KR102194215B1 - Biomarkers for Diagnosing Gastric Cancer And Uses Thereof - Google Patents

Abstract

The present invention relates to a gastric cancer biomarker composition and a diagnosis kit using an mRNA or miRNA expression level of a gene as an index for Koreans to diagnose or screen gastric cancer, and a method for providing information for diagnosing gastric cancer. According to the present invention, a gene expression level in the saliva of a suspected cancer patient among Koreans is measured by a formulation capable of measuring mRNA expression levels of the genes SPINK7, PPL, and SEMA4B, and miRNA expression levels of MIR140-5p and MIR301a, wherein SPINK7, PPL, SEMA4B, MIR140-5p, and MIR301a are biomarkers for diagnosing gastric cancer. Accordingly, the salvia of the patient is used in a non-invasive manner, and thus gastric cancer can be simply analyzed with high accuracy and can be diagnosed when the gene expression level is lower than that of a normal person.

Description

Biomarkers for Diagnosing Gastric Cancer And Uses Thereof}

The present invention relates to a biomarker composition and a diagnostic kit for diagnosing gastric cancer for diagnosing gastric cancer by using the mRNA or miRNA expression level of a gene as an index for Koreans, and a method of providing information for diagnosing gastric cancer.

Gastric cancer is the fourth most common cancer diagnosed worldwide, and the third leading cause of cancer-related deaths. This gastric cancer is a typical type of cancer diagnosed in East Asian countries. In Korea, the incidence of gastric cancer is high due to genetics, diet containing salted foods, smoking, and the high prevalence of Helicobacter pylori . In Korea, most early gastric cancers are found to be symptom-free (74.2 to 78.1%) compared to symptoms (25.9 to 35.7%). Once gastric cancer progresses and causes serious symptoms and complications, the prognosis is poor and the survival rate decreases from about 65% (at early detection) to less than 20%. In 1999, due to the high prevalence of gastric cancer, the National Cancer Screening Program in Korea implemented an early cancer detection program that recommends that all people over the age of 40 undergo endoscopy every two years. However, an endoscope for diagnosis of gastric cancer is expensive, time-consuming, and invasive. Since the start of the screening program, less than 30% of the subjects participated. Therefore, there is a need for a predictive biomarker that can be used as a reliable screening tool for early detection and screening of gastric cancer. Such biomarkers are highly desirable for improving disease outcomes and reducing unnecessary endoscopy.

There have been studies to find potential biomarkers in serum as a non-invasive method of screening for gastric cancer. Low concentrations of serum pepsinogen I, II and their ratio were considered as indicators of preneoplastic gastric lesions, but the results depend on the location of the cancer and the cutoff values used in other studies. lost. The most commonly used gastric cancer markers, such as carcinoembryonic antigen (CEA), CA19-9, CA-50, and CA72-4, are the area under the ROC curve (AUC) of the receiver operating characteristic (ROC). Values of 0.54 to 0.73 were reported, and these gastric cancer markers were not sensitive or specific enough to screen gastric cancer patients. Meanwhile, in addition to proteins, various types of RNA have been reported as biomarkers for gastric cancer. There has been a study of systematic microRNA (miRNA) profiling of blood samples obtained from gastric cancer patients. The expression patterns of MIR221, MIR744 and MIR376c in blood showed value as a biomarker that differentiates gastric cancer patients from healthy people. Plasma miRNA biomarkers for gastric cancer were discovered through reverse transcription quantitative real-time PCR, and AUC values for MIR185, MIR20a, MIR210, MIR25 and MIR92b were 0.65 to 0.75. In another study, MIR181a-1 and KAT2B mRNA were identified as complex predictors with an AUC value of less than 0.95, and suggested as potential biomarkers for diagnosis and prognosis of gastric cancer. However, there have been no studies that have performed definitive demonstration of these mRNA biomarkers in a sufficiently large cohort. Therefore, there is a need to further confirm the value of mRNA biomarkers in gastric cancer patients.

Saliva extracellular RNA (exRNA) biomarkers containing miRNA have been developed to screen for various local and systemic diseases such as oral cancer, Sjogren syndrome, pancreatic cancer, breast cancer, and lung cancer. In the present invention, researchers collect samples from patients with suspected cancer (before cancer diagnosis), and based on the'retrospective blinded evaluation (PRoBE)' guidelines, develop a saliva exRNA biomarker for gastric cancer diagnosis for Korean high-risk groups. I did.

One aspect of the present invention is a biomarker for diagnosis of gastric cancer comprising an agent measuring the mRNA expression level of the first gene group including SPINK7, PPL and SEMA4B and the miRNA expression level of the second gene group including MIR140-5p and MIR301a It is an object to provide a composition.

Another aspect of the present invention is to provide a kit for diagnosing gastric cancer comprising the composition.

Another aspect of the present invention is (a) the mRNA expression level of the first gene group including SPINK7, PPL and SEMA4B and the miRNA expression of the second gene group including MIR140-5p and MIR301a in a biological sample isolated from a Korean subject. Measuring the level; And (b) it provides a method of providing information for diagnosis of gastric cancer comprising the step of comparing the measured gene expression level with a normal non-gastric cancer control.

<Biomarker composition for diagnosis of gastric cancer>

According to one embodiment of the present invention, the present invention is the mRNA expression level of the first gene group comprising SPINK7, PPL and SEMA4B; And it provides a biomarker composition for diagnosis of gastric cancer comprising an agent for measuring the miRNA expression level of the second gene group including MIR140-5p and MIR301a.

The gene SPINK7 is ECRG2 (esophagus cancer-related gene 2), which has been found to be rapidly reduced in expression in stage I esophageal squamous carcinoma. It is a tumor suppressor gene that suppresses cancer cell invasion through the plasmin activator receptor/β1 integrin pathway. Gene PPL (Periplakin) is known to be reduced in expression in esophageal squamous cell carcinoma and urinary tract carcinoma, and acts as a tumor suppressor to inhibit the progression of colon cancer. The gene SEMA4B acts as a tumor suppressor that inhibits the invasion of non-small cell lung cancer through the PI3K/AKT pathway. Gene MIR140 has been found to be significantly reduced in tissues and cell lines of breast and non-small cell lung cancer, and acts as a tumor suppressor. Gene MIR301 is known to be overexpressed in lung cancer and colon cancer, and is a potential oncogene that forms tumors. The genes SPINK7, PPL, SEMA4B, MIR140-5p, and MIR301a are exRNAs present in saliva, and the correlation with gastric cancer in Koreans has not been clearly identified.

Accordingly, the present inventors limited the cohort to Koreans and compared the expression levels of saliva exRNA in 31 normal subjects and 63 gastric cancer patients. It was confirmed that the miRNA expression level was lower than that of normal subjects. The results of these gene expression levels were the same when tested in 100 normal and gastric cancer patients. Therefore, the five genes can be usefully used as biomarkers for gastric cancer diagnosis.

The agent for measuring the mRNA or miRNA expression level of the gene is SPINK7 represented by SEQ ID NO: 1, PPL represented by SEQ ID NO: 2, SEMA4B represented by SEQ ID NO: 3, MIR140-5p represented by SEQ ID NO: 4, and SEQ ID NO: 5 It may be one or more selected from the group consisting of a primer set, a probe, and an antisense oligonucleotide specifically binding to all or part of the sequence of MIR301a represented by. In particular, the preparation is preferably a primer set or probe designed to specifically amplify a specific region of a gene based on the nucleic acid sequence information of the genes.

Here, "specifically binds" means that the binding power to the target substance is superior to that of other substances so as to detect the presence or absence of the target substance by binding.

"Diagnosis" of the present invention means to confirm the presence or characteristics of a pathological condition, to determine the susceptibility of an individual to a specific disease or disease, whether or not an individual currently has a specific disease or disease And determining the prognosis of an individual with a particular disease or condition, or therametrics (eg, monitoring the condition of the subject to provide information on treatment efficacy).

The "biomarker" of the present invention generally includes all organic biomolecules such as polypeptides, proteins, nucleic acids, genes, lipids, glycolipids, glycoproteins, sugars, etc. that can detect changes in a living body as a substance detectable in a biological sample. In particular, the biomarker is abundantly present in a saliva sample isolated from a gastric cancer patient, and may be a nucleic acid, particularly RNA, having a significant difference in expression between a normal person and a gastric cancer patient.

