Open Access

Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer

  • Authors:
    • Xiang Yin
    • Fumin Zhang
    • Zhongwu Guo
    • Weiyuan Kong
    • Yuanyuan Wang
  • View Affiliations

  • Published online on: June 27, 2019     https://doi.org/10.3892/ol.2019.10536
  • Pages: 2330-2339
  • Copyright: © Yin et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Gastric cancer (GC) is one of the most common types of malignant cancer and is associated with poor prognosis. Although the prognosis of patients with GC is associated with grade, stage and lymph node metastases, these traditional clinical features are inadequate to predict the outcome of GC. Therefore, there has been an increased focus on identifying novel molecular biomarkers for early diagnosis and prognosis, in order to improve outcomes in GC. In the present study, an integrative analysis of microRNA (miRNA) expression profiles, mRNA expression profiles and clinical characteristics was performed in a large cohort of patients with GC in order to identify an integrative prognostic model for improving postoperative risk classification. An integrative mRNA/miRNA signature (IMMIS), comprised of three miRNAs and one mRNA, was identified from a large number of differentially expressed miRNAs and mRNAs using univariate and multivariate Cox regression analysis. The prognostic value of the IMMIS was validated in the discovery cohort, testing cohort and The Cancer Genome Atlas (TCGA) cohort. The present results suggested that the identified signature had a reliable predictive performance and could classify the patients into high‑ and low‑risk groups with significantly different overall survival times. In the discovery cohort, the hazard ratio (HR) was 2.805 with a 95% CI=1.722‑4.567 (P<0.001). The median overall survival time as 1.49 vs. 3.85 years. In the testing cohort, the HR was 1.625 with a 95% CI=1.004‑2.638 (P=0.039) and the median overall survival time was 2.17 vs. 4.62 years. In the TCGA cohort, the HR was 2.139 with a 95% CI=1.519‑3.012 (P<0.001) and the median overall survival time was 1.53 vs. 4.62 years. The IMMIS constituted a reliable independent prognostic factor compared with clinical covariates, including age, sex, grade and stage, as indicated by multivariate and stratified analyses. Furthermore, comparative analysis revealed that the predictive value of the IMMIS was superior to the mRNA‑based signature alone. The present results suggested the potential value of the IMMIS as a promising novel biomarker for improving the clinical management of patients with GC.

Introduction

Gastric cancer (GC), additionally referred to as stomach cancer, is the one of most common types of digestive cancer worldwide and is the third leading cause of cancer-related death (1). China is one of the high-risk areas for digestive cancer, and GC is ranked the second most common cancer behind lung cancer in China (2). Notably, an estimated 477.7 individuals per 100,000 newly diagnosed cases and 158.7 per 100,000 mortalities were reported in China in 2015 (2). Gastric adenocarcinomas are the most common histological type of GC, which account for ~90% of GC cases (3). Although the survival rate of patients with GC has improved over the past years, the prognosis remains unsatisfactory, with a 5-year survival rate of 10% for patients with localized gastric adenocarcinoma and 50% for patients with early resectable gastric adenocarcinoma (4). Although the prognosis of patients with GC is associated with grade, stage and lymph node metastases, these traditional clinical features are inadequate to predict the outcome of GC. Therefore, there is an increasing focus on identifying novel molecular biomarkers for early diagnosis and prognosis to improve outcomes in patients with GC.

With the development of high throughput sequencing technologies for transcriptional analysis, including microarray and RNA sequencing (-Seq), researchers have increased their efforts in trying to identify molecular biomarkers for predicting the outcome of patients with GC at the transcriptional levels. Several mRNA or micro RNA (miRNA) expression signatures have been proposed to predict the survival of patients with GC. For example, Chen et al (5) developed a three-mRNA survival prediction model using cDNA microarray data from 18 pairs of cancerous and noncancerous gastric tissues. A previous study performed by Cho et al (6) constructed a six-gene expression-based prognostic risk score to predict the likelihood of relapse after curative resection. For miRNAs, Ding et al (7) identified an eight-miRNA signature for predicting the overall survival in 380 patients with gastric adenocarcinoma. However, previous studies on other types of cancer have shown that, notwithstanding the importance of individual RNA, intrinsic multi-RNA-based expression signatures have greater prognostic value (810). However, to the best of our knowledge, no integrated studies concerning their prognostic significance have been reported yet.

