www.nature.com/scientificdata OPEN Cross-sectional study of human Data Descriptor coding- and non-coding RNAs in progressive stages of Helicobacter pylori infection Sergio Lario 1,2,7 ✉, María J. Ramírez-Lázaro 1,2,7, Aintzane González-Lahera1,3, José L. Lavín3, Maria Vila-Casadesús1,4, María E. Quílez2, Anna Brunet-Vega5,7, Juan J. Lozano1,4,7, Ana M. Aransay 1,3,7 & Xavier Calvet1,2,6 Helicobacter pylori infects 4.4 billion individuals worldwide and is considered the most important etiologic agent for peptic ulcers and gastric cancer. Individual response to H. pylori infection is complex and depends on complex interactions between host and environmental factors. The pathway towards gastric cancer is a sequence of events known as Correa’s model of gastric carcinogenesis, a stepwise inflammatory process from normal mucosa to chronic-active gastritis, atrophy, metaplasia and gastric adenocarcinoma. This study examines gastric clinical specimens representing different steps of the Correa pathway with the aim of identifying the expression profiles of coding- and non-coding RNAs that may have a role in Correa’s model of gastric carcinogenesis. We screened for differentially expressed genes in gastric biopsies by employing RNAseq, microarrays and qRT-PCR. Here we provide a detailed description of the experiments, methods and results generated. The datasets may help other scientists and clinicians to find new clues to the pathogenesis of H. pylori and the mechanisms of progression of the infection to more severe gastric diseases. Data is available via ArrayExpress. Background & Summary Helicobacter pylori is one of the most successful human bacterial pathogens, infecting 4.4 billion individuals worldwide1. Infection can induce gastric pathologies ranging from chronic gastritis in all infected individuals to peptic ulcers (in 15–20% of patients) and gastric cancer (0.5–1% of patients)2. Individual response to H. pylori infection is complex and depends on a combination of environmental factors, genetic background, host response and strain virulence3. The pathway towards gastric cancer is a sequence of events known as Correa’s model of gastric carcinogenesis, a stepwise inflammatory process from chronic-active gastritis (CAG), atrophy (AT), intestinal metaplasia (IM) and gastric adenocarcinoma4. This study examines gastric clinical specimens representing different steps of the Correa pathway with the aim of identifying the expression profiles of coding- and non-coding RNAs (microRNAs and small RNAs) that may have a role in Correa’s model of gastric carcinogenesis and, potentially, to develop novel clinical biomarkers. RNAseq (for microRNAs and non-coding RNAs) and microarrays (for coding RNAs) were used to screen for differentially expressed genes in gastric biopsies (antrum/corpus). The expression of a selection of genes was con- firmed in a validation cohort of patients using quantitative real-time PCR (RT-qPCR). The general study design is illustrated in Fig. 1. Here we provide a detailed description of the experiments conducted, methods used and results generated. The datasets may help other scientists and clinicians to find new clues to the pathogenesis of 1 Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain. 2Digestive Diseases Service, Hospital Universitari Parc Taulí, Institut d’Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain. 3Genome Analysis Platform, CIC bioGUNE, Bizkaia Technology Park, Derio, Bizkaia, Spain. 4Bioinformatics Platform, CIBEREHD, Barcelona, Spain. 5 Oncology Service, Hospital Universitari Parc Taulí, Institut d’Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain. 6Departament de Medicina, UAB, Sabadell, Spain. 