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. 2026 Jan 18;16:5655. doi: 10.1038/s41598-026-36388-3

3’-phosphoadenosine 5’-phosphosulfate synthetase 2 (PAPSS2) is a potential diagnostic and prognostic biomarker in colon adenocarcinoma

Andong Jin 1,#, Fengjing Yang 2,#, Hang Li 1, Geng Wang 1, Sihua Wang 2, Song Tong 2,✉, Jinbo Gao 1,✉
PMCID: PMC12891730  PMID: 41549136

Abstract

Colon cancer represents a global health challenge. The 3’-phosphoadenosine 5’-phosphosulfate (PAPS) synthase 2 (PAPSS2) is the key enzyme to generate PAPS, which is the universal sulfonate donor for all sulfation reactions. However, the correlation between PAPSS2 and diagnosis, prognosis, and immune cell infiltration in colon adenocarcinoma (COAD) has rarely been mentioned. We analyzed PAPSS2 expression levels in pan-cancer from The Cancer Genome Atlas (TCGA) database; and validated it in the Gene Expression Omnibus (GEO) database. RNA-seq data were analyzed using the R package to identify differentially expressed genes (DEGs) between COAD tissues with high and low PAPSS2 expression from multiple databases. The ssGSEA algorithm was used to analyze the correlation between PAPSS2 and immune cell infiltration in COAD. Tumor tissues and normal tissues were classified and assayed at the single-cell level to analyze differences in PAPSS2 expression. CCK8 and EdU assays were used to validate proliferative capacity; wound healing assays to validate migratory capacity; and Transwell assays to examine changes in invasive capacity. The PAPSS2 expression level was significantly lower in the tumor tissues and associated with worse clinical parameters and prognosis in COAD patients. And we constructed a transcriptional regulatory network involving FLI1 and hsa-miR-152-3p targeting PAPSS2 to support the role of PAPSS2.Enrichment analysis revealed that PAPSS2 is involved in O-glycan biosynthesis, TP53 pathway and extracellular matrix formation. PAPSS2 was found to be positively associated with the infiltration of numerous immune cells, immunomodulatory factors and chemokines. Cytological experiments demonstrated that PAPSS2 knockdown enhanced the proliferation, migration, and invasion of HCT116 and HT-29 cells. Our study suggests that PAPSS2 acts as a promising diagnostic and prognostic biomarker, which facilitates malignant progression in part through its regulation of the p53 signaling pathway.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-36388-3.

Subject terms: Biomarkers, Cancer, Computational biology and bioinformatics, Oncology

Introduction

Colorectal cancer (CRC) is the third most common cancer in the world, accounting for approximately 10% of all annual diagnosed cancers and cancer-related deaths worldwide1–3. Although the treatment regimen of surgery combined with chemotherapy has improved, high rates of metastasis and recurrence result in persistently low 5-year survival rates4–6. Colon adenocarcinoma (COAD) is the most prevalent histologic type of CRC, and the efficacy of treatment is hindered by its late diagnosis. Therefore, developing biomarkers with early diagnostic value and prognostic prediction function has become the key to improving the clinical treatment of COAD.

The enzyme encoded by the PAPSS2 gene plays an essential role in regulating intracellular sulfurization reactions. As the rate-limiting enzyme of the sulfation reaction, its main function is to catalyze the synthesis of the universal sulfate donor adenosine 3′-phosphate 5′-phosphosulfate (PAPS). The sulfation process entails the transfer of sulfate groups (SO42−) from the universal sulfate PAPS to the corresponding acceptor molecules7. These acceptor molecules include exogenous substances, hormones, lipids, steroids, proteins, and proteoglycans. Proper sulfation of endogenous molecules is a ubiquitous phenomenon that is essential for growth and development8,9.

In this study, we performed an in-depth bioinformatics analysis utilizing the TCGA and GEO databases and explored the association between PAPSS2 and COAD malignant behavior by cellular experiments. Our results suggest that PAPSS2 not only serves as a diagnostic and prognostic marker for COAD, but also participates in remodeling the tumor immune microenvironment (TME). This finding may provide novel ideas and insights for future cancer treatments.

Materials and methods

Public databases

We downloaded RNA-seq data and corresponding clinicopathologic information for 455 COAD patients from the Colon Adenocarcinoma Project in the TCGA database (https://portal.gdc.cancer.gov/) (TCGA-COAD). Two COAD datasets (GSE39582 10 and GSE14473511) were also downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The R package “TCGAplot” was employed for analyses involving pan-cancer12. The CRC_EMTAB1807 and CRC_GSE166555 datasets were acquired and analyzed online as single cell RNA-seq (scRNA-seq) using the TISCH database.

