Two kinds of human PDAC cell lines, PANC-1 and MIA PaCa-2, were obtained from the American Type Culture Collection (ATCC). The cells were cultured in DMEM (Dulbecco’s Modified Eagle Medium) added with 10% fetal bovine serum (FBS), penicillin (Thermo Fisher Scientific, USA), and 100 µg/mL of streptomycin (Thermo Fisher Scientific, USA) at 37°C and 5% CO₂.

The single guide RNA (sgRNA) sequences were obtained based on the ENO1 gene sequence and cloned into the LentiCRISPR v2 plasmid using the BsmBI site and primers 5’- TCGCGGGAATCCCACTGTTG-3’(forward) and 5’-CAACAGTGGGATTCCCGCGA-3’ (reverse). For ENO1 knockout cells, we generated the recombinant lentiviruses using ViraPower Packaging Mix (Invitrogen, CA). The two knockout cell lines were obtained by infecting with lentiviruses for 48 h, followed by 2mg/L puromycin (Thermo Fisher Scientific, USA) selection for 14 days. The selected cells were subcultured to obtain the monoclonal cells. Eventually, ENO1 knockouts were confirmed by detecting ENO1 protein deficiency in corresponding cells using Western blotting.

Total cell proteins were extracted after cell lysis in RIPA lysis buffer (Solarbio, R0010). The protein-transferred PVDF membrane (Millipore, PVH00010) was incubated in 5% non-fat milk at 25°C for 1 h, followed by washing in 1×TBST. Next, the membrane was incubated with ENO1 antibodies (Sigma-Aldrich, WH0002023M1) for 1 h. After washing in TBST, the membrane was incubated with HRP-conjugated goat anti-mouse antibodies (ZSGB-BIO, ZB-2305, 1:100000) for 1 hr at room temperature. Immobilon™ Western Chemiluminescent HRP substrate (Millipore, WBKLS0500) was used to illuminate the protein bands.

Cells (5×10³ cells/well) were seeded in 96-well plates. The cell viability was detected every 24 h for 4 days using the CCK-8 Kit (Dojindo, CK04) according to the manufacturer’s instructions. Briefly, 10% CCK-8 solution was added to each well for 2 h, and then sample optical density (OD) was recorded at 485 nm using a microplate reader (BMG LABTECH) to determine the cell proliferation rate.

Cancer cells (1000 cells/well) were seeded into 6-well plates. After 2 to 3 weeks, colonies were immobilized with 4% paraformaldehyde (PFA) for 15 min. Then, the cells were stained with 0.1% crystal violet (CV) for 15 min. The stained cells were photographed with a Canon digital camera EOS M50 and counted by ImageJ software after washing. Colony formation rate is determined by counting the colonies numbers formed per 100 cells.

Cells resuspended in DMEM without FBS were seeded into the upper chamber of the transwell chamber (Corning, CLS3422) at a cell density of 4×10⁴ cells/well. Then, 800 μL DMEM supplemented with 20% FBS was added to the well under the chamber. The cells adhering to the upper layer of the chamber were swabbed after 24 h and then immobilized in 4% PFA for 15 min. Cells were stained with CV and then photographed and counted in four randomly selected fields under a stereomicroscope (Olympus, Japan). For the invasion assay, Matrigel (Corning, 356237) was coated on the 8 μm pores polycarbonate membrane of the transwell chamber. 4×10⁴ cells were planted into each well and cultured for 48 h and the cell migration was estimated. The migration and invasion capacity was assessed by the number of cells that migrated and stained by crystal violet in a photograph. Each experiment was repeated thrice.

Cells were seeded into 6-well plates and the DMEM medium was changed every 12 h. 500 μl of the medium was collected at 0 and 8 h of incubation to measure the initial and final concentrations of glucose and lactate, respectively, using a Silman M900 bioprocess biochemistry analyzer. Finally, the cells were digested with 0.25% trypsin solution (Thermo Fisher Scientific, USA) and counted through the countess 3 automated cell counter (Thermo Fisher Scientific, USA). The lactate and glucose levels were normalized based on cell count.