7These authors contributed equally: Sergio Lario, María J. Ramírez-Lázaro, Anna Brunet-Vega, Juan J. Lozano, Ana M. Aransay. ✉e-mail: SLario@tauli.cat Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 1 www.nature.com/scientificdata/ www.nature.com/scientificdata Gastroscopy corpus antrum Gastric biopsies -1 H. pylori rapid urease test and culture -2 Molecular analysis -2 Histology Non-acve gastris Chronic-acve gastris Atrophy Intesnal metaplasia transcriptomics (RNAseq , microarray) Differenal gene expression analysis Selecon of: 12 miRNAs and 24mRNAs RT-qPCR Validaon Validation of mRNA microarray results using RT−qPCR Validation of miRNA sequencing results using RT−qPCR CPS1 TFF3 hsa−miR−146a−5p hsa−miR−155−5p ILMN_1792748 ILMN_1811387 20 12 18 10 10 18 log2 (qPCR N0) log2 (qPCR N0) 16 5 8 16 14 6 0 R = 0.97 , p = 1e−07 R = 0.99 , p = 1.4e−10 R = 0.97 , p = 2.1e−07 14 R = 0.67 , p = 0.013 6 8 10 12 8 10 12 14 8 10 12 8 9 10 11 Normalized microarray intensity log2 (sequencing reads) Fig. 1 Outline of the experimental design and workflow for this study from biopsy collection to data analysis. During the endoscopy procedure, antrum and corpus biopsies were collected for molecular analysis, rapid urease test and histology. Each specimen was analyzed for H. pylori, chronic-active gastritis, atrophy and intestinal metaplasia. Patients with H. pylori and neutrophil infiltrate (activity) were classified as chronic-active gastritis (CAG). Patients without activity and negative for H. pylori were classified as non-active gastritis (NAG). H. pylori and the mechanisms of progression to severe disease states. The transcriptomics data is available in the ArrayExpress database5. Methods Patient selection. The Digestive Service has assembled a collection of samples from dyspeptic patients. The study was undertaken in accordance with the Declaration of Helsinki, with the approval of the ethics committee at our institution (code: 2005511; approval date: 2006/1/11). Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 2 www.nature.com/scientificdata/ www.nature.com/scientificdata Fig. 2 Microscopic images from representative 10x hematoxylin-eosin–stained cases of non-active gastritis (a, e), chronic-active gastritis (b,f), atrophy (c,g) and intestinal metaplasia (d,h) are shown for antrum (a–d) and corpus (e–h). At the time the transcriptomic experiments were performed, the collection included samples from 439 patients, enrolled from 2007–2012. The enrolment process was as follows: Dyspeptic patients referred for upper gastrointestinal endoscopy because of dyspepsia were contacted by phone and invited to participate. Those who agreed were instructed not to take antisecretory drugs for two weeks before undergoing the procedure. Exclusion criteria were: patients who were not able to stop antisecretory drugs, those who had received antibiotics in the four weeks before the endoscopy and those with a history of prior treatment for H. pylori. Before the endoscopy, a 13[C]-urea breath test (UBT) (Cat.No. 654057, UBiTest 100 mg, Otsuka Pharmaceutical Europe Ltd, UK) was administered. During the endoscopy procedure, biopsies were taken for histology, rapid urease testing (RUT, Cat. No. 1100090, JATROX HP test CHR Heim Arzneimittel GmbH, Germany) and molecular analysis (RNAlater, Cat.No AM7021, ThermoFisher, MA, USA). After a positive RUT test, biopsies were plated on Pylori Agar (Cat. No. 413193, bioMérieux SA, Spain) in microaerophilic jars (Jar Gassing System, Don Whitley Scientific Limited, UK). After a maximum of a week, grown H. pylori isolates were subcultured on Columbia plates (Cat.No. 43041, bioMérieux) and identified by colony morphology, Gram-negativity and positivity for urease, catalase, and oxi- dase tests. VacAs, VacAm and cagA virulence factor genes of H. pylori were determined by PCR on isolated strains or biopsy samples by using custom locked nucleic acids primers (LNA, Exiqon, Denmark) and SensiMix SYBR Low-ROX Kit (Cat. No. QT625-05, Bioline, UK). Details on VacAs, VacAm and cagA amplification are described in detail elsewhere6. For histopathological evaluation, sections were stained with haematoxylin-eosin (Fig. 2) and evaluated for H. pylori, CAG, atrophy, intestinal metaplasia, and presence of lymphoid follicles by a pathologist specializing in digestive