The "biomarker composition" of the present invention may include primers, probes, antisense oligonucleotides, and the like capable of specifically binding to a biomarker and measuring its expression level.

The "primer" of the present invention is a short-stranded RNA or DNA sequence that recognizes a target gene sequence, and may be a primer set including forward and reverse primers. Since the nucleic acid sequence of the primer is a sequence that is inconsistent with the non-target sequence present in the sample, only the target gene sequence containing the complementary primer binding site is amplified, and a primer that does not induce non-specific amplification can give high specificity. .

The "probe" of the present invention means a substance capable of specifically binding to a target substance to be detected in a sample, and the presence of a target substance in a sample can be confirmed through specific binding. At this time, the probe may be a peptide nucleic acid (PNA), a locked nucleic acid (LNA), a peptide, a polypeptide, a protein, RNA, DNA, etc. known in the art.

The "antisense oligonucleotide" of the present invention is a DNA or RNA derivative containing a nucleic acid sequence that is complementary to a specific mRNA sequence, and acts to inhibit translation of the mRNA into a protein by binding to a complementary sequence in the mRNA. The length of the antisense oligonucleotide may be 6 to 100 bases, preferably 8 to 60 bases, more preferably 10 to 40 bases.

<Stomach Cancer Diagnosis Kit>

According to another embodiment of the present invention, the present invention provides a kit for diagnosis of gastric cancer comprising a biomarker composition for diagnosis of gastric cancer. Specifically, the kit includes a set of primers specifically binding to the genes SPINK7, PPL, SEMA4B, MIR140-5p and MIR301a for diagnosis of gastric cancer; And it may include a reagent for amplifying nucleic acid.

The primers are as described above, and are used in a nucleic acid amplification reaction. In this case, the primer may be labeled with a fluorescent material, chemiluminescent material, or radioactive isotope at the end or inside of the nucleic acid.

The "nucleic acid amplification reagent" of the present invention may include a polymerase, deoxyribo nucleoside triphosphate (dNTP), a buffer, a nucleic acid, a coenzyme, a fluorescent substance, and the like used to amplify a gene in large quantities using a primer. For example, the reagent for amplifying nucleic acid may include Taq polymerase.

The "diagnosis kit" of the present invention refers to a substance capable of diagnosing gastric cancer by separating a biological sample collected from a patient suspected of gastric cancer from a biological sample collected from a normal person. The biological sample may be saliva rich in exRNA and easy to collect. The kit may be applied to a method of measuring the expression level of a gene, and may be an RT-PCR kit, a competitive RT-PCR kit, a real-time RT-PCR kit, an RNase protection assay kit, a Northern blot kit, a DNA microarray kit, and the like. .

Such a kit may further include a reagent for separating nucleic acids. The reagent for separating nucleic acids may include a salt capable of lysis, a surfactant, a metal ion, a sugar, a reducing agent (eg, DTT), and the like.

When using such a kit to diagnose gastric cancer, a sample taken from a patient suspected of cancer or a normal person may be brought into contact with the primers and nucleic acid amplification reagents included in the kit. At this time, the sample may be extracted using a nucleic acid separation reagent, and may be prepared by diluting it to an appropriate concentration before contacting the primer. After mixing the prepared sample with a primer and a nucleic acid amplification reagent, a large amount of genes can be amplified under a temperature condition that induces a polymerase reaction. It is possible to diagnose gastric cancer by comparing the amplified gene expression level of a patient suspected of cancer and a normal person to determine whether the expression level has increased or decreased significantly.

<Method of providing information for diagnosis of gastric cancer>

According to another embodiment of the present invention, the present invention provides a method of providing information for diagnosis of gastric cancer. Specifically, the method comprises (a) the mRNA expression level of the first gene group including SPINK7, PPL and SEMA4B and the miRNA expression of the second gene group including the genes MIR140-5p and MIR301a in a biological sample isolated from a Korean subject. Measuring the level; And (b) comparing the measured gene expression level with a normal non-gastric cancer control.

Step (a) is a process of confirming the mRNA expression level of the biomarkers SPINK7, PPL, and SEMA4B and the miRNA expression level of the genes MIR140-5p and MIR301a in a biological sample of a Korean cancer suspected patient (subject) to diagnose gastric cancer. to be.

The biological sample is suitable for a non-invasive diagnostic method, and is preferably saliva rich in exRNA. At this time, after amplifying a small amount of genes using primers that specifically bind to the gene, the expression level of each gene can be quantitatively measured. For measurement of the mRNA or miRNA expression level of a gene, an RT-PCR kit, a competitive RT-PCR kit, a real-time RT-PCR kit, an RNase protection assay kit, a Northern blot kit, a DNA microarray kit, and the like can be used.

The step (b) is a process of comparing the mRNA or miRNA expression level of a gene measured in a patient suspected of cancer based on a (normal) Korean person who does not have gastric cancer as a reference value. At this time, if all of the five measured gene expression levels are significantly lower than the reference value, gastric cancer can be diagnosed.

Thereafter, by combining the mRNA or miRNA expression levels of each gene measured in step (a), the sensitivity and specificity of the five biomarkers and the accuracy of diagnosis by these biomarkers can be analyzed. The analysis method may use a statistical analysis method known in the art, for example, a linear or nonlinear regression analysis method, an advance or nonlinear classification analysis method, ANOVA, neural network analysis method, genetic analysis method, support vector machine analysis method, hierarchical Analysis or clustering analysis method, hierarchical algorithm or kernel principal components analysis method using decision tree, Markov Blanket analysis method, recursive feature elimination or entropy-basic recursive feature elimination analysis method, forward floating search or rear floating search analysis method, etc. Or they can be used in combination. In order to increase the reliability of the analysis, it is preferable to use linear regression analysis or ROC analysis.

The ROC analysis is a graph expressing the correlation between sensitivity and specificity in a specific diagnosis method, and may indicate the accuracy of a specific diagnosis model. The "sensitivity" of the present invention refers to the ratio of determining positive for an individual with an actual disease when using a specific diagnostic model, and the "specificity" of the present invention does not have an actual disease when using a specific diagnostic model. It refers to the rate at which non-negative individuals are judged negative.

When both the specificity and the sensitivity are high in the diagnostic method, the accuracy of the test is increased. Specifically, when the total area is 1, it can be determined that the accuracy is higher when the AUC is 0.5 or more, 0.6 or more, 0.7 or more, 0.8 or more, or 0.9 or more, and the closer the curve is to the upper left vertex in the ROC graph, the higher the accuracy. It can be judged higher. In the ROC graph, the X-axis is 1-specificity and the Y-axis is sensitivity, and the positions on the graphs of all diagnostic models are indicated as points, and then a curve is drawn by connecting the points. In the ROC curve, as the area under the curve (AUC) increases, it can be determined that the accuracy of the corresponding diagnostic model is higher. Therefore, in the case of diagnosing gastric cancer by analyzing the expression levels of the genes SPINK7, PPL, SEMA4B, MIR140-5p, and MIR301a for Koreans according to the present invention, the diagnosis accuracy (AUC value) is 0.87 or more, and is not limited to Koreans. Diagnosis accuracy is higher than that of the five genes (AUC value = 0.81) and general gastric cancer markers (AUC value = 0.54 ~ 0.73).

In the present invention, by measuring the expression level of the gene in the saliva of a suspected cancer patient among Koreans using a formulation capable of measuring the mRNA expression levels of the genes SPINK7, PPL, and SEMA4B, which are biomarkers for diagnosis of gastric cancer, and the miRNA of MIR140-5p and MIR301a, It is possible to analyze with simple and high accuracy using saliva without biopsy, and it is possible to diagnose gastric cancer when the expression of the gene is lower than that of a normal person.

