Research Article Computational Analysis of Specific MicroRNA Biomarkers for Noninvasive Early Cancer Detection

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1 Hndaw BoMed Research Internatonal Volume 0, Artcle ID 00, pages Research Artcle Computatonal Analyss of Specfc McroRNA Bomarkers for Nonnvasve Early Detecton Tanc Song, Yanchun Lang,, Zhongbo Cao, We Du, andyngl College of Computer Scence and Technology, Key Laboratory of Symbol Computaton and Knowledge Engneerng of the Mnstry of Educaton, Jln Unversty, Changchun 00, Chna Zhuha Laboratory of Key Laboratory of Symbolc Computaton and Knowledge Engneerng of Mnstry of Educaton, Zhuha College of Jln Unversty, Zhuha 0, Chna Correspondence should be addressed to We Du; and Yng L; Receved November 0; Accepted February 0; Publshed March 0 Academc Edtor: Stephen H. Safe Copyrght 0 Tanc Song et al. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. s a complex dsease resdng n varous tssues of human body, accompaned wth many abnormaltes and mutatons n genomes, transcrptome, and epgenome. Early detecton plays a crucal role n extendng survval tme of all major cancer types. Recent advances n mcroarray and sequencng technques have gven more support to dentfyng effectve bomarkers for early detecton of cancer. McroRNAs (mrnas) are more and more frequently used as canddates for bomarkers n cancer related studes due to ther regulaton of target gene expresson. In ths paper, the comparatve analyss s used to dscover mrna expresson patterns n cancer versus normal samples on early stage of eght prevalent cancer types. Our work focuses on the specfc mrnas bomarkers dentfcaton and functon analyss. Several dentfed mrna bomarkers n ths paper are matched well wth those reported n exstng researches, and most of them could serve as potental canddate ndcators for clncal early dagnoss applcatons.. Introducton s a hghly complex dsease that contans many abnormaltes and mutatons n the genomes, transcrptome, and epgenome. These abnormaltes play mportant roles n the cancer cell growth []. s one of the leadng causes of death all over the world. Accordng to the world cancer report of 0, there are about mllon new cases and more than mllon deaths related to cancer n 0. The number wll contnue to rse f there are no effectve predcton and treatment of cancer. It s expected that the number of new cases wll rse to mllon over the next two decades []. Early detecton of cancer s undoubtedly mportant. Durng the development of cancer, some genes or protens, assocated wth genetc mutatons, transcrpton, or epgenetc alteratons, could be detected from the tssue of cancer or nflammaton when beng compared to the normal tssue. These genes or protens could gve a quanttatve measurement to the severty of a cancer. There have been many genes or protens as bomarkers used n the clncal detecton of cancer ncludng alpha-fetoproten (AFP) for lver cancer [], BRCA and BRCA for breast and ovaran cancer [], prostate specfc antgen (PSA) for prostate cancer [], and epdermal growth factor receptor (EGFR) for nonsmall-cell lung carcnoma []. In recent years, more and more researches on mcrorna (mrna) bomarkers have been publshed. mrnas are small noncodng RNA molecules that contan nucleotdes. They play mportant roles n the posttranscrptonal regulaton of target gene expresson []. The mrna bomarker dentfcaton has been extensvely studed n recent years. Hgh-throughput mcroarray and sequencng technques are wdely used for transcrptome analyss. We can acqure lots of transcrptome nformaton for varous knds of cancers on gene expresson level from publc databases, such as Gene Expresson Omnbus (GEO) [], Stanford Mcroarray Database (SMD) [], Oncomne [0], and the Genome Atlas (TCGA) []. Dysregulaton of mrna expresson s mportant for cancer development through varous

