Research Article Identification of Three Lncrnas As Potential Predictive Biomarkers of Lung Adenocarcinoma

Hindawi BioMed Research International Volume 2020, Article ID 7573689, 13 pages https://doi.org/10.1155/2020/7573689

Research Article Identification of Three lncRNAs as Potential Predictive Biomarkers of Lung Adenocarcinoma

Donghui Jin ,1 Yuxuan Song ,2 Yuan Chen ,1 and Peng Zhang 1

1Cardiovascular oracic Surgery Department, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China 2Department of Urology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China

Correspondence should be addressed to Peng Zhang; [email protected]

Received 20 October 2019; Accepted 25 January 2020; Published 20 February 2020

Academic Editor: Rosario Caltabiano

Copyright © 2020 Donghui Jin et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Lung cancer is the most common cancer and the most common cause of cancer-related death worldwide. However, the molecular mechanism of its development is unclear. It is imperative to identify more novel biomarkers. Methods. Two datasets (GSE70880 and GSE113852) were downloaded from the Gene Expression Omnibus (GEO) database and used to identify the differentially expressed genes (DEGs) between lung cancer tissues and normal tissues. *en, we constructed a competing endogenous RNA (ceRNA) network and a protein-protein interaction (PPI) network and performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analyses to identify potential biomarkers that are related to the diagnosis and prognosis of lung cancer. Results. A total of 41 lncRNAs and 805 mRNAs were differentially expressed in lung cancer. *e ceRNA network contained four lncRNAs (CLDN10-AS1, SFTA1P, SRGAP3-AS2, and ADAMTS9- AS2), 21 miRNAs, and 48 mRNAs. Functional analyses revealed that the genes in the ceRNA network were mainly enriched in cell migration, transmembrane receptor, and protein kinase activity. mRNAs DLGAP5, E2F7, MCM7, RACGAP1, and RRM2 had the highest connectivity in the PPI network. Immunohistochemistry (IHC) demonstrated that mRNAs DLGAP5, MCM7, RACGAP1, and RRM2 were upregulated in lung adenocarcinoma (LUAD). Survival analyses showed that lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 were associated with the prognosis of LUAD. Conclusion. lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9- AS2 might be the biomarkers of LUAD. For the first time, we confirmed the important role of lncRNA CLDN10-AS1 in LUAD.

1. Introduction Protein-coding genes are widely studied in lung cancer over the past decades, but the human genome transcribes less Lung cancer has the highest incidence of all cancers (11.6% than 2% of the protein-coding genes. 85% are noncoding of the total cases) and the highest death rate (18.4% of the RNAs, including long noncoding RNAs (lncRNAs) [6]. total cancer deaths) [1]. It contains two main types, small cell lncRNAs account for a large part of the human genome [7], (SCLC, 15% cases) and non-small-cell lung cancer (NSCLC, and they were once considered to be the insigniﬁcant “noise” 85% cases). Lung adenocarcinoma (LUAD) and squamous in genome’s repertoire of non-protein-coding transcripts. cell carcinoma (LUSC) are the main histological subtypes of However, recent studies have revealed the roles of lncRNAs in NSCLC [2]. *e underlying biological and molecular many biological processes, including transcriptional regula- mechanisms of lung cancer are gradually being understood tion and cell diﬀerentiation [8,9]. It is well recognized that over the past decades [3]. *e molecular biomarkers may dysregulation of lncRNAs plays an important role in cancer, improve the early lung cancer detection [4]. Furthermore, including lung cancer [10,11]. Nevertheless, a large number of traditional chemotherapy that is based on histopathology is lncRNAs are still unexplored [12]. *erefore, it is imperative replaced by the individualized precision treatment which is to recognize more lncRNAs as biomarkers of lung cancer for based on carcinogenic factors [5]. better diagnosis, therapy, and prediction of the prognosis. 2 BioMed Research International

Table 1: Details of datasets from Gene Expression Omnibus database. Series accession Platform Tumor Normal Contributors GSE70880 GPL19748 20 20 Yuan J., Yue H, Luo J., Chen R. GSE113852 GPL16847 27 27 Polycarpou-Schwarz M., Roth A, et al.

*is study aimed to explore more biomarkers of lung predicted by the miRcode database (http://www.mircode. cancer via integrated bioinformatics analysis. Gene Ex- org/) [13]. lncRNAs could competitively bind to the shared pression Omnibus (GEO) is a NCBI’s publicly available miRNAs. *e upregulation and downregulation of one genomics database (https://www.ncbi.nlm.nih.gov/gds/), lncRNA result in more and less sequestrated copies of which provides us a large amount of genomic data about shared miRNAs, respectively [14]. (2) miRNA-mRNA lung cancer. Two datasets (GSE70880 and GSE113852) were interactions were predicted by miRTarBase (http:// downloaded from GEO and used to identify the differentially mirtarbase.mbc.nctu.edu.tw/) [15], TargetScan (http:// expressed genes (DEGs) between lung cancer tissues and www.targetscan.org/) [16], and miRDB (http://www. normal tissues. *en, we constructed a competing endog- mirdb.org/miRDB/) [17]. *e interactions should match enous RNA (ceRNA) network and a protein-protein in- all the three databases. We selected the mRNAs that were teraction (PPI) network and performed gene ontology (GO) differentially expressed in our study from the target analysis, Kyoto Encyclopedia of Genes and Genomes mRNAs of miRNAs for the construction of the ceRNA (KEGG) pathway analysis, and survival analyses to identify network. We used the software Cytoscape (v3.7.1) to vi- potential biomarkers that are related to the diagnosis and sualize the network. prognosis of lung cancer. We verified the expression differences and measured the diagnostic roles of lncRNAs through *e Cancer Genome Atlas (TCGA) database 2.4. Functional Enrichment Analysis and PPI Network. (https://portal.gdc.cancer.gov/). Functional enrichment analysis includes GO and KEGG analysis. Genes in the ceRNA network were analyzed by GO 2. Materials and Methods and KEGG analysis by package clusterprofiler in software R. In GO analysis, functions were divided into biological 2.1. Gene Expression Microarray Datasets. We downloaded processes (BP), molecular functions (MF), and cellular gene expression microarray datasets GSE70880 and component (CC). PPI network was constructed by using GSE113852 from the GEO database. *e criteria they met STRING (http://string-db.org/cgi/input.pl). were as follows: (1) the studies were about lung cancer, (2) tissue samples included the tumor and corresponding adjacent tissues or normal tissues, (3) the number of total 2.5. Online Survival Analyses in LUAD and LUSC. To con- samples was not less than 40, and (4) the datasets must firm whether lncRNA is associated with the survival of include lncRNAs and mRNAs. LUAD and LUSC was one of our research contents. Due to lack of clinical information, we performed Kaplan–Meier (KM) analyses of lncRNAs in the ceRNA network and genes 2.2. Integrated Analysis of Microarray Datasets. We merged in the PPI network through the Kaplan–Meier Plotter the two datasets not only to increase the sample size but also (http://kmplot.com/analysis/), respectively [18]. We only to facilitate subsequent analyses. Based on different plat- performed the survival analyses of these lncRNAs and forms, days, environments, and people, the samples had mRNAs in LUAD and LUSC. Log rank P < 0.05 was con- heterogeneity and potential variables, which might lead to a sidered statistically significant. Genes related to the prog- bias. *erefore, we batch-normalized the merged dataset by nosis of LUAD or LUSC would be considered as the hub suing limma and sva packages in software R (v3.6.1). Next, genes. we performed gene differential analysis between tumor and normal tissues by limma package. |LogFC| > 1 and adjusted P value <0.05 (the correction for P value was done by 2.6. e Differences of Expression Levels and Diagnostic Roles Benjamini–Hochberg method) were considered statistically of lncRNAs Selected from ceRNA Network. To confirm significant for the DEGs. whether the expression levels of lncRNAs in the ceRNA network were different between normal and tumor tissues, 2.3. Construction of ceRNA Network. In order to find out the we downloaded the gene expression profile from TCGA competing endogenous regulating network mediated by database and performed gene differential analysis of lncRNAs and miRNAs, the ceRNA network was con- lncRNAs selected from the ceRNA network. P < 0.05 means structed. CeRNA networks link the functions of protein- there is a statistical difference. In order to measure the coding mRNAs with that of noncoding RNAs such as diagnostic values of these lncRNAs, we used the data from microRNA, long noncoding RNA, pseudogenic RNA, and TCGA database to perform receiver operating characteristic circular RNA [12]. *e construction of the ceRNA network (ROC) curves by GraphPad Prism 7.0, and the values of included two steps. (1) Interactions between differentially cutoff, specificity, sensitivity, and area under the curve expressed lncRNAs (DElncRNAs) and miRNAs were (AUC) were also calculated. *e cutoff value is the best BioMed Research International 3