Comparison of the PAPSS2 expression level

The TCGA and GEO datasets were used to analyze the mRNA expression of PAPSS2 in COAD. The CPTAC Data Portal (https://cptac-data-portal.georgetown.edu/) and Human Protein Atlas (HPA) database (http://www.proteinatlas.org/) were used to verify the expression of PAPSS2 in COAD at the protein level. TNM staging and pathologic staging were defined according to the National Comprehensive Cancer Network (NCCN) guidelines (2022) (https://www.nccn.org/).

Survival and prognostic analysis

The R packages “survival” and “survminer” were used to estimate the correlation between PAPSS2 expression and survival of COAD patients in the COAD and GSE39582 cohorts. ROC curves using the “pROC” and “timeROC” package. Nomogram model of survival probability using the “rms” package.

TF-miRNA -mRNA regulatory network

Transcription factors (TFs) targeting PAPSS2 are based on JASPAR (https://maayanlab.cloud/Harmonizome/dataset/JASPAR+Predicted+Transcription+Factor+Targets), KnockTF (https://bio.liclab.net/KnockTF/index.php), GTRD (https://gtrd20-06.biouml.org/bioumlweb/#) and ChIP_Atlas (https://chip-atlas.org/target _genes) were predicted. The miRNAs targeting PAPSS2 were predicted based on four different databases: miRWalk (http://mirwalk.umm.uni-heidelberg.de/), TargetScan (https://www.targetscan.org/vert_80/), miRNet (https://www.mirnet.ca/) and mirDIP (http://ophid.utoronto.ca/mirDIP/). The miRNA data from TCGA-COAD were downloaded for subsequent analysis using the R package “TCGAbiolinks”. Overlapping results are represented by the “VennDiagram” package.

Enrichment analysis

The COAD patients were divided into two groups based on PAPSS2 gene expression. The DEGs corresponding to COAD tissues were obtained and subjected to analysis of signaling pathways in which PAPSS2 may be involved using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The DEGs were subjected to functional annotation and gene set enrichment analysis (GSEA), The curated reference genesets from the MgDB files: “c5.all.v7.0.symbols.gmt” and “c2.all.v7.0.symbols.gmt” were selected for GSEA.

Immune infiltration analysis

COAD patients were divided into two groups based on PAPSS2 expression. The ssGSEA algorithm in the R package “GSVA” was used to assess the tumor infiltration status of 28 immune cell types. Correlation heatmaps between pan-cancer and immunostimulants, chemokines and chemokine receptors were plotted using the R package “TCGAplot”. Spearman correlation analysis was performed to further determine the relationship between PAPSS2 expression levels and immune cell infiltration status, immunostimulants, chemokines and chemokine receptors.

Cell culture and transfection

Human COAD cell lines were purchased from the Shanghai Cell Bank of the Chinese Academy of Sciences (Shanghai, China). HT-29 was cultured in McCoy’s 5 A medium (PM150710, Pricella), and the other COAD cell lines (HCT116, LoVo, SW480, SW620 and RKO) were cultured in DMEM (PM150210, Pricella) supplemented with 10% fetal bovine serum (FBS). HCT116 and HT-29 cells were inoculated at a density of 4 × 105/mL in 6-well plates for 24 h. Synthesized small interfering RNA (siRNA) was transfected into the cells using Lipofectamine2000 (Invitrogen, USA) according to the reagent instructions. The knockdown efficiency was tested by quantitative reverse transcription polymerase chain reaction (RT-qPCR) after 24 h of transfection, and by Western blot after 48 h of transfection. siRNA sequences were as follows: siPAPSS2-1: 5′- GCAGAAAUCCACCAAUGUA − 3′, siPAPSS2-2: 5′- GCAAGAGCAGAACAUUGUA − 3′.

RNA extraction and RT-qPCR

Total RNA was extracted using Trizol reagent (9109, Takara) according to the manufacturer’s protocol. Then, isolated RNA was reverse transcribed via cDNA synthesis reagent (6215 A, Takara). PrimeScript One Step RT-PCR kit (RR055A, Takara) was used for PCR amplification on StepOne real-time fluorescent quantitative PCR software. The 2−ΔΔCt values were calculated for quantitative analysis. The primer sequences are as follows:

PAPSS2 forward: 5′- AGGAACGCTGTTCCCGTGTTTG-3′, reverse: 5′- GAGGTGTCAGACGGTATTGGTC-3′;

β-actin forward: 5′- CACCATTGGCAATGAGCGGTTC-3′, reverse: 5′- AGGTCTTTGCGGATGTCCACGT-3′;

β-actin as an internal reference.