The aim of the present study was to develop and assess the predictive value of an integrated multi-gene signature by analyzing mRNA and miRNA expression profiles, and clinical information in a large cohort of patients with GC.

Materials and methods

Patient datasets

The Cancer Genome Atlas (TCGA) database (cancergenome.nih.gov/) was used to identify the following data: i) Normalized level-3 RNA-Seq data of 375 GC samples and 32 normal tissue samples obtained with HTSeq (version 0.6.1; cancergenome.nih.gov/); ii) normalized level-3 miRNA-Seq data of 436 GC samples; and iii) 41 normal tissue samples obtained with Illumina HiSeq (Illumina, Inc.). For all datasets, corresponding clinical data were collected and analyzed. The Ensembl gene ID of protein-coding genes was retrieved from the HUGO Gene Nomenclature Committee database (www.genenames.org/). Cross-referencing of the Ensembl gene ID with tumor barcodes was performed. Furthermore, data of patients with incomplete expression data and follow-up information were removed from the analysis. A total of 18,528 mRNAs in 407 samples, 1,573 miRNAs in 477 patients and 438 samples with survival information were retained for further analysis in the present study.

Development of an integrative mRNA/miRNA signature (IMMIS)

Differentially expressed mRNAs and miRNAs in GC and normal tissue samples were detected using the DEGseq2 package (11). An adjusted P<0.05 was used with Benjamini-Hochberg (B-H) correction and a |log2 (fold change)| >1 applied. Subsequently, univariate Cox proportional hazards analyses were used to identify prognostic mRNAs and miRNAs from the differentially expressed mRNAs and miRNAs. Finally, an IMMIS was constructed as the linear combination of expression values of each prognostic mRNA and miRNA, which was weighted by their estimated regression coefficients in the multivariate Cox regression analysis (1218).

Statistical analysis

The differences in overall survival time between the high- and low-risk groups were assessed using Kaplan-Meier survival plots and log-rank tests. Univariate and multivariate Cox regression analyses for overall survival were performed on the individual clinical variables with and without the IMMIS in each cohort. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. All statistical analyses were performed using R-version3.5.2 (http://www.R-project.org/) (19).

Results

Identification of differentially expressed mRNAs and miRNAs between GC samples and normal tissue samples

By comparing the expression profiles of mRNAs and miRNAs between GC samples and normal tissue samples, a total of 4,221 mRNAs and 201 miRNAs were identified to be differentially expressed in GC samples and normal tissue samples according to the DEGseq2 package method with |log2 (fold change)| >1 and an adjusted P<0.05 after B-H adjustment. These profiles were used for subsequent survival analyses. Among the differentially expressed autophagy-associated mRNAs and miRNAs, 2,055 mRNAs and 138 miRNAs were upregulated, and 2,166 mRNAs and 63 miRNAs were downregulated in GC samples compared with normal tissue samples.

Identification of independent prognostic mRNAs and miRNAs

In order to identify and validate an IMMIS, samples, corresponding clinical data, and mRNA and miRNA expression profiles were initially cross-referenced with tumor barcodes. A total of 361 samples were retained for subsequent survival analyses. All 361 samples with survival information were randomly divided into two equal patient cohorts: i) The discovery cohort (n=181); and ii) testing cohort (n=180). The detailed clinical information of the discovery cohort and testing cohort are provided in Table I. Statistical analysis indicated that the clinical characteristics were similar in the discovery and testing cohorts (Table I). The association between the expression level of differentially expressed mRNAs and miRNAs with the overall survival of patients with GC using the univariate Cox analyses was then examined. A total of 12 RNAs (including 11 mRNAs and 1 miRNA; FDR<0.05) were observed to be significantly associated with overall survival, and were considered candidate prognostic RNAs. All 12 prognostic RNAs were identified as risk factors with positive coefficients (Table II), and high expression of these RNAs was associated with a poor outcome.

Table I.

Clinical features of the patients with gastric cancer in the discovery cohort, testing cohort and the entire TCGA cohort.

Table I.

Clinical features of the patients with gastric cancer in the discovery cohort, testing cohort and the entire TCGA cohort.

