The study included a total of 125 CON and patients diagnosed with AG and GC, who were divided into the profiling cohort of 60 subjects and validation cohort of 65 subjects. Tissue samples of profiling cohort were collected during the years 2007-2015, while plasma of participants in validation cohort was collected from years 2011-2019 at the Departments of Surgery and Gastroenterology, Hospital of Lithuanian University of Health Sciences (Kaunas, Lithuania). Clinical and phenotypic characteristics of subjects investigated in profiling and validation cohorts are presented in Table 1. H. pylori status was assessed using indirect ELISA to detect serum-specific IgG antigen (Virion/Serion GmbH, Germany). Control group consisted of subjects, who had no signs of atrophy or IM according to Operative Link on Gastritis Assessment (OLGA) staging system (stage 0)[12]. AG group consisted of individuals that had stage I-IV atrophy score in gastric mucosa by OLGA classification. Gastric adenocarcinoma in GC patients was verified by histology and classified according to the American Joint Committee on Cancer TNM Staging Classification and Lauren Classification[13,14]. Adjacent GC (GCaj) samples were biopsy samples obtained from endoscopically healthy appearing gastric mucosa at least 2 cm away from the primary tumor.

The study was approved by the Kaunas Regional Biomedical Research Ethics Committee (approval No BE-2-10 and BE-2-31) and performed in accordance with the Declaration of Helsinki. All study participants provided written informed consent before enrollment.

Total RNA, including small RNA fraction, was isolated from CON, AG and GC tissues using miRNeasy Mini Kit (Qiagen, Germany) according to the manufacturer’s instructions. Quantification of RNA was performed using Nanodrop2000 spectrophotometer (Thermo Fisher Scientific, United States) and quality of RNA samples was evaluated by Agilent 2100 Bioanalyzer (Agilent Technologies, United States). Circulating nucleic acids, including circulating miRNA fraction, was isolated using QIAamp Circulating Nucleic Acid Kit (Qiagen, Germany) according to manufacturer’s instructions. All isolated samples were stored at -80 °C prior to further analysis.

Small RNA libraries were prepared using Illumina TruSeq Small RNA Sample Preparation Kit (Illumina, United States) according to the manufacturer’s protocol with 1 μg RNA input per sample followed by RNA 3’ adapter ligation, RNA 5’ adapter ligation, cDNA synthesis, polymerase chain reaction (PCR) amplification using unique barcode sequences for each sample and gel size-selection of small RNA library. The yield and quality of sequencing libraries were assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, United States). The small RNA libraries were randomized, pooled 24 samples per lane and sequenced using Illumina HiSeq 2500 (1 × 50 bp single-end reads).

Analysis of raw small RNA-seq data was performed by nf-core/smrnaseq pipeline v.1.0.0 including Nextflow v.20.07.1[15], Java v.11.0.7, and Docker v.19.03.12. In brief, all steps consisted of read quality control using FastQC v.0.11.9, removing 3’ adapter sequences with TrimGalore! v.0.6.5, mapping to mature and hairpin miRNAs (miRBase v.22.1[16]), and GRCh37 human reference genome with Bowtie v.1.3.0[17]. After alignment and trimming sorted BAM files were used for further analysis with edgeR v.3.32.1[18] and mirtop v.0.4.23. MiRNA quality was assessed and summarized using MultiQC v.1.9[19]. Normalized counts were generated using isomiRs package and differential expression analysis was carried out using the DESeq2 Bioconductor package v.1.26.0[20]. The threshold for significant differential expression was Bonferroni[21] adjusted P-value < 0.05 and absolute value of log2 fold change (FC) |log2FC| > 1.

To validate differentially expressed miRNAs in plasma samples, isolated plasma circulating microRNA was reverse transcribed to cDNA using the TaqMan™ MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific, United States). The material was preamplified using the TaqMan PreAmp Master Mix (Applied Biosystems, United States) according to the manufacturer’s protocol. Quantitative real-time PCR (RT-PCR) was performed using the TaqMan MicroRNA Assays: Hsa-miR-129* (Assay ID: 002298), hsa-miR-196a (Assay ID: 241070_mat) on 7500 Fast Real-Time PCR System (Applied Biosystems, United States). All RT-qPCR reactions were run in duplicate in a 20 μL reaction and the relative fold change in miRNA expression was estimated using the 2^-ΔΔCt method[22]. Ct values were normalized to the RNU6B (Assay ID: 001093, Thermo Fisher Scientific, United States) endogenous control.