Western blot

Protein extraction was performed using RIPA lysis buffer (p0013B, Beyotime), followed by separation through 10% SDS-PAGE electrophoresis and transfer onto PVDF membranes. Subsequently, the membranes were blocked with 5% skim milk and then trimmed based on the protein molecular weight markers (near 70, 50, and 30 kDa). The membranes were probed overnight at 4 °C with the following primary antibodies: anti-PAPSS2 (MG467111, Abmart), anti-CDKN1A(p21) (10355-1-AP, Proteintech), anti-TP53(p53) (A19585, ABclonal), or anti-β-actin (66009-1-Ig, Proteintech). The following day, they were incubated with the corresponding secondary antibodies, either HRP-conjugated anti-mouse IgG (SA00001-1, Proteintech) or HRP-conjugated anti-rabbit IgG (SA00001-2, Proteintech). The visualization of the protein bands was attained using ECL luminescence reagent (MA0186-1, Meilunbio), and Image J software was employed to calculate the optical densities to determine PAPSS2 protein expression levels.

Proliferation, migration and invasion assays

The proliferation rate of HCT116 and HT-29 cells was measured using the CCK-8 assay (BS350A, Biosharp). The COAD cells were cultured in 96-well plates at a density of 4,000 cells per well in appropriate media. After 24, 48 and 72 h of culture, the cells were assayed for CCK-8. Subsequently, absorbance was measured at 450 nm using an automated enzyme marker. For the Edu assay, cells were inoculated in 6-well plates, and the EdU adulteration rate was determined by the EdU Assay Kit (C0078S, Beyotime) according to the manufacturer’s instructions. The HCT116 and HT-29 cell lines were inoculated into 6-well plates and scraped with a 200-µL pipette tip when cell fusion approached 90%. After incubation in medium containing 1% FBS for 24 h (HT-29 cells for 48 h), the width of the wound was examined under a microscope (magnification 100x). 24-well Transwell chambers (8 μm, 3422, Corning) were prepared overnight with 80 µL of matrix gel (356234, Corning). Transfected COAD cells (15 × 104 cells) were inoculated into 200 µL of serum-free DMEM in the upper chamber and 500 µL of DMEM containing 20% FBS in the lower chamber. After incubation at 37 °C for 24–60 h, the cells were fixed with 4% paraformaldehyde and stained with crystal violet. The uninvaded upper cells were erased. Cell invasion was captured under an inverted microscope.

Statistical analysis

Data processing and statistical analysis were performed using GraphPad Prism 10, Image J software, and R (version 4.3.2). The data from TCGA and GEO databases were performed by the R package. The differences among groups were detected with t-test. The survival analyses were determined by the Kaplan-Meier curve and log-rank test. The correlation analysis was evaluated using spearman’s test. In all analyses, P-value < 0.05 indicated statistical significance, *, **, and *** indicate P < 0.05, P < 0.01 and P < 0.001, respectively.

Results

Expression of PAPSS2 in COAD

Analysis of RNA-seq data based on the TCGA-COAD database showed that the expression level of PAPSS2 mRNA in COAD tissues was significantly lower than that in normal colon tissues (p < 0.001) (Fig. 1A). This trend of downregulated expression was prevalent in a variety of solid tumors, including Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cholangiocarcinoma (CHOL), Kidney Chromophobe (KICH), Kidney Renal Clear Cell Carcinoma (KIRC), Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Pheochromocytoma and Paraganglioma (PCPG), Rectum Adenocarcinoma (READ), Thyroid Carcinoma (THCA), and Uterine Corpus Endometrial Carcinoma (UCEC) (Fig. 1B). Notably, the down-regulation of PAPSS2 expression remained significant (p < 0.05) in paired cancer versus normal tissue analyses of BRCA, COAD, KICH, KIRC, LUAD, LUSC, THCA and UCEC (Fig. 1C). To verify the reliability of RNA-seq, we further integrated proteomics analysis and found that PAPSS2 expression showed a trend consistent with transcription (Fig. 1D). The IHC results from the HPA database showed that the expression level of PAPSS2 protein was significantly lower than that of normal colonic mucosa in COAD tissues (Fig. 1E). Furthermore, an independent cohort analysis based on the GEO database colon cancer dataset (GSE39582) demonstrated that the trend of low expression of PAPSS2 mRNA in colon cancer tissues exhibited a high degree of consistency with the TCGA-COAD cohort (p < 0.001) (Fig. 1F). The above multi-omics evidence suggests that PAPSS2 exhibits synergistic downregulation of transcriptional and translational levels in COAD, suggesting that it may serve as a potential biomarker for colon cancer.