Table 2: Differentially expressed lncRNAs and mRNAs of merged dataset. MNX1-AS1, FEZF1-AS1, CLDN10-AS1, LINC00511, LINC01635, LINC02544, LINC02362, AC105760, AC100861, Upregulated LINC02483, AC108134, AL391056, CASC9, LINC01876 lncRNAs PICSAR, AC007743, MIR3945HG, MIR99AHG, AC007405, NFIA-AS2, LINC00987, AC046195, LINC00968, COLCA1, Downregulated LINC00636, PTPRD-AS1, LINC01197, HHIP-AS1, LHFPL3-AS2, SMIM25, PCAT19, ADAMTS9-AS2, TBX5-AS1, TARID, AC008268, RAMP2-AS1, FENDRR, AC245041, SRGAP3-AS2, LINC01936, SFTA1P FAM83A, MMP12, ANLN, TOP2A, CTHRC1, TPX2, CST2, ASPM, SLC2A1, KIF20A, UBE2C, CDCA2, KIF4A, KIF2C, DNAJC22, BUB1, PITX1, RRM2, CCNB2, CKAP2L, E2F8, MCM10, CDC20, KIF14, UHRF1, TTK, DLGAP5, CDCA7, PLPP2, SAPCD2, CP, LGSN, FOXM1, KRT80, CLSPN, MMP11, CDK1, CDKN3, TOX3, SCG5, SULF1, HMMR, NUF2, KIF11, CDC25C, NDC80, SLC22A18AS, SPP1, CHEK1, CENPA, GPT2, PRAME, TK1, GALNT14, CCNA2, IGFL2, CENPU, FAP, STIL, SLC44A5, ARHGEF39, MAP7D2, CCNO, ESPL1, COL3A1, RAD51AP1, SGPP2, CCNE1, GPX2, OCIAD2, SEMA4B, E2F7, KIF15, AUNIP, ARNTL2, SYT12, RAB26, EZH2, LMNB1, SPC24, ATP10B, AGMAT, PLK1, PLOD2, RECQL4, FAM72A, CDKN2A, FERMT1, EGLN3, SERINC2, AGR2, BRIP1, FGF11, TFAP2A, ZWINT, S100P, CENPF, PI15, MCM4, GALNT6, HELLS, MB, ORC6, CELSR3, LY6K, ATAD2, RALGPS2, CYP27B1, SLC2A5, STYK1, GTSE1, MMP13, RHOV, POLE2, RNF183, RHBDL2, ITPKA, TMEM45B, SGO2, ONECUT1, PRSS3, KNL1, KCNN4, GYG2, ABCC3, TIMELESS, ECT2, XRCC2, EPCAM, DSG2, JPT1, FANCI, GRHL1, DEPDC1, BPIFA2, CIT, FAM83B, GPC2, LOXL2, CEACAM1, PARPBP, GALNT7, CHGB, WDR62, FHL2, HIST1H2BE, CENPM, VWDE, TRIM31, AKR1B10, SFXN1, HILPDA, MYBL2, HIST1H2BI, FGL1, SLC16A14, MELTF, CXCL9, UGT8, AK4, STK32A, Upregulated ADGRF1, NGEF, TFR2, KIF18A, HNF4G, PFKP, MZB1, UCHL1, ADAMTS16, RACGAP1, SLC17A9, CD19, P4HA3, ETV4, TNS4, TTYH3, SLFN13, STRA6, CARD14, CDCA8, CKS1B, FAM83D, CHI3L1, TNFRSF21, HIST1H2BC, FOXP3, CCDC34, FBXO32, GLYATL1, GSDMB, ESRP1, ARHGEF16, GAD1, SRPK1, TEX11, SLC7A5, HIST1H3H, TDRD5, FAM83F, FANCA, IL4I1, IGSF9, ESCO2, PPAT, HCN4, HIST1H2BG, KIF26B, MYO19, BICDL1, TDO2, CNTNAP5, BAIAP2L1, CPNE4, SLC12A8, LGI2, POC1A, LARGE2, RCC1, KIF18B, C12orf56, ZBED6CL, DBNDD1, CDC7, SYNJ2, NLN, FCN2, NECTIN4, LSR, CA12, MFAP2, UNC5CL, PTGES, BRCA1, PROM2, CGREF1, VTCN1, MTNR1A, SLC35F2, COL22A1, HNF4A, ERO1A, GSDMC, VCAN, PDCD2L, BZW2, KRTCAP3, AKR1B15, DTL, NCAPG2, SMKR1, PRSS1, SERPINB5, SHMT2, CPXM1, CHEK2, MCIDAS, EEF1AKMT4, GPR87, TONSL, GRHL2, SLC50A1, KCNQ5, CNTNAP2, C1orf53, BPIFB6, PRR19, PRR11, POSTN, KCNK1, MCM7, CIP2A, HIST3H2A, GAPDH, TUBB2B, BCO1, SUSD4, MTBP, LRRC59, STRIP2, PTHLH, PPIF, CLDN4, RNFT2, SYT7, SSR4, E2F1, VIL1, FCRL2, KIF22, BRDT, RNF43, TPD52, AOC1, JPT2, CDH1, DIRAS1, SCIN, TIGIT, PMAIP1, B3GNT5, MYEOV, VPREB3, P3H4, KLK6, BRI3BP, SLC39A11 SLC25A27, FAM184A, ZNF106, TJP1, TEKT2, PCYT1B, ITPRIP, EDIL3, GFOD1, SFTA3, TTLL10, AMIGO1, MT1E, SMIM10, LONRF1, PROK2, CFAP157, PODXLRSPO3, ELMO1, KLHDC1, SHROOM4, SMAD6, GPR146, SULT1A2, RASGRF1, NPR3, CD55, MACF1, TTLL11, MATN2, MRAS, ZNF608, PPP1R14C, RND3, IER2, DCN, PTPRG, TAL1, SLC2A3, FRAS1, PIFO, AMOTL2, MAP6, SFRP5, PLA1A, SLC27A3, ARHGAP20, RAB11FIP1, COBL, GNG7, TCF4, SLCO1A2, USP2, MAP7D3, SIRPB1, SORBS3, BMX, MYLK, DUOXA1, CNGA4, CACHD1, C1orf194, RASSF2, EFCAB12, SECISBP2L, SNRK, RGS22, SYNPO2, PTPRN2, ITGA10, NHSL1, HOXA4, CCL3, RAB11A, BMP6, PHLDB2, MYOCD, TSLP, MFSD2A, CADM1, GALNT16, VIM, MVB12B, TFPI, ADGRG6, CLEC12A, NXF3, ADPRH, mRNAs