1 is a schematic diagram showing the progress of a study on the development of a saliva exRNA biomarker for diagnosis of gastric cancer; The experiment is carried out in one step (part 1) of discovering biomarkers and two steps (part 2) of verifying biomarkers.
2 is a heat map image created using the top 150 genes obtained from microarray results; The difference between the two groups was revealed using saliva obtained from the gastric cancer patient group (n = 63) and the non-gastric cancer control group (n = 31). Red is a sample from a gastric cancer patient group, and blue is a sample from a non-gastric cancer control group.
Figure 3 is a ROC curve graph for the combination of three Korean saliva mRNA biomarkers (SPINK7, PPL and SEMA4B) and two miRNA biomarkers (MIR140-5p and MIR301a); Using the ROC curve graph, AUC values for the combination of five biomarker combinations (black dotted line), demographic characteristics for Koreans (black solid line), and biomarker and demographic characteristics (gray dotted line) were calculated, respectively. .

Hereinafter, the biomarker for gastric cancer diagnosis of the present invention and its use will be described in more detail with reference to the accompanying drawings. However, these descriptions are provided by way of example only to aid understanding of the present invention, and the scope of the present invention is not limited by these exemplary descriptions.

1. Materials and methods

1-1. Sample collection and study design

This study was designed according to the PRoBE guidelines. All participants were recruited from Samsung Medical Center, and 294 saliva samples (163 gastric cancer patients, 131 normal non-gastric cancer patients) were collected before endoscopy. Detailed patient registration and cell-free saliva collection procedures can be found at http://www.clinchem.org/content/vol64/issue10 . Sample randomization was performed based on demographic information of all subjects. Demographic information used in this study is shown in Tables 1 and 2 below. In Tables 1 and 2, age, race, and alcohol use balanced the number of gastric cancer patients and non-gastric cancer control groups, whereas alcohol and Helicobacter pylori carriers were a risk factor for gastric cancer.

Demographic information Characteristic Steps to discover transcript biomarkers Gastric cancer patients
(n = 63) Gastric cancer control
(n =31) P value age Mean±standard deviation 56.2 ± 11.1 54.8 ± 10.4 0.56 gender male 43 (68.3%) 13 (41.9%) 0.02 female 20 (31.7%) 18 (59.1%) race Asian 63 (100%) 31 (100%) smoking 27 (42.8%) 5 (16.1%) 0.01 Drinking 28 (44.4%) 9 (29.0%) 0.15 H. pylori 37 (58.7%) 8 (25.8%) 0.003

Demographic information Characteristic Steps to discover miRNA biomarkers Gastric cancer patients
(n = 10) Gastric cancer control
(n = 10) P value age Mean±standard deviation 58.4 ± 7.6 52.4 ± 7.9 0.10 gender male 6 (60.0%) 4 (40.0%) 0.66 female 4 (40.0%) 6 (60.0%) race Asian 10 (100%) 10 (100%) smoking 3 (30.0%) 0 (0.0%) 0.21 Drinking 4 (40.0%) 5 (50.0%) 1.00 H. pylori 5 (50.0%) 4 (40.0%) 1.00

Referring to FIG. 1, a biomarker development study consists of a discovery step and a validation step.

Step 1 is the step of discovering and confirming biomarkers using transcriptomic and miRNA. Transcripts were profiled for 63 gastric cancer patient samples and 31 non-gastric cancer control samples using Affymetrix HG U133 + 2.0 microarray, and mRNA candidates were identified by RT-qPCR. For miRNA, 10 initial gastric cancer samples and 10 non-gastric cancer control samples were selected and profiled using TaqMan miRNA array (Applied Biosystems), and miRNA candidates were identified by TaqMan miRNA assay (Thermo Scientific).

Step 2 is verified for the exRNA biomarker candidate identified in Step 1 using an exRNA sample extracted from an independent cohort containing 100 gastric cancer patient sample and 100 non-gastric cancer control sample, as shown in Table 3 below. I did.

Demographic information Characteristic Verification step Gastric cancer patients
(n=100) Gastric cancer control
(n=100) P value age Mean±standard deviation 51.9 ± 9.2 55.7 ± 8.1 0.003 gender male 79 (79.0%) 47 (47.0%) <0.001 female 21 (21.0%) 53 (53.0%) race Asian 100 (100%) 100 (100%) smoking 66 (44.0%) 33 (33.0%) <0.001 Drinking 55 (55.0%) - H. pylori - -

1-2. Saliva transcriptome profiling and data analysis

Total RNA was extracted from 300 μl of saliva supernatant using the miRNeasy micro kit (QIAGEN). DNase I (Ambion) was treated on the extracted RNA to remove DNA contaminated by RNAse. The amount of saliva mRNA was calculated by measuring the expression level of the saliva internal reference gene (GAPDH) using RT-qPCR. The isolated saliva mRNA (about 10 ng) was amplified using the RiboAmp RNA Amplification kit (Molecular Devices), and labeled with biotin using GeneChip Expression 3-Amplification Reagents (Affymetrix) for in vitro transcription labeling. Biotin-labeled complementary RNA (about 20 μg) was fragmented and sent to the University of California, a microarray core facility in Los Angeles, USA for chip hybridization and scanning. An Affymetrix Human Genome U133 Plus 2.0 Array with less than 47,000 transcripts and variants was used for saliva transcript profiling. Microarray data were recorded in the GEO database (access number GSE64951) based on the guidelines of'Minimum Information About a Microarray Experiment'.

1-3. Confirmation of mRNA biomarkers using RT-qPCR

Candidate mRNA biomarkers selected by microarray profiling were verified by superimposed RT-qPCR (RT-PCR using individual SYBR green qPCR) using the same sample (n = 94) used in microarray analysis. qPCR primers were designed using Primer3 software, and synthesized by Sigma-Genosys after performing Primer-BLAST investigation. The primer sequence was designed to avoid single nucleotide variation (SNP) regions in the target gene. All amplicons were in the intron spanning. RT-qPCR analysis was performed in duplicate for each biomarker candidate according to the guidelines of'Minimum Information for Publication of Quantitative Real-Time PCR Experiment'. Detailed test methods can be found at http://www.clinchem.org/content/vol64/issue10 . The specificity of the PCR product for each gene was confirmed by melting curve analysis and 3% agarose gel analysis. The ΔCq value was calculated as [ΔCq = (existing Cq value of each biomarker candidate)-(Cq value of GAPDH or ACTB, which is a housekeeping gene)]. The mRNA biomarker candidates and their primer sequences used for identification and verification of transcript biomarkers are shown in Table 4 below.

