Serum microRNAs as potential new biomarkers for cisplatin resistance in gastric cancer patients.

Background microRNAs (miRNAs) have been studied for their role in the early detection of several diseases. However, there is no current information on the systematic screening of serum-derived cisplatin resistance biomarkers in gastric cancer (GC). Methods Cisplatin-resistant GC cell lines were screened for dysregulated miRNAs using small RNA sequencing (sRNA-seq) and miRNAs were functionally annotated using bioinformatics analyses. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to validate the miRNA-relative transcription levels in GC cells and in 74 GC patients. We analyzed the associations between the clinical characteristics of the patients and their miRNA expression. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic value for serum-derived cisplatin resistance. Results Seven miRNAs were identified from 35 differentially expressed miRNAs between the MGC803/DDP and MGC803 cells in a public database. We found four miRNA candidates (miR-9-3p, miR-9-5p, miR-146a-5p, and miR-433-3p) that were significantly associated with chemotherapy responses in GC cells and patients. miR-9-5p (AUC = 0.856, 95% CI [0.773–0.939], p < 0.0001) and a combined group (miR-9-5p + miR-9-3p + miR-433-3p) (AUC = 0.915, 95% CI [0.856–0.975], P < 0.0001) distinguished chemoresistant GC patients from chemosensitive GC patients. Conclusions Our study reveals the potential therapeutic use of two serum-based biomarkers, miR-9-5p and a combined group (miR-9-5p + miR-9-3p + miR-433-3p), as indicators for the successful use of cisplatin in GC patients.


INTRODUCTION
Gastric cancer is the fourth most commonly diagnosed malignant cancer and the second leading cause of tumor-related deaths worldwide (Siegel, Miller & Jemal, 2019). Surgery combined with chemotherapy is an emerging and treatment for GC. Cisplatin (DDP) is recognized as a first-line chemotherapy drug for GC patients in a progressive stage of the disease. The current long-term survival rate of GC patients is poor due to the high prevalence of drug-resistance, metastasis, and recurrence (Zong et al., 2016). DDP resistance in most patients is inevitable and results in failed treatment, while the long-term use and repeated administration of DDP leads to severe side effects (Kovalchuk et al., 2008). The development of novel biomarkers for chemotherapy-resistant GC is critical for improving the prognostic efficacy in patients.
miRNAs are a discovered class of small noncoding RNAs containing 19-25 nucleotides (Bartel, 2004). miRNAs have been identified in many biofluids, including serum, plasma, and urine (Etheridge et al., 2011), suggesting that circulating miRNAs could be used as minimally invasive biomarkers for cancer and other diseases (Etheridge et al., 2011;Vychytilova-Faltejskova et al., 2016;Schultz et al., 2014). The use of biomarkers is noninvasive, more comfortable than endoscopic examination, and may reflect the heterogeneity of the disease. Dysregulation of miRNAs may lead to DDP resistance in many tumors (Sorrentino et al., 2008;Yu et al., 2010), but the role of miRNAs in the chemo-response of GC is not fully understood. miRNAs in human tumor serum specimens may act as markers to predict the treatment sensitivity and prognosis in many cancer types (Ueda et al., 2010;Goswami et al., 2013;Hur et al., 2015). Emerging evidence reveals that specific miRNAs in serum specimens may aid in the early diagnosis of malignancies (Masuda & Izpisua Belmonte, 2014;Lin et al., 2015) and in the determination of the survival prognosis for cancer patients after surgery (Hu et al., 2010). However, it is not understood how specific miRNAs in serum might predict a chemotherapy response in GC patients.
Small RNA-sequencing was used to analyze the microRNA profiles in GC cells to find the dysregulated miRNAs. RT-qPCR and correlation analyses were used to validate the results of sRNA-seq, which was used to select the miRNA candidates. We also attempted to develop specific biomarkers to determine the chemotherapy response of GC.