ERG, GCOM1, KANK3, C1orf189, VSIR, SLC9A3R2, WNT11, HGF, LMO7, CES4A, PNPLA6, SPEF1, C1orf198, CST6, ABLIM1, PRICKLE2, ID2, PKNOX2, RPS6KA2, EFHB, SLC18A2, APOLD1, SPI1, ACACB, ID3, ADAMTS15, ECM2, SYNDIG1L, ELF5, DCDC2, HYAL2, HACD4, CD300LG, RBP7, TLL1, FAT4, BMPR2, GMFG, TMEM130, CCDC68, SIGLEC6, DRC3, SOX5, ARAP3, TSPAN19, JPH2, SYDE2, MYRIP, THSD4, CSRP1, FILIP1, VWA3A, LAMA4, SCTR, SPATA4, LBH, PTPN13, LAMA3, DISP1, HMGCLL1, ABHD6, STARD9, DENND3, NCALD, WNT2B, NFASC, MAPK4, DTHD1, ESYT3, NEXN, DNAJB4, TMEM139, ACOXL, ST6GALNAC3, MAP2, MCC, ATOH8, AKAP12, NEBL, CAV1, ZEB1, CLDN5, EPB41L2, RECK, TRPC6, TTLL7, SERTAD1, ANGPT1, SLC14A1, PYGM, CYB5A, IGSF22, DOCK4, SESN1, DGKG, SELENBP1, TUBB6, C1orf162, NLRC4, MSR1, NKD2, ALDH1A2, MEIS1, ETS2, PHACTR1, PRDM6, TSPAN18, AC023509, ADCY4, STAC, CDH19, JCAD, SERPINA1, SOBP, FLRT3, MAL, COLEC12, PIK3R1, IQCN, FEZ1, CASS4, NAPSA, NAV3, PLXNA2, NDRG4, CLDN18, RGS9, MSLN, FGR, EML1, RASGEF1B, PLCE1, RBMS2, NIM1K, SH3D19, METTL7A, RUNX1T1, LRRN4, PIGR, CALCOCO1, C14orf132, KL, SCEL, C16orf89, CFAP43, PDZRN3, FRMD3, CTSG, KLF6, GATA2, NR4A1, FGFR2, GIMAP1, CRIM1, C9orf24, FLI1, downregulated MGP, CXCL12, MFAP5, DNAAF1, PGM5, AATK, WFS1, SGIP1, HYAL1, SOX13, WNT7A, STARD13, NFIX, KCNK17, MDH1B, ROS1, ADGRE1, GATA6, DMD, ART4, USHBP1, SEMA6A, CA1, ADGRL2, CTXND1, TACC1, FAT3, SEMA6D, CPED1, ABCG2, IL7R, CD34, ARHGAP6, HECW2, ITLN1, ENG, DKK2, PRKG1, LRRC36, CHST9, SEMA3D, CFAP221, GADD45B, PTGFR, IRX2, PLCL1, SH3GL2, CAVIN1, SORBS1, SEMA3B, DNAI2, ARHGEF26, RADIL, GLIPR2, NKD1, SCN4B, RAI2, CHIA, PIP5K1B, RSPH9, SYNE1, RASIP1, TTN, SELENOP, ABCA6, TEX14, RAPGEF4, PECAM1, SLCO2A1, AKAP14, OTUD1, NTNG1, HEG1, MAB21L4, SPTBN1, RSPO2, ZBTB16, DPEP2, FBLN5, TPPP, SLC46A2, SASH1, PCDHGC3, LRRC32, CORO2B, TNS2, OLFML1, COL6A5, PALMD, CASKIN2, ADTRP, COL13A1, PDE5A, JAML, DPYSL2, NRN1, LMCD1, GYPC, PLCB4, CRTAC1, RANBP3L, P3H2, GUCY1A2, GPM6B, NDRG2, PTPRM, EDNRB, PGC, PRX, NEDD9, DENND2A, ESAM, PLEKHH2, LEPR, PDZRN4, LMO2, TMEM204, FBLN1, ODAM, MYH11, ADGRD1, THSD1, TMEM212, WASF3, MYRF, ATF3, MSRB3, ZNF366, VSIG4, FGD5, PTPRB, DLC1, TNS1, FGF10, HIF3A, C1QTNF7, RHOJ, CLEC1A, PPP1R15A, ATP13A4, CXCL2, KLF2, LAMC3, ANXA3, TIE1, SULT1C4, SUSD2, SELP, PPARG, CSRNP1, WFDC1, ITIH5, SFTPB, NOSTRIN, JAM2, SMTNL2, PKHD1L1, PID1, GRK5, KCNK3, PDZD2, DUSP1, LRRK2, LIMCH1, PCOLCE2, LDB2, GPRC5A, IL1RL1, FAM13C, SVEP1, AFF3, HLF, RP1, NCKAP5, ERICH3, DUOX1, EMP2, EPAS1, CCDC141, PPP1R14A, SLIT2, CLEC14A, MMRN1, PDK4, SLC39A8, EDN1, ADAMTS1, VSIG2, COX4I2, ABCA8, GRASP, ABCA3, MYCT1, ROBO2, CALCRL, ANXA8, ANKRD29, CDH5, LIMS2, TEKT1, ZNF385B, NECAB1, ROBO4, SLIT3, ADAMTSL3, PLA2G4F, VIPR1, LTBP4, GPC3, KLF4, C11orf88, CACNA2D2, CNTFR, VEPH1, ADAMTS8, PI16, FGFR4, RAMP3, RGCC, ABI3BP, HHIP, CA4, TGFBR3, CLIC5, RXFP1, CCL14, COL6A6, CYP4B1, SCARA5, TPPP3, ACKR1, AOC3, ACTN2, GRIA1, MFAP4, SCUBE1, STXBP6, DNASE1L3, GPM6A, CLEC3B, RTKN2, TNXB, LGI3, MAMDC2, LYVE1, BTNL9, FOSB, GKN2, FCN3, DES, FHL1, FAM107A, SFTPC 4 BioMed Research International expression of a lncRNA for the diagnosis of lung cancer. P < 0.05 indicated significant difference. 4