gene Subscription number Primer sequence (5'> 3') Sequence number Amplification product size ^* (bp) ANXA1 NM_000700.1 OF: CCACAAGCAAACCAGCTTTC 6 61 OR: AATTTCAGAACGGGAAACCA 7 IF: TCAAGCCATGAAAGGTGTTG 8 IR: ACGGGAAACCATAATCCTGA 9 CD24 NM_013230.2 OF: TGAGAATCCCAAATTTGATTGA 10 56 OR: TTGGATGTTGCCTCTCCTTC 11 IF: TGCCAATATTAAATCTGCTGGA 12 IR: GGATGTTGCCTCTCCTTCAT 13 CSTB NM_000100.3 OF: GCCGAGACCCAGCACATC 14 60 OR: CACCTGGCTCTTGAATGACA 15 IF: GTGAGGTCCCAGCTTGAAGA 16 IR: TGACACGGCCTTAAACACAG 17 EIF3G NM_003755.3 OF: AAGTTCAAGATTGTCCGCAC 18 112 OR: GCAGTTCAGGTCCTCTTTG 19 IF: TTCAAAGGCTGTCGCAAGGA 20 IR: CGTCATAGAGACATCGTCACTG 21 ERO1A NM_014584.1 OF: GCAAATATGCCAGAAAGTGGA 22 81 OR: CCTGAAGTTTTCTAATTCTTTCACA 23 IF: TGCCAGAAAGTGGACCTAGT 24 IR: AAATTCTTCCAAATGCGTTGA 25 KRT4 NM_002272.3 OF: CCTCCCATGGACAGAGAAGA 26 55 OR: CTAGTGGGAGATGGCATTGG 27 IF: CCAGGAGTGTCATCTCCAGAA 28 IR: TTGGACTGGGAAGGGACATA 29 KRT6A NM_005554.3 OF: CCTCTGCTCCTTTTCATTGC 30 63 OR: GGTGGGGGTTCACAACACT 31 IF: AAAATTGCCAGGGGCTTATT 32 IR: GAGAGTTTGAGAGCCAGTGGA 33 PPL NM_002705.4 OF: GAGAAACAAAGGCAAATACAGC 34 66 OR: TGTGTCCACGATGTTCTTCTC 35 IF: CCGGAGCATCTCTAACAAGGA 36 IR: ACCTGGTCGGCATTCTTCTG 37 RANBP9 NM_005493.2 OF: ATGGCAAAACCCCAAAAGA 38 109 OR: CCAACCTGGTAGTCTATTCA 39 IF: AGCCACGCATCCAATACCAG 40 IR: TGAGCAGAAAGACCAATTCCCA 41 S100A10 NM_002966.2 OF: CAGTGTAGAGATGGCAAAGTG 42 92 OR: TTATCAGGGAGGAGCGAAC 43 IF: CCAGAGCTTCTTTTCCCTAATTGC 44 IR: CTGCCTACTTCTTTCCCTTCTG 45 SEMA4B NM_198925.2 OF: CAGCCTCTACCAGCCTCA 46 77 OR: CTGGAACTGGACTTGCTCA 47 IF: ATCCAGGACATCGAGGGAGC 48 IR: GTTGGTACAAAAGACGGGGAC 49 SPINK7 NM_032566.2 OF: CCTGCCCCATCACATACCTA 50 79 OR: AGAGCCTGGGATGATGAAGATG 51 IF: CATCACCTATGGGAATGAATGTC 52 IR: TCCATCGTGAAGAAACTGAACTC 53 GAPDH ^# NM_002046.4 OF: CAACAGCCTCAAGATCATCA 54 112 OR: CCATCACGCCACAGTTTC 55 IF: CCAACTGCTTAGCACCCCTG 56 IR: GGGCCATCCACAGTCTTCTG 57 ACTB ^# NM_001101.3 OF: CAGAGCCTCGCCTTTGCC 58 73 OR: ATGCCGGAGCCGTTGTCG 59 IF: CCTCGCCTTTGCCGATCC 60 IR: GAGCGCGGCGATATCATCA 61 O=outer, I=inner, F=forward, R=reverse
*: The amplification product size is the overlapped PCR product size in which IF and IR primers are used.
#: Saliva internal reference (SIR) gene

1-4. Saliva miRNA profiling

Total RNA was extracted from 300 μl of saliva supernatant using the mirVana PARIS extraction kit (Ambion). On-column DNase treatment (Qiagen) was used to remove contaminating DNA during RNA extraction. Total RNA (3 ng) was converted to complementary DNA using the TaqMan miRNA Reverse Transcription Kit (Applied Biosystems). As shown in Table 5 below, two sets of stem-loop RT primers (human pool A and human pool B) (Megaplex RT primers, Applied Biosystems) were used. After reverse transcription, the RT product was pre-amplified with TaqMan PreAmp Master Mix (Applied Biosystems) and Megaplex PreAmp primer (Applied Biosystems). The preamplification product was not diluted prior to quantification of miRNA. To quantify miRNA, TaqMan Human miRNA array set version 3.0 (Applied Biosystems) and TaqMan Universal PCR Master Mix (no AmpErase uracil N-glycosylase) were used. All reactions were performed in a 7900HT Fast Real-Time PCR System containing a special cardholder (Applied Biosystems). Data was analyzed using RQManager software version 1.2 and DataAssist software version 3.0 (Applied Biosystems). The ΔCq value was calculated as [ΔCq = (existing Cq value of each biomarker candidate)-(Cq value of RNA polymerase III transcribed into U6 micronuclear RNA, which is a reference gene)].

Primer (SEQ ID NO:) Base sequence (5'> 3') miR-140-5p (SEQ ID NO: 62) CAGUGGUUUUACCCUAUGGUAG miR-301a (SEQ ID NO: 63) CAGUGCAAUAGUAUUGUCAAAGC

1-5. Identification and verification of miRNA biomarkers in saliva

Biomarker candidates selected by TaqMan miRNA array profiling were confirmed by TaqMan miRNA assay (Applied Biosystems) using the same sample (n = 20) used in miRNA array analysis. TaqMan miRNA assays containing specific miRNA genes were ordered from Applied Biosystems. The experimental method is the same as suggested by the manufacturer for the custom RT and pre-amplification pool using TaqMan miRNA assay. Samples were not diluted before the real-time PCR reaction. The qPCR reaction for each candidate miRNA was performed in duplicate with Roche LightCycler 480 II (Roche). The average threshold cycle (Cq) was checked, and U6 micronuclear RNA was used as a reference gene for data standardization. In addition, TaqMan miRNA assay was used to analyze miRNA in later cohort validation.

1-6. Statistical analysis

First, demographic characteristics within each cohort were summarized, and then demographic characteristics were compared between the gastric cancer patient group and the non-gastric cancer control group in the cohort using χ ² test and t-test.

Microarray Analysis: CEL files derived from the entire data set were extracted with statistical software R 3.0.2. Data pre-processing was performed using Probe Logarithmic Intensity Error Estimation expression measurement after background correction and displacement standardization for each microarray expression data set. Probe set-level displacement normalization was performed on all samples. Finally, the Wilcoxon rank-sum test for all probe sets was used to compare gene expression between gastric cancer patients and non-gastric cancer controls.

Saliva mRNA and miRNA biomarker candidates met the following criteria: (a) the P value of the Wilcoxon test was less than 0.05, and (b) the fold change exceeded 1.2. It was selected to identify the top 30 mRNAs and the top 15 miRNAs with the lowest P values. In the RT-qPCR confirmation step, ΔCq values for mRNA and miRNA were compared between the gastric cancer patient group and the non-gastric cancer control group using the Wilcoxon rank-sum test. 12 biomarker mRNAs and 6 miRNAs with a P value of less than 0.05 were selected and verified.

1-7. Verification and model building

To verify each of the 12 mRNA and 6 miRNA candidates selected from the excavation set, a Wilcoxon rank-sum test was performed comparing 100 gastric cancer patient groups and 100 non-gastric cancer control groups. First, the Wilcoxon rank-sum test was used to compare ΔCq transformed values for each marker between the gastric cancer patient group and the non-gastric cancer control group. Thereafter, a multiple logistic model was designed to identify the best combination of markers capable of distinguishing gastric cancer in the samples of the non-gastric cancer control group.

To construct a logistic regression model, the LASSO variable selection technique/estimation was used. The tuning parameter (λ) was selected through 10-fold cross-validation from R to the GLMnet package. The diagnostic ability of each model was evaluated using the AUC calculated based on the predicted probability in the model. Statistical analysis was derived using R 3.0.2 and SPSS V22 (IBM Corp). Each value is'mean (standard deviation)', and a P value <0.05 was considered statistically significant.

2. Results

2-1. Discovery of candidate biomarkers for saliva transcriptome

1, the gene expression profile of the saliva samples obtained from the gastric cancer patient group (n = 63) and the non-gastric cancer control group (n = 31) was investigated using Affymetrix Human Genome U133 Plus 2.0 Array. To ensure the accuracy of microarray profiling, the amount and quality of RNA in each saliva sample was evaluated. On average, 117.51 ± 70.67 ng of total RNA (n = 94) was obtained in 300 µl of saliva supernatant. There was no significant difference in total RNA isolated from the gastric cancer patient group and the non-gastric cancer control group (P = 0.39; n = 94). In all saliva samples, the RT-qPCR results of the reference gene GAPDH were not significantly different in the expression levels of the gastric cancer patient group and the non-gastric cancer control group (P = 0.71; n = 94). Amplification twice to obtain a constant amplification magnitude, and the yield of biotin-labeled complementary RNA was 58.58 ± 14.76 ㎍. There was no significant difference in the complementary RNA yield between the gastric cancer patient group and the non-gastric cancer control group (P = 0.23; n = 94).

In the expression microarray results, 38 exRNAs increased and 2601 exRNAs decreased when the saliva of the gastric cancer patient group was compared with that of the non-gastric cancer control group (n = 94; P <0.05; fold change> 1.2). As shown in FIG. 2, in a heat map made as a result of microarray analysis of the top 150 genes, the unadjusted P value cutoff is 0.002, and there is a potential difference between the saliva profiles of the gastric cancer patient group and the non-gastric cancer control group. appear.

