR-652 hsa-miR-193a-3p hsa-miR-30d hsa-miR-30a hsa-miR-143 hsa-miR-766 hsa-miR-491-3p hsa-miR-320 hsa-miR-886-3p hsa-let-7d* hsa-miR-18a hsa-miR-29b-1* hsa-miR-101 hsa-let-7i hsa-miR-886-5p hsa-miR-24 hsa-miR-765 hsa-miR-576-5p hsa-miR-22 hsa-miR-299-5p hsa-miR-106b hsa-miR-20a hsa-miR-625* hsa-miR-185 hsa-miR-483-5p hsa-miR-939 hsa-miR-26b hsa-miR-222 hsa-miR-369-3p hsa-miR-129-5p hsa-miR-106a kshv-miR-K12-4-3p hsa-miR-26a hcmv-miR-UL36 hsa-miR-574-3p hsa-miR-634 hsa-miR-23a* hsa-miR-923 ebv-miR-BHRF1-1 ebv-miR-BART16 hsa-miR-363* hsa-miR-15a hsa-let-7e hsa-miR-32* hsa-miR-801 hsa-miR-17

0.417 0.239 0.515 0.221 0.207 0.389 0.574 0.333 0.635 0.566 0.616 0.457 0.499 0.291 0.506 0.537 0.642 0.345 0.629 0.542 0.424 0.519 0.579 0.522 0.637 0.479 0.643 0.454 0.474 0.533 0.589 0.560 0.358 0.371 0.661 0.501 0.473 0.588 0.659 0.351 0.610 0.349 0.393 0.440 0.502 0.655 0.544

6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

0.033 0.021 0.042 0.019 0.009 0.091 0.102 0.021 0.592 0.166 0.186 0.212 0.202 0.021 0.071 0.231 0.252 0.183 0.199 0.186 0.156 0.249 0.136 0.331 0.073 0.358 0.288 0.051 0.129 0.203 0.061 0.386 0.271 0.041 0.423 0.243 0.301 0.264 0.275 0.163 0.251 0.199 0.034 0.243 0.284 0.187 0.263

Pvalue

Chromosomal localization

Putative targetsb

0.023 0.084 0.041 0.020 0.004 0.063 0.140 0.052 0.240 0.408 0.124 0.379 0.218 0.012 0.039 0.386 0.195 0.239 0.123 0.178 0.158 0.238 0.126 0.360 0.051 0.364 0.292 0.087 0.133 0.207 0.062 0.230 0.377 0.057 0.466 0.250 0.356 0.320 0.283 0.151 0.348 0.223 0.031 0.340 0.332 0.182 0.271

2q36.3 Xq22.3 17q11.2 8q24.22 6q13 5q33.1 Xq24 9p21.3 8p21.3 5q31.2 9q22.32 13q31.3 7q32.3 1p31.3 12q14.1 5q31.2 9q22.32 1q23.1 4q25 17p13.3 14q32.31 7q22.1 13q31.3 14q23.3 22q11.21 11p15.5 8q24.3 2q35 Xp11.3 14q32.31 7q32.1 Xq26.2 d 3p22.2 d 4 17q24.2 19p13.12 17q12 d d Xq26.2 13q14.3 19q13.33 9q31.3 1p35.3 13q31.3