600 μl of 0.6% agar (Sigma, A1296) was added to each well of a 24-well plate. After the 0.6% agar solidified, the digested cells mixed with 0.3% agar were inoculated into the same at 600 μl/well; each well contained 1000 cells. The colonies that were >50 μm in diameter were photographed and counted under a stereomicroscope (Olympus, Japan) after three weeks.

8-week-old male NOD-SCID IL-2 receptor gamma null (NOG) mice were used to access the tumorigenesis role of ENO1. The mice were divided into two groups (control and test groups) with 6 mice each. 2×10⁶ cells were subcutaneously transplanted into the two sides of the NOG mice. The corresponding tumor sizes in mice were recorded every 5 days. The tumor volume was calculated using the formula V =L*W²*0.5, where L and W represent the largest and the smallest diameters, respectively. After 5-6 weeks, the mice were sacrificed for tumor collection. All animal protocols were approved by the Peking University Cancer Hospital Animal Care and Use Committee, China.

The cells were lysed with TRIzol reagent (Invitrogen,15596018); the library construction and RNA-sequencing were conducted by Novogene Co., Ltd (Tianjin, China). The raw sequencing data have been deposited in the Genome Sequence Archive (GSA) database under accession number HRA001089. All of the data are also available as the sequence read archive (SRA) format in the National Center for Biotechnology Information (NCBI) with the accession number of SRP414959. The gene expression differences were analyzed using the DEseq2 software (1.20.0). Data with p-value <0.05 after adjustment were considered significantly different. The Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses of differentially expressed genes (DEGs) were accomplished by clusterProfiler (3.4.4); GO terms with a p-value <0.05 were considered significantly enriched. Gene set enrichment analysis (GSEA) results were visualized by Omicshare Online tools.

Total cell RNA was extracted using the RNeasy^® Mini Kit (QIAGEN,74104) as previously described (18). 2 μg of total RNA, Oligo-(dT)₁₅, dNTPs, and MMLV reverse transcriptase (Invitrogen, 28025-021) were mixed for cDNAs synthesis. qRT-PCR was performed using the Biosystems 7500 real-time PCR system. The gene expressions were estimated by the 2^−ΔΔCt method. The used primers are listed as follows:

GPX7:5′-CGACTTCAAGGCGGTCAACATC-3′ (forward), 5′-TCGGTAGTGCTGGTCTGTGAAG-3′ (reverse); RGN:5′-GGAGGAAGTGTCCAACTCTCTG-3′ (forward), 5′-CAATGGTGGCAACATAGCCTCC-3′ (reverse); GMPPA:5′-GGACAGTGAGAGCCTCTTCAAG-3′ (forward), 5′-TCGAGTTCAGGATGAGCACCTC-3′ (reverse); G6PD:5′-CTGTTCCGTGAGGACCAGATCT-3′ (forward), 5′-TGAAGGTGAGGATAACGCAGGC-3′(reverse); GFPT2:5′-GCTCATCGTGATTGGCTGTGGA-3′ (forward), 5′-CAACCATCACAGGAAGCTCAGTC-3′ (reverse); MGST1:5′-GCCAATCCAGAAGACTGTGTAGC-3′ (forward), 5′-AGGAGGCCAATTCCAAGAAATGG-3′ (reverse); GSTM4:5′-TGGAGAACCAGGCTATGGACGT-3′ (forward),5′-CCAGGAACTGTGAGAAGTGCTG-3′ (reverse); GAPDH:5′-GACCCCTTCATTGACCTCAAC-3′ (forward), 5′-CTTCTCCATGGTGGTGAAGA-3′ (reverse).

The TIMER (https://cistrome.shinyapps.io/timer/) and GEPIA databases (http://gepia2.cancer-pku.cn/#analysis) were used to analyze the difference in ENO1 expression levels between the human cancers and paired normal tissue. ENO1 expression levels (Log₂TPM) are displayed using box plots, with statistical significance of differential expression evaluated using the Wilcoxon test in the TIMER 2.0 version. We use log2 (TPM + 1) for log-scale and match TCGA normal and GTEx data in GEPIA databases analysis. Furthermore, The Kaplan-Meier Plotter database (http://kmplot.com/analysis/) was used to find the prognostic value of ENO1 expression level in human cancers. We choose mRNA (RNA-seq) to start KM Plotter for pan-cancer. Patients are splited by selecting best cutoff value automatically.