2-2. Identification of mRNA candidate biomarkers for diagnosis of gastric cancer

Candidate mRNA markers obtained from microarray profiling were identified before verification. The top 30 mRNA candidates with the smallest P value (25 candidates with reduced expression and 5 increased candidates) were selected. RT-qPCR was performed to confirm the results of the excavated sample set (gastric cancer patient group = 63, non-gastric cancer control group = 31), and showed a significant difference (P = 0.05) between the gastric cancer patient group and the non-gastric cancer control group while consistent with the microarray data. Among the 30 exRNAs, 12 differences in RNA expression levels were confirmed. The Cq value of the gastric cancer patient group calculated using the GAPDH gene was 24.85 ± 1.53, whereas the Cq value of the non-gastric cancer control group was 25.03 ± 2.32, showing no significant difference (P = 0.65). As shown in Table 6 below, 11 reduced exRNAs (S100A10, ANXA1, CSTB, KRT6A, ERO1A, PPL, SPINK7, RANBP9, KRT4, CD24 and SEMA4B) and 1 increased exRNA (EIF3G) were identified. The expression pattern of the confirmed biomarker was consistent with the microarray profiling result, and the AUC value was 0.63 ~ 0.74.

gene ΔCq value Gastric cancer patients
(Mean (standard deviation)) Gastric cancer control
(Mean (standard deviation)) AUC
(95% CI) ANXA1 -0.90 (2.29) -1.96 (2.65) 0.724 CD24 1.18 (2.15) 0.31 (1.46) 0.651 CSTB -1.84 (1.79) -2.31 (2.89) 0.655 EIF3G 1.84 (0.26) 2.48 (0.23) 0.639 ERO1A 4.50 (4.84) 2.91 (4.18) 0.663 KRT4 -0.76 (2.56) -1.58 (1.81) 0.634 KRT6A -0.04 (3.08) -1.19 (1.51) 0.666 PPL -1.03 (2.26) -2.42 (1.52) 0.719 RANBP9 5.90 (4.45) 3.53 (3.48) 0.730 S100A10 1.69 (2.80) 0.76 (2.57) 0.644 SEMA4B 8.19 (3.45) 6.71 (1.88) 0.637 SPINK7 1.05 (3.31) -0.96 (3.16) 0.737

2-3. Saliva miRNA expression profile, candidate discovery and validation

The miRNA expression profiles of 10 early gastric cancer patient groups (stage 1a or 1b) and 10 non-gastric cancer control groups were used to discover miRNA. TaqMan Human miRNA array set version 3.0 (A+B) was used to profile each sample (total of 40 cards). Prior to data normalization, only miRNAs with a Cq value of less than 35 in a minimum of 80% of the sample (16 out of 20 patients) were included to ensure the accuracy of miRNA identification using RT-qPCR. The number of detectable miRNAs between the gastric cancer patient group and the non-gastric cancer control group was similar (218 ± 3). Using this criterion, 15 miRNA candidates (12 with reduced expression and 3 with increased expression) were selected to perform identification by RT-qRCR using the sample set of the excavation step. The Cq value of the gastric cancer patient group for the reference gene U6 micronuclear RNA was 18.79 ± 1.37, and the Cq value of the non-gastric cancer control group was 18.67 ± 1.90 (P = 0.87). From the RT-qPCR results, it was confirmed that the relative expression levels of the 6 miRNAs were consistent with the TaqMan miRNA array data, and there was a significant difference between the gastric cancer patient group and the non-gastric cancer control group. As shown in Table 7 below, six miRNAs with reduced expression (MIR140-5p, MIR374a, MIR454, MIR15b, MIR28-5p, and MIR301a) were identified, and their AUC values were 0.79 to 0.88.

gene ΔCq value Gastric cancer patients
(Mean (standard deviation)) Gastric cancer control
(Mean (standard deviation)) AUC
(95% CI) miR140-5p 1.77 (1.16) -0.18 (1.43) 0.840 miR374a 4.05 (1.50) 2.41 (1.48) 0.790 miR454 4.85 (1.42) 2.71 (1.81) 0.830 miR15b 1.79 (1.52) 0.01 (1.33) 0.820 miR28-5p 6.57 (1.36) 4.50 (1.34) 0.880 miR301a 6.02 (1.18) 4.32 (1.45) 0.820

2-4. Validation of mRNA and miRNA candidate biomarkers

For further validation using an independent cohort (100 gastric cancer patients and 100 non-gastric cancer control), 12 mRNA candidates and 6 miRNA candidates identified from 30 top mRNA candidates and 15 miRNA candidates were selected. As shown in Table 8 below, 9 of the 12 mRNA candidates (ANXA1, CD24, CSTB, ERO1A, KRT4, KRT6A, PPL, S100A10 and SPINK7) were verified (P <0.05), and the AUC values were 0.59 to 0.64. Four of the six miRNA candidates (MIR140-5p, MIR374a, MIR454, and MIR15b) were verified, and these showed significant differences between the gastric cancer patient group and the non-gastric cancer control group (P <0.05; n = 200), and the AUC value was 0.63 ~ 0.70 Was.

gene ΔCq value Gastric cancer patients
(Mean (standard deviation)) Gastric cancer control
(Mean (standard deviation)) P value AUC
(95% CI) ANXA1 -2.58 (2.07) -3.36 (1.63) 0.008 0.61
(0.53, 0.69) CD24 1.20 (1.90) 0.32 (1.66) 0.001 0.63
(0.56, 0.71) CSTB -2.83 (2.15) -3.74 (1.79) 0.004 0.62
(0.54, 0.70) EIF3G 6.98 (3.08) 7.08 (3.21) 0.945 0.50
(0.42, 0.58) ERO1A 4.53 (2.07) 3.70 (1.96) 0.002 0.63
(0.55, 0.71) KRT4 -2.28 (2.35) -3.02 (2.00) 0.035 0.59
(0.51, 0.67) KRT6A -0.34 (2.34) -1.21 (2.15) 0.001 0.63
(0.56, 0.71) PPL 1.08 (2.23) 0.34 (2.20) 0.007 0.61
(0.53, 0.69) RANBP9 4.26 (3.11) 3.56 (2.77) 0.157 0.56
(0.48, 0.64) S100A10 2.21 (2.02) 1.55 (2.04) 0.006 0.61
(0.54, 0.69) SEMA4B 11.47 (3.98) 10.57 (4.14) 0.149 0.56
(0.48, 0.64) SPINK7 2.37 (2.72) 1.18 (1.98) 0.001 0.64
(0.56, 0.72) miR140-5p 1.54 (3.68) -1.08 (3.27) <0.001 0.70
(0.63, 0.78) miR374a 6.95 (5.69) 4.26 (4.59) <0.001 0.65
(0.57, 0.73) miR454 4.61 (3.40) 3.14 (3.40) 0.003 0.63
(0.55, 0.70) miR15b 2.92 (3.52) 1.00 (3.42) <0.001 0.65
(0.57, 0.72) miR28-5p 5.15 (4.17) 3.59 (3.94) 0.024 0.59
(0.51, 0.67) miR301a 8.46 (4.17) 6.95 (3.82) 0.01 0.61
(0.53, 0.69)

2-5. Construction of predictive model using proven saliva exRNA biomarkers

Referring to FIG. 3, in a combination model of three mRNA biomarkers (SPINK7, PPL and SEMA4B) and two miRNA biomarkers (MIR140-5p and MIR301a) selected by the LASSO method from 12 mRNA candidates and 6 miRNA candidates, The AUC value was 0.81 (95% CI, 0.72 ~ 0.89). On the ROC curve that maximizes sensitivity and specificity, it was found that the sensitivity was 75% and the specificity was 83%. By setting the sensitivity to 80% or 90%, the expected specificity was obtained as 54% or 40%, respectively. To evaluate the predictive ability (accuracy) of biomarkers, a demographic characteristic-only model was constructed based on the gastric cancer patient group database, and the AUC value was 0.69 (95% CI, 0.59 ~ 0.79). . In the model incorporating five biomarkers and demographic characteristics (smoking, sex, and age), the AUC value was 0.87 (95% CI, 0.80 to 0.93). Calibration was performed for the three models using the Hosmer-Lemeshow test. All three models had P> 0.05, and there was no shortage of calibration. Delong's test was performed to compare the AUC values of the three models. There was a significant difference in ΔAUC between the combination model of biomarker and demographic characteristics (AUC = 0.87) and the demographic model (AUC = 0.69). On the ROC curve that maximizes sensitivity and specificity, the sensitivity was 82% and the specificity was 77%, the positive predictive value was 82%, and the negative predictive value was 77%. When the threshold was set with high sensitivity (90%), the specificity, positive predictiveness and negative predictiveness were 65%, 76%, and 84%, respectively.