2 BoMed Research Internatonal mechansms ncludng deletons, amplfcatons, or epgenetc slencng []. The crculatng mrnas are suggested to be effectve ndcators of dsease. They are mportant for clncal applcatons such as dsease dagnostcs, montorng therapeutc effect, and predctng recurrence n cancer patents []. Crculatng mrnas are wdely used as bomarkers for varous human cancers, such as prostate cancer and breast cancer [ ]. Some gene or mrna bomarkers are dentfed usng statstcal methods to the hgh-throughput omcs data. MRNAs are wdely used as bomarkers for human cancers. Resnck et al. acqured dfferentally expressed mrnas through comparng mcroarray data between patents wth ovaran cancer and normal samples. Fve dfferentally expressed mrnas wth overexpresson and mrnas wth underexpresson n patents were evaluated through real-tme PCR []. Xe et al. dd the research on aberrant mrnas used as bomarkers for the dagnoss of non-small-cell lung cancer (NSCLC). Ther result ndcated that mr- was detected wth hgh expresson n sputum specmen of patent []. Du Reu et al. dd the study on whether abnormal mrna producton for nonnvasve precursor pancreatc ntraepthelal neoplasa (PanIN) can be used as a potental early bomarker of pancreatc ductal adenocarcnoma (PDAC). They ndcated that mr- was aberrantly expressed n early development of PanIN and t was worthy for further study as a bomarker for early detecton of PDAC []. Habbe et al. dd the research on the aberrant expressed mrnas n ntraductal papllary mucnous neoplasms (IPMNs). They showed that aberrant mrna expresson was an early event for pancreatc cancer, and mr- was worthy of further study as a bomarker for IPMNs n clncal samples [0]. Although several mrna bomarkers have been reported n the above researches, only one or very few mrnas are dentfed n each experment whch s nvolved n a sngle cancer type. The accuracy and specfcty of some mrnas are not so promsng. In ths paper, we analyze the mrna expresson patterns comparatvely n cancer versus normal samplesonearlystageofeghtprevalentcancertypes.the datasets used n our paper are all RNA-Seq data from TCGA database to make a comprehensve dentfcaton of mrna bomarkers for varous cancers. We focus on dentfyng the specfc mrnas bomarkers ncludng four aspects: (a) detectng dfferentally expressed mrnas for each cancer type; (b) detectng specfc dfferentally expressed mrnas; (c) detectng specfc mrnas bomarkers; and (d) analyzng functon and pathway of these mrnas. Several dentfed mrna bomarkers have been reported n exstng researches andmostofthemcouldserveaspotentalcanddatebomarkers for clncal early dagnoss.. Materals and Methods.. mrna Expresson Datasets. The orgnal mrna expresson data based on mrna-seq for eght prevalent cancer types are downloaded from the TCGA database []. The eght cancer types nclude prostate, thyrod, breast, head and neck, kdney, stomach, lung, and lver cancer. For each cancer type,weselectthecancersampleswhchhavecorrespondng Table : The detaled nformaton of cancer mrna expresson datasets. type Abbrevaton Number of normal samples Number of cancer samples Breast BRCA Colorectal CRAD Kdney KIDC 0 0 Lver LIHC 0 Lung LUNG Prostate PRAD Stomach STAD Thyrod THCA All 0 CRAD contans COAD (colon adenocarcnoma) and READ (rectum adenocarcnoma). KIDC contans KIRC (kdney renal clear cell carcnoma) and KIRP (kdney renal papllary cell carcnoma). LUNG contans LUSC (lung squamous cell carcnoma) and LUAD (lung adenocarcnoma). samples from ther normal tssues as the pared samples to dentfy cancer related mrna bomarkers of dfferent cancer types. Bomarkers can ensure that the predcton results could