Cell culture
The MGC803 cell line was obtained from the Chinese Academy of Medical Sciences (Beijing, China). DDP-resistant GC cells were established by culturing MGC803 cells in a continuous stepwise fashion with gradually increasing concentrations of DDP from 0.05 to 5 µg/ml over a period of 15 months. This resulted in a shift towards cell proliferation and an apoptosis phenotype (Hong et al., 1988;Yu, Ma & Chang, 2000). The cell line was continuously cultured with 2 µg/ml DDP to maintain its resistance. These two cell lines were cultured following standard culturing procedures and identified by short tandem repeat (STR) profile data compared with the ATCC, DSMZ, or JCRB databases (Cobioer Biosciences Co., Ltd, Los Altos, CA, USA).

Patient enrollment and ethics statement
Serum specimens were collected from 74 GC patients who met the following criteria: (1) histologically confirmed gastric adenocarcinoma; (2) DDP-based chemotherapy used as first-line treatment; (3) received at least 2 cycles of neoadjuvant chemotherapy or palliative treatment; (4) chemotherapy efficacy could be evaluated by unenhanced and enhanced computed tomography (CT) after 2 or 3 cycles; (5) serum samples were collected before the first chemotherapy. All patients provided informed consent prior to the collection of a blood sample. This study was approved by the Ethics Committee (Approval Number: 2018-P2-045-01) of Beijing Friendship Hospital, Capital Medical University and met the ethical requirements of the Declaration of Helsinki. Prechemotherapy serum samples were collected between January 2015 and January 2019 from patients who received neoadjuvant chemotherapy or palliative treatment. The serum samples were collected and processed following the standard operating procedure of the Early Detection Research Network. The chemotherapy principle was executed according to the guidelines of the American Society of Clinical Oncology (ASCO). The chemotherapy response effect was evaluated according to CHOI's principle (Choi et al., 2004). Treatment resistance was evaluated by the existing progressive disease (PD) in <3 treatment cycles, and the treatment response was evaluated by the existing complete response (CR), stable disease (SD), or partial response (PR) lasting for 2 or 3 cycles. The chemotherapy response-sensitive group included CR and PR patients, whereas the chemotherapy response-resistant group included SD and PD patients.

RNA extraction
Total RNA was isolated from the GC cells using Trizol (Invitrogen, USA). The same amount of Caenorhabditis elegans cel-39-3p miRNA was spiked into each serum sample as an external calibration for RNA extraction, reverse transcription, and miRNA amplification. Total RNA was extracted and purified from serum using the miRNeasy Serum/Plasma kit (Qiagen, cat. 217184). The quantity and integrity of the RNA yield was assessed using the Qubit2.0 fluorometer (Life Technologies, USA) and Agilent 2200 TapeStation (Agilent Technologies, USA), separately.

sRNA-seq and data analysis
Total RNA (1 µg) of GC cells were used to prepare small RNA libraries by NEBNext Multiplex Small RNA Library Prep Set for Illumina (NEB, USA) according to the manufacturer's instructions. The libraries were sequenced by HiSeq 2500 (Illumina, USA) with single-end 50 bp reads at RiboBio Co. Ltd (RiboBio, China). Raw reads were processed by FastQC to get clean reads by filtering out those containing adapter, poly 'N', were of low quality, or were smaller than 17nt reads. Mapping reads were obtained by mapping clean reads to the reference genome from the BWA software. miRDeep2 was used to identify known mature miRNA based on miRBase21 (http://www.miRBase.org) and to predict novel miRNA. The databases of Rfam12.1 (http://www.rfam.xfam.org) and piRNABank (http://www.pirnabank.ibab.ac.in) were used to identify ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), and piwiinteracting RNA (piRNA) by BLAST. miRNA expressions were calculated in RPM (reads per million) values (RPM = (number of reads mapping to miRNA/number of reads in clean data) × 10 6 ). The expression levels were normalized by RPM [(number of reads mapping to miRNA/number of reads in clean data) × 10 6 ].

Bioinformatics analysis
The differential expression between the two sample sets was calculated using the edgeR algorithm according to the criteria of |log2 (Fold Change) | ≥1 and Pvalue <0.05. TargetScan, miRDB, miRTarBase, and miRWalk were used to predict the target genes of selected miRNA. Patients with different expression levels of miRNAs were determined to have shorter or longer overall survival (OS) times for 10-year OS using the Kaplan-Meier survival curve data according to the Kaplan-Meier Plotter database (http://kmplot.com) (Szász et al., 2016;Nagy et al., 2018), the OncoLnc database (http://www.oncolnc.org/) (Anaya, 2016), and the OncomiR database (http://www.oncomir.org/) (Wong et al., 2018).