<110> SAMSUNG LIFE PUBLIC WELFARE FOUNDATION <120> Biomarkers for Diagnosing Gastric Cancer And Uses Thereof <130> PN190164 <160> 63 <170> KoPatentIn 3.0 <210> 1 <211> 258 <212> DNA <213> Artificial Sequence <220> <223> SPINK7 <400> 1 atgaagatca ctgggggtct ccttctgctc tgtacagtgg tctatttctg tagcagctca 60 gaagctgcta gtctgtctcc aaaaaaagtg gactgcagca tttacaagaa gtatccagtg 120 gtggccatcc cctgccccat cacataccta ccagtttgtg gttctgacta catcacctat 180 gggaatgaat gtcacttgtg taccgagagc ttgaaaagta atggaagagt tcagtttctt 240 cacgatggaa gttgctaa 258 <210> 2 <211> 5271 <212> DNA <213> Artificial Sequence <220> <223> PPL <400> 2 atgaactcgc tcttcaggaa gagaaacaaa ggcaaataca gccccactgt gcagacccgg 60 agcatctcta acaaggagct ctcggagctg atcgagcagc tgcagaagaa tgccgaccag 120 gtggagaaga acatcgtgga cacagaggcc aagatgcaga gtgacctggc tcggctgcag 180 gagggtcggc agcctgagca ccgggacgtg accctgcaga aggtgttgga ctctgagaag 240 ctgctctatg tgctagaggc ggatgcggcc attgccaagc acatgaagca cccacagggg 300 gacatgatcg ccgaggatat ccgccagctg aaggagcgtg tgaccaacct gcgcgggaaa 360 cacaagcaga tctacaggct ggcggtgaag gaagtggatc cacaggtcaa ctgggcggca 420 ctggtggagg agaagctgga caagctgaac aaccagagct ttgggactga cctgccgctg 480 gtggaccacc aagtggagga gcataacatc ttccacaatg aggtcaaggc catcgggccc 540 cacctggcca aggacgggga caaggagcag aacagcgaac tccgggccaa gtaccagaaa 600 ctgctggcag catcacaggc ccggcagcag cacctgagtt cgctgcagga ctacatgcag 660 cgctgcacca atgagctgta ctggctggac cagcaggcca agggccgcat gcagtacgac 720 tggagtgacc gcaacctcga ctaccccagc cgccggcgcc agtatgagaa tttcatcaac 780 cggaacctgg aggccaaaga ggagagaatc aacaaactgc acagcgaggg cgaccagctg 840 ctggcggccg agcaccccgg gaggaactcc attgaggcgc acatggaggc tgtgcacgca 900 gactggaagg agtacctgaa cctgctcatc tgcgaggaga gccacctcaa gtacatggag 960 gactaccacc agtttcacga agacgtgaag gacgctcagg agctgctgcg caaggtggac 1020 tcggacctga accagaagta tggccctgac ttcaaggacc ggtaccagat tgagctgctg 1080 ctgcgggagc tggatgacca ggagaaggtg ctggacaagt atgaggacgt ggtgcagggg 1140 ctgcagaagc gaggccagca ggtggtgccc ctcaagtacc gccgggagac tccgctcaag 1200 cccatccccg tggaggcact ctgtgacttt gagggggagc agggcctgat ctcgcggggc 1260 tacagctaca ccctgcagaa gaacaacggg gagagctggg agctcatgga cagcgctggg 1320 aacaagctga ttgctccggc cgtgtgtttt gtgatccccc ccacagaccc tgaggccctg 1380 gctctggctg acagcctggg cagccagtac cggagcgtgc ggcagaaggc agctgggagc 1440 aaacgcacgc tgcagcagcg gtatgaggtg ctgaagaccg agaatcccgg agatgcctct 1500 gacctacagg ggcggcagct gctggctggc ttggacaagg tggccagcga cctggaccgg 1560 caggagaagg ccatcacagg gatcctgcgg ccaccactgg agcaaggccg ggctgtgcag 1620 gacagtgccg agcgggccaa ggacctcaag aacatcacca acgagctact gcggattgaa 1680 cctgagaaga cgcggagcac ggctgagggc gaagccttca tccaggccct cccaggcagt 1740 ggcaccacac ccctgctgag gacccgggtg gaggacacca accggaaata cgagcacctc 1800 ctgcagctgc tggacttggc ccaggagaag gttgatgtgg ccaaccgcct ggagaagagc 1860 ctgcagcaga gctgggagtt gctggccaca cacgagaacc atctgaatca ggatgacaca 1920 gtgcctgaga gcagccgtgt cctggacagc aaggggcagg agctggcggc catggcctgt 1980 gagttacagg cccagaagtc cctcctgggt gaggtggagc agaacttgca ggcggccaag 2040 cagtgctcga gcacactggc cagccgcttc caggagcact gtccggacct ggagcgccag 2100 gaggccgagg tgcacaagct gggccagcgt ttcaacaacc tgcgccagca ggtggaacgc 2160 agggcgcaga gcctacagag cgccaaggca gcctacgagc acttccaccg cggccatgac 2220 cacgtgctgc agttcctagt cagcatcccc agttacgagc cccaggagac agacagcctc 2280 agccagatgg agaccaagct gaagaaccag aagaacctgc tagatgagat agcaagtagg 2340 gagcaggaag tacagaagat ctgtgccaat tcccagcagt accagcaagc tgtaaaggac 2400 tatgagttag aagcagaaaa actaaggtct cttctcgact tggagaatgg aaggagaagc 2460 cacgtgagca agagagccag gctccaatct cctgccacca aagtgaagga agaggaagca 2520 gcacttgccg ccaagttcac tgaagtttat gccatcaaca gacagaggct gcagaatctg 2580 gagtttgctc tgaatctcct cagacagcag ccggaagtag aagtgaccca tgagaccctg 2640 caaaggaata ggccggactc tggagtggag gaggcgtgga agatcaggaa ggaactggat 2700 gaggagactg agcggaggcg gcagctggag aacgaggtca agagcaccca ggaagaaatc 2760 tggaccttga ggaatcaggg gcctcaggaa tcggtggtga ggaaggaggt gctcaagaag 2820 gtgccggatc ccgtgctgga ggagagcttc cagcagctgc agcggacgct ggcagaggag 2880 cagcacaaga accagctgct gcaggaggag ctggaggcac tgcagctgca gctgcgtgcc 2940 ctggagcagg agaccagaga cggggggcag gagtacgtgg tcaaggaggt cctgcgcatc 3000 gagcctgaca gggcccaggc ggatgaggtc ttgcagctgc gggaggagct ggaggcactg 3060 aggcggcaga agggcgcccg ggaggcagag gtgctcctcc tgcagcagcg tgtggccgcc 3120 ctggctgaag agaagagccg ggcgcaggag aaggtcacag agaaagaggt ggtgaaactg 3180 cagaatgacc cccagctgga ggcagagtac cagcagctgc aggaggacca ccagcgccag 3240 gaccagctca gggagaagca ggaggaggag ctgagcttcc tccaggacaa gctcaagagg 3300 ctagagaagg agcgggccat ggccgagggc aagatcaccg tcaaggaggt gctcaaggtg 3360 gagaaggacg cggccaccga gagggaggtc agcgatctca cccgccaata tgaggacgag 3420 gctgccaagg ctcgcgctag ccagagggag aagacggagc tgctccgaaa gatatgggcc 3480 ttggaggagg agaacgccaa agtggtggtg caggagaagg tgcgggagat cgtgcggcca 3540 gaccccaagg cggaaagtga agtggcgaac ctccgcctgg agcttgtgga gcaggagcga 3600 aagtaccggg gtgccgagga gcagctccgg agctaccaga gtgagctgga ggccctcagg 3660 aggcgaggcc cccaggtgga agtcaaagag gtgactaagg aagtcattaa gtacaagact 3720 gaccctgaga tggagaagga gcttcagcgg ctcagggagg agatcgtgga caagaccaga 3780 ctgatcgaaa ggtgtgattt agagatctac cagctgaaaa aggaaatcca ggccctgaaa 3840 gacaccaaac cccaggtcca gaccaaagag gtggtccagg agatcctcca attccaagaa 3900 gaccctcaaa ccaaggagga ggtggcgtct ctgagggcaa agctctcaga ggagcagaag 3960 aaacaagtgg atctggagag ggaaagagct tcccaggaag agcagatcgc ccggaaagag 4020 gaggagctct cgcgggtgaa ggaaagggtg gtgcagcagg aggtggtcag gtatgaggag 4080 gagccaggcc tgcgggccga ggcgagcgcc tttgccgaga gcatcgatgt ggagctgcgg 4140 cagattgaca agctgcgggc agagctgcgg cggctgcagc gccggcgcac cgagcttgag 4200 cggcagctgg aggagctaga gcgcgagcgg caggcccgca gggaggccga gcgcgaggta 4260 cagcggttgc agcagcggct ggcagcgctg gagcaggaag aagctgaggc ccgtgagaag 4320 gtaacccata cgcagaaggt ggtgctgcag caggacccgc agcaggcgcg agagcatgcc 4380 ctgctccgac tccagctgga agaagagcag caccggcggc agctcctgga gggggagctc 4440 gagaccctcc ggaggaaact ggctgcactg gagaaggcgg aggtcaagga gaaggtggtg 4500 ctctccgaga gtgtccaggt ggagaagggc gacaccgagc aagagatcca gaggctcaag 4560 agcagcctgg aggaggagag ccgcagcaag cgcgagctgg acgtcgaggt gagccggctg 4620 gaagccaggc tttcggagct ggaattccat aactccaagt catccaagga actagacttt 4680 ctgagggaag agaaccacaa attacagctg gagaggcaaa acctgcagct ggagacccga 4740 aggctccaat cggaaatcaa catggcagcg acggaaacac gagacctgcg gaacatgacc 4800 gtggcggact ctgggaccaa ccatgactcc agactgtggt ccctggagag ggaactggat 4860 gacctcaaga ggctctccaa ggacaaagac ctcgagatcg acgagctgca gaagcgcctg 4920 ggctccgtgg ccgtcaagcg ggagcagcgg gagaaccacc tgcggcgctc catcgtagtc 4980 atccaccctg acacaggccg cgagctgtcc ccggaggaag cccaccgtgc cgggctcatt 5040 gactggaaca tgttcgtgaa actcagaagc caggagtgcg actgggagga gatctcagtg 5100 aagggtccca atggggagtc ctcagtgata cacgacagga agtctggcaa gaagttctcc 5160 atcgaagagg ccctgcagag tggcaggctg acccctgctc agtatgaccg ctatgtcaac 5220 aaggatatgt ccatccagga gctggcggtc ttggtatctg ggcagaagta g 5271 <210> 3 <211> 2514 <212> DNA <213> Artificial Sequence <220> <223> SEMA4B <400> 3 atgctgcgca ccgcgatggg cctgaggagc tggctcgccg ccccatgggg cgcgctgccg 60 cctcggccac cgctgctgct gctcctgctg ctgctgctcc tgctgcagcc gccgcctccg 120 acctgggcgc tcagcccccg gatcagcctg cctctgggct ctgaagagcg gccattcctc 180 agattcgaag ctgaacacat ctccaactac acagcccttc tgctgagcag ggatggcagg 240 accctgtacg tgggtgctcg agaggccctc tttgcactca gtagcaacct cagcttcctg 300 ccaggcgggg agtaccagga gctgctttgg ggtgcagacg cagagaagaa acagcagtgc 360 agcttcaagg gcaaggaccc acagcgcgac tgtcaaaact acatcaagat cctcctgccg 420 ctcagcggca gtcacctgtt cacctgtggc acagcagcct tcagccccat gtgtacctac 480 atcaacatgg agaacttcac cctggcaagg gacgagaagg ggaatgtcct cctggaagat 540 ggcaagggcc gttgtccctt cgacccgaat ttcaagtcca ctgccctggt ggttgatggc 600 gagctctaca ctggaacagt cagcagcttc caagggaatg acccggccat ctcgcggagc 660 caaagccttc gccccaccaa gaccgagagc tccctcaact ggctgcaaga cccagctttt 720 gtggcctcag cctacattcc tgagagcctg ggcagcttgc aaggcgatga tgacaagatc 780 tactttttct tcagcgagac tggccaggaa tttgagttct ttgagaacac cattgtgtcc 840 cgcattgccc gcatctgcaa gggcgatgag ggtggagagc gggtgctaca gcagcgctgg 900 acctccttcc tcaaggccca gctgctgtgc tcacggcccg acgatggctt ccccttcaac 960 gtgctgcagg atgtcttcac gctgagcccc agcccccagg actggcgtga cacccttttc 1020 tatggggtct tcacttccca gtggcacagg ggaactacag aaggctctgc cgtctgtgtc 1080 ttcacaatga aggatgtgca gagagtcttc