be well generalzed to clncal research and utlty by usng these pared samples. Detaled nformaton of the datasets used n ths paper s lsted n Table. In ths paper, we only usethesamplesofcancerfrompathologcstageitodetect mrna bomarkers for cancers of early stage. The pathologc stage s collected from clncal patent nformaton of TCGA database. The value of reads per mllon mrna mapped s used as expresson value of each mrna... Identfcaton of Dfferentally Expressed mrnas. For each cancer type, frstly, Wlcoxon sgned-rank test s used to dentfy dfferentally expressed mrnas between cancer samples and normal samples. Then, FDR controllng method s used to elmnate false dscovery rate of the result by Wlcoxon sgned-rank test. After the above processes, an mproved fold change method s appled to dentfy the dfferental expressed mrnas between cancer samples and normal samples. Inthspaper,weusequantlestocalculatethefoldchange of each mrna. A quantle s a cut pont whch dvdes a set of observatonal data nto equal szed groups. So, the q-quantles ndcate the value that parttons a set of fnte data nto q groups of equal szes. The number of q-quantles of value s q.foravarablex,thekth q-quantle (0<k<q)Q k can be estmated usng the followng formula: Q k =x h + (h h ) (x h + x h ), () where h = (N )(k/q) + and N sthesamplesze.theset of q-quantles s defned as follows: SQ {Q,...,Q k,...,q q }. () Let E R m s be the transcrpt measurements n a mrna expresson matrx wth m mrnas and s samples. Here,

3 BoMed Research Internatonal varable X s E[, :] (0 < m) and the set of q-quantles of mrna canbecalculatedusngformulae()and(). For mrna on normal samples and cancer samples, we can obtan two sets of q-quantles for normal and cancer samples as NQ and CQ,respectvely.Thefoldchangevalue of the mrna of kth q-quantle can be calculated usng the followng formula: CQ k (CQ { NQ k NQ k ) k FC k = { NQ k (CQ { CQ k <NQ k ), k where CQ k and NQ k are the expresson values of mrna of kth q-quantle n cancer and normal samples, respectvely. Andthen,theorgnalfoldchangeOFC of mrna, whch s calculated by the average of FC k across all the samples, s defned as follows: q () OFC = FC q k, () where q stheselectednumberofquantles. In ths paper, the mpact of standard devaton s also ntroducedtotheorgnalfoldchange.themprovedfold change denoted by IFC of mrna s shown as follows: j= IFC = OFC, σ(cq )σ(nq ) where σ s the standard devaton of sets of q-quantles. In formula (), not only the mean value of fold change but alsothenfluenceofvaranceacrossdfferentsampless consdered, whch s a more effectve and robust statstcal analyss. In ths paper, q s set to 00 to calculate the quantles. And then, we select dfferentally expressed mrnas for each ndvdual cancer type usng the followng rules: () the mproved fold change (IFC) s less than 0. or greater than 0.; and () q-value by FDR controllng for Wlcoxon sgnedrank test s less than... Identfcaton of Specfc Dfferentally Expressed mr- NAs. The specfc mrna bomarkers for each cancer are selected, whch can be used as dscrmnators for other cancers. We select specfc dfferentally expressed mrnas based on the dfferental expressed mrna results from last secton for each cancer. Let the mproved fold change of mrna n cancer C be IFC C and n other cancers C,C,...,C k (k =,,...,) be IFC C, IFC C,...,IFC C k. The mrna s regarded as a specfc mrna bomarker for cancer C,fandonlyfmRNAcompled wth the followng formula: k j= ( IFCC IFCC >t, IFC C j ) δc j >t t (k ), () () where k sthenumberofcancertypesandk=n ths paper. t means the threshold of the rato of dfferent cancer types (the number of cancer types beng consdered); here we set t to be 0.. t means the threshold of the mproved fold change (IFC value); here