CRK, IRS1, VEGF, PIK3R2 (alias p85-b), VCAM-1 EEF2, CCKAR, USP47, XPOT, PTPN4 ABI2, IL17RD, ERBB4, FHDC1, SLC16A6 No targets KLHL28, NEDD4, TMEM170B, STIM2, FAM160B1 KRAS, ABL2, VASH1, HIPK2, ASAP3 (previously DDEFL1) ZNF763, NR3C2, POU2AF1, NUP210, ZNF609 BNC2, PYROXD1, PCGF5, GABPA, EEA1 ARPP19, MBD2, FTL, CDK6, TMEM47 (previously TM4SF10) RALGPS1, NLGN3, MARK4, SEPT5, PITX1 GPR98, PCDH8, CECR5, KRT15, GKAP1 ESR1 (alias ERA, ER-a)c, NEDD9, GLRB, PHC3 SLC22A7, COL4A5, COL4A1, ROBO1, ADAMTS9 PTGS2 (alias COX2), MCL1, TNPO1, GLTSCR1, FLRT3, EZH2 HMGA2, IGDCC3 (previously PUNC ), ARID3B, CLCN5, LIMD2 KPNA6, CNGB1, BCAT2, TRIM54, CPNE7 CALCR, KDM5A (previously JARID1A), TMEM50B, CDV3, VCPIP1 POU2F2, TIMP3, KCND1, PPP1R12B, PTPRT ITGBL1, WDR72, NRIP1, ICA1L, CUL3 FUT9, CCDC67, CBL, TET2, H3F3B NAV3, IKZF2, ITIH5, SLC5A12, ROBO1 CDKN1A (alias P21), YOD1, ATL3, ACPL2, WNK3 PDCD1LG2, CAMTA1, FAM129A, TANC1, PRRG1 ESD, DDX1, HIPK2, RFPL3, FRAS1 SLC16A2, PALM2, BSN, ABCG4, PCDHAC1 HLA-DOA, FAM160B2, ELK1, AQP7, ELMO3 SLC34A2, ZNRF1, MN1, CPLX2, LDOC1L CHORDC1, POLR3G, SLC2A13, TNRC6B, TET3 CDKN1B (alias P27KIP1), SNX4, RGS6, OSTM1, TCF12 NF1, YOD1, RAB11FIP2, STK38L, ZEB2 TNRC6B, HRNBP3, TCF4, CACNG2, LDB3 DOCK4, PFKP, PTPN4, TLE4, FGD4 d ATP11C, B3GNT5, KLHDC5, STRADB (previously ALS2CR2), SLC7A11 d RXRA, KLF12, CUL2, DAB2IP, NDUFA4L2 PDIK1L, ENAH, BRWD1, KIAA1462, NRXN3 NADSYN1, NFATC2, DYNLRB1, PYGM, GPT a frgament of the 28S rRNA d d COPE, PTCRA, PDZD7, KANK3 (previously ANKRD47), BIN3 BCL2, KIF1B, DCLK1, EDA, ABL2 XKR8, GALE, TARBP2, CDC34, CYP4F8 DIP2A, UPP2, KRT78, LPAR6 (previously P2RY5), RPS6KA6 a fragment of U11 spliceosomal RNA ZNFX1, PKD2, MYT1L, ITGB8, SCN1A

Abbreviations: ebv, EpsteineBarr virus; hsa, Homo sapiens; hcmv, human cytomegalovirus; kshv, Kaposi’s sarcoma-associated herpesvirus; miRNA, microRNA; SCC, squamous cell carcinoma; SD, standard deviation. a An asterisk is part of the miRNA nomenclature system and is not linked to any footnote specific to this table. b For each row, the top five putative targets identified with TargetScan are reported. c The software in use identified ES-a as a gene distinct from ESR1; however, ES-a is an alias for ESR1, and the two symbols represent a single gene. Eliminating the duplication leaves four genes.

thresholds typically set between 0.05 to 0.1 and paired with a baseline starting at 1e3 Ct and finishing at 15e17 Ct (where Ct is the threshold crossing point). The let-7i, miR125a-5p, miR-126, and Run48 were reversely transcribed and amplified by ABI reaction (081006-H, 0810007-B, 0812017-G, and 0811271-H; Applied Biosystems).

2.4. qRT-PCR data analysis For the target gene, changes were determined by relative quantification [17]. The change in amplification of the target gene was normalized to U6. The fold change in the target gene for the results of quantitative real-time PCR

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was calculated for each sample using 2DDCt: DDCt 5 (Cttargetgene  CtU6)cancer  (Cttargetgene  CtU6)normal. Differentially expressed miRNA was defined by a 2DDCt value of O1.5 or !0.67. 3. Results 3.1. Differentially expressed miRNAs in lung SCC The data from miRNA expression profiling indicated that 65 miRNAs with O1.5-fold change could be differentially expressed between the lung normal tissue and SCCs, with higher expression in SCC for 16 miRNAs and lower expression for 49 miRNAs. The top five putative targets identified with TargetScan online software (version 4.1; Whitehead Institute for Biomedical Research; http://www.targetscan. org) for each miRNA are listed in Tables 2 and 3. Nine miRNAs were detected to pass a Student’s t-test with statistical significance (P ! 0.05): miR-185*, 125a-5p, miR-126, miR-193a-3p, miR-30d, miR-30a, miR-101, let-7i, and miR-15a. Hierarchical clustering analysis based on the miRNA array data led to two groupings for the samples, SCC and normal tissue, suggesting that the miRNA expression profile was consistent among the different SCC samples (Fig. 1). The potential tumor suppressors were observed to be downregulated and were clustered into one group, including miR-26a [18], miR-126 [19], miR-17 [20], miR-193a-3p [21], miR-320 [22], let-7i [23], miR-15a [24], and miR-101 [25]. The potential oncogenes were observed to be upregulated and were clustered into another group, such as miR185 [26], miR-221* [27], and miR-338 [28]. 3.2. Validation of miRNA microarray results by qRT-PCR in lung SCC To validate the results of miRNA microarray from three pairs of lung SCCs and normal tissue, miR-551b and miR765 were selected and assayed by qRT-PCR in the same samples as used in the miRNA microarray experiment. The qRT-PCR results indicated that miR-551b was upregulated and miR-765 was downregulated in each of the three lung SCCs samples used for validation, which was consistent with the results of miRNA microarray (Fig. 2). 3.3. Expression of let-7i, miR-125-5p, and miR-126 in 20 pairs of SCC samples The miRNAs with O1.5-fold changes were defined to be differentially expressed between the lung normal tissue and SCCs. We assayed the expression of let-7i, miR-1255p, and miR-126 from another 20 pairs of samples by