diseases. Patients were considered to be H. pylori positive when two or more diagnostic tests (RUT, UBT, histology, cul- ture) were positive and /or two or more H. pylori virulence PCR assays were positive. Patients with less than two positive diagnostic tests and with less than two positive PCR assays were considered uninfected. Seventy antral and 26 corpus biopsies from 76 patients were selected. Due to chip restrictions, 2 antral biop- sies (B294, B311) were not included in microarray analysis. In 18 cases, antrum and corpus biopsies were paired. The biopsy samples were classified into different groups based on histology: non-active gastritis (NAG, n = 16), chronic-active gastritis (CAG, n = 28), atrophic gastritis (AT, n = 15) and intestinal metaplasia (IM, n = 37). Demographic and clinical characteristics of patients can be found in Online only Table 1. RNA extraction and quality control. Two antrum and two corpus biopsies were used to isolate total RNA. Total RNA was extracted using the mirVana miRNA isolation kit (ThermoFisher, MA, USA) as per the manufacturer’s protocol and stored at −80 °C for downstream analysis. DNase treatment was performed as described in the DNA-free Kit protocol (Cat. No. AM1906, ThermoFisher, MA, USA). Total RNA was quantified ® with the Qubit RNA Assay Kit (ThermoFisher, MA, USA). Quality was assessed using Agilent RNA 6000 Nano chips (Cat.No. 5067-1511) on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), including calculation of the RNA integrity number (RIN). The RIN score was 7.72 ± 0.6 (Fig. 3). mRNA microarrays. Biotin-labeled cRNA samples for hybridization were prepared from 200 ng total RNA using Epicentre TargetAmp Nano-g Biotin-aRNA Labeling Kit for Illumina system (Cat. No. TAN091096; Epicentre, WI, USA). Labeled cRNA was hybridized to the HumanHT-12_V4.0 expression arrays (Cat. No. BD-103-0204; Illumina Inc., San Diego, CA) as described in the protocol/instructions. HumanHT-12 v. 4 Expression arrays were scanned with the iScan system (Illumina Inc., San Diego, CA, USA) and raw data were decoded using GenomeStudio Gene Expression Module (Illumina Inc., San Diego, CA, USA). Intensities were quantile-normalised and differentially detected transcripts were calculated using the Bioconductor limma package7. miRNA and small RNA sequencing. TruSeq miRNA and small RNA library preparation. Briefly, 3′ adapter ligation was performed by incubating 1 µg of total RNA of each sample with the adapter for 2 minutes at Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 3 www.nature.com/scientificdata/ www.nature.com/scientificdata Fig. 3 Quality control of the RNA samples, sequencing libraries and sequencing reads. (a) Agilent Bioanalyzer electropherogram showing total RNA from sample #47 (left) and violin plot of RIN values according to disease state (right). (b) Library Size Selection by resolution of total RNA on 6% Novex TBE PAGE Acrylamide gels. Original acrylamide gel for sample #47 (left), after 145–160 bp miRNA bands (middle) and small- RNA fragments (200–300 bp) (right) were excised. (c,d) Final library QC. Precise library quantification was performed using real-time PCR and size distribution was assessed with Agilent BioAnalyzer High Sensitivity DNA Chips. Upper and lower panels show sample #47 miRNA and small-RNA libraries, respectively. (e) Sequencing of the 192 libraries generated over 9.8 × 108 raw reads. Mean library counts were 5.9 × 106 and 4.4 × 106 for miRNAs and small RNAs, respectively. The panels show the distribution of quality scores per base (upper panels) and the read count per library (lower panels) for both miRNA (left panels) and small-RNAs (right panels). Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 4 www.nature.com/scientificdata/ www.nature.com/scientificdata 70 °C. Then 5′-adapter was added alongside using a truncated T4-RNA ligase 2 (Cat. No. M0351S, New England Biolabs, MA, USA) in an incubation at 28 °C for 1 hour. Half of the ligation product was used for the reverse transcription performed with SuperScript II reverse transcriptase (Cat. No. 18064-014, ThermoFisher, MA, USA) in a thermocycler for 1 hour at 50 °C. Next, enrichment of the cDNA was performed using PCR cycling: 98 °C for 30 secs; 11 cycles of 98 °C for 10 secs, 60 °C for 30 secs and 72 °C for 15 secs; a final elongation of 72 °C for 10 mins, and pause at 4 °C. PCR products were resolved on 6% Novex TBE PAGE gels (Cat. No. EC6265BOX, ThermoFisher, MA, USA). microRNA and Small_Non-coding-RNA fragments between 145–160 and 200–300 bp respectively, were cut from the gel. microRNA and Small_Non-coding-RNA libraries were extracted from poly- acrylamide gel with the MinElute gel extraction kit (Cat. No. 28604, Qiagen, Germany) using an adapted proto- col, in which gel slices were dissolved in a diffusion buffer (0.5 M ammonium acetate; 10 mM magnesium acetate; 1 mM EDTA, pH 8.0; 0.1% SDS) overnight at room temperature plus 3 hours and 30 min at 50 °C. The libraries were visualized on an Agilent 2100 Bioanalyzer with the Agilent High Sensitivity DNA kit (Cat. No. G2938- 90320, Agilent Technologies, Santa Clara, CA) and quantified using quantitative PCR with the Kappa Library Quantification Kit (Master Mix and DNA Standards, Cat.No. KK4824, Roche-Kappa, Basel, Switzerland). Next-generation sequencing (NGS). The libraries were pooled, and 12pM 12xmicroRNA-libraries and 14pM 12xSmall_Non-codingRNA-library pools were sequenced. Multiplexed libraries were hybridized to flow cells on a cBot Cluster Generation System (Illumina, San Diego, CA, USA) using TruSeq SR Cluster Kit v3-cBot-HS (Cat. No. GD-401-3001; Illumina, San Diego, CA, USA). The clustered flow cells were loaded onto a HiScanSQ sequencer. The sequencing was performed using the TruSeq SBS Kit v3-HS (Cat. No. FC-401-3002; Illumina, San Diego, CA, USA) for 50 cycles. NGS Data analysis. Base calling was performed with the Illumina Real Time Analysis software (RTA, version 1.13.48) and the FASTQ files were generated with CASAVA (version: 1.8.1). Secondary data analysis was done using the sRNAbench package8. Briefly, reads were aligned to the human genome (UCSC hg19) using Bowtie 1.1.29. miRNA annotations were obtained from miRBase10 (version 21). Sequencing analysis was done by using the sRNAbench package11. Briefly, after adapter trimming and unique read grouping, reads were aligned to the human genome (UCSC hg19) using Bowtie9 allowing for one mismatch. To provide annotations for RNA ele- ments that mapped to the human genome, miRBase (version 21) for mature and pre-miRNA sequences was used and a matrix of counts were created. To process count and to identify differentially expressed miRNAs we use edgeR package12.Transcripts were considered differentially expressed provided their edgeR FDR-adjusted P value was < 0.05. Quantitative PCR validation. Twenty-five RNAs were reanalyzed to validate 24 messenger RNAs and 12 miR- NAS. The RNAs used were a subset (n = 25) of the aliquots of the same RNA samples we used for sequencing and microarray analysis. Studied genes are summarized in Table 1. cDNA synthesis. miRNA validation was performed using the miRCURY LNA Universal RT microRNA PCR system (Exiqon, Denmark). miRNAs were reverse transcribed according to the manufacturer’s protocol using ™ 10 ng of total RNA (Cat. No. 203301; miRCURY LNA Universal RT microRNA PCR, Polyadenylation and cDNA synthesis kit II). For coding RNAs, 1.0 µg of total RNA was converted into cDNA using PrimeScript RT Reagent Kit (Cat. No. RR037A, Takara, Japan). Quantitative PCR. Coding RNAs were amplified using predesigned PrimeTime 5’ Nuclease Assays (IDT, Iowa, USA) (assay catalog numbers are in Table 1) and PremixExTaq Probe qPCR mastermix (Cat. No. RR390W; Takara, Japan). miRNAs were quantified using predesigned microRNA LNA PCR Primer sets (Exiqon, Denmark) and SensiMix SYBR Low-ROX Kit (Cat. No. QT625-05, Bioline, UK). Amplification was performed in duplicate on a QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) using 384-well