Clinicopathological characteristicDiscovery cohort, n=181Testing cohort, n=180Entire TCGA cohort, n=361P-valueHigh risk group, n=180Low risk group, n=181P-value
Age 0.424a 0.492a
  <688794181 9586
  ≥689282174 8490
Sex 0.713a 0.773a
  Male117112229 116113
  Female6468132 6468
Stage 0.128a 0.133a
  I292049 2722
  II5850108 5256
  III6282144 8064
  IV211637 1324
  Unknown111223 815
Grade 0.489b 0.404b
  G1268 35
  G26467131 6071
  G3109104213 112101
  Unknown639 54
Status 0.942a <0.001a
  Alive106111217 87130
  Deceased7569144 9351
Therapy 0.916a 0.193a
  Chemotherapy6772139 6376
  Ancillary8715 105
  Unknown106112207 107100

a χ2-test

b Fisher's exact test. TCGA, The Cancer Genome Atlas.

Table II.

Information of mRNAs and microRNAs in the integrative signature.

Table II.

Information of mRNAs and microRNAs in the integrative signature.

Ensembl IDGene symbolGenomic locationP-valueHazard ratioCoefficient
ENSG00000146090RASGEF1CChr 5: 179,527,795-179,636,153(−)P<0.0012.2480.810
ENSG00000120129DUSP1Chr 5: 172,195,093-172,198,198(−)P<0.0011.0040.004
ENSG00000157927RADILChr 7: 4,836,686-4,923,350(−)P<0.0012.3580.858
ENSG00000134917ADAMTS8Chr 11: 130,274,820-130,298,888(−)P<0.0011.1070.102
ENSG00000144476ACKR3Chr 2: 237,476,430-237,491,001(+)P<0.0011.0610.059
ENSG00000130005GAMTChr 19: 1,397,091-1,401,569(−)P<0.0011.0410.040
ENSG00000183287CCBE1Chr 18: 57,098,172-57,364,612(−)P<0.0011.4960.403
ENSG00000106366SERPINE1Chr 7: 100,770,370-100,782,547(+)P<0.0011.0100.010
ENSG00000163673DCLK3Chr 3: 36,753,913-36,781,352(−)P<0.0017.4972.014
ENSG00000163530DPPA2Chr 3: 109,012,635-109,035,364(−)P<0.0011.7560.563
ENSG00000029559IBSPChr 4: 88,720,733-88,733,074(+)P<0.0011.1840.168
hsa-mir-184miR-184Chr 15: 79,209,788-79,209,871(+)P<0.0011.0100.010

[i] RASGEF1C, RasGEF domain family member 1C; DUSP1, dual specificity phosphatase 1; RADIL, Rap associating with DIL domain; ADAMTS8, ADAM metallopeptidase with thrombospondin type 1 motif 8; ACKR3, atypical chemokine receptor 3; GAMT, guanidinoacetate N-methyltransferase; CCBE1, collagen and calcium binding EGF domains 1; SERPINE1, serpin family E member 1; DCLK3, doublecortin like kinase 3; DPPA2, developmental pluripotency associated 2; IBSP, integrin binding sialoprotein; miR-, microRNA.