we set t to be 0.. δ C j s a factor to flter the mrnas wth low expresson value and s defned as follows: δ C j =, (X C X C j ), δ C j =0, (X C < X C j ), where X C j mrna on cancer C j and X C j () s a vector whch means the expresson value of s the average of X C j. For example, f mrna s upexpressed n cancer C (the frst nequalty n () s satsfed, whch s IFC C >t ) and downexpressed or changed very lttle n other cancer types(thesecondnequaltyn()ssatsfed,whchs k j= ( IFCC IFC C j ) δ C j >t t (k )), then ths mrna s a specfc dfferentally expressed mrna for cancer C.Its a good dscrmnator for dstngushng cancer types between C and other C j (j=,,...,)... Identfcaton of Specfc mrna Bomarkers. After obtanng the specfc dfferentally expressed mrnas for each sngle cancer type, we further select the crculatng and upregulated mrnas as bomarkers. The nformaton of extracellular crculatng mrnas s downloaded from mrandoladatabase[].basedonthesourceofextracellular mrnas, the mrnas n mrandola database are dvded nto four categores: Ago, exosome, HDL, and crculatng. In ths paper, we only select the crculatng mrnas whch aresourceofplasmaandserumnmrandoladatabaseas canddate mrna bomarkers. For mprovng the senstvty and specfcty, we dentfy the combned mrna bomarkers based on the specfc sngle mrna bomarkers. The rules of selectng combned bomarkers are consdered here n order to dentfy k-mrna dscrmnators. These k-mrna dscrmnators are used as combned bomarkers for multple cancer types, specfc bomarkers for cancer types wth smlar survval rates (hgh survval rate, medum survval rate, and low survval rate), and a specfc cancer type. A computatonal process of fndng the k-mrna (k =,,,, ) combnaton bomarkers s proposed n ths paper to gve a best dstncton among the dfferent cancer groups usng a lnear classfer []. Lnear Dscrmnant Analyss (LDA) [] s employed to evaluate the performance of kmrna combnaton bomarkers for multple cancer types, cancers of smlar survval rates, and a sngle cancer type. The overall accuracy s defned as the fracton of the total number of true postves and true negatves and the number of all the samples n the followng: (TP + TN) OA =, () N where TP s the number of true postves, TN s the number of true negatves, and N s the total number of samples.

4 BoMed Research Internatonal Table : The dfferentally expressed mrna n eght cancer types. types Upregulaton Downregulaton All Rato of up/down BRCA 0. CRAD 0. KIDC 0. LIHC LUNG. PRAD. STAD.00 THCA 0. The performance of all the k-mrna combnatons s evaluated usng Leave-One-Out Cross Valdaton (LOOCV) method []. In each LOOCV step, one sngle sample s randomly chosen as the valdaton data, and the remanng samples are chosen as the tranng data to buld the classfer usnglda.theoavaluescalculatedastheaccuracyn ths LOOCV step. Ths process s terated untl all of samples are selected as the valdaton data. Due to the computatonal complexty, the number of k combnatons s set to be,,,, and n ths paper... Functon and Pathway Analyss. The functonal annotaton and pathway analyss are conducted usng meaa, a mrna Enrchment Analyss and Annotaton tool []. meaa s a web-based system, whch offers mrna set enrchment analyss smlar to Gene Set Enrchment Analyss (GSEA) []. The tool also provdes rch functonalty n terms of mrna categores and contans over,000 mrna sets, ncludng pathways, dseases, organs, and target genes. In ths paper, we perform the functonal annotaton and pathway analyss on specfc upexpressed mrnas for each cancer type among multple cancer types, respectvely.. Results and Dscusson The dfferentally expressed mrnas n each cancer type are dentfed n order to study the specfc roles of mrnas nvolved n dfferent cancer types. Due to the varous