The collection of human placentas was authorized by the Ethics Committee of Human Research of Ningxia Medical University. Informed consent was signed with each mother prior to delivery. Three human full-term placentas were obtained from healthy mothers after elective caesarean section in the General Hospital of Ningxia Medical University. Human PTMSCs were isolated as described previously (Wang et al., 2012, Zhu et al., 2014, Zhu et al., 2015). Cells from the three mothers were pool together and cultivated in MesenCult-XF Basal Medium added to MesenCult-XF Supplement (STEMCELL Technologies Inc., Grenoble, France). At about 90% confluence, cells were digested using the MesenCult-ACF Dissociation Kit (STEMCELL Technologies Inc., Grenoble, France) and passaged. The hPTMSCs at passage 3 were treated with 3-MC (SIGMA-ALDRICH, Saint Louis, USA)) at a concentration of 5 μg/mL for serial 7 days to induce carcinogenesis. All the studies were conducted with five passages of the isolated hPTMSCs. Human lung adenocarcinoma cell A549 were purchased from the Cell Resource Center of the Chinese Academy of Sciences (Shanghai, China) and cultured in RPMI-1640 medium added to 10% fetal bovine serum (Gibco, Grand Island, NY, USA). Cells were collected to spread assays when confluence reached about 90% using TrypLE™ Express (Invitrogen, USA). All cultures were maintained at 37°C in a sterile humidified incubator with 5% CO₂.

The immunological characterization of hPTMSCs was identified by flow cytometry on a FACS Calibur flow cytometer (BD Biosciences, San Diego, CA, USA). All antibodies were purchased from BD Pharmingen (Franklin Lakes, NJ, USA). Briefly, cells (1 × 10⁶) in 100 μL PBS were incubated with the antibody for 30 min on the ice: IgG1-PE, CD34-PE, CD73-PE, IgG2a-FITC, CD14-FITC, CD45-FITC, CD90-FITC, CD105-FITC. After extensive washing, cells were resuspended in 500 μL PBS and used for flow cytometry analysis.

Total RNA was extracted from DMSO control and 3-MC treated hPTMSCs using TRIzol Reagent (Ambion, America). Sequencing libraries were generated using NEBNext UltraTM RNA Library Prep Kit for Illumina (NEB, USA), and index codes were added to attribute sequences to each sample. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v4-cBot-HS (Illumia). After cluster generation, the library preparations were sequenced on an Illumina Hiseq 2500 platform, and paired-end reads were generated. Gene expression levels were estimated by FPKM (fragments per kilobase of transcript per million fragments mapped) using Cuffquant and Cuffnorm (Florea et al., 2013). Differential expression analysis of two samples was performed using the DEGseq R package. P-value was adjusted using q-value. q-value < 0.005 and |log2 (foldchange)| ≥ 1 was set as the threshold for significantly differential expression.

Human LV-CON, LV-sfrp1, LV-sfrp1-RNAi, LV-ptgs2, and LV-ptgs2-RNAi were purchased from Genechem (Shanghai, China). hPTMSCs and A549 cells were infected with LV-CON, LV-sfrp1, LV-sfrp1-RNAi, LV-ptgs2, or LV-ptgs2-RNAi, respectively. sfrp1-targeting small hairpin RNA (shRNA) (ACCTTTCAGTCCGTGTTTA) and ptgs2-targeting shRNA (TGAATTTAACACCCTCTAT) were cloned into the GV Lentivirus plasmid (Genechem, China). The scramble sequence (TTCTCCGAACGTGTCACGT) of LV-CON was a control shRNA that does not complement any human gene.

Total RNA was separated using TRIzol Reagent (Ambion, USA). RNA degradation was monitored on 1% agarose gels. RNA purity and concentration were checked using a Nanodrop™ spectrophotometer (Thermo Fisher Scientific Inc., San Diego, CA, USA). Then, 1μg of total RNA was converted to cDNA with cDNA Synthesis SuperMix (TransGen Biotech, Beijing, China). Afterwards, real-time PCR was carried out using TransStart Tip Green qPCR SuperMix (TransGen Biotech, Beijing, China) on Bio-rad IQ5 (Bio-Rad Inc., USA). The expression levels of gene transcripts were estimated according to the respective standard curves and normalized to the amount of actb or gapdh endogenous control transcripts by the comparative Ct value. Specific primers sets were synthesized by Shenggong (Shanghai, China) and illustrated in Tab. 1.