RT-qPCR
The primers for miRNA detection were purchased from FulenGene Company (Guangzhou, China) and their sequence is listed in Table 1. Total RNA was reverse transcribed using the All-in-One TM miRNA RT-qPCR Kit (GeneCopoeia, USA) for miRNA analysis. The All-in-One TM miRNA RT-qPCR Detection Kit (GeneCopoeia, USA) was used to measure miRNAs quantitatively. All samples were normalized by the initial biofluid input volume used for RNA extraction and were calibrated by the spike-in cel-39-3p to eliminate the minute bias caused by different RNA isolation efficiencies and PCR efficiencies among samples. U6 or let-7g-5p was used as an endogenous control to normalize the relative number of miRNAs. ABI 7500 real-time fast PCR system (Applied Biosystems, USA) was used to achieve the relative quantitation of miRNA expression and the data was analyzed using the affiliated software. The Ct attenuation value of each type of miRNA in each of the serum samples was corrected by the internal reference cel-39-3p and the two housekeeping genes, U6 and let-7g-5p. The 2 − method was used to calculate the relative expression of each miRNA. Each sample per reaction was performed in triplicate.

Statistical analysis
miRNA expression levels in the serum samples were divided into high and low groups taking the median as the cutoff value by RT-qPCR. Statistical differences for in vitro experiments were analyzed using student's unpaired t tests. Associations between the clinical parameters of the patients and their miRNA expression were analyzed using the Mann-Whitney test. The AUC was used to assess the diagnostic accuracy of the predictors. Logistic regression was used to develop a panel of combined biomarkers to predict the probability of GC chemotherapy response. All data were expressed as mean ± SD. Each experiment was repeated independently at least three times. Quantitative data were analyzed and graphed using SPSS 23.0, MedCalc, and GraphPad Prism 7. Differences were considered to be significant at ****P < 0.0001, ***P < 0.001, **P < 0.01, and *P < 0.05.

Analysis of sRNA-seq data
The results of sRNA-seq in MGC803/DDP cells (Fig. 1A) and MGC803 cells (Fig. 1B) showed that the sRNA sequence included miRNA, tRNA, rRNA, snRNA, snoRNA, piRNA, and other RNA. Thirty-five miRNAs were selected and intersected, of which 11 miRNAs were up-regulated, and 24 down-regulated at higher levels in MGC803/DDP cells than the MGC803 cells (Fig. 1C). These 35 differentially expressed miRNAs in sRNA-seq data are shown in Table 2.

Bioinformatics analysis of selected miRNA
A large cohort analysis was conducted using Kaplan-Meier survival data according to the Kaplan-Meier Plotter database in order to determine the role of the 35 differentially expressed miRNAs as a potential prognostic factor. We observed that a total of seven differentially expressed miRNAs showed statistical differences (p < 0.05) for 10-year overall survival (OS) ( Table 2), which indicated that these miRNAs could be used as valid biomarkers for chemotherapy response and overall survival. The differentially expressed miRNAs observed were miR-9 (including miR-9-3p and miR-9-5p, Fig (Table S1) was added to the common target genes of the 7 miRNAs (Fig. 3). These 7 miRNAs were selected as miRNA candidates.