alteratons accumulated n the development of the oncogeness, dfferent cancers may have ther specfc mrnas havng dfferental expresson. These mrnas may be nvolved n some bologcal processes n the formaton and progresson of cancers. Smlar to genes, several specfc mrnas are dentfed andusedastargetsofthepreventonanddagnossofcancers... Dfferentally Expressed mrnas. In ths part, we use the strategy n the mentoned methods to dentfy the dfferentally expressed mrnas n eght cancer types. We detect the upregulated and downregulated mrnas, respectvely, most of whch may be nvolved n some mportant bology processes or pathways. The dfferentally expressed mrnas of eght cancer types are summarzed n Table. There are dfferentally expressed mrnas n breast cancer, among whch mrnas are upregulated and mrnas are downregulated. There are dfferentally expressed mrnas n colorectal cancer, among whch mrnas are upregulated and mrnas are downregulated. In the lung cancer results, mrnas are found to be dfferentally expressed, among whch mrnas are upregulated and mrnas are downregulated. Interestngly, the number of dfferentally expressed mrnas of dfferent cancer types vares a lot. One reasonable explanaton s that these mrnas may reflect dfferent regulatory mechansms n dfferent cancers and dfferent cancers may accumulate dfferent set of mrnas. Another nterestng observaton s that the number of upregulated and downregulated mrnas n each cancer dffers too much; that s, the rato of upregulated and downregulated mrnas dffers a lot. In Table, we can see that there are more upregulated mrnas than downregulated mrnas n prostate and stomach cancers. Dfferently, the downregulated mrnas n thyrod and lver cancers account for a major porton among the dfferently expressed mrnas. It may possbly ndcate the unque and smlar characterstcs of dfferent cancer types... Specfc Dfferentally Expressed mrnas. A detaled statstcal analyss on specfc dfferentally expressed mr- NAs for sngle cancer type s conducted. The results are summarzed n Table. There are specfc dfferentally expressed mrnas n breast cancer, among whch mrnas are upregulated and 0 mrnas are downregulated. There are 0 specfc dfferentally expressed mrnas n colorectal cancer, among whch mrnas are upregulated and mrnas are downregulated. In lung cancer, mrnas are found as specfc dfferentally expressed mrnas, among whch mrnas are upregulated and mrnas are downregulated. Smlarly, whle the majorty of dfferentally expressed crculatng mrnas n prostate and stomach cancers are upregulated, n kdney and lver cancers, the majorty of such mrnas are downregulated. The detals of the specfc dfferentally expressed mrnas n eght cancers are llustrated n Table... Specfc mrnas Bomarkers. The dentfcaton of specfc mrna bomarkers for each cancer s also performed n ths part. For each cancer type, we use the strategy mentoned nmateralsandmethodstofndthemrnasthathavelarger upregulaton n each cancer type and have hgh expresson value,andmeanwhlethemrnashavedfferentchanges or expresson values n other cancer types. Such mrnas

5 0 BoMed Research Internatonal Table : The specfc dfferentally expressed mrnas n eght cancer types. types Upregulaton Downregulaton All Rato of up/down BRCA 0 0 CRAD 0 0. KIDC 0. LIHC 0 0. LUNG. PRAD.00 STAD. THCA 0 0 BRCA: MIMAT0000 CRAD: MIMAT00000 KIDC: MIMAT0000 LIHC: MIMAT Densty Densty Densty Densty Expresson (log ) Expresson (log ) Expresson (log ) Expresson (log ) Other normal tssues Other normal tssues Other normal tssues Other normal tssues 0. LUNG: MIMAT0000 PRAD: MIMAT0000 STAD: MIMAT0000 THCA: MIMAT Densty 0. Densty 0. Densty 0. Densty Expresson (log ) Expresson (log ) Expresson (log ) Expresson (log ) Other normal tssues Other normal