plates. qPCR data analysis. The raw PCR data was exported from QuantStudio Real-Time PCR Software v1.2 (Applied Biosystems) onto a RDML13 file and imported into LinRegPCR (v2016.1)14. LinRegPCR was used to determine PCR efficiencies (E) and to calculate the starting concentration per sample (N0). First, the program determines the baseline fluorescence and performs baseline subtraction. Then a Window-of-Linearity for all PCR samples per amplicon is set and then the algorithm determines: the mean PCR efficiency per amplicon (Emean), the quanti- fication cycle (Cq) value per sample and the fluorescence threshold set to determine the Cq (Nq). With these data, N0 is calculated using N0 = Nq / (Emean)Cq. Data Records Individual miRNA and small-RNA FASTQ files and a tab-delimited file for the processed microarray data have been deposited in the ArrayExpress public repository5. The accession numbers are: E-MTAB-889015 for miRNAs, E-MTAB-889616 for small RNAs and E-MTAB-888917 for mRNAs. The sample metadata records are provided in Online only Table 1. Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 5 www.nature.com/scientificdata/ www.nature.com/scientificdata GENE Symbol Refseq accession Detects all variants(a) Exon location(a) Mean PCR efficiency(b) Company and catalogue number AGPAT2 NM_006412(1) No 4–5 1,86 IDT Hs.PT.58.1470724 ACTB NM_001101(1) Yes 6–6 1,86 IDT hs.PT.56a.40703009.g ANXA13 NM_004306(2) Yes 10–11 1,90 IDT Hs.PT.56a.20938889.g APOB NM_000384(1) Yes 8–9 1,87 IDT Hs.PT.56a.19389676 C3 NM_000064 Yes 27–28 1,90 IDT Hs.PT.56a.2840009 CDX1 NM_001804(1) Yes 1–2 1,89 IDT Hs.PT.58.468499 CFH NM_001014975(1) No 9–10 1,89 IDT Hs.PT.58.41054235 CPS1 NM_001122633(3) Yes 27–28 1,88 IDT Hs.PT.58.2708374 CREB1 NM_134442 No 3–5 1,89 IDT Hs.PT.58.4988504 CXCR5 NM_001716(1) No 1–2 1,89 IDT Hs.PT.56a.1692541 EIF4G2 NM_001042559(3) Yes 3–5 1,91 IDT Hs.PT.58.6917393 FUT9 NM_006581 Yes 2–3 1,89 IDT Hs.PT.58.22395619 HIPK3 NM_005734(2) Yes 3–4 1,89 IDT Hs.PT.58.2927056 HNF4G NM_004133(1) Yes 2–3 1,86 IDT Hs.PT.58.26995600 IL8 NM_000584(1) Yes 3–4 1,82 IDT Hs.PT.58.38869678.g KRT20 NM_019010(1) Yes 5–6 1,89 IDT Hs.PT.58.39027228 MEG3 NR_002766(8) No 5–10 1,86 IDT Hs.PT.58.25426100 MMP9 NM_004994(1) Yes 3–4 1,84 IDT Hs.PT.58.22814824.g MTTP NM_000253(1) Yes 18–19 1,87 IDT Hs.PT.58.94887 MUC2 NM_002457(1) Yes 28–30 1,89 IDT Hs.PT.58.4321237 POFUT1 NM_015352(1) No 6–7 1,78 IDT Hs.PT.58.19361092 RUNX2 NM_001024630(3) Yes 6–7 1,88 IDT Hs.PT.56a.19568141 SDHA NM_004168(1) Yes 3–4 1,88 IDT Hs.PT.58.41017719 TFF3 NM_003226(1) Yes 1–2 1,89 IDT Hs.PT.58.1814807 WDR1 NM_017491(1) No 4–5 1,88 IDT Hs.PT.58.40308614 MIR103A1 NR_029520.1 NA NA 1,89 Exiqon 204063 MIR146A NR_029701 NA NA 1,86 Exiqon 204688 MIR153–1 NR_029563 NA NA 1,81 Exiqon 204338 MIR155 NR_030784.1 NA NA 1,90 Exiqon 204308 MIR182 NR_029614.1 NA NA 1,89 Exiqon 206070 MIR191 NR_029690.1 NA NA 1,86 Exiqon 204306 MIR192 NR_029578.1 NA NA 1,91 Exiqon 204099 MIR196B NR_029911.1 NA NA 1,88 Exiqon 204555 MIR19B1 NR_029490.1 NA NA 1,85 Exiqon 204450 MIR204 NR_029621.1 NA NA 1,89 Exiqon 206072 MIR215 NR_029628.1 NA NA 1,87 Exiqon 204598 MIR340 NR_029885.1 NA NA 1,89 Exiqon 206068 Table 1. qPCR Primer assays used for mRNA and miRNA validation. aPrimer assays targeting all splicing variants were chosen for validation purposes, and when possible, in the same exon where the Illumina probe was positioned. bPCR efficiency was calculated by LinRegPCR software. Using the raw qPCR data, the algorithm computes iteratively a Window-of-Linearity for a specific amplicon and calculates the Cq and PCR efficiency for each individual reaction and amplicon. NA: not applicable. Technical Validation Quality control. Sample collection. In order to ensure the collection of biopsy tissue samples would pro- vide high-quality results for microbiology, molecular analysis and histology, a two round biopsy protocol was followed. During the endoscopy, a first set of biopsy samples was collected for microbiological (in sterile saline) and molecular