Establishment of an IMMIS for survival prediction in the discovery cohort

To construct an IMMIS for survival prediction, the 12 RNAs were fitted into a multivariate Cox regression model in the discovery cohort. Notably, the IMMIS was constructed using the determined expression of 12 prognostic RNAs and multivariate Cox regression coefficient as follows: Risk score=(0.5677× RasGEF domain family member 1C) + [0.0035× dual specificity phosphatase 1 (DUSP1)] + (0.0807× Rap associating with DIL domain) + [0.0281× ADAM metallopeptidase with thrombospondin type 1 motif 8 (ADAMTS8)] + [(−0.0095× atypical chemokine receptor 3 (ACKR3)] + (0.0096× guanidinoacetate N-methyltransferase) + [(−0.1882× collagen and calcium binding EGF domains 1 (CCBE1)] + (0.0024× serpin family E member 1) + (0.4322× doublecortin like kinase 3) + (0.6609× developmental pluripotency associated 2) + (0.1356× integrin binding sialoprotein) + (0.0110× miRNA-184). With the identified IMMIS, 181 patients with GC from the discovery cohort were divided into a high-risk group (n=91) and a low-risk group (n=90) according to the median risk score value (0.646), which was considered the cut-off value. Survival analysis showed that patients in the high-risk group had significantly shorter overall survival compared with those in the low-risk group, and median survival was 1.49 vs. 3.85 years, respectively. (P<0.001; Fig. 1A). The result of univariate Cox regression analysis indicated that the HR of the high-risk group compared with the low-risk group regarding overall survival was 2.805 (P<0.001; 95% CI=1.722–4.567; Table III). The 3- and 5-year overall survival rates of patients in the high-risk group were 25.7 and 22%, respectively, which were significantly lower compared with those of patients in the low-risk group. Specifically, he 3- and 5- year overall survival rates of patients in the low-risk group were 61.8 and 47.7%, respectively. Notably, the area under the curve for the IMMIS prognostic model was 0.724 for the 3-year overall survival rate (Fig. 1B). In addition, the distribution of risk scores, the survival status of patients and expression patterns of the 12 prognostic RNAs in the IMMIS are presented in Fig. 1C.

Table III.

Univariate and multivariate Cox regression analysis of the IMMIS and other clinicopathological variables. Discovery cohort, n=181

Table III.

Univariate and multivariate Cox regression analysis of the IMMIS and other clinicopathological variables. Discovery cohort, n=181

VariablesUnivariate modelMultivariate model



CharacteristicsComparison groupsHazard ratio95% Confidence intervalP-valueHazard Ratio95% Confidence intervalP-value
IMMISHigh- Vs. Low-risk2.8051.722–4.567<0.0012.6131.560–4.375<0.001
Age<68 vs. ≥68 years1.0321.008–1.0560.0071.0391.011–1.0670.005
SexMale vs. female1.0250.634–1.6580.9201.0070.601–1.6880.979
StageI and II vs. III and IV1.5490.950–2.5270.0791.8681.096–3.1810.022
GradeG1 vs. G2 and G30.9870.612–1.5930.9570.9370.552–1.5910.811

[i] IMMIS, integrative mRNA/microRNA signature; TCGA, The Cancer Genome Atlas.

Validation of the IMMIS for survival prediction in the testing cohort and entire TCGA cohort

To confirm the predictive value of the IMMIS for survival prediction, the predictive ability of the IMMIS in the testing cohort and entire TCGA cohort was analyzed. With the same risk score model and cut-off derived from the discovery cohort, the IMMIS classified 89 and 91 patients of the testing cohort into high- and low-risk groups, respectively. Consistent with the findings in the discovery cohort, the overall survival time of the high-risk group patients was significantly shorter than that of the patients in the low-risk group. Specifically, the median survival time was 2.17 and. 4.62 years in patients in the high- and low-risk groups, respectively. (P=0.047; Fig. 2A). The 3- and 5-year overall survival rates of patients in the high-risk group were 44.5 and 26.7%, whereas the corresponding rates were 53 and 42.4% in the low-risk group, respectively. In the entire TCGA cohort, 361 patients were classified in either the high-risk (n=180) and low-risk (n=181) groups. Notably, overall survival was significantly different between the high- years and low-risk groups according to the IMMIS and the cut-off value derived from the discovery cohort. The median survival was 1.53 and 4.62 years in the high- and low-risk groups, respectively. (P<0.001; Fig. 2B). The 3- and 5-year overall survival rates of patients in the high-risk group were 35.6 and 26.1%, whereas the corresponding rates were 57.6 and 45.3% in the low-risk group, respectively. The HR of the high-risk group compared with the low-risk group regarding overall survival was 1.625 (P=0.039, 95% CI=1.004–2.638) in the testing cohort and 2.139 (P<0.001, 95% CI=1.519–3.012) in the entire TCGA cohort (Table IV).

Table IV.

Univariate and multivariate Cox regression analysis of the IMMIS and other clinicopathological variables.

Table IV.

Univariate and multivariate Cox regression analysis of the IMMIS and other clinicopathological variables.