Fig. 1.

Fig. 1

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Expression of PAPSS2 in various tumors. (A) PAPSS2 mRNA expression in COAD tissues in the TCGA database. (B) A pan-cancer analysis of PAPSS2 expression. (C) A pan-cancer analysis of PAPSS2 mRNA expression in paired tumor and adjacent normal tissues. (D) Protein expression of PAPSS2 in COAD according to CPTAC samples. (E) PAPSS2 protein expression in COAD and normal colon tissue in the HPA database. (F) The expression of PAPSS2 in the GSE39582 database.

Diagnostic and prognostic value of PAPSS2 in COAD

The relationship between PAPSS2 and various clinical and pathologic variables was investigated using the COAD cohort. The results of the study showed that PAPSS2 expression was significantly lower (p < 0.05) in tumor tissues of stage III-IV compared with those of early stage (stage I-II) patients. Meanwhile, its expression level in lymph node metastasis (stage N1-N3) and distant metastasis (stage M1) groups was significantly lower than that in the group with no metastasis (stage N0: p < 0.01; stage M0: p < 0.05) (Fig. 2A-C). In the GSE39582 cohort, there was also an association between PAPSS2 expression and M stage (p < 0.05) (Fig. 2D). To explore the diagnostic efficacy of PAPSS2, ROC curve analysis showed that the AUC values of PAPSS2 expression levels differentiating COAD from normal tissue amounted to 0.963 and 0.954 in the COAD and GSE39582 cohorts (Fig. 2E, F). Further evaluating the prognostic value of PAPSS2, Kaplan-Meier survival analysis showed that patients in the low-expression group had a significantly shorter overall survival (OS) than those in the high-expression group (p = 0.0068) (Fig. 2G). We similarly validated this in the GEO colon cancer cohort (GSE39582) (p = 0.0049) (Fig. 2H). The above findings demonstrated that PAPSS2 not only serves as a molecular marker for malignant progression of COAD, but also exhibits excellent diagnostic and prognostic assessment potential.

Fig. 2.

Fig. 2

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Correlation between PAPSS2 and Diagnostic and prognostic of COAD. PAPSS2 expression levels correlate with (A) stage, (B) M and (C) N in TCGA database and (D) M. in GSE39582 database. (E, F) Diagnostic ROC curves to distinguish COAD tissues and normal tissues based on the PAPSS2 expression levels in COAD and GSE39582. (G, H) K-M survival analysis of patients with high and low papss2 expression.

We constructed a nomogram model that included age, T stages, pathologic stages, and PAPSS2 expression levels as parameters (Fig. 3A). The calibration curves for 1-year, 2-year, and 3-year predictions displayed a high level of concordance between the predicted and actual outcomes (Fig. 3B). Moreover, the nomogram showed a significantly high clinical value in predicting the 1 -, 2-, and 3-year survival probability of the COAD patients (Fig. 3C).

Fig. 3.

Fig. 3

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PAPSS2 based nomogram and its prognostic performance. (A) The nomogram model that includes age, T stages, pathologic stages and PAPSS2 expression levels to predict the 1-, 2-, and 3-year survival rates of COAD patients. (B) Calibration curve at 1,2,3 year. (C) Time-dependent survival ROC curves to predict 1-, 2-, and 3-year survival rates of COAD patients.

Construction of the upstream regulatory network of PAPSS2

We analyzed the upstream regulatory mechanism of PAPSS2. Firstly we searched 4 transcription factor (TF) databases, KnockTF, JASPAR, GTRD, and ChIP_Atlas, and found that FLI1 was the only TF predicted to target and regulate PAPSS2 in all the datasets (Fig. 4A). In the COAD cohort, FLI1 expression was downregulated in cancer tissues, which was consistent with PAPSS2. And the two expressions in the COAD tissues were significantly positively correlated (Spearman’s ρ = 0.15, p = 0.0017) (Fig. 4B, C). We also predicted possible binding sequences of FLI1 to the PAPSS2 promoter region in the JASPAR database (Fig. 4D). Then we integrated miRNET, miRDB, mirDIP and TargetScan databases to screen miRNAs regulated upstream of PAPSS2, and identified three miRNAs potentially targeting PAPSS2: hsa-miR-363-3p, hsa-miR-25-3p and hsa-miR-152-3p (Fig. 4E). Next, we used expression analysis and prognostic analysis for further screening. The results showed that the miRNA most likely to be involved in the post-transcriptional regulation of PAPSS2 was hsa-miR-152-3p (Fig. 4F-H). We also predicted the complementary sequence of PAPSS2 to hsa-miR-152-3p in miRanda (Fig. 4I). Based on this, we have established a PAPSS2 transcriptional network within COAD involving FLI1 and hsa-mir-152-3p.