analysis (in RNAlater) and a second set were fixed in formalin for histopathological examination. By doing this, we ensured that formalin contamination of biopsy forceps did not interfere with the RUT and H. pylori culture. Histological examination was performed by a pathologist specialized in digestive diseases. In order to increase the total RNA yield and because intestinal metaplasia is typically present as small mucosal patches, we isolated RNA from two gastric biopsies per anatomical location. The reason is that the biopsy cores examined by the pathologist are different from the biopsy specimens used for molecular analysis. By using two biopsies, we were more confident that if the pathologist reported intestinal metaplasia in the histology specimens, intestinal metaplasia would also be present in the molecular biology cores. Additionally, two biopsies are the minimum recommended by the Updated Sydney System18. RNA processing. Figure 2 shows the quality control procedures used in this study for RNA integrity, library preparation and sequencing. Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 6 www.nature.com/scientificdata/ www.nature.com/scientificdata a Validation of miRNA sequencing results using RT−qPCR hsa−miR−103a−3p hsa−miR−146a−5p hsa−miR−153−3p hsa−miR−155−5p 20 18 20 19 18 17 18 18 16 16 17 16 14 15 16 R = 0.39 , p = 0.057 R = 0.88 , p = 4.3e−09 R = 0.44 , p = 0.027 R = 0.82 , p = 6.7e 07 12 14 14 8.00 8.25 8.50 8.75 9.00 9.25 8 10 12 14 4 5 8 9 10 11 hsa−miR−182−5p hsa−miR−191−5p hsa−miR−192−5p hsa−miR−196b−5p 19 20.5 18 20.0 24 16 log2 (qPCR N0) 18 19.5 23 14 19.0 17 22 18.5 12 16 18.0 21 R = 0.44 , p = 0.027 R = 0.31 , p = 0.13 R = 0.73 , p = 3.7e−05 10 R = 0.82 , p = 5.6e−06 11.5 12.0 12.5 13.0 13.5 12.5 13.0 13.5 16.0 16.5 17.0 17.5 18.0 2.5 5.0 7.5 10.0 hsa−miR−19b−3p hsa−miR−204−5p hsa−miR−215−5p hsa−miR−340−5p 17 24 24 16 23 18 23 22 15 22 16 21 14 21 20 20 R = −0.078 , p = 0.71 14 R = 0.71 , p = 7.9e−05 19 R = 0.4 , p = 0.05 13 R = 0.43 , p = 0.033 8 9 10 11 5 6 7 8 9 10 7.5 10.0 12.5 15.0 7 8 9 10 log2 (sequencing reads) b Validation of mRNA microarray results using RT−qPCR ACTB ILMN_2152131 AGPAT2 ILMN_1732176 ANXA13 ILMN_2412490 APOB ILMN_1664024 C3 ILMN_1762260 17 12 12 10 8 16 8 10 5 15 6 4 8 0 14 R = 0.76 , p = 1.2e−05 4 R = 0.67 , p = 0.00023 0 R = 0.93 , p = 1.2e−11 R = 0.96 , p = 1.4e−14 6 R = 0.79 , p = 2.6e−06 −5 14.0 14.5 8.0 8.5 9.0 9.5 10.0 6 8 10 4 6 8 10 5 6 7 8 CDX1 CFH CPS1 CREB1 CXCR5 ILMN_1815619 8 ILMN_1810910 ILMN_1792748 ILMN_2334243 ILMN_2337928 8 10.0 10 7 7 7.5 5 6 5 5.0 6 5 2.5 0 0 5 R = 0.92 , p = 4.3e−11 4 R = 0.86 , p = 3.4e−08 0.0 R = 0.98 , p < 2.2e−16 R = 0.057 , p = 0.79 R = 0.93 , p = 7.6e−11 4 6 8 10 6 7 8 4 6 8 10 12 10 11 12 5 6 7 8 EIF4G2 FUT9 HIPK3 HNF4G IL8 ILMN_2380946 ILMN_1878007 ILMN_1746941 ILMN_1743394 ILMN_1666733 12.5 8 13 8.0 log2 (qPCR N0) 8 10.0 7 7.5 7 12 7.5 6 7.0 5.0 11 6 5 R = −0.063 , p = 0.76 R = 0.84 , p = 1.1e−07 6.5 R = −0.42 , p = 0.036 R = 0.82 , p = 6.5e−07 2.5 R = 0.83 , p = 2.4e−07 10 5 12.2 12.4 12.6 12.8 13.0 6 7 8 4.5 5.0 5.5 6.0 6 7 8 5.5 6.0 6.5 7.0 7.5 8.0 KRT20 MEG3 MTTP MUC2 POFUT1 ILMN_2219867 ILMN_2061435 ILMN_1774742 ILMN_2205622 ILMN_2276758 4 10.0 0 2 14 13 2 7.5 −1 1 12 5.0 −2 0 11 2.5 −3 0 10 −2 R = 0.76 , p = 9.7e−06 R = 0.88 , p = 5e−09 0.0 R = 0.97 , p = 7.6e−16 −4 R = 0.89 , p = 0.00012 R = −0.19 , = 0.4 9 5 6 7 6 7 8 3 5 7 9 5 6 7 8 9 9 10 11 RUNX2 SDHA TFF3 WDR1 ILMN_1716651 ILMN_1744210 ILMN_1811387 ILMN_1675844 8 12 11.0 10 10.5 6 9 10.0 9 4 6 9.5 R = 0.84 , p = 1.4e−07 R = 0.25 , p = 0.23 R = 0.98 , p < 2.2e−16 9.0 R = 0.44 , p = 0.026 8 3 4 5 6 7 9.0 9.5 10.0 10.5 8 10 12 14 6 7 8 9 Normalized microarray intensity Fig. 4 Validation of miRNAs (a) and messenger RNAs (b) by RT-qPCR. A panel 12 of miRNAs and 24 mRNAs were selected for validation of 25 RNA samples. Aliquots of the same RNA samples were used for sequencing, microarray and qPCR measurements. Raw qPCR data was exported to LinRegPCR software. N0 (an estimate of the target starting