A, Testing cohort, n=180

VariablesUnivariate modelMultivariate model



CharacteristicsComparison groupsHazard ratioIMMISHigh- vs. Low-riskHazard ratioIMMISHigh- vs. Low-risk
IMMISHigh vs. Low1.6251.004–2.6380.0391.2691.001–2.1120.049
Age<68 vs. ≥68 years1.0160.992–0.4020.1911.0261.001–1.0520.045
SexMale vs. female1.6070.955–2.7040.0741.6960.971–2.9620.063
StageI and II vs. III and IV2.1571.259–3.6940.0051.8041.033–3.1500.038
GradeG1 vs. G2 and G31.7571.057–2.9210.0301.8511.050–3.2650.033

B, Entire TCGA cohort, n=361
VariablesUnivariate modelMultivariate model



CharacteristicsComparison groupsHazard ratioIMMISHigh- vs. Low-riskHazard ratioIMMISHigh- vs. Low-risk

IMMISHigh vs. Low2.1391.519–3.012<0.0011.8441.288–2.6380.001
Age, n=355<68 vs. ≥68 years1.0241.007–1.0410.0041.0291.011–1.0470.001
SexMale vs. female1.2820.901–1.8250.1671.2690.876–1.8400.208
Stage, n=338I and II vs. III and IV1.7511.228–1.8660.0021.7231.195–2.4850.004
GradeG1 vs. G2 and G31.3190.932–1.8660.1181.2500.862–1.8140.239

[i] IMMIS, integrative mRNA/microRNA signature; TCGA, The Cancer Genome Atlas.

Independence of the IMMIS from other clinicopathological factors

To examine whether the predictive value of the IMMIS was independent of other clinicopathological factors associated with patients with GC, multivariate Cox regression analysis was used to compare the performance of the IMMIS and other clinicopathological factors, including age, sex, stage and grade. The results from the multivariate Cox regression analysis indicated that the IMMIS, stage and age were significantly associated with overall survival of patients with GC (Table III). Therefore, stratification analysis on age and stage to determine whether the IMMIS was independent of age and stage was also performed. First, 355 patients (excluding 6 patients without age data) were classified into a younger stratum (n=181) and an elder stratum (n=174) according to the median age (68 years). Results of stratification analysis for age revealed that, within each age stratum, the IMMIS could further subdivide the patients into the high- and low-risk groups with significantly different overall survival. The median survival was 1.61 and 5.75 years in the high- and low-risk groups, respectively (P<0.001), for the younger group. The median survival was 1.53 and 2.17 years in the high- and low-risk groups, respectively, (P=0.041) for the elder group (Fig. 3A and B). Subsequently, 338 patients (excluding 23 patients without stage data) were classified into an early-stage stratum (stage I/II; n=157) and an advanced-stage stratum (stage III/IV; n=181) according to the GC stage (20). Results of stratification analysis based on stage indicated that, within each stage stratum, the IMMIS could further subdivide the patients into the high- and low-risk groups with significantly different overall survival. The median survival was 4.62 and 4.96 years in the high- and low-risk groups, respectively (P=0.002), for the early-stage group, and 1.49 and 3.85 years in the high- and low-risk groups, respectively (P=0.006) for the advanced-stage group (Fig. 3C and D). Multivariate and stratification analysis demonstrated that the survival prediction ability of the IMMIS was independent of other clinicopathological factors associated with survival prediction in patients with GC.

Performance comparison of IMMIS with mRNA-based signature

The predictive value of the IMMIS compared with an mRNA-based signature was also evaluated. A total of 11 mRNAs of the IMMIS were used to construct an mRNA-based signature. When applied to the entire TCGA cohort, the IMMIS and mRNA-based signatures reliably classified the patients into high-risk group and low-risk group with significantly different overall survival (Fig. 4). However, Kaplan-Meier survival curves revealed that patients in the high-risk group predicted by the IMMIS had a poorer prognosis compared with those in the high-risk group predicted by the mRNA-based signature, and patients in low-risk group predicted by the IMMIS had an improved prognosis compared with those in the low-risk group predicted by the mRNA-based signature (Fig. 4). The present results suggested that the IMMIS may have improved prognosis prediction ability compared with the mRNA-based signature.