Fig. 4.

Fig. 4

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The TF-miRNA-mRNA regulatory network of PAPSS2. (A) FLI1 was filtered from four TFs databases. (B) The expression of PAPSS2 in COAD tissues in the TCGA database. (C) Correlation scatter plot of FLI1 and PAPSS2. (D) Predicted TF binding motifs in the PAPSS2 promoter region. (E) Venn diagram found that has-miR-363-3p, has-miR-25-3p and has-miR-152-3p were potential miRNAs of PAPSS2. (F) The expression of has-miR-363-3p, has-miR-25-3p and has-miR-152-3p in the TCGA database. Note: has-miR-363-3p: ns. (G) Correlation scatter plot of has-miR-25-3p, has-miR-152-3p and PAPSS2. Note: has-miR-25-3p: p = 0.05. (H) The prognosis curve of has-miR-152-3p. (I) Predicted interaction of PAPSS2 and has-miR-152-3p.

Potential mechanisms of PAPSS2 in COAD

To elucidate the biological processes associated with PAPSS2 in colon adenocarcinoma, we divided the COAD cohort into high and low expression groups based on the median expression level of PAPSS2 (Fig. 5A), and performed a comprehensive pathway enrichment analysis. KEGG analysis showed PAPSS2 involved in Endocytosis, Protein processing in endoplasmic reticulum, mTOR signaling pathway, p53 signaling pathway, other types of O−glycan biosynthesis (Fig. 5B). PAPSS2 was associated with positive regulation of cytokine production, extracellular matrix organization, extracellular structure organization, external encapsulating, proliferation on biological process (BP); external side of plasma membrane, collagen-containing extracellular matrix and Fc receptor complex on cellular component (CC); immune receptor activity, glycosaminoglycan binding, extracellular matrix structural constituent, cytokine receptor and sulfur compound binding on molecular function (MF) (Fig. 5C). Additionally, our Gene Set Enrichment Analysis (GSEA) analysis revealed strong evidence of a significant correlation between elevated expression of PAPSS2 and pathways related to epithelial structure maintenance, O − glycan processing and Fc receptor mediated stimulatory signaling pathway (Fig. 5D), and enhances p53-mediated senescence and CDKN1A apoptosis-related pathways (Fig. 5E).

Fig. 5.

Fig. 5

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The potential mechanisms of PAPSS2 in COAD. (A) DEGs in PAPSS2 high and low expression groups. (B, C) Displaying enriched biological processes using GO and KEGG (www.kegg.jp/kegg/kegg1.html). (D, E) GSEA showed the enriched pathways in the high and low PAPSS2 groups.

PAPSS2 is associated with immune infiltration in COAD and correlation analysis with immunostimulants, chemokines and chemokine receptors

To elucidate the regulatory role of PAPSS2 in the TME, we quantified the infiltration levels of 28 immune cells in the COAD cohort by single-sample gene set enrichment analysis (ssGSEA). The results showed that CD4⁺ T and memory T cell infiltration was significantly increased in the PAPSS2 high-expression group, whereas the level of natural killer cell infiltration was negatively correlated with the level of PAPSS2 expression (Fig. 6A, B), suggesting that it may influence antitumor immunity by modulating adaptive immune responses. Further analysis revealed that PAPSS2 expression was significantly associated with a variety of immunostimulator molecules. Among them, TNFSF13 (ρ = 0.39, p = 2.89e-18), NT5E (ρ = 0.34, p = 7.92e-14) and HHLA2 (ρ = 0.30, p = 6.35e-11) showed the strongest positive correlation, while TNFRSF25 exhibited a negative correlation (ρ = -0.20, p = 2.45 × 10-⁵) (Fig. 6C, D). At the chemokine level, PAPSS2 was positively correlated with CCL28 (ρ = 0.39, p = p = 1.31e-17), CXCL1 (ρ = 0.21, p = 7.04e-06), and CXCL6 (ρ = 0.21, p = 7.07e-06), but negatively correlated with CCL14(ρ = -0.13, p = 3.98e − 03) (Fig. 6E, F). The results of the chemokine receptor expression profiling study showed that high PAPSS2 expression was significantly positively correlated with CCR4 (ρ = 0.22, p = 1.30e-06), CXCR4 (ρ = 0.18, p = 1.18e-04) and CXCR6 (ρ = 0.16, p = 6.69e- 04), while negatively correlated with CXCR3 (ρ = -0.18, p = 8.39e-05) (Fig. 6G, H).