3. Results 2 3.1. Gene Expression Proﬁle Data. *ere were two datasets and a total of 47 lung cancer tissue samples and 47 normal 0

tissue samples in this study (Table 1). We merged the two LogFC datasets into one dataset and then batch-normalized it to reduce variability. 846 genes (321 genes were upregulated −2 and 525 genes were downregulated) were confirmed as DEGs. We divided the DEGs into mRNA group (a total of 805 mRNAs, 307 mRNAs were upregulated and 498 mRNAs −4 were downregulated) and lncRNA group (a total of 41 lncRNAs, 14 lncRNAs were upregulated and 27 lncRNAs 0 5 10152025 were downregulated) (Table 2). Volcano plot showed the −log10 (adj.P.Val) results of gene differential analysis (Figure 1). Heatmaps Figure 1: Volcano plot of differentially expressed genes in the showed gene expression changes (Figure 2). merged dataset. Red dots represent genes upregulation and based on adjusted P < 0.05 and logFC > 1; green dots represent genes P . − 3.2. ceRNA Network. A total of 4 lncRNAs (CLDN10-AS1, downregulation and based on adjusted < 0 05 and logFC < 1; black dots represent genes being not significantly differentially SFTA1P, SRGAP3-AS2, and ADAMTS9-AS2), 21 miRNAs, expressed between tumor tissues and normal tissues. FC � fold and 48 mRNAs were involved in the ceRNA network change. (Figure 3). In tumor tissues, CLDN10-AS1 was upregulated, while SFTA1P, SRGAP3-AS2, and ADAMTS9-AS2 were downregulated. *e number of nodes and lines in the had the highest connectivity (DLGAP5, E2F7, MCM7, network was 73 and 85, respectively. Among the 4 lncRNAs, RACGAP1, and RRM2, and the number of genes connected the number of miRNAs that connected with ADAMTS9- with each of the five genes was seven). All of them were AS2 was larger than that of other lncRNAs, which indicated upregulated. Immunohistochemistry (IHC) in *e Human the important role of ADAMTS9-AS2. miRNAs miR-125a- Protein Atlas (THPA) (https://www.proteinatlas.org/) da- 5p and miR-125b-5p were only connected with lncRNA tabase demonstrated that DLGAP5, MCM7, RACGAP1, and CLDN10-AS1, which showed the special role of CLDN10- RRM2 were upregulated in LUAD (Figure 6). AS1 among the four lncRNAs. 3.5. Survival Analyses in LUAD and LUSC. We uploaded the 3.3. GO and KEGG Pathway Analysis. GO terms and KEGG four lncRNAs and five mRNAs to the Kaplan–Meier Plotter pathway analyses were performed by package clusterProfiler (http://kmplot.com/analysis/) to perform survival analyses in software R. *e results showed that biological processes in LUAD and LUSC, respectively (Figures 7(a) and 7(b)). were mainly associated with epithelial cell migration, reg- *e results showed that lncRNAs CLDN10-AS1 (logrank ulation of epithelial cell migration, ameboidal-type cell P � 0.026), SFTA1P (logrank P � 0.00071), and ADAMTS9- migration, endothelial cell migration, endothelium devel- AS2 (logrank P � 0.0082) were associated with prognosis of opment, and so on, while molecular functions were asso- LUAD. Upregulation of CLDN10-AS1 predicted poor ciated with carbohydrate binding, transmembrane receptor prognosis, while upregulation of the SFTA1P and protein serine/threonine kinase activity, transmembrane ADAMTS9-AS2 was related to good prognosis. mRNAs receptor protein tyrosine phosphatase activity, transmem- DLGAP5 (logrank P � 4.7e − 05), E2F7 (logrank brane receptor protein phosphatase activity, carboxylic acid P � 9.8e − 05), MCM7 (logrank P � 0.031), RACGAP1 binding, and organic acid binding. Cellular component (logrank P � 2.3e − 05), and RRM2 (logrank P � 0.0062) gathered in the basal part of cell, MCM complex, pore were associated with prognosis of LUAD. *eir high ex- complex, cell-cell adherens junction, and basal plasma pression related to poor prognosis. No lncRNAs and membrane (Figure 4). KEGG pathway analysis revealed the mRNAs were associated with the prognosis of LUSC. genes were only enriched in cell adhesion molecules (CAMs) DLGAP5, E2F7, MCM7, RACGAP1, and RRM2 were and axon guidance (Figure 5(a)). considered as hub genes. *e heatmap showed the expression changes of the three lncRNAs and five mRNAs in the merged dataset (Figure 8). 3.4. Protein-Protein Interaction. We uploaded the mRNAs Furthermore, we performed survival analyses of the in the ceRNA network to STRING to construct a PPI three lncRNAs and five mRNAs in the cohorts of stage I network (Figure 5(b)). *ere were a total of 25 nodes and 42 LUAD patients, stage II LUAD patients, LUAD patients with edges in the network. PPI enrichment P value was smoking history, and LUAD patients without smoking 1.07e − 07. We calculated the number of genes connected to history. *e results showed that lncRNAs SFTA1P (logrank each gene in the PPI network. Results revealed that five genes P � 0.00027) and ADAMTS9-AS2 (logrank P � 0.00048) BioMed Research International 5