tssues Other normal tssues Fgure : Specfc dfferentally expressed mrnas n eght cancer types. Other normal tssues can make a better dstncton between one cancer and other cancer types. The detaled nformaton s llustrated n Fgure. MRNA MIMAT0000 s taken as an example (the frst one n Fgure ). It shows the expresson dstrbuton of mrna MIMAT0000 n breast cancer, normal breast, other cancers, and other normal tssues. MIMAT0000 gets a fold changevalueofnearly0.nbreastcancer,whchndcates ts upregulaton obvously. The approxmate mean expresson values are 0 and 0 n breast cancer and normal samples, respectvely. The approxmate mean expresson values are and 0 n other cancer and normal samples, respectvely. So MIMAT0000 gets a better performance n prostate cancer compared to other cancers. It could be used to dstngush prostate cancer from other cancer types. In some cases, usng one sngle gene or mrna s not enough to dstngush the specfc cancer. The dentfcaton of the combnaton of some mrnas s an mportant step

6 BoMed Research Internatonal Table : Fve-mRNA bomarker for eght cancers. types Markers Accuracy Accuracy Mean BRCA MIMAT( ) CRAD MIMAT( ) KIDC MIMAT( ) LIHC MIMAT( ) LUNG MIMAT( ) PRAD MIMAT( ) STAD MIMAT( ) THCA MIMAT( ) of dfferentally expressed mrna analyss. In order to fnd the better combnatons of mrnas as bomarkers whch canbeusedtodstngushthespecfccancer,k-mrna (k =,,,,) combnatons of dfferentally expressed mrnas are selected n ths secton. By dentfyng dfferent mrna combnatons whch have smlar expresson patterns n sngle cancer type but have dfferent expresson patterns n other cancers, we explore useful nformaton of dfferent and varous mechansms about carcnogeness wth sngle cancer type. In each k-mrna combnaton, we calculate the classfcaton capablty n two aspects. One s the measurement of the k-mrna n a sngle cancer dataset, whch calculates the classfcaton capablty between cancer samples and the pared normal samples of the same cancer type. The other s the measurementofthe k-mrna n the datasets of multple cancer types, whch calculates the classfcaton capablty between one cancer type and the other cancer types usng thefoldchangevaluesofthecancersamplesandthenormal samples. Table gves the results of dstngushng ablty of the top fve-mrna specfc bomarker combnatons n eght cancers, respectvely. In Table, accuracy s the results obtaned by classfer between cancer samples and ther correspondng pared normal samples; and accuracy s the results obtaned by classfer between fold change values from each cancer typeandfoldchangevaluesfromothercancertypes.these mrnas could be used as bomarkers for correspondng cancer, and smultaneously they are found to be effectve dscrmnators for dstngushng each cancer from other cancer types. For example, n breast cancer the top fve-mrna of MIMAT( ) gets the accuracy of.0% n sngle dataset evaluatonandaccuracyof.%nmultplecancerdatasets evaluaton process, whch are the best results among other combnatons. The mean value can reach.%, whch ndcates that the fve-mrna combnaton could be good bomarkers for breast cancer. As noted, several mrnas n each combnaton have been reported to be related to dfferent cancers. For BRCA, MIMAT00000 (hsa-mr-) s reported to be overexpressednhumanbreastcancerandassocatedwthclncal stage, metastass, and prognoss []. MIMAT0000 (hsa-mr-) and MIMAT000 (hsa-mr-b) are found to be overexpressed n human breast tumor [, ]. MIMAT0000 (hsa-mr-) s reported to relate to the regulaton of tumorgencty of human breast cancer through the canoncal WNT sgnalng pathway [0], and MIMAT0000 (hsa-mr-) regulates BRCA expresson through modulaton of ID n breast cancer []. For CRAD, MIMAT (hsa-mr-) s found overexpressed n colon cancer and the