Data were analyzed by GraphPad Prism 8. The data differences were assessed by t-test and those with p-value <0.05 were considered statistically significant.

The results from the TIMER database showed that ENO1 expression was significantly higher in most human cancers tissue compared to adjacent normal tissue, such as in BLCA (bladder urothelial carcinoma), BRCA (breast invasive carcinoma), CHOL (cholangiocarcinoma), COAD (colon adenocarcinoma), ESCA (esophageal carcinoma), HNSC (head and neck cancer), KIRC (kidney renal papillary cell carcinoma), KIRP (kidney renal papillary carcinoma), LIHC (liver hepatocellular carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), PRAD (prostate adenocarcinoma), READ (rectum adenocarcinoma), STAD (stomach adenocarcinoma), THCA (thyroid carcinoma) and UCEC (uterine corpus endometrial carcinoma). Notably, ENO1 expression was significantly lower only in KICH (kidney chromophobe) ( Figure 1A ). Similarly, GEPIA data also showed that ENO1 expression was high in most cancer types, which was consistent with TIMER analysis. The expression of ENO1 is significantly greater in PAAD (pancreatic adenocarcinoma) tumor tissues (T) than in normal tissues (N) with the highest Log₂FC value ( Figure 1B and Supplementary Figure 1A ). Altogether, these results suggested that ENO1 may promote the occurrence of various cancers including pancreatic cancer.

The prognostic value of ENO1 expression level in human cancers was analyzed using the Kaplan-Meier plotter database. We found that ENO1 upregulation was associated with poor overall survival (OS) in BLCA (n = 404, HR = 1.6, P = 0.0016; Figure 2A ), BRCA (n = 1089, HR = 1.52, P = 0.015; Figure 2B ), CESC (cervical squamous cell carcinoma) (n = 304, HR = 1.84, P = 0.0098; Figure 2C ), ESCC (esophageal squamous cell carcinoma) (n = 81, HR = 2.34, P = 0.038; Figure 2D ), HNSC (n = 499, HR = 1.35, P = 0.029; Figure 2E ), LIHC (n = 370, HR = 2.29, P = 1.7e-06; Figure 2F ), LUAD (n = 504, HR = 1.63, P = 0.0017; Figure 2G ), PAAD (n = 177, HR = 1.7, P = 0.017; Figure 2H ) and SARC (sarcoma) (n = 259, HR = 1.73, P = 0.0066; Figure 2I ). In addition, patients with higher ENO1 expression level had poor relapse-free survival (RFS) in BRCA, LIHC, PAAD, SARC, and TGCT (testicular germ cell tumors). More details of ENO1-RFS relationships were analyzed by the Kaplan-Meier plotter database ( Supplementary Figure 1B–F ). Concisely, overexpression of ENO1 was found associated with poor prognosis in multiple tumor types.

To examine the biological function(s) of ENO1 in PC, we knocked out ENO1 in PANC-1 and MIA PaCa-2 cells ( Figure 3A ). Both the ENO1-knockout and corresponding control cells were cultured and tested for cell viability using the CCK-8 assay. The cell growth curves (reflected by OD values) revealed that ENO1 knockout markedly inhibited the cell proliferation in both the PDAC cells ( Figure 3B ). The cells were cultured for 10-15 days, the colonies were immobilized and stained, and then photographed and counted. We found that the colony numbers of the ENO1 knockout cells were sharply decreased compared to the corresponding control cells. ENO1 knockout decreased the proliferation abilities of PANC-1 and MIA PaCa-2 cells significantly compared with the control cells ( Figures 3C, D ). Thus, knockout of ENO1 could inhibit the proliferation of PDAC cells.

The effect of ENO1 knockout on cell migration and invasion in PDAC cells was evaluated by transwell assay. We found that fewer cells migrated through the polycarbonate membrane in the ENO1 knockout groups than in the control groups ( Figures 4A, B ). Consistently, when the polycarbonate membrane of the chamber was coated with Matrigel, the number of cells invading through the polycarbonate membrane was lower in the ENO1 knockout groups than in the control groups ( Figures 4C, D ). These results revealed that ENO1 knockout significantly decreased the migration and invasion of PDAC cells. To examine the effect of ENO1 knockout on glycolysis, we detected the possible change in glucose and lactate concentrations in the two PDAC cells. The corresponding cell culture media were collected at 0 and 8 h to estimate the amounts of glucose and lactate, reflecting any possible change in glucose uptake and lactate secretion after ENO1 knockout. We found that the glucose consumption and lactate production in both the ENO1 knockout PDAC cells were significantly lower compared to that in corresponding control cells ( Figures 4E, F ). This indicated that ENO1 knockout reduces glucose metabolism levels in PDAC cells.