agcggcctct acaaggaggt gaaccgtgag 1140 acacagcagt ggtacaccgt gacccacccg gtgcccacac cccggcctgg agcgtgcatc 1200 accaacagtg cccgggaaag gaagatcaac tcatccctgc agctcccaga ccgcgtgctg 1260 aacttcctca aggaccactt cctgatggac gggcaggtcc gaagccgcat gctgctgctg 1320 cagccccagg ctcgctacca gcgcgtggct gtacaccgcg tccctggcct gcaccacacc 1380 tacgatgtcc tcttcctggg cactggtgac ggccggctcc acaaggcagt gagcgtgggc 1440 ccccgggtgc acatcattga ggagctgcag atcttctcat cgggacagcc cgtgcagaat 1500 ctgctcctgg acacccacag ggggctgctg tatgcggcct cacactcggg cgtagtccag 1560 gtgcccatgg ccaactgcag cctgtacagg agctgtgggg actgcctcct cgcccgggac 1620 ccctactgtg cttggagcgg ctccagctgc aagcacgtca gcctctacca gcctcagctg 1680 gccaccaggc cgtggatcca ggacatcgag ggagccagcg ccaaggacct ttgcagcgcg 1740 tcttcggttg tgtccccgtc ttttgtacca acaggggaga agccatgtga gcaagtccag 1800 ttccagccca acacagtgaa cactttggcc tgcccgctcc tctccaacct ggcgacccga 1860 ctctggctac gcaacggggc ccccgtcaat gcctcggcct cctgccacgt gctacccact 1920 ggggacctgc tgctggtggg cacccaacag ctgggggagt tccagtgctg gtcactagag 1980 gagggcttcc agcagctggt agccagctac tgcccagagg tggtggagga cggggtggca 2040 gaccaaacag atgagggtgg cagtgtaccc gtcattatca gcacatcgcg tgtgagtgca 2100 ccagctggtg gcaaggccag ctggggtgca gacaggtcct actggaagga gttcctggtg 2160 atgtgcacgc tctttgtgct ggccgtgctg ctcccagttt tattcttgct ctaccggcac 2220 cggaacagca tgaaagtctt cctgaagcag ggggaatgtg ccagcgtgca ccccaagacc 2280 tgccctgtgg tgctgccccc tgagacccgc ccactcaacg gcctagggcc ccctagcacc 2340 ccgctcgatc accgagggta ccagtccctg tcagacagcc ccccggggtc ccgagtcttc 2400 actgagtcag agaagaggcc actcagcatc caagacagct tcgtggaggt atccccagtg 2460 tgcccccggc cccgggtccg ccttggctcg gagatccgtg actctgtggt gtga 2514 <210> 4 <211> 22 <212> RNA <213> Artificial Sequence <220> <223> miR-140-5p <400> 4 cagugguuuu acccuauggu ag 22 <210> 5 <211> 86 <212> RNA <213> Artificial Sequence <220> <223> miR-301a <400> 5 acugcuaacg aaugcucuga cuuuauugca cuacuguacu uuacagcuag cagugcaaua 60 guauugucaa agcaucugaa agcagg 86 <210> 6 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> ANXA1_OF <400> 6 ccacaagcaa accagctttc 20 <210> 7 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> ANXA1_OR <400> 7 aatttcagaa cgggaaacca 20 <210> 8 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> ANXA1_IF <400> 8 tcaagccatg aaaggtgttg 20 <210> 9 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> ANXA1_IR <400> 9 acgggaaacc ataatcctga 20 <210> 10 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> CD24_OF <400> 10 tgagaatccc aaatttgatt ga 22 <210> 11 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> CD24_OR <400> 11 ttggatgttg cctctccttc 20 <210> 12 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> CD24_IF <400> 12 tgccaatatt aaatctgctg ga 22 <210> 13 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> CD24_IR <400> 13 ggatgttgcc tctccttcat 20 <210> 14 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> CSTB_OF <400> 14 gccgagaccc agcacatc 18 <210> 15 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> CSTB_OR <400> 15 cacctggctc ttgaatgaca 20 <210> 16 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> CSTB_IF <400> 16 gtgaggtccc agcttgaaga 20 <210> 17 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> CSTB_IR <400> 17 tgacacggcc ttaaacacag 20 <210> 18 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> EIF3G_OF <400> 18 aagttcaaga ttgtccgcac 20 <210> 19 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> EIF3G_OR <400> 19 gcagttcagg tcctctttg 19 <210> 20 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> EIF3G_IF <400> 20 ttcaaaggct gtcgcaagga 20 <210> 21 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> EIF3G_IR <400> 21 cgtcatagag acatcgtcac tg 22 <210> 22 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> ERO1A_OF <400> 22 gcaaatatgc cagaaagtgg a 21 <210> 23 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> ERO1A_OR <400> 23 cctgaagttt tctaattctt tcaca 25 <210> 24 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> ERO1A_IF <400> 24 tgccagaaag tggacctagt 20 <210> 25 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> ERO1A_IR <400> 25 aaattcttcc aaatgcgttg a 21 <210> 26 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> KRT4_OF <400> 26 cctcccatgg acagagaaga 20 <210> 27 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> KRT4_OR <400> 27 ctagtgggag atggcattgg 20 <210> 28 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> KRT4_IF <400> 28 ccaggagtgt catctccaga a 21 <210> 29 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> KRT4_IR <400> 29 ttggactggg aagggacata 20 <210> 30 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> KRT6A_OF <400> 30 cctctgctcc ttttcattgc 20 <210> 31 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> KRT6A_OR <400> 31 ggtgggggtt cacaacact 19 <210> 32 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> KRT6A_IF <400> 32 aaaattgcca ggggcttatt 20 <210> 33 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> KRT6A_IR <400> 33 gagagtttga gagccagtgg a 21 <210> 34 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> PPL_OF <400> 34 gagaaacaaa ggcaaataca gc 22 <210> 35 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> PPL_OR <400> 35 tgtgtccacg atgttcttct c 21 <210> 36 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> PPL_IF <400> 36 ccggagcatc tctaacaagg a 21 <210> 37 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> PPL_IR <400> 37 acctggtcgg cattcttctg 20 <210> 38 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> RANBP9_OF <400> 38 atggcaaaac cccaaaaga 19 <210> 39 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> RANBP9_OR <400> 39 ccaacctggt agtctattca 20 <210> 40 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> RANBP9_IF <400> 40 agccacgcat ccaataccag 20 <210> 41 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> RANBP9_IR <400> 41 tgagcagaaa gaccaattcc ca 22 <210> 42 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> S100A10_OF <400> 42 cagtgtagag atggcaaagt g 21 <210> 43 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> S100A10_OR <400> 43 ttatcaggga ggagcgaac 19 <210> 44 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> S100A10_IF <400> 44 ccagagcttc ttttccctaa ttgc 24 <210> 45 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> S100A10_IR <400> 45 ctgcctactt ctttcccttc tg 22 <210> 46 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> SEMA4B_OF <400> 46 cagcctctac cagcctca 18 <210> 47 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> SEMA4B_OR <400> 47 ctggaactgg acttgctca 19 <210> 48 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SEMA4B_IF <400> 48 atccaggaca tcgagggagc 20 <210> 49 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> SEMA4B_IR <400> 49 gttggtacaa aagacgggga c 21 <210> 50 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SPINK7_OF <400> 50 cctgccccat cacataccta 20 <210> 51 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> SPINK7_OR <400> 51 agagcctggg atgatgaaga tg 22 <210> 52 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> SPINK7_IF <400> 52 catcacctat gggaatgaat gtc 23 <210> 53 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> SPINK7_IR <400> 53 tccatcgtga agaaactgaa ctc 23 <210> 54 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> GAPDH_OF <400> 54 caacagcctc aagatcatca 20 <210> 55 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> GAPDH_OR <400> 55 ccatcacgcc acagtttc 18 <210> 56 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> GAPDH_IF <400> 56 ccaactgctt agcacccctg 20 <210> 57 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> GAPDH_IR <400> 57 gggccatcca cagtcttctg 20 <210> 58 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> ACTB_OF <400> 58 cagagcctcg cctttgcc 18 <210> 59 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> ACTB_OR <400> 59 atgccggagc cgttgtcg 18 <210> 60 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> ACTB_IF <400> 60 cctcgccttt gccgatcc 18 <210> 61 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> ACTB_IR <400> 61 gagcgcggcg atatcatca 19 <210> 62 <211> 22 <212> RNA <213> Artificial Sequence <220> <223> miR-140-5p primer <400> 62 cagugguuuu acccuauggu ag 22 <210> 63 <211> 23 <212> RNA <213> Artificial Sequence <220> <223> miR-301a primer <400> 63 cagugcaaua guauugucaa agc 23