Type 16 Type 12 GEO 14 GEO 10 12 AC008268 8 10 LHFPL3−AS2 8 AC007743 6 6 4 LINC00987 4 LINC01197 MIR3945HG HHIP−AS1 FENDRR LINC01936 TBX5−AS1 AC108134 SFTA1P AC245041 CLDN10−AS1 FEZF1−AS1 LINC01876 AC105760 LINC01635 LINC02483 AL391056 LINC02362 SRGAP3−AS2 TARID LINC00968 NFIA−AS2 RAMP2−AS1 PTPRD−AS1 COLCA1 SMIM25 AC007405 MIR99AHG ADAMTS9−AS2 PCAT19 AC046195 LINC00636 PICSAR CASC9 MNX1−AS1 LINC02544 LINC00511 AC100861 GSM1821205 GSM3121286 GSM3121300 GSM3121314 GSM3121328 GSM1821195 GSM1821197 GSM1821199 GSM1821201 GSM1821203 GSM1821207 GSM1821209 GSM1821211 GSM1821213 GSM1821215 GSM1821217 GSM1821219 GSM1821221 GSM1821223 GSM1821225 GSM1821227 GSM1821229 GSM1821231 GSM1821233 GSM1821196 GSM1821198 GSM1821200 GSM1821202 GSM1821204 GSM1821206 GSM1821208 GSM1821210 GSM1821212 GSM1821214 GSM1821216 GSM1821218 GSM1821220 GSM1821222 GSM1821224 GSM1821226 GSM1821228 GSM1821230 GSM1821232 GSM1821234 GSM3121285 GSM3121287 GSM3121288 GSM3121289 GSM3121290 GSM3121291 GSM3121292 GSM3121293 GSM3121294 GSM3121295 GSM3121296 GSM3121297 GSM3121298 GSM3121299 GSM3121301 GSM3121302 GSM3121303 GSM3121304 GSM3121305 GSM3121306 GSM3121307 GSM3121308 GSM3121309 GSM3121310 GSM3121311 GSM3121312 GSM3121313 GSM3121315 GSM3121316 GSM3121317 GSM3121318 GSM3121319 GSM3121320 GSM3121321 GSM3121322 GSM3121323 GSM3121324 GSM3121325 GSM3121326 GSM3121327 GSM3121329 GSM3121330 GSM3121331 GSM3121332 GSM3121333 GSM3121334 GSM3121335 GSM3121336 GSM3121337 GSM3121338 GSM1821195 GSM1821197 GSM1821199 GSM1821201 GSM1821203 GSM1821207 GSM1821211 GSM1821213 GSM1821215 GSM1821217 GSM1821219 GSM1821221 GSM1821223 GSM1821225 GSM1821229 GSM1821233 GSM1821196 GSM1821198 GSM1821200 GSM1821202 GSM1821204 GSM1821206 GSM1821212 GSM1821214 GSM1821216 GSM1821218 GSM1821220 GSM1821222 GSM1821226 GSM1821230 GSM1821232 GSM1821234 GSM3121285 GSM3121286 GSM3121287 GSM3121288 GSM3121291 GSM3121292 GSM3121293 GSM3121294 GSM3121295 GSM3121296 GSM3121297 GSM3121299 GSM3121301 GSM3121302 GSM3121303 GSM3121304 GSM3121305 GSM3121306 GSM3121307 GSM3121310 GSM3121311 GSM3121312 GSM3121313 GSM3121314 GSM3121315 GSM3121316 GSM3121317 GSM3121319 GSM3121321 GSM3121322 GSM3121323 GSM3121324 GSM3121325 GSM3121327 GSM3121328 GSM3121329 GSM3121330 GSM3121331 GSM3121332 GSM3121333 GSM3121334 GSM3121335 GSM3121336 GSM1821205 GSM1821209 GSM1821227 GSM1821231 GSM1821208 GSM1821210 GSM1821224 GSM1821228 GSM3121289 GSM3121290 GSM3121298 GSM3121300 GSM3121308 GSM3121309 GSM3121318 GSM3121320 GSM3121326 GSM3121337 GSM3121338 Type GEO Type GEO N GSE113852 N GSE113852 T GSE70880 T GSE70880 (a) (b)

Type 16 GEO 14 12 10 8 6 4 GSM1821195 GSM1821197 GSM1821199 GSM1821201 GSM1821211 GSM1821213 GSM1821215 GSM1821217 GSM1821219 GSM1821229 GSM1821231 GSM1821233 GSM1821196 GSM1821198 GSM1821200 GSM1821208 GSM1821210 GSM1821212 GSM1821214 GSM1821216 GSM1821218 GSM1821228 GSM1821230 GSM1821232 GSM1821234 GSM3121285 GSM3121286 GSM3121290 GSM3121291 GSM3121292 GSM3121293 GSM3121294 GSM3121295 GSM3121300 GSM3121301 GSM3121302 GSM3121303 GSM3121304 GSM3121309 GSM3121310 GSM3121311 GSM3121312 GSM3121313 GSM3121314 GSM3121316 GSM3121318 GSM3121319 GSM3121321 GSM3121322 GSM3121323 GSM3121328 GSM3121329 GSM3121330 GSM3121331 GSM3121332 GSM3121337 GSM3121338 GSM1821203 GSM1821205 GSM1821207 GSM1821209 GSM1821221 GSM1821223 GSM1821225 GSM1821227 GSM1821202 GSM1821204 GSM1821206 GSM1821220 GSM1821222 GSM1821224 GSM1821226 GSM3121287 GSM3121288 GSM3121289 GSM3121296 GSM3121297 GSM3121298 GSM3121299 GSM3121305 GSM3121306 GSM3121307 GSM3121308 GSM3121315 GSM3121317 GSM3121320 GSM3121324 GSM3121325 GSM3121326 GSM3121327 GSM3121333 GSM3121334 GSM3121335 GSM3121336 Type GEO N GSE113852 T GSE70880 (c)

Figure 2: Heatmaps of differentially expressed genes in the merged dataset. (a) Heatmap of all differentially expressed genes. (b) Heatmap of differentially expressed lncRNAs. (c) Heatmap of differentially expressed mRNAs. *e color represents the level of gene expression. From green to black to red, the levels of gene expression are increasing. lncRNA: long noncoding RNA; mRNA: messenger RNA; N: normal tissues; T: tumor tissues. 6 BioMed Research International

PPIF GATA2 LRRC59 PPARG LONRF1 VCAN hsa-miR-613 ZEB1 NLN TGFBR3 SEMA6D SRGAP3-AS2 hsa-miR-206 hsa-miR-27a-3p RUNX1T1