Expression levels and functions of miRNAs in human GC clinical specimens
RT-qPCR tests were run on the serum samples from 74 GC patients to identify the potential miRNAs. 2 housekeeping genes (U6 and let-7g-5p) and a spike-in cel-39-3p served as the internal references. The relative levels of miR-9-3p (B), miR-9-5p (C), miR-146a-5p (D), and miR-433-3p (E) between 34 chemotherapy response sensitive gastric cancer serums and 40 chemotherapy response resistant gastric cancer serums were significantly different, which were measured using RT-qPCR. (F-H) The relative levels of miR-370-3p (F), miR-519a-5p (G), and miR-522-5p (H) between 34 chemotherapy response sensitive gastric cancer serums and 40 chemotherapy response resistant gastric cancer serums didn't show significantly different, which were measured using RT-qPCR. Each assay was conducted in triplicate. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05 and mean ± SD were utilized to show the data. The clinical parameter findings of 74 patients with neoadjuvant chemotherapy or palliative treatment are shown in Table 4. Using the median ratio as the cutoff for the relative miRNA expression (7 miRNA candidates; Fold Change = 0) in serum, patients were classified into two groups: low miRNA (7 miRNA candidates) and high miRNA (7 miRNA candidates). Clinical data was collected and efficacy was evaluated by CT in 2-3 cycles following chemotherapy. Independent measurements were made by two radiologists according to CHOI's principle (Choi et al., 2004). Unenhanced-combinedenhanced CT was applied to accurately evaluate the tumor response using tumor density measurement. RT-qPCR analysis was used to detect miRNA (7 miRNA candidates) levels in 34 chemotherapy response-sensitive (CR and PR) and 40 chemotherapy response-resistant (SD and PD) patients to validate miRNA (7 miRNA candidates) expression levels.

Pairwise comparison of ROC curves
MedCalc software was used to display the data for the different ROC curves and the results of pairwise comparison of all ROC curves (difference between the areas (DBA), standard error (SE), 95% confidence interval for the difference (CI), and P-value) ( Table 6).

Validate selected miRNAs in public database
To determine the role of miR-9-3p, miR-9-5p, miR-146a-5p, miR-433-3p, or their combination as a potential prognostic factor, a large cohort analysis was conducted using Kaplan-Meier survival data according to the Kaplan-Meier Plotter database, the OncoLnc database, and the OncomiR database (Table 7).