Whole protein was isolated using Whole Cell Lysis Assay kit (KeyGEN, Nanjing, China). Equal protein from all samples was separated by 10% or 12% SDS-PAGE and transferred to polyvinylidene fluoride membranes (Millipore, Billerica, MA, USA), then probed with rabbit anti SFRP1, PTGS2, SOX2, CMYC, OCT4, NANOG, and ACTB antibody (Proteintech, Wuhan, China) at 4°C overnight followed by appropriate secondary antibodies (BOSTER, Wuhan, China). The protein bands were detected via Superstar ECL Plus Ready-to-use (BOSTER, Wuhan, China) and quantified using Gel Imaging System (Bio-Rad Inc., USA). By using ACTB as an internal control, the ratio of grey level between the target bands and the internal control was considered the relative expression of target proteins.

Human LV-CON, LV-sfrp1, LV-sfrp1-RNAi, LV-ptgs2, and LV-ptgs2-RNAi infected A549 cells (3 × 10³) were seeded in 96-well plates and cultured overnight in a CO₂ incubator. CCK-8 assay (Dojindo, Kumamoto, Japan) was carried out. The plates were incubated for 4 h and then the absorbance was read at 450 nm in a plate reader (Bio-Rad Laboratories Inc., Hercules, USA). The following formula was used to calculate the percentage of viable cells. Percentage of Cell Viability = OD sample at 450 nm/OD MOCK at 450 nm × 100%.

Human LV-CON, LV-sfrp1, LV-sfrp1-RNAi, LV-ptgs2, and LV-ptgs2-RNAi infected A549 cells (1 × 10³) were seeded in 6-well plates for colony formation. The cells were incubated in a humidified CO₂ incubator for 10 to 14 days. The colonies were fixed with 4% paraformaldehyde and stained with crystal violet. Colonies with more than 50 cells were considered clonogenic survivors.

Human LV-CON, LV-sfrp1, LV-sfrp1-RNAi, LV-ptgs2, and LV-ptgs2-RNAi infected A549 cells were made artificial wounds and cultured for another 24 h. Cell migration ability was represented by the wound gap distance in the microscopic field at the time points of 0 and 24 h.

All data collected in this study was obtained from at least three independent experiments for each condition. SPSS19.0 analysis software (USA) and PRISM5 (GraphPad, USA) were used for the statistical analysis. Statistical evaluation of the data was performed by one- way ANOVA when more than two groups were compared with a single control, and a t-test was employed for comparison of differences between two groups. Quantitative data were presented as the mean ± standard deviation (SD). The statistical significance was set at P < 0.05. *P < 0.05, **P < 0.01 denote significant differences compared with the MOCK or sh-NC group.

The Illumina HiSeq 2500 RNA sequencing platform was used to sequence the whole transcriptome of hPTMSCs. After stringent data filtration and quality inspection, approximately 62 million high-quality clean reads were obtained with 87.11% and 86.44% Q30 bases (base quality was more than 30) for DMSO control and 3-MC treated hPTMSCs, respectively. 29,251,305 and 32,739,136 cleanpaired-end reads as well as 8,730,066,236 and 9,771,496,192 clean bases were generated from DMSO control and 3-MC treated hPTMSCs, respectively (Tab. 2). These clean reads were then mapped to the human reference genome sequence (GRCh38) using STAR (version 2.5.2b) (Dobin et al., 2013). Only reads with a perfect match or mismatch were further analysed and annotated based on the reference genome.

Unigene expression was calculated by FPKM (fragments per kilobase of transcript per million fragments mapped) (Florea et al., 2013) using Cuffquant and Cuffnorm. Differential expression analysis was determined by EBseq. The threshold of the false discovery rate (FDR) was set to less than 0.01 and with a fold change greater than or equal to 2. Through this calculation, the upregulation and downregulation of both DMSO control- and 3-MC-treated transcripts were determined. Ultimately, 1081 differentially expressed genes (DEGs) were identified, including 498 upregulated and 583 downregulated genes (Figs. 1A–B and Suppl. Tab. S1). Hierarchical cluster analysis of DEGs is presented in Fig. 1C.