= Fig. 1. Hierarchical clustering of lung squamous cell carcinoma (SCC) samples. Samples are in columns, miRNAs in rows. The miRNA clustering tree is shown on the left, and the sample clustering tree appears at the top. The color scale along the top illustrates the relative expression level of a miRNA (red, high expression level; green, low expression level).

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qRT-PCR. For the 20 SCC samples, let-7i was upregulated in 1 and downregulated in 9; miR-125-5p was upregulated in 5 and downregulated in 5; and miR-126 was upregulated in 1 and downregulated in 16 (Fig. 3).

4. Discussion Lung cancer is now the leading cause of cancer mortality worldwide [29]. The current 5-year survival rate for lung cancer is a discouraging 15%, although there has been an incremental improvement in survival rate over the last several decades. The survival advances seen in other common malignancies have not been realized in lung cancer. A number of studies have directly profiled miRNA expression in lung cancers, and unique groups of miRNAs were identified to either characterize the neoplastic tissues or identify patients with poor prognosis [30e32]. We used miRNA expression arrays to determine the miRNA profiles for lung SCC and normal lung tissue. The miRNA expression profiles distinguished SCC from normal lung tissue, and the samples were classified into two clusters: normal and SCC (Fig. 1). The expression levels of miR-551b and miR-765 determined by qRT-PCR were consistent with the results of miRNA microarray (Fig. 2) in the same samples. A few of the miRNAs, including miR-30a, let-7i and miR-126, were found to be downregulated in lung SCCs; these results were consistent with findings for lung adenocarcinomas in Baltimore, Maryland [31], but were not consistent with findings from lung SCC in Michigan [33], suggesting the possibility of different miRNA expression for lung SCC in different ethnic groups. The let-7 miRNA, one of the best-studied miRNAs to date, is involved in inhibition of cancer growth. Reduced let-7i expression significantly increased the resistance of ovarian and breast cancer cells to the chemotherapy drug cis-platinum, and was significantly associated with shorter progression-free survival of patients with late-stage ovarian cancer [23]. In the present study, let-7i, a member of the let-7 miRNA family, was downregulated in 7 of the 20 samples. MiR-126 expression decreased in SCC, relative to paired uninvolved tissue (Fig. 1 and Fig. 3C). MiR-126 is located in chromosome 9q34.3, within the host gene encoding for epidermal growth factor like-7 (EGFL7) [34,35]. MiR126 expression is the greatest within highly vascularized tissues, such as the lung, heart, and kidney [36]. Recent studies indicate that miR-126 might function as a tumor suppressor [19,37]. A few genes targeted by miR-126 are involved in cancer growth, including CRK [37], IRS1 [21], VEGF [38], and PIK3R2 (alias p85-b) [39]. Upregulation of miR-126 induced cell cycle G1 arrest in a few cancer cell lines [19,38], suggesting that miR-126 could be a promising treatment in anticancer therapy. MiR-15a acts as a putative tumor suppressor by targeting the oncogenes BCL2 [40] and CCND1 and WNT3A [24]. The

Fig. 2. Validation of miRNA microarray results by quantitative real-time polymerase chain reaction (qRT-PCR) in lung SCC samples for (A) miR551b, (B) miR-765, and (C) miR-126 in three pairs of samples checked by miRNA microarray and by qRT-PCR.

miR-15a level is significantly decreased in advanced prostate tumors, whereas the expression of BCL2, CCND1, and WNT3A is inversely upregulated [24], a finding that is consistent with the present study (Table 3). In line with our result of a downregulation of miR-101 in lung SCC, miR-101 has been observed to be decreased in human hepatocellular carcinoma [41] and colon cancer [42]. Furthermore, downregulation of

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miR-101 that was associated with upregulation of PTGS2 (alias COX2) and MCL1 contributed to the growth, invasiveness, and antiapoptotic character of tumor cells. Finally, only miR-185* and miR-125-5p were significantly upregulated in three SCC samples. MiR-185 was overexpressed in both kidney and bladder cancers [26]. The present findings demonstrate that some miRNAs are deregulated in lung SCC, suggesting the possible involvement of these genes in the development and progression of SCC.

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Fig. 3. The expression levels of (A) let-7i , (B) miR-125a-5p, and (C) miR126 in 20 pairs of SCC samples, along with (D) the folds of miRNA expression between lung SCC and normal lung tissue in 20 pairs of SCC samples.

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