TPPP PIK3R1 hsa-miR-107 SNRK SFTA1P hsa-miR-23b-3p MCM7 GALNT7 MMP11 SEMA6A CADM1 LIMCH1 hsa-miR-137 E2F7 hsa-miR-125a-5p hsa-miR-363-3p hsa-miR-216b-5p hsa-miR-10a-5p MCM4 hsa-miR-129-5p CLDN10-AS1 hsa-miR-3619-5p ADAMTS9-AS2 BMPR2 PPAT hsa-miR-761 PNPLA6 hsa-miR-338-3p hsa-miR-125b-5p hsa-miR-1297 hsa-miR-507 ZWINT PTPRM ZNF608 CDH5 RACGAP1 STARD13 DEPDC1 hsa-miR-33a-3p LOXL2 PMAIP1 PTPRG hsa-miR-301b-3p RRM2

EDN1 hsa-miR-24-3p DLGAP5 SECISBP2L DPYSL2 EGLN3 ARHGEF26 CD34 MCC AMOTL2 EMP2 SESN1 MT1E

Figure 3: Competing endogenous RNA (ceRNA) network. Red diamond represents lncRNAs, green triangle represents miRNAs, and blue circle represents mRNAs. lncRNA: long noncoding RNA; miRNA: micro-RNA; mRNA: messenger RNA.

Dotplot of biological processes Dotplot of molecular functions Epithelial cell migration Epithelium migration Tissue migration Carbohydrate binding Ameboidal−type cell migration Regulation of epithelial cell migration Count Endothelial cell migration 2 Negative regulation of cellular component movement Carboxylic acid binding Regulation of angiogenesis 3 Epithelial cell proliferation Regulation of vasculature development 4 Regulation of endothelial cell migration Negative regulation of cell migration Organic acid binding 5 Negative regulation of cell motility Negative regulation of locomotion P.adjust Response to oxygen levels Transmembrane receptor protein serine/ Endothelium development Endothelial cell proliferation threonine kinase activity Response to hypoxia P.adjust Response to decreased oxygen levels Regulation of epithelial cell proliferation Transmembrane receptor protein 0.04234551 Regulation of body fluid levels tyrosine phosphatase activity Negative regulation of epithelial cell migration 0.01 Regulation of blood pressure Sprouting angiogenesis 0.02 Transmembrane receptor protein Axon guidance phosphatase activity Neuron projection guidance 0.03 Cellular response to oxygen levels Connective tissue development 0.04 G1/S transition of mitotic cell cycle 0.04 0.06 0.08 0.10 cell cycle G1/S phase transition Gene ratio Endothelial cell differentiation Regulation of fat cell differentiation (b) Placenta development Count Actomyosin structure organization Negative regulation of epithelial cell proliferation 2 Regulation of endothelial cell proliferation 4 Dotplot of cellular component Response to starvation 6 Negative regulation of angiogenesis 8 Negative regulation of blood vessel morphogenesis Blood vessel endothelial cell migration 10 Basal part of cell Count Cartilage development Negative regulation of vasculature development 2.00 Response to prostaglandin 2.25 Negative regulation of fat cell differentiation Activation of cysteine−type endopeptidase activity involved in apoptotic process Cell−cell adherens junction 2.50 Animal organ regeneration 2.75 Vasculogenesis Regulation of systemic arterial blood pressure 3.00 Response to fatty acid Regulation of actomyosin structure organization MCM complex Positive regulation of phagocytosis, engulfment P.adjust Positive regulation of membrane invagination Regulation of phagocytosis, engulfment 0.02 Regulation of vasculogenesis Pore complex Regulation of membrane invagination 0.03 Regulation of receptor biosynthetic process Definitive hemopoiesis Glomerular filtration 0.04 Response to prostaglandin E Renal filtration Basal plasma membrane Receptor biosynthetic process Regulation of odontogenesis 0.05 0.10 0.15 0.20 0.045 0.050 0.055 0.060 0.065 Gene ratio Gene ratio (a) (c) Figure 4: Dotplots of gene ontology (GO) analysis. (a) Dotplot of biological processes. (b) Dotplot of molecular functions. (c) Dotplot of cellular component. BioMed Research International 7

Dotplot of KEGG pathway P.adjust Count 0.018 5 Cell adhesion molecules (CAMs) 0.019 0.020 0.021 Axon guidance 0.022 0.023 0.150 0.175 0.200 0.225 Gene ratio

(a) PPI network

(b)

Figure 5: Dotplot of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network. (a) Dotplot of KEGG pathway analysis. (b) PPI network.

DLGAP5 MCM7 RACGAP1 RRM2 Normal

Tumor

Figure 6: Immunohistochemistry (IHC) of DLGAP5, MCM7, RACGAP1, and RRM2 expression in lung adenocarcinoma (LUAD) and paired with normal tissue based on *e Human Protein Atlas (THPA). DLGAP5, MCM7, RACGAP1, and RRM2 were upregulated in LUAD. 8 BioMed Research International

CLDN10−AS1 ADAMTS9−AS2 SFTA1P DLGAP5 1.0 1.0 1.0 1.0 HR = 1.31 (1.03 − 1.67) HR = 0.72 (0.57 − 0.92) HR = 0.66 (0.52 − 0.84) HR = 1.63 (1.29 − 2.07) Logrank P = 0.026 Logrank P = 0.0082 Logrank P = 0.00071 Logrank P = 4.7e −05 0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4 Probability Probability Probability Probability 0.2 0.2 0.2 0.2

0.0 0.0 0.0 0.0 0 50 100 150 200 0 50 100 150 200 0 50 100 150 200 0 50 100 150 200 Time (months) Time (months) Time (months) Time (months) Expression Expression Expression Expression Low Low Low Low High High High High Number at risk Number at risk Number at risk Number at risk Low 337 189 35 9 0 Low 338 171 29 11 1 Low 336 149 30 11 1 Low 361 199 33 7 1 High 336 156 34 10 1 High 335 174 40 8 0 High 337 196 39 8 0 High 359 149 36 12 0 (a) E2F7 MCM7 RACGAP1 RRM2 1.0 1.0 1.0 1.0 HR = 1.61 (1.27 − 2.06) HR = 1.29 (1.02 − 1.63) HR = 1.67 (1.31 − 2.11) HR = 1.39 (1.1 − 1.75) Logrank P = 9.8e −05 Logrank P = 0.031 Logrank P = 2.3e −05 Logrank P = 0.0062 0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4 Probability Probability Probability Probability 0.2 0.2 0.2 0.2

(b) ADAMTS9−AS2 SFTA1P RACGAP1 RRM2 1.0 1.0 1.0 1.0 HR = 0.48 (0.32 − 0.73) HR = 0.47 (0.31 − 0.71) HR = 2.76 (1.79 − 4.26) HR = 1.63 (1.09 − 2.42) Logrank P = 0.00048 Logrank P = 0.00027 Logrank P = 1.7e −06 Logrank P = 0.015 0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4 Probability Probability Probability Probability 0.2 0.2 0.2 0.2