Statistical analysis was performed using RStudio software (R v.3.6.3). Shapiro-Wilk normality test was used to test the normal distribution of data. For normally distributed data, statistical significance was assessed by Student's t-test. If the data did not pass normality tests was performed non-parametric Wilcoxon rank-sum test. A P < 0.05 was considered statistically significant. Area under the receiver operating characteristic curve (AUC-ROC) analysis was performed using pROC R package.

Small RNA sequencing of CON, AG, and paired GC (cancerous and adjacent) tissues in total identified 1037 miRNAs annotated in the miRBase v22.1. Sequencing yielded approx 250 M raw sequencing reads (from 359 K to 16 M reads per sample). After quality control steps 396 low-abundant and non-variable miRNAs and 5 outlying samples were removed resulting in 641 miRNAs and 75 samples which were used for further analysis (Supplementary Figures 1 and 2). The number of deregulated miRNAs corresponded to pathological cascade of GC development. The highest number of deregulated miRNAs were determined when comparing GC and CON groups (129 differentially expressed miRNAs, 82 up-regulated and 47 down-regulated; Supplementary Table 1). Next, 99 differentially expressed miRNAs were identified analyzing GC compared to AG (67 up-regulated and 32 down-regulated; Supplementary Table 2). The lowest number, 20 miRNAs, were found to be deregulated comparing AG and CON (6 up-regulated and 14 down-regulated; Supplementary Table 3). Differential expression results comparing GC vs GCaj, AG vs GCaj, and CON vs GCaj are presented in Supplementary Tables 4, 5 and 6 respectively.

Differential expression results and top five deregulated miRNAs in each case are represented in Figure 1A. Multidimensional scaling analysis of normalized expression values, assessing the similarity structure of miRNomes (Spearman’s correlation distance), revealed 4 clusters, corresponding to the CON, AG, GC cancerous and adjacent tissues (Figure 1B). The AG cluster was intermediate between GC and CON, whereas GCaj was overlapping with AG and CON groups.

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Figure 1 Results of microRNA differential expression analysis . A: Differentially expressed gastric tissue microRNAs among different conditions. P-adjusted < 0.05 and |log2 fold change| > 1; B: Multidimensional scaling plot based on normalized data showing a clustering corresponding to control, atrophic gastritis, gastric cancerous and adjacent tissues. The density plots show distributions of the first and second dimensions. CON: Control; AG: Atrophic gastritis; GC: Gastric cancerous; GCaj: Gastric adjacent tissue; MDS: Multidimensional scaling.

To further study miRNome profiles, altered expression of miRNAs was analyzed in three main comparison groups: AG vs CON, GC vs CON and AG vs GC according to clinical significance. Analyzing uniquely deregulated miRNAs, 40 differentially expressed miRNAs were found when compared GC to CON (25.8% of all deregulated miRNAs), 18 (11.6%) - AG compared to GC, and 6 (3.9%) - AG compared to CON (Figure 2). Most of the deregulated miRNAs (n = 79, 68.7%) were similar between GC vs CON and GC vs AG comparison groups. 12 miRNAs (7.7%) were deregulated in both AG and GC groups when compared to CON. Four miRNAs (2.6%) were similarly deregulated between AG vs CON and AG vs GC groups. Finally, only 2 miRNAs (hsa-miR-129-1-3p and hsa-miR-196a-5p) (1.29%) were identified as deregulated between all comparison groups. AUC-ROC analysis revealed that expression level of hsa-miR-129-1-3p in tissues resulted in AUCs: 68.1%; 86.3%, and 78.1%, CON vs AG, CON vs GC, and AG vs GC, respectively (Figures 3A, 3B and 3C). In addition to this, expression level of hsa-miR-196a-5p could be significant for discrimination of CON vs AG, CON vs GC and AG vs GC and resulted in AUCs: 88.0%, 93.1% and 66.3% (Figures 3D, 3E and 3F).

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Figure 2 Venn diagram representing the number of commonly and uniquely differentially expressed microRNAs in three different comparison groups. P-adjusted < 0.05 and |log2 fold change| > 1. CON: Control; AG: Atrophic gastritis; GC: Gastric cancer.