Fig. 6.

Fig. 6

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The correlation between PAPSS2 and immune cell infiltration, immunostimulators, chemokines and chemokine receptors. (A, B) Correlation analysis of PAPSS2 with immune cell infiltration in COAD. (C, D) Correlation analysis of PAPSS2 with immunostimulators. (E, F) Correlation analysis of PAPSS2 with chemokines. (G, H) Correlation analysis of PAPSS2 with chemokine receptors.

scRNA-seq analysis of PAPSS2 in COAD

We obtained two independent scRNA-seq data on COAD based on the TISCH database (CRC_EMTAB1807; CRC_GSE166555) to explore the relevance of PAPSS2 expression at the single-cell level. In the CRC_EMTAB1807 dataset, PAPSS2 was predominantly expressed in Epithelial cells and Fibroblasts (Figure S1A); in addition, the CRC_GSE166555 dataset also showed high expression in Dendritic cells, and monocytes/macrophages (Figure S1B). Therefore, we analyzed COAD scRNA-seq data obtained from GEO database (GSE144735) for validation. The results showed that PAPSS2 was mainly expressed in Epithelial cells, Stromal cells and Myeloid cells (Fig. 7A). We further performed cell fractionation, PAPSS2 expression was significantly down-regulated in epithelial cells from tumor and paracancerous tissues (Fig. 7B), while it showed a trend of up-regulation in the stromal cell fraction (Fig. 7C), but no significant level change in myeloids (Fig. 7D). A comprehensive analysis reveals that PAPSS2 is predominantly expressed in epithelial cells, fibroblasts, dendritic cells, and monocyte macrophages. The downregulation of PAPSS2 in tumor tissues is predominantly attributable to the decreased expression of PAPSS2 in tumor epithelial cells. For further studies, we used subclustering analysis to categorize epithelial cells into 4 clusters (Fig. 7E). Cluster 0 was identified as colon cancer cells cluster 1 with high ANXA10 expression, cluster 1 was identified as normal colonocytes cluster 1 with high ASCL2 expression cluster 1, cluster 2 was identified as colon cancer cells cluster 2 with high KRT16 expression and cluster 3 was identified as normal colonocytes cluster 2 with high ADH1C expression cluster 1 (Fig. 7F). We also analyzed the tissue sources of the 4 clusters of cells (Fig. 7G). Pseudotime trajectory revealed that the epithelial cells showed a cancerous transformation from normal colonocytes cluster 2 to colon cancer cell cluster 2 (Fig. 7H). Concurrently, the expression of PAPSS2 underwent a gradual decrease (Fig. 7I).

Fig. 7.

Fig. 7

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scRNA-seq analysis of PAPSS2 in COAD. (A) Uniform manifold approximation and projection (UMAP) plot and Violin plot of PAPSS2 in GSE144735. (B-D) Expression of PAPSS2 in epithelial, stromal and myeloid cells from normal, border and tumor tissue. (E) UMAP plot of epithelial cells. (F) The dot plot showing the average expression levels of representative marker genes in different clusters of epithelial cells. (G) Tissue source of different epithelial cell clusters. (H) Trajectory analysis of epithelial cells. (I) Dynamic expression of PAPSS2 genes along pseudo time.

Knockdown of PAPSS2 promote COAD cell proliferation, migration and invasion and downregulates p53/p21