concentration per reaction) was calculated using the formula N0 = Nq/ECq where E is the amplicon PCR efficiency and Nq is the fluorescence threshold set to determine Cq. The Pearson correlation coefficient (R), the p-value and 95% confidence interval are indicated. Additional correlations to genes having multiple probes can be found in ref. 29. Gene expression validation by qPCR. We used LinRegPCR14 for calculating individual and mean PCR efficien- cies. Amplicons showed high PCR efficiencies, ranging from 1.78 to 1.91. PCR inhibition can be detected using individual PCR efficiency values. Samples showing PCR efficiencies greater than 5% of the PCR mean efficiency per amplicon were excluded. The algorithm also calculates N0. N0 is the starting quantity of mRNA or miRNA Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 7 www.nature.com/scientificdata/ www.nature.com/scientificdata (expressed in arbitrary fluorescence units). Quantitative N0 values have been used in previous publications19–24. Determining N0 has several advantages over relative quantification. First, the selection of a housekeeping gene is often controversial since the expression of all genes is regulated. Second, the expression of a housekeeping gene varies to a greater or lesser extent under experimental conditions25. Third, to solve this issue a quantitative PCR approach with a correction factor according to the starting amount of RNA used in the reverse transcription has been recommended (i.e. µg of RNA)26 instead of relative quantification. To evaluate the concordance in gene expression between microarray or RNA-seq and qPCR, we calculated the correlation between normalized microarray/RNA-seq and qPCR log transformed N0 values (Fig. 4). Overall, high R and low p-values values (R > 0.8, p < 0.001) were observed between microarray and qPCR measurements. Some of them were probe dependent (i.e. C3 probe ILMN_1762260: R = 0.79, p < 0.001, but C3 ILMN_1662523 was not correlated). Five miRNA showed high correlation (R > 0.7, p < 0.001), 4 were poorly correlated (R ∼ 0.4, p < 0.05) and 3 were not correlated. Usage Notes miRNA, small-RNA raw sequencing data (FASTQ) and normalized microarray data can be analysed by a variety of freely accessible packages and platforms, such as R/Bioconductor27. Some R/Bioconductor packages can be used without prior programming knowledge by using the Galaxy platform28. The authors encourage proper citation of data sources for any work based on this dataset. Received: 6 April 2020; Accepted: 19 June 2020; Published: xx xx xxxx References 1. Hooi, J. K. Y. et al. Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. Gastroenterology 153, 420–429 (2017). 2. Kusters, J. G., van Vliet, A. H. M. & Kuipers, E. J. Pathogenesis of Helicobacter pylori infection. Clin. Microbiol. Rev. 19, 449–490 (2006). 3. Chmiela, M., Karwowska, Z., Gonciarz, W., Allushi, B. & Stączek, P. Host pathogen interactions in Helicobacter pylori related gastric cancer. World J. Gastroenterol. 23, 1521–1540 (2017). 4. Amieva, M. & Peek, R. M. Pathobiology of Helicobacter pylori-Induced Gastric Cancer. Gastroenterology 150, 64–78 (2016). 5. Athar, A. et al. ArrayExpress update - from bulk to single-cell expression data. Nucleic Acids Res. 47, D711–D715 (2019). 6. Companioni, O. et al. Genetic variation analysis in a follow-up study of gastric cancer precursor lesions confirms the association of MUC2 variants with the evolution of the lesions and identifies a significant association with NFKB1 and CD14. Int. J. Cancer 143, 2777–2786 (2018). 7. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). 8. Aparicio-Puerta, E. et al. sRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression. Nucleic Acids Res. 47, W530–W535 (2019). 9. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009). 10. Kozomara, A. & Griffiths-Jones, S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39, D152–7 (2011). 11. Barturen, G. et al. sRNAbench: profiling of small RNAs and its sequence variants in single or multi-species high-throughput experiments. Methods Gener. Seq. 1, (2014). 12. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinforma. Oxf. Engl. 26, 139–140 (2010). 13. Lefever, S. et al. RDML: structured language and reporting guidelines for real-time quantitative PCR data. Nucleic Acids Res 37, 2065–9 (2009). 14. Ramakers, C., Ruijter, J. M., Deprez, R. H. L. & Moorman, A. F. M. Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci. Lett. 339, 62–66 (2003). 15. Lario, S. et al. miRNA sequencing data from patients with non-active gastritis, chronic active gastritis and precursor lesions of gastric cancer. ArrayExpress https://identifiers.org/arrayexpress:E-MTAB-8890 (2020). 16. Lario, S. et al. small-RNA sequencing data from patients with non-active gastritis, chronic active gastritis and precursor lesions of gastric cancer. ArrayExpress https://identifiers.org/arrayexpress:E-MTAB-8896 (2020). 17. Lario, S. et al. mRNA microarray data from patients with non-active gastritis, chronic active gastritis and precursor lesions of gastric cancer. ArrayExpress https://identifiers.org/arrayexpress:E-MTAB-8889 (2020). 18. Dixon, M. F., Genta, R. M., Yardley, J. H. & Correa, P. Classification and grading of gastritis. The updated Sydney System. International Workshop on the Histopathology of Gastritis, Houston 1994. Am J Surg Pathol 20, 1161–81 (1996). 19. Aggarwal, S. D. et al. Function of BriC peptide in the pneumococcal competence and virulence portfolio. PLoS Pathog. 14, e1007328 (2018). 20. Bhavsar, S. P., Løkke, C., Flægstad, T. & Einvik, C. Hsa-miR-376c-3p targets Cyclin D1 and induces G1-cell cycle arrest in neuroblastoma cells. Oncol. Lett. 16, 6786–6794 (2018). 21. Boissière, A. et al. Application of a qPCR assay in the investigation of susceptibility to malaria infection of the M and S molecular forms of An. gambiae s.s. in Cameroon. PloS One 8, e54820 (2013). 22. Koenis, D. S. et al. Nuclear Receptor Nur77 Limits the Macrophage Inflammatory Response through Transcriptional Reprogramming of Mitochondrial Metabolism. Cell Rep. 24, 2127–2140.e7 (2018). 23. van Wijk, B. et al. Epicardium and myocardium separate from a common precursor pool by crosstalk between bone morphogenetic protein- and fibroblast growth factor-signaling pathways. Circ Res 105, 431–41 (2009). 24. Lario, S. et al. Expression profile of circulating microRNAs in the Correa pathway of progression to gastric cancer. United Eur. Gastroenterol. J. 6, 691–701 (2018). 25. Lion, T. Current recommendations for positive controls in RT-PCR assays. Leukemia 15, 1033–7 (2001). 26. Bustin, S. A. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29, 23–39 (2002). 27. Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015). 28. Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018). 29. Lario, S. et al. Validation of microarray expression by RT-qPCR. figshare https://doi.org/10.6084/m9.figshare.11932854 (2020). Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 8 www.nature.com/scientificdata/ www.nature.com/scientificdata Acknowledgements This study was funded by the Fondo de Investigación en Salud (FIS), Instituto de Salud Carlos III, ISCiii (PI12/01802). We are also indebted to A. Salvador for her help with the English. Author contributions A.M.A., A.GH and J.L.L processed the microarras, performed the RNA sequencing and discussed and contributed to writing. J.J.L. and M.V.C. performed omics data analyses and discussed and contributed to writing. A.B.V. and M.E.Q. performed RNA extraction, QC and qPCR experiments. X.C. designed the study and discussed and contributed to writing. S.L. and M.J.R.L. designed the study, contributed to the patient consent, collection of samples, qPCR data analysis, wrote and edited the manuscript. All authors read and approved the manuscript. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to S.L. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. © The Author(s) 2020 Scientific Data | (2020) 7:296 | https://doi.org/10.1038/s41597-020-00636-6 9