expresson s correlated wth low survval rate n colorectal cancer patents [], and MIMAT0000 (hsa-mr-0a) s reported to be overexpressed n human colon cancer []. MIMAT00000 (hsalet-g) s assocated wth tumorgeness n colon cancer [], and MIMAT0000 (hsa-mr-) s assocated wth cell prolferaton, mgraton, and nvason n human cancer []. MIMAT000 (hsa-mr-) s used as a novel bomarker for metastatc colon cancer []. For KIDC, MIMAT00000 (hsa-mr-a) s reported to be upregulated n urne of patents wth renal cell carcnoma and undetectable n oncocytoma, other tumors, and urnary tract nflammaton []. MIMAT00000 (hsa-mr- ), MIMAT000 (hsa-mr--p), and MIMAT0000 (hsa-mr-0) are upregulated n renal cell carcnoma []. MIMAT0000 (hsa-mr-a) s reported as a specfc oncogenc mrna and shown to experence hypermethylaton n kdney cancer cells []. For LIHC, MIMAT00000 (hsa-mr-0a) s upregulated n lver cancer and correlated wth hepatts C vrusmedated lver dsease progresson []. MIMAT0000 (hsa-mr-a) and MIMAT 000 (hsa-mr-0) are reported as a potental therapeutc target n human cancer [0, ]. MIMAT0000 (hsa-mr-) s upregulated n lver cancer tssues [], and MIMAT000 (hsa-mr-00) s related to cancer survval []. For LUNG, MIMAT0000 (hsa-mr-) s reported to be overexpressed n lung cancer cell and suggested that hgh levels of CO ncrease these mrna levels and t s related to mtochondral oxygen consumpton, ATP producton, and cell prolferaton []. MIMAT (hsa-mr-b) s found to medate NF-κB sgnalng n KRAS-nduced nonsmall-cell lung cancers (NSCLC) []. MIMAT0000 (hsamr-) and MIMAT00000 (hsa-mr-b) are upregulated n lung cancer []. MIMAT000 (hsa-mr--p) s reported as bomarker to stratfy for the subtype of lung cancer []. For PRAD, MIMAT00000 (hsa-mr-b) s reported to be related to prostate cancer and promoted prostate cell prolferaton by targetng PTEN, PIK/Akt pathway, and cycln

7 BoMed Research Internatonal Table : Enrched pathway nformaton of eght cancer types. Category Term Hnt rato KEGG Apoptoss (hsa00).000 Wk Pathways mrnas nvolved n DDR (WP).000 Wk Pathways Leptn sgnalng pathway (WP0).000 KEGG Adpocytokne sgnalng pathway (hsa00) 0. Wk Pathways Estrogen sgnalng pathway (WP) 0. Wk Pathways Insuln sgnalng (WP) 0. Wk Pathways DNA damage response (WP0) 0. Wk Pathways IL sgnalng pathway (WP) 0. KEGG Wnt sgnalng pathway (hsa00) 0. KEGG Endocytoss (hsa0) 0. D []. MIMAT00000 (hsa-mr-b) s overexpressed n prostate cancer []. MIMAT0000 (hsa-mr-) s reported as a promsng bomarker for prostate cancer [0]. MIMAT0000 (hsa-mr-) plays a dual role n prostate carcnogeness [], and the expresson of MIMAT000 (hsa-mr-0b) s assocated wth progresson of prostate cancer cells []. For STAD, MIMAT00000 (hsa-mr-) has sgnfcantly hgher expresson and plays a role n regulatng cellular apoptoss, prolferaton, and nvason n gastrc cancer []. MIMAT00000 (hsa-mr--p) and MIMAT0000 (hsa-mr-00b) are reported to be overexpressed n gastrc cancer patents [, ]. MIMAT00000 (hsa-mr-) s used as potental dagnostc and prognostc bomarkers for gastrc cancer [], and MIMAT0000 (hsa-mr--p) s appled as prognostc sgnature n gastrc cancer []. For THCA, MIMAT0000 (hsa-mr-a) s overexpressed n thyrod carcnoma and has been proposed to play a role n the pathogeness, development, progresson, metastass, prognoss, and therapeutc response to chemoand radotherapy []. MIMAT0000 (hsa-mr-b) s reported as a key regulator of the oncogenc process n cancer []. MIMAT0000 (hsa-mr-), MIMAT0000 (hsa-mr-), and MIMAT000 (hsa-mr-a--p) are upregulated n thyrod carcnoma []... Functon and Pathway Analyss. In ths secton, the functon annotaton and pathway enrchment are performed usng meaa database. Frstly, we analyze the enrched pathways on specfc upexpressed mrnas for each cancer type among multple cancer