Cells were seeded into 24-well plates and cultured in 0.3% agar for 2-3 weeks. We found that the colony numbers were significantly lower in the ENO1 knockout groups than in the control groups ( Figures 5A, B ). This indicated that ENO1 knockout markedly reduced the tumorigenicity of PDAC cells in vitro. For in vivo assay, 2×10⁶ control or ENO1 knockout cells were subcutaneously inoculated into NOG mice. The tumor size was recorded every 5 days to obtain the growth curves. The results showed that tumor sizes were significantly smaller in ENO1 knockout mice than in control mice, indicating that ENO1 knockout reduced the tumorigenicity of PDAC cells ( Figures 5C, D ).

After examining the biological function of ENO1 in PC, we tried to understand its underlying molecular mechanism in abnormal cell metabolism in PC. Accordingly, RNA-seq was conducted to find the DEGs after ENO1 knockout in PANC-1 cells ( Supplementary Table 1 ). The heatmap from the transcriptome sequencing data revealed 727 DEGs (p-value <0.05), including 370 upregulated and 357 downregulated genes ( Figure 6A ). These DEGs were also analyzed by a volcano map, which showed the overall changes in gene expression after ENO1 knockout. The red and green dots represent the upregulated and downregulated DEGs, respectively ( Figure 6B ).

Furthermore, the identified DEGs were subjected to GO and KEGG pathway enrichment analysis. GO terms are defined as biological processes (BP), molecular functions (MF), and cellular components (CC). Based on the GO enrichment analysis, the top 20 GO terms of upregulated and downregulated genes were selected based on the -log₁₀ (P-value) ( Supplementary Figures 2A–F ). The top 10 GO terms indicated that the main BP terms enriching the DEGs were heart development and the regulation of signaling receptor activity; the enriched CC terms were extracellular matrix and dendrites; the enriched MF terms were receptor ligand and regulator activity ( Figure 6C ). KEGG pathway analysis was used to annotate the DEGs related physiological and biochemical reaction pathways; the top 20 pathways were selected based on -log₁₀ level (P-value). Many pathways showed substantial changes between the knockout and control cells ( Figure 6D ). These pathways include the cancer pathways, Rap1 signaling pathway, Wnt signaling pathway, and many metabolic pathways. The number and percentages of the DEGs belonging to these pathways are listed in Figure 6D .

To understand how ENO1 participates in the abnormal metabolism of PDAC cells, we mainly focused on the identified cell metabolism-associated pathways, including the pentose phosphate pathway and metabolism of fructose, mannose, glutathione, amino, and nucleotide sugar. The DEGs involved in these four metabolic pathways and their relative expression levels (sgENO1 vs control) are listed in Supplementary Table 2 . These DEGs may be the regulatory targets of ENO1. We randomly selected 7 DEGs to validate the RNA-seq data by qRT-PCR, including 5 upregulated and 2 downregulated genes. We found that RNA-seq data were consistent with qRT-PCR ( Figure 6E ).

In addition, we performed GSEA to explore the potential downstream pathways of ENO1. GSEA highlighted that ENO1 knockout in PANC-1 cells positively associated with various genes related to oxidative phosphorylation (KO00190), ether lipid metabolism (KO00565), arachidonic acid metabolism (KO00590), linoleic acid metabolism (KO00591), alpha-linolenic acid metabolism (KO00592), retinol metabolism (KO00830), glycerophospholipid metabolism (KO00564), and metabolism of xenobiotics by cytochrome P450 (KO00980) ( Figure 6F ). The GSEA plots, nominal p-value, FDR q-value, enrichment score (ES) and normalized ES nominated specifical metabolism pathways upregulated after ENO1 knockout are shown in Supplementary Figures 3A–H . The pathways were mainly associated with the activation of mitochondrial oxidative phosphorylation and lipid metabolism.