0.6

0.4 Probability 0.2

0.0 0 50 100 150 Time (months) Expression Low High Number at risk Low 59 25 4 1 High 59 18 2 0

(d) Figure 7: Continued. BioMed Research International 9

ADAMTS9−AS2 SFTA1P RACGAP1 1.0 1.0 1.0 HR = 0.43 (0.25 − 0.72) HR = 0.5 (0.31 − 0.83) HR = 2.52 (1.53 − 4.18) Logrank P = 0.0012 Logrank P = 0.006 Logrank P = 0.00019 0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4 Probability Probability Probability 0.2 0.2 0.2

0.0 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Time (months) Time (months) Time (months) Expression Expression Expression Low Low Low High High High Number at risk Number at risk Number at risk Low 122 98 71 47 11 3 2 Low 116 93 59 38 9 3 2 Low 123 111 82 47 13 4 1 High 109 100 69 41 12 4 0 High 115 105 81 50 14 4 0 High 123 101 66 42 10 3 1 (e) CLDN10−AS1 ADAMTS9−AS2 DLGAP5 1.0 1.0 1.0

0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4 Probability Probability Probability 0.2 HR = 3.68 (1.37 − 9.91) 0.2 HR = 0.36 (0.14 − 0.93) 0.2 HR = 3.3 (1.31 − 8.33) Logrank P = 0.0057 Logrank P = 0.027 Logrank P = 0.0074 0.0 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Time (months) Time (months) Time (months) Expression Expression Expression Low Low Low High High High Number at risk Number at risk Number at risk Low 70 70 58 38 12 4 0 Low 70 66 57 38 15 4 0 Low 72 70 61 47 11 3 0 High 70 66 57 38 12 1 0 High 70 70 58 38 9 1 0 High 71 68 56 29 13 2 0

(f)

Figure 7: Kaplan–Meier (KM) analysis results of three lncRNAs and ﬁve mRNAs in cohorts of (a, b) LUAD patients, (c) stage I LUAD patients, (d) stage II LUAD patients, (e) LUAD patients with smoking history, and (f) LUAD patients without smoking history. lncRNA: long noncoding RNA. LUAD: lung adenocarcinoma.

Type 12 GEO

CLDN10−AS1 10

SFTA1P 8

ADAMTS9−AS2 6

DLGAP5 4

E2F7

MCM7

RACGAP1

RRM2B GSM1821195 GSM1821197 GSM1821199 GSM1821201 GSM1821203 GSM1821205 GSM1821207 GSM1821209 GSM1821211 GSM1821213 GSM1821215 GSM1821217 GSM1821219 GSM1821221 GSM1821223 GSM1821225 GSM1821227 GSM1821229 GSM1821231 GSM1821233 GSM1821196 GSM1821198 GSM1821200 GSM1821202 GSM1821204 GSM1821206 GSM1821208 GSM1821210 GSM1821212 GSM1821214 GSM1821216 GSM1821218 GSM1821220 GSM1821222 GSM1821224 GSM1821226 GSM1821228 GSM1821230 GSM1821232 GSM1821234 GSM3121285 GSM3121286 GSM3121287 GSM3121288 GSM3121289 GSM3121290 GSM3121291 GSM3121292 GSM3121293 GSM3121294 GSM3121295 GSM3121296 GSM3121297 GSM3121298 GSM3121299 GSM3121300 GSM3121301 GSM3121302 GSM3121303 GSM3121304 GSM3121305 GSM3121306 GSM3121307 GSM3121308 GSM3121309 GSM3121310 GSM3121311 GSM3121312 GSM3121313 GSM3121314 GSM3121315 GSM3121316 GSM3121317 GSM3121318 GSM3121319 GSM3121320 GSM3121321 GSM3121322 GSM3121323 GSM3121324 GSM3121325 GSM3121326 GSM3121327 GSM3121328 GSM3121329 GSM3121330 GSM3121331 GSM3121332 GSM3121333 GSM3121334 GSM3121335 GSM3121336 GSM3121337 GSM3121338 Type GEO N GSE113852 T GSE70880 Figure 8: *e heatmap showed the expression of the three lncRNAs and ﬁve mRNAs in the merged dataset. lncRNA: long noncoding RNA. N: normal tissues. T: tumor tissues. 10 BioMed Research International

15 P = 3.507e − 17 300 P = 2.162e − 23

250

10 200

150

5 100 SFTA1P expression 50 CLDN10−AS1 expression

0 0 Normal Tumor Normal Tumor (a) (b) 3.0 P = 2.735e − 23 40 P = 9.904e − 13

2.5 30 2.0

1.5 20

1.0 10

0.5 SRGAP3−AS2 expression ADAMTS9−AS2 expression 0.0 0 Normal Tumor Normal Tumor (c) (d)

Figure 9: Expression levels of four lncRNAs in lung adenocarcinoma (LUAD) in TCGA. lncRNA: long noncoding RNA. TCGA: the cancer genome atlas. and mRNAs RACGAP1 (logrank P � 1.7e − 06) and RRM2 4. Discussion (logrank P � 0.015) were associated with the prognosis of stage I LUAD patients (Figure 7(c)); only mRNA E2F7 In the present study, we investigated the gene expression (logrank P � 0.031) was associated with the prognosis of patterns of lncRNAs and mRNAs in lung cancer cells and stage II LUAD patients (Figure 7(d)); lncRNAs SFTA1P corresponding adjacent tissues or normal tissues. We found (logrank P � 0.006), ADAMTS9-AS2 (logrank P � 0.0012), that 525 genes were downregulated and 321 genes were and mRNA RACGAP1 (logrank P � 0.00019) were asso- upregulated in lung cancer cells. *e ceRNA network ciated with the prognosis of LUAD patients with smoking revealed the correlation among lncRNAs, miRNAs, and history (Figure 7(e)); lncRNA CLDN10-AS1 (logrank mRNAs. Four lncRNAs (CLDN10-AS1, SFTA1P, SRGAP3- P � 0.0057), ADAMTS9-AS2 (logrank P � 0.027), and AS2, and ADAMTS9-AS2) were involved in ceRNA network mRNA DLGAP5 (logrank P � 0.0074) were associated with and survival analyses showed that CLDN10-AS1, SFTA1P, the prognosis of LUAD patients without smoking history and ADAMTS9-AS2 were associated with prognosis of (Figure 7(f)). LUAD. *e PPI network showed the interaction among these mRNAs in ceRNA network and five mRNAs (DLGAP5, E2F7, MCM7, RACGAP1, and RRM2) had the 3.6. Expression Levels and Diagnostic Values of Four lncRNAs highest connectivity. Survival analyses also revealed the in LUAD Confirmed through TCGA. A total of 551 samples relationship between the five mRNAs and the prognosis of (54 normal samples and 497 tumor samples) of LUAD were LUAD, and they were confirmed as hub genes of LUAD downloaded from the TCGA database. After gene differ- finally. However, survival analyses indicated that these ential analysis, we observed that, in tumor samples, lncRNAs and mRNAs were not related to the prognosis of CLDN10-AS1 was upregulated, while SFTA1P, SRGAP3- LUSC. To increase the credibility of our results, we verified AS2, and ADAMTS9-AS2 were downregulated (Figure 9). the expression levels and measured the prognostic values of *e results were consistent with our previous conclusions. the four lncRNAs in LUAD through TCGA database. *e Furthermore, the ROC curves showed that all of the four results confirmed our conclusions and revealed that all the lncRNAs had diagnostic values in LUAD (Table 3, four lncRNAs were associated with the diagnosis of LUAD. Figure 10). lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2, and BioMed Research International 11