To further study how hPTMSCs regulate their biological functions in response to 3-MC at the molecular level, we focused on tumour transformation-related and pluripotency-related DEGs. A total of 268 tumour transformation-related genes were identified, among which 17 upregulated (c1qtnf1, cd14, chi3l1, cxcl8, cxcl16, gata3, gch1, irak3, mn1, pid1, plxdc2, ptgs2, ptk2b, tnfaip3, tnfaip6, tnfaip8, and tnfsf13b) and 12 downregulated (il18, krt18, mtus1, mtus2, prame, pttg1, sfrp1, tacstd2, tlr4, tnfaip8l1, tnfrsf19, and txnip) (Figs. 2A and 2C, Suppl. Tab. S2). In addition, 66 pluripotency-related genes were identified, including jag1, lfng, and maml3, which were downregulated in the NOTCH signalling pathway (Figs. 2B and 2C and Suppl. Tab. S3).

Functional annotation provided information on protein function annotation, pathway annotation, COG annotation, and GO annotation. Unigenes were subjected to sequence alignment with protein databases, such as NR, Swiss-Prot, GO, KOG, Pfam, and KEGG, using BLAST software, which is based on sequence similarities to public databases. In total, 1077 DEGs were successfully annotated. All DEGs were annotated using the NR (1077) followed by eggNOG (1063), Swiss-Prot (1055), Pfam (996), GO (948), KEGG (709), KOG (695), and COG databases (325). The remaining DEGs had no matches. The GO database is an international standard biological annotation system with three ontologies: biological process, cellular component, and molecular function (Wang et al., 2010). The GO classification of DEGs is presented in Fig. 3A. The pathway-based analysis provides information and further understanding of how hPTMSCs regulate their biological functions in response to 3-MC at the molecular level. KEGG is a database resource for understanding the high-level functions and utilities of biological systems, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. We used KOBAS software to test the statistical enrichment of DEGs in KEGG pathways. In total, 865 DEGs were mapped to the KEGG database. These DEGs were classified into 50 KEGG pathways of four main KEGG categories, including cellular processes (129 DEGs), environmental information processing (294 DEGs), human diseases (343 DEGs), and organismal systems (99 DEGs) (Fig. 3B). Out of 50 pathways, the first 20 pathways (q-value ≤ 1.00) are presented in Fig. 3C and Suppl. Tab. S4.

To verify the results of tumour transformation-related and pluripotency-related DEGs in RNA sequencing, we characterized the expression of these DEGs by qPCR. The expression trend of 27 tumour transformation-related DEGs, as well as 3 pluripotency-related DEGs in the NOTCH signalling pathway, exhibited consistency with the RNA sequencing, whereas the other 2 tumour transformation-related DEGs did not change (Fig. 4A). Among these, sfrp1 and ptgs2 showed higher deregulation as seen by qPCR. To elucidate the possible roles of sfrp1 and ptgs2 in the 3-MC induced tumorigenesis of hPTMSCs, stable sfrp1 and ptgs2 overexpression or silencing in hPTMSCs and A549 cells were achieved using the respective lentiviruses. mRNA expression analysis of sox2, cmyc, oct4, and nanog in hPTMSCs and A549 cells with sfrp1 or ptgs2 overexpression and silencing showed that the cells express the four pluripotency gene markers (Fig. 4B). Western blot analysis of all samples showed that only CMYC and OCT4 were expressed at the protein level (Figs. 4C and 4D). Since these four genes have critical roles in the regulation of pluripotency, these results indicate that sfrp1 and ptgs2 alteration may affect the pluripotent state of hPTMSCs. To analyse the tumorigenic capacity of sfrp1 and ptgs2, CCK-8, colony formation, and wound healing assays were performed in A459 cells. Overexpression of sfrp1 led to reduced cell viability, colony formation, and migration of A549 cells. In contrast, the overexpression of ptgs2 resulted in significantly increased cell viability, colony formation, and migration. These results suggest that A549 cells with high ptgs2 expression but low sfrp1 expression may have more potential tumorigenic capacity, low ptgs2 expression, and high sfrp1 expression may exhibit less tumorigenic capacity (Fig. 5).