Claims (10)

MRNA expression level of the first gene group comprising SPINK7, PPL and SEMA4B; And
It includes an agent for measuring the miRNA expression level of the second gene group comprising MIR140-5p and MIR301a,
The first gene group and the second gene group are detected in saliva, a biomarker composition for diagnosing gastric cancer in Koreans.
delete The method according to claim 1,
The agent is one or more selected from the group consisting of a primer set, a probe, and an antisense oligonucleotide that specifically binds to each gene constituting the first gene group and the second gene group.
A kit for diagnosing gastric cancer comprising the composition of claim 1 or 3.
The method of claim 4,
The kit includes a set of primers specifically binding to respective genes constituting the first gene group and the second gene group; And gastric cancer diagnostic kit comprising a reagent for nucleic acid amplification.
(a) measuring the mRNA expression level of the first gene group including SPINK7, PPL and SEMA4B and the miRNA expression level of the second gene group including MIR140-5p and MIR301a in a biological sample isolated from a Korean subject; And
(b) comparing the measured gene expression level with a normal non-gastric cancer control
Including,
The biological sample of step (a) is saliva, the method of providing information for diagnosis of gastric cancer.
delete The method of claim 6,
The mRNA or miRNA expression level of step (a) is RT-PCR (reverse transcription polymerase chain reaction), competitive RT-PCR (competitive reverse transcription polymerase chain reaction), real-time reverse transcription polymerase chain reaction (RT-PCR). , RNase protection assay (RNase protection assay), Northern blot (Northern blot) and DNA microarray (microarray) that is measured by one or more methods selected from the group consisting of a method of providing information for gastric cancer diagnosis.
The method of claim 6,
In the step (b), when the measured gene expression level is lower than that of the normal non-gastric cancer control group, the method of providing information for gastric cancer diagnosis is diagnosed as gastric cancer.
The method of claim 6,
The method is a method of providing information for diagnosis of gastric cancer, wherein the AUC value is 0.87 or more with diagnostic accuracy.

Images