Combined with the results of scRNA-seq analysis; to further investigate the role of PAPSS2 in COAD cells, we investigated the impact of the PAPSS2 gene on COAD proliferation, migration, and invasion. First, we compared the expression levels of PAPSS2 in six colon cancer epithelial cells, and the highest levels of PAPSS2 expression were found in HT-29 and HCT116 (Fig. 8A; Original blots are presented in Figure S2A). si-PAPSS2 successfully knocked down the expression of PAPSS2 in HCT116 and HT29 cells (Fig. 8B). CCK8 and EdU assays demonstrated that PAPSS2 knockdown promoted the proliferative ability of colon cancer cells (Fig. 8C, D). Wound healing assays were performed to detect the effect of PAPSS2 knockdown on cell migration ability. Compared with the negative control, cell migration was significantly increased after knockdown of PAPSS2 (Fig. 8E). In addition, the Transwell assay showed that the invasive ability of COAD cells was significantly enhanced after PAPSS2 knockdown (Fig. 8F). The above findings support the evidence that impaired PAPSS2 expression plays an oncogenic role in COAD. Furthermore, we observed that knockdown of PAPSS2 led to decreased levels of p53 and p21(Fig. 8G, Original blots are presented in Figure S2B), which corroborates the conclusions from GSEA analysis suggesting that PAPSS2 may be associated with p53-mediated senescence and p21-related apoptotic pathways.

Fig. 8.

Fig. 8

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Knockdown of PAPSS2 promote COAD cell proliferation, migration and invasion and downregulates p53/p21. (A) The level of PAPSS2 expression in 6 colon cancer cell lines. (B) PAPSS2 knockdown efficiency in HCT116 and HT-29 cell lines. (C, D) CCK-8 and EdU assays to detect proliferative capacity of HCT116 and HT29. (E) Effect of PAPSS2 knockdown on cell migration in HCT116 and HT29. (F) Effect of PAPSS2 knockdown on cell invasion in HCT116 and HT29. (G) PAPSS2 knockdown affected p53 and p21 levels in HCT116 and HT29 cells.

Discussion

The PAPSS family is a central regulator of intracellular sulfation reactions. The primary function of this family is to catalyze the synthesis of the universal sulfate donor known as PAPS. In humans, this family contains two main isoforms, PAPSS1 and PAPSS2, with PAPSS1 being highly expressed mainly in the brain and skin, and PAPSS2 dominating in tissues such as liver, cartilage, intestine, and adrenal gland8. Sulfonyl coupling reaction is an important metabolic pathway for various exogenous and endogenous compounds in the human body, involved in various physiological processes. PAPSS2 deficiency can lead to downregulation of chondroitin sulfate levels, resulting in chondrodysplasia13–15. In the process of androgen metabolism, decreased PAPSS2 expression has been shown to inhibit the sulfation of dehydroepiandrosterone (DHEA), thereby enhancing its conversion into active androgens16,17. Sulfation also serves as an important post-translational modification that occurs mainly in secreted proteins and transmembrane proteins, regulating their functional activity and subcellular localization18,19. Recent studies have revealed that sulfation can occur on nuclear proteins. A novel histone sulfation modification exists on the H3Y99 residue of histones (H3Y99sulf). This modification has been shown to regulate histone H4R3me2a modification in transcriptionally active regions of chromatin by recruiting the protein arginine methyltransferase PRMT1, thereby activating the transcription of downstream genes20. However, the mechanism of PAPSS2 in tumors is rarely reported. In hepatocellular carcinoma, hypoxic environment can induce the expression of PAPSS2, increase the level of H3Y99sulf modification, and further promote the transcription of PDK121. Another study indicated that PAPSS2 could promote invasion and migration of breast cancer cells22.

The regulatory role of PAPSS2 in sulfation response and tumor microenvironment remodeling makes it a key molecular determinant in the pathogenesis of COAD. In this study, we employed bioinformatics analysis to identify PAPSS2 as a potential biomarker for diagnosis and prognosis in colon cancer patients. Our analysis revealed that PAPSS2 expression was significantly downregulated in COAD tissues compared to adjacent normal tissues. The decreased expression level of PAPSS2 was associated with advanced stage and poor prognosis. Subsequent analysis of colon cancer scRNA-seq data obtained from the GEO database (GSE144735) revealed that the expression level of PAPSS2 was significantly lower in epithelial cell subpopulations of tumor and paracancerous tissues than in normal tissues. These bioinformatics findings were experimentally validated through in vitro studies, where PAPSS2 knockdown significantly promoted tumor cell proliferation, migration, and invasion.

To investigate the potential role of PAPSS2 in TME remodeling, we performed a comprehensive immune infiltration analysis. PAPSS2 expression showed a positive correlation with the infiltration level of CD4 + T cells as well as memory T cells. CD4 + T cells, as the core coordinator of adaptive immunity, have been shown to activate macrophages and CD8 + T cells by differentiating into different subsets of T cells, thereby enhancing tumor immune response23,24. The accumulation of memory T cells in the TME suggests that PAPSS2 may be involved in maintaining the long-term surveillance capacity of the immune system. These cells are able to rapidly proliferate and differentiate upon re-exposure to tumor antigens, preventing tumor recurrence or metastasis25,26. On the contrary, downregulation of PAPSS2 in COAD may lead to CD4 + T cell functional depletion and memory T cell pool atrophy, thereby accelerating tumor immune escape. However, the exact molecular mechanisms remain unclear. Further studies are needed to elucidate the molecular mechanisms of PAPSS2 and TME remodeling in COAD.