DISCUSSION
DDP resistance is a barrier to the effective treatment of GC. The identification of unique biomarkers for drug resistance can help to accurately diagnose and treat GC. The involvement of non-coding nucleic acids, such as miRNAs in response to DDP treatment, is not well understood. miRNAs play vital roles in the progression and development of tumors, and may be correlated with resistance to chemotherapeutics miR-9-3p + miR-9-5p + miR-433-3p None 0.0081** 0.0047** miR-9-3p + miR-146a-5p + miR-433-3p None 0.5808 0.0020** miR-9-5p + miR-146a-5p + miR-433-3p None 0.5782 0.0020** miR-9-3p + miR-9-5p + miR-146a-5p + miR-433-3p None 0.4551 0.0020** in tumor cells (Xia et al., 2008;Yang et al., 2008;Li et al., 2009). The downregulation of miR-21 has been shown to alter survival rates by increasing DDP sensitivity in GC cells (Yang et al., 2013). Liquid biopsies can identify markers from blood and various body fluids and are minimally invasive, safe, economical, and convenient versus the use of more invasive tests or biopsies for the diagnosis of GC (Tsujiura et al., 2014). Liquid biopsies are suitable for screening a wide range of people, which can improve the diagnosis and treatment of GC. Several studies have been conducted on the use of miRNAs in GC patients related to the occurrence, development, diagnosis, treatment, and prognosis of the disease. Fang et al. (2013) reported that certain carcinogenesis-related miRNAs (miR-10b, miR-21, miR-223, and miR-338) and tumor suppressor miRNAs (miR-30a-5p, miR-126, and let-7a) can be used as prognosis markers in GC patients. Numerous studies have reported on the use of combinations of circulating miRNAs for greater diagnostic accuracy, which is indicated by an area under the ROC curve larger than 0.8 (Fang et al., 2013;Ng et al., 2009).
Relatively few studies have reported the application of miRNAs in the diagnosis and screening of the chemotherapy response of GC. In this study, the differences in the expression profiles of miRNAs in chemotherapy response-sensitive and chemotherapy response-resistant GC cells were established using sRNA-seq and the potential miRNAs were screened using bioinformatics analyses.
Our results showed that miR-9-3p, miR-9-5p, and miR-433-3p were significantly up-regulated and miR-146a-5p was significantly down-regulated in the MGC803/DDP cells and in chemotherapy response-resistant GC patients. While some of these miRNAs Figure 9 Three selected miRNAs were ascertained in two public databases for prognostic analysis. (A, B) Kaplan-Meier survival curves suggested that patients with high miR-9-3p levels had shorter OS times than those with low miR-9-3p levels in the OncoLnc database (A, P = 0.00836) and in the OncomiR database (B, P = 0.06643). (C, D) Kaplan-Meier survival curves suggested that patients with high miR-9-5p levels had shorter OS times than those with low miR-9-5p levels in the OncoLnc database (C, P = 0.00181) and in the OncomiR database (D, P = 0.0044). (E, F) Kaplan-Meier survival curves suggested that patients with high miR-433-3p levels had shorter OS times than those with low miR-433-3p levels in the OncoLnc database (E, P = 0.0311) and in the OncomiR database (F, P=0.05651).
Full-size DOI: 10.7717/peerj.8943/ fig-9 are considered to be diagnostic or prognostic biomarkers in GC, ours is the first study to explore their potential use in determining the chemotherapy response in GC patients. Numerous studies have shown that a combination of multiple miRNAs could more easily identify early-stage CC patients than a single miRNA biomarker (Vychytilova-Faltejskova et al., 2016;Ng et al., 2009). We combined the three miRNAs in our study (miR-9-3p, miR-9-5p and miR-433-3p) to achieve an AUC of 0.915. The equation used in this A B C D Figure 10 Different miRNA combinations were ascertained in two public databases for prognostic analysis. (A) Kaplan-Meier survival curves suggested that patients with high miR-9-3p + miR-9-5p levels had shorter OS times than those with low miR-9-3p + miR-9-5p levels in the OncomiR database (P = 0.003421). (B) Kaplan-Meier survival curves suggested that patients with high miR-9-5p + miR-433-3p levels had shorter OS times than those with low miR-9-5p + miR-433-3p levels in the OncomiR database (P = 0.01428). (C) Kaplan-Meier survival curves suggested that patients with high miR-9-3p + miR-9-5p + miR-433-3p levels had shorter OS times than those with low miR-9-3p + miR-9-5p + miR-433-3p levels in the OncomiR database (P = 0.008066). (D) Kaplan-Meier survival curves suggested that patients with high miR-9 + miR-433 levels had shorter OS times than those with low miR-9 + miR-433 levels in the Kaplan-Meier Plotter database (P = 0.0047). Full-size DOI: 10.7717/peerj.8943/ fig-10 determination was risk score factor (RSF) = 0.514 × miR-9-3p + 0.526 × miR-9-5p + 0.614 × miR-433-3p + 0.240. There was a significant difference between the two compared areas of the combined group (miR-9-3p + miR-9-5p + miR-433-3p) and the miR-9-5p group (p = 0.045), whereas the two compared areas of the other combined group and the miR-9-5p group was not significantly different (p > 0.05). A large cohort analysis was conducted using Kaplan-Meier survival data according to the Kaplan-Meier Plotter database, the OncoLnc database, and the OncomiR database to determine the role of miR-9-3p, miR-9-5p, miR-433-3p or their combination as a potential prognostic factor. The results indicate that the high expression of miR-9-5p and the combined group (miR-9-5p + miR-9-3p + miR-433-3p) can be used as a valid biomarker for chemoresistance and poor survival. Serum-derived miRNAs have the potential to be used as novel noninvasive tumor markers for the chemotherapy response in GC patients. Previous studies have shown that miR-9-5p expression may act as a potential tumor suppressor gene and is closely related to the malignant progression of GC (Fan et al., 2019). miR-9-3p may play an important role in tumor invasion and have potential effects on the prognosis of gastric cancer (Meng et al., 2017). miR-433-3p may function as a potential diagnostic marker and therapeutic target for glioma (Sun et al., 2017). These miRNAs serve as biomarkers for tumors and tumor progression.

CONCLUSIONS
In conclusion, we reported on the potential for 2 serum-based biomarkers, miR-9-5p and a combined group (miR-9-5p + miR-9-3p + miR-433-3p), to predict the therapeutic benefit of DDP for GC patients. Additional clinical samples should be collected to validate these serum biomarkers.