SFTA1P ADAMTS9-AS2 1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 Sensitivity AUC = 0.9126 Sensitivity AUC = 0.9116 0.2 P < 0.0001 0.2 P < 0.0001

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 1 – specificity 1 – specificity Sensitivity Sensitivity Identity Identity

SRGAP3-AS2 CLDN10-AS1 1.0 1.0

0.8 0.8

0.6 0.6

AUC = 0.8470 0.4 0.4

Sensitivity Sensitivity P < 0.0001 AUC = 0.7945 0.2 P < 0.0001 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.1 0.2 0.3 0.4 1.5 1 – specificity 1 – specificity Sensitivity Sensitivity Identity Identity

Figure 10: Receiver-operating characteristic (ROC) curves of four lncRNAs in lung adenocarcinoma (LUAD).

Table 3: Diagnostic values of the four lncRNAs. lncRNA Cutoff value AUC (95% CI) Sensitivity (%) Specificity (%) SFTAP1 39.89 0.9126 (0.886–0.935) 82.29 100.00 ADAMTS9-AS2 0.212 0.9116 (0.885–0.934) 81.49 96.30 SRGAP3-AS2 0.921 0.7945 (0.758–0.828) 73.04 75.93 CLDN10-AS1 0.078 0.847 (0.814–0.876) 72.03 96.30 AUC: area under the curve; CI: confidence interval. Cutoff value is the best expression of lncRNA for the diagnosis of lung adenocarcinoma. their related mRNAs DLGAP5, E2F7, MCM7, RACGAP1, as potential drug targets [6]. HOTAIR might be a thera- and RRM2 might be biomarkers of LUAD. peutic target in NSCLC because of its role in the chemo- Previous studies have explored the functions of lncRNAs resistance to cisplatin [6]. Liu et al. indicated that MEG3 as diagnostic and prognostic biomarkers [6]. HOTAIR is a might be a potential therapeutic target in lung cancer, for famous cancer-related lncRNA and it is highly expressed in tumor cells would be sensitive to cisplatin when MEG3 was NSCLC and SCLC [10]. MALAT1 is another important overexpressed in A549 cells [23]. lncRNA, and in patients with non-small-cell lung cancer, it lncRNA SFTA1P had been reported previously. Huang is significantly related to metastasis potential and poor et al. demonstrated that SFTA1P and CASC2 were associated prognosis [19, 20]. Schmidt et al. indicated that MALAT1 with the regulation and development of LUSC and could be could be considered as an independent prognostic param- used as prognostic and predictive indicators of LUSC via eter for both LUAD and LUSC [19, 20, 22]. Due to the high integrated bioinformatics analysis [24]. Zhang et al. reported expression in LUAD, CCAT2 is a potential diagnostic that the downregulation of SFTA1P affected LUAD patients’ biomarker for LUAD [22]. lncRNAs have also been studied survival time but had no influence on LUSC patients. 12 BioMed Research International

SFTA1P exerted tumor inhibition in LUAD [25]. *ere were values in LUAD. lncRNAs CLDN10-AS1, SFTA1P, and no experiments on the mechanism of SFTA1P in lung cancer ADAMTS9-AS2 and their related mRNAs DLGAP5, E2F7, so far. However, this opens up many avenues of study to MCM7, RACGAP1, and RRM2 might be biomarkers of pursue on this topic. LUAD. For the first time, we confirmed the important role of *e number of miRNAs that connected with lncRNA lncRNA CLDN10-AS1 in LUAD. It might be related to the ADAMTS9-AS2 was the largest among the four lncRNAs, diagnosis, ability of migration, and prognosis of LUAD. suggesting it was a critical lncRNA. Studies about lncRNA ADAMTS9-AS2 in lung cancer were rare. Liu et al. dem- Data Availability onstrated that lncRNA ADAMTS9-AS2 was lowly expressed in lung cancer tissues by qRT-PCR. In their study, high All data can be obtained from GEO (https://www.ncbi.nlm. expression of lncRNA ADAMTS9-AS2 reduced prolifera- nih.gov/gds/), TCGA (https://portal.gdc.cancer.gov/), and tion ability and inhibited migration and elevated their ap- THPA (https://www.proteinatlas.org/). optosis rate. *ey also verified the relationship among lncRNA ADAMTS9-AS2, mRNA TGFBR3, and miRNA Conflicts of Interest miR-223-3p. ADAMTS9-AS2 increased TGFBR3 expression, but miR-223-3p decreased both of them. miR-223-3p *e authors declare that they have no conflicts of interest. targeted TGFBR3 to enhance the ability of proliferation, migration, and invasion of lung cancer. *ey came to a Authors’ Contributions conclusion that DAMTS9-AS2, TGFBR3, and miR-223-3p might provide potential therapeutic targets in lung cancer Donghui Jin designed the study, analyzed the data, and [26]. wrote the manuscript. Yuxuan Song designed the study and miRNAs miR-125a-5p and miR-125b-5p only connected analyzed the data. Yuan Chen revised the paper critically for with lncRNA CLDN10-AS1, indicating lncRNA CLDN10- important intellectual content. Peng Zhang designed the AS1 had a different binding trend among the four lncRNAs. study and revised the paper critically for important intel- CLDN10-AS1 might play a role in the development and lectual content. progression of lung cancer, by regulating the G1/S transition of mitotic cell cycle through miR-125b-5p/PPAT and by Acknowledgments regulating endothelium development, angiogenesis, and cell-cell adherens junction through miR-125b-5p/CDH5, *e authors gratefully acknowledge contributions from the respectively. Furthermore, CLDN10-AS1 was associated GEO, TCGA, and THPA database. with the prognosis of LUAD (logrank P � 0.026), indicating the potential functions in the prognosis of LUAD patients. It References is noteworthy that CLDN10-AS1 has not been reported in [1] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, lung cancer. 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BioMed Research International 13

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