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Figure 3 Receiver operating characteristic curves showing prediction performances of expression levels . A-C: Hsa-miR-129-1-3p; D-F: Hsa-miR-196a-5p in tissue samples between different comparison groups: Control vs atrophic gastritis; control vs gastric cancer; and atrophic gastritis vs gastric cancer. AUC: Area under the curve; CON: Control; AG: Atrophic gastritis; GC: Gastric cancer.

Differential expression analysis of NGS data in tissue samples revealed that hsa-miR-129-1-3p was significantly down-regulated and hsa-miR-196a-5p was up-regulated in AG and GC tissues compared to CON (P = 0.002 and P = 0.00018; P = 1.2 × 10^-5 and P = 3.1 × 10^-5, respectively). Moreover, hsa-miR-129-1-3p was significantly down-regulated in the case of AG compared to GC (P = 0.0021) and reflected a stepwise process of a pathology (Figure 4A). Therefore, to identify whether the expression changes of these two miRNAs can be detected noninvasively in the body fluids of the patients, hsa-miR-129-1-3p and hsa-miR-196a-5p were selected for RT-qPCR analysis in plasma samples of independent cohort. The analysis showed similar expression patterns in the case of hsa-miR-129-1-3p, which was significantly down-regulated when comparing AG and GC groups (P = 0.024). There were no other significant findings between the groups (Figure 4B).

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Figure 4 Hsa-miR-129-1-3p and hsa-miR-196a-5p expression levels in study comparison groups . A: Atrophic gastritis and gastric cancer tissue samples compared to controls; B: Atrophic gastritis and gastric cancer plasma samples compared to controls. Box plot graphs; boxes correspond to the median value and interquartile range. CON: Control; AG: Atrophic gastritis; GC: Gastric cancerous; GCaj: Gastric adjacent tissue.

To investigate role of miRNAs in AG atrophy progression (OLGA classification) and H. pylori-induced GC, differential miRNAs profile analysis in the subgroups of the study was performed. The analysis revealed a minor clustering in AG tissues corresponding to OLGA stages (Supplementary Figures 3A and 3H). H. pylori status in GC tissues (Supplementary Figure 3B). However, no significantly deregulated miRNAs were determined comparing I-II OLGA stages vs III-IV OLGA stages (AG tissue samples). On the other hand, analyzing GC group based on H. pylori infection status [H. pylori (neg.) vs H. pylori (pos.)], three miRNAs were shown to be significantly deregulated: Hsa-miR-215-3p (log2FC = -4.52, P-adjusted = 0.02), hsa-miR-215-5p (log2FC = -4.00, P-adjusted = 0.02), and hsa-miR-934 (log2FC = 6.09, P-adjusted = 0.02).

Gastric cancer (GC) is a complex disease arising from the interaction of environmental (e.g., diet, smoking, etc.) and host-associated factors [e.g., Helicobacter pylori (H. pylori) infection, genetics, etc.]. Due to its silent course, it is also one of the most lethal cancers worldwide as it is usually diagnosed at the advanced stages.

Novel biomarkers that would help to improve GC patients’ diagnosis and prognosis are highly needed. Studies show that microRNAs (miRNAs) play an important role in many cancers and could be a promising biomarker or even therapeutic target.

The objectives of the study were to analyze whole miRNome profiles of control, premalignant and malignant gastric tissues, and select the potential miRNA markers that could have a potential for minimally invasive GC diagnostics.

Total RNA from gastric tissue samples was subjected for small RNA sequencing (smRNA-seq). Plasma total circulating nucleic acids were used for the expression analysis of the most tissue deregulated miRNAs by real-time quantitative polymerase chain reaction. Statistical analysis involved the differential expression and discrimination analyses.

The abundance of altered expression miRNAs corresponded to a pathological cascade of GC development. Hsa-miR-129-1-3p and has-miR-196a-5p were shown to be deregulated in healthy-premalignant-malignant sequence. In addition to this, we showed that down-regulation of hsa-miR-129-1-3p could also be detected non-invasively in GC patients’ plasma samples. Finally, results indicated that hsa-miR-215-3p/5p and hsa-miR-934 were significantly deregulated based on H. pylori infection status for GC patients.

Gastric tissue miRNome study provides extensive profiling of control, premalignant and malignant cases. Based on smRNA-seq results several miRNAs were shown as potential gastric carcinogenesis (hsa-miR-196a-5p and hsa-miR-129-1-3p); and H. Pylori-related (hsa-miR-215-3p/5p and hsa-miR-934) biomarkers.

This study provides novel insights into complex GC pathogenesis cascade and could serve as a reference for future research to support our findings.