In addition, to further explore the mechanism of PAPSS2 regulation in malignant progression and immune infiltration, combined with the results of KEGG enrichment analysis, we found that PAPSS2 may be involved in the p53 signalling pathway and the biosynthesis process of O-glycans. TP53, as a key oncogene, is involved in a variety of biological processes, including cell cycle arrest, DNA damage repair, cellular senescence, metabolic homeostasis, apoptosis, and autophagy27,28. Research has demonstrated that Papss2 can sulfate the Tyr160 site of p53, enhance the p53-Mdm2 interaction and the ubiquitination-mediated degradation of p53, thereby exacerbating cellular oxidative stress and APAP-induced acute liver injury in mice29. However, this mechanism presents contradictions with our findings in COAD: GSEA results showed that PAPSS2 could enhance p53-mediated senescence and p21 apoptosis related pathways, and combined with our experimental conclusion, knockdown of PAPSS2 could reduce the expression levels of p53 and p21. We preliminarily speculate that PAPSS2 may be involved in the modification and targeted clearance of p53 mutants during the development of tumors30. Furthermore, high expression of PAPSS2 has been shown to enhance the biosynthesis of O-glycans, which are critical components of mucins and play an important role in maintaining the intestinal barrier31. Research has demonstrated that sulfation of O-glycans has a positive effect on intestinal barrier function32. However, most of the current studies have focused on the role of sulfotransferases of O-glycans. A recent study suggests that Papss2ΔIE mice exhibit reduced levels of intestinal sulfonamide mucin, decreased stability of the intestinal mucosal barrier, and are more susceptible to inducing the formation of colitis and colon cancer33. Based on the above conclusions, we can propose the hypothesis that PAPSS2 plays a dual role in COAD: firstly, it enhances the intestinal barrier to protect the intestinal epithelium, reducing external stimuli and the possibility of epithelial carcinogenesis; secondly, it enhances the tumor surveillance effect of p53 in epithelial cells, timely clearing tumor cells.

There are some limitations in this study. Firstly, our analysis relied on RNA-seq data from public databases, which may lead to selection bias. This is because these datasets may not fully represent the diversity of all COAD patients. This is due to the fact that these datasets may not adequately represent the diversity of all COAD patients. In addition, given the crosstalk of PAPSS2 on the relevant pathways in the enrichment analysis, we should conduct a large prospective cohort to explore whether defects in this gene are predictive of colon tumor development.

Conclusion

In this study, we demonstrated the diagnostic and prognostic values of PAPSS2 in COAD. PAPSS2 expression was decreased in the tumor tissues of COAD patients. Our findings suggest that PAPSS2 may contribute to COAD progression, potentially through mechanisms involving the p53 signalling pathway and O-glycan biosynthesis. Further functional studies are needed to validate these specific mechanisms. COAD expression levels correlated with tumor infiltration status of many immune cell types and may play a role in the response to immunotherapy in COAD patients.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (488.5KB, docx)
Supplementary Material 2 (21.4KB, xlsx)

Author contributions

Andong Jin: Study design, writing, data analysis; Fengjing Yang: Data collection and analysis; Hang Li: Technical Support; Geng Wang: Reviewing and editing; Sihua Wang: Technical Support; Song Tong: Supervision and investigationJinbo Gao：Supervision, investigation and resourcesAll authors reviewed the manuscript.

Funding

No funds to support this study.

Data availability

The available datasets could be retrieved from the TGCA database (https://portal.gdc.cancer.gov/) and GEO database (http://www.ncbi.nlm.nih.gov/geo/). The code applied in the study is available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Andong Jin and Fengjing Yang contributed equally to this work.

Contributor Information

Song Tong, Email: tongsong110@126.com.

Jinbo Gao, Email: jgao@hust.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (488.5KB, docx)
Supplementary Material 2 (21.4KB, xlsx)

Data Availability Statement

The available datasets could be retrieved from the TGCA database (https://portal.gdc.cancer.gov/) and GEO database (http://www.ncbi.nlm.nih.gov/geo/). The code applied in the study is available from the corresponding author upon reasonable request.

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