Discussion

The Tumor-Node-Metastasis (TNM) classification of malignant tumors is widely used for the staging and risk stratification of patients with GC (20). However, patients with similar clinical features and those who belong to the same TNM stage may have different clinical outcomes (20). Advances in molecular biology and particularly in the omics sciences have recently demonstrated the complex heterogeneity of GC characterized by genetic and epigenetic changes (21). The findings imply the potential of molecular aberrations as alternative biomarkers for aiding TNM staging and improving prognosis predictions of patients with GC (13,14). Accumulating evidence has revealed that the dysregulated expression of mRNAs and miRNAs has critical roles in the development and progression of GC (2029), highlighting the applications of miRNAs and mRNAs as molecular biomarkers for predicting the prognoses of patients with GC in clinical practice. Although increasing efforts have been made to discover novel mRNA- or miRNA-based expression signatures for improving prognosis prediction, the predictive signatures previously developed mainly consider a single type of RNA. Several studies have investigated and revealed the prospect of the combination of multiple RNA types for risk stratification in several types of cancer, including colon cancer, breast cancer, glioblastoma multiforme and ovarian cancer (8,9,30). However, it is still unknown whether multi-RNA-based signature could substantially increase the prognostic value in GC.

In the present study, an integrative analysis of mRNA expression profiles, miRNA expression profiles and clinical information in a large cohort of patients with GC was performed. After dividing the entire TCGA cohort into a discovery cohort and a testing cohort, a novel multi-RNA-based signature, consisting of 11 mRNAs and one miRNA, was identified. The predictive value of the identified signature was successfully validated in the testing cohort, which reflects the reproducibility of the integrative signature. Moreover, the integrative signature could act as an independent factor for survival prediction in patients with GC. Notably, comparative analysis revealed that the predictive value of the IMMIS was superior compared with an mRNA-based signature alone. Therefore, the present results suggested that the IMMIS may be a promising signature to effectively identify patients with GC who are at high risk of mortality and guide individualized therapy choices.

The majority of genes included in the integrative signature have been experimentally demonstrated to be associated with GC. For example, DUSP1, a member of the threonine-tyrosine dual-specificity phosphatase family, is involved in cellular proliferation, differentiation and apoptosis, and is additionally associated with tumor carcinogenesis progression (31). Teng et al (32) demonstrated that DUSP1 can induce apatinib resistance by activating the mitogen-activated protein kinase-signaling pathway in patients with GC. The expression status of ADAMTS8 has been investigated in GC, and the expression levels are significantly increased in patients with GC (33). In addition, ADAMTS8 is associated with grade and tumor size (34). Kim et al (35) investigated the expression of ACKR3 and its clinical relevance in GC, and found that ACKR3 expression is associated with aggressive behavior and poor prognosis in GC. A recent study has suggested that CCBE1 may have an important role in the progression of gastrointestinal stromal tumors by enhancing angiogenesis and mediating resistance to imatinib (36). Notably, miRNA-184 has been reported to inhibit cell proliferation and invasion, and functions as a potential oncogene in several types of cancer (37,38). A recent study has indicated that overexpression of miRNA-184 is associated with poor outcome in GC (7).

In conclusion, in the present study, a novel IMMIS was constructed, which could effectively stratify patients into low- and high-risk groups of mortality. With further prospective study, the IMMIS may add more information to the current TNM staging system and could improve prognosis prediction. However, there were some limitations in the present study. Firstly, the integrative signature was only based on available TCGA datasets, and no other independent patient datasets were tested. Secondly, the biological functions of the mRNAs and miRNA incorporated in the integrated signature require further experimental studies.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and material

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

YYW conceived and designed the experiments. XY, FMZ, ZWG and WYK performed the experiments and analyzed the data. YYW wrote the paper. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Only publicly available datasets were used and ethical approval was not received in the present study, as it was not necessary.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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September 2019
Volume 18 Issue 3

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Copy and paste a formatted citation
APA
Yin, X., Zhang, F., Guo, Z., Kong, W., & Wang, Y. (2019). Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer. Oncology Letters, 18, 2330-2339. https://doi.org/10.3892/ol.2019.10536
MLA
Yin, X., Zhang, F., Guo, Z., Kong, W., Wang, Y."Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer". Oncology Letters 18.3 (2019): 2330-2339.
Chicago
Yin, X., Zhang, F., Guo, Z., Kong, W., Wang, Y."Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer". Oncology Letters 18, no. 3 (2019): 2330-2339. https://doi.org/10.3892/ol.2019.10536