types, respectvely. Then, we selecttheenrchedpathwaysnmostcancertypesandsort them by the hnt rato. Table gves top enrched pathway nformaton wth top ranks accordng to ther hnt rato by meaa []. The category means the source of pathway databases,thetermmeanstheenrchedpathways,andthe hnt rato means the enrchment score of mrnas. From Table,wecanseethatthetopenrchedpathwaysarederved from Kyoto Encyclopeda of Genes and Genomes (KEGG) [0] and Wk Pathways []. The frst enrched pathway s apoptoss, whch s descrbed by ts morphologcal characterstcs and contrbutedtothehghrateofcelllossnmalgnanttumors []. There are two enrched pathways, mrnas nvolved n DDR and DNA damage response, whch are related to DNA damage. There s an ncontrovertble lnk between DNA damage and neoplastc phenotype n cancer []. Another nterestng pathway s endocytoss, whch entals selectve packagng of cell-surface protens and can be modfed n cancer []. There are sx sgnalng pathways n the enrchment results. Cellular sgnalng pathways are nterconnected to form complex sgnalng networks and altered n cancer cells representng a major ntellectual challenge [].. Conclusons Early detecton of cancer s a very mportant and necessary way for cancer preventon. The bomarkers, as effectve ndcators to dstngush between cancer and normal samples or among dfferent groups of cancer samples, are more and more used n cancer mechansm studes and clncal detecton. The research of mrna bomarkers s attractng more attenton. But some of the bomarkers lack specfcty and there are no effectve common bomarkers for multple cancer types. In ths paper, we manly focus on the dentfcaton of specfc mrnas and the common mrna bomarkers. The mrna expresson data from RNA-Seq for eght cancer types are obtaned from TCGA database, and the crculatng mrna nformaton s collected from mrandola database. For each cancer type, we apply Wlcoxon sgned-rank test, whch elmnates false dscovery rate by usng FDR controllng method, and mproved fold change (FC) to dentfy mrnas whch have dfferental expressons between cancer samples and ther correspondng normal samples. Then, the specfc mrna bomarkers for each cancer are further selected to act as dscrmnators for other cancers. After obtanng the specfc dfferentally expressed mrnas for each sngle cancer type, we further select the crculatng and upregulated mrnas as bomarkers. Several dentfed mrna bomarkers n ths paper are matched well wth those reported n exstng lteratures and most of them could be taken as potental canddate ndcators for clncal early dagnoss applcatons. Competng Interests The authors declare that there s no conflct of nterests regardng the publcaton of ths paper.

8 BoMed Research Internatonal Acknowledgments The authors are grateful for the support of the Natonal Natural Scence Foundaton of Chna (0,, and ), the Scence-Technology Development Project from Jln Provnce (0000JH), Chna Postdoctoral Scence Foundaton (0T0), and the Premer-Dscplne Enhancement Scheme supported by Zhuha Government and the Premer Key-Dscplne Enhancement Scheme supported by Guangdong Government funds. References [] M. Hejmad, Introducton to Bology, Ventus Publshng ApS, 00. [] B.W.StewartandC.P.Wld,World Report,IARC,0. [] O. Arreta, B. Cacho, D. Morales-Espnosa, A. Ruelas- Vllavcenco, D. Flores-Estrada, and N. Hernández-Pedro, The progressve elevaton of alpha fetoproten for the dagnoss of hepatocellular carcnoma n patents wth lver crrhoss, BMC, vol., artcleno., 00. [] N. Petrucell, M. B. Daly, G. L. Feldman et al., BRCA and BRCA heredtary breast and ovaran cancer, n GeneRevews, R. A. Pagon, M. P. Adam, H. H. Ardnger et al., Eds., Unversty of Washngton, Seattle, Wash, USA,. 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