Research Article

S100P as a Shared Biomarker in Inflammatory Bowel Disease, Colorectal Cancer, and Pancreatic Adenocarcinoma: An Integrated Transcriptomic Analysis

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DOI:

10.3791/71735

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August 11th, 2026

 ,  ,  ,  , 
1Medical School of Tianjin University, 2Peking University People's Hospital, 3Zhaoyuan Maternal and Child Health Service Center, 4Yantai Center for Disease Control and Prevention, 5Yantaishan Hospital

* These authors contributed equally

In This Article

Summary

This protocol integrates public transcriptomic datasets and colonic epithelial cell validation to identify S100P as a shared biomarker associated with inflammatory bowel disease, colorectal cancer, and pancreatic adenocarcinoma.

Abstract

Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD), yet the molecular features shared among these diseases remain incompletely understood. This study aimed to identify common genes and biological pathways associated with IBD, CRC, and PAAD through integrated transcriptomic analysis and experimental validation. Gene expression datasets for IBD, CRC, and PAAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and differential expression analysis were performed to identify disease-associated and shared genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (analyses were used to explore enriched biological functions and pathways. Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts. Receiver operating characteristic analysis was performed to assess the diagnostic performance of common genes. Single-cell RNA sequencing analysis was conducted to examine the cellular distribution of S100P. In addition, the effects of S100P downregulation were evaluated in lipopolysaccharide (LPS)-stimulated colonic epithelial cells. A total of 162 disease-associated genes and four common genes were identified. Functional enrichment analyses indicated significant enrichment of immune- and inflammation-related pathways, including the interleukin-17 signaling pathway. Immune infiltration analysis revealed similar trends in several immune cell populations across IBD, CRC, and PAAD. Single-cell analysis showed elevated S100P expression in epithelial cells from all three diseases. Downregulation of S100P restored the proliferative capacity of LPS-stimulated colonic epithelial cells and reduced inflammatory cytokine expression. Integrated transcriptomic analysis identified S100P as a biomarker associated with IBD, CRC, and PAAD and highlighted shared immune-related features across these diseases.

Introduction

Inflammatory bowel disease (IBD) represents a spectrum of immune-related disorders that affect the gastrointestinal tract, including ulcerative colitis and Crohn’s disease1. The etiology of IBD is highly complex, involving mucosal immune abnormalities, dysbiosis, and genetic susceptibility2. IBD is a global health concern with increasing incidence rates and substantial economic burdens, and its growing prevalence has attracted considerable attention1. Importantly, patients with IBD have a significantly increased risk of developing colorectal cancer (CRC)3 and pancreatic adenocarcinoma (PAAD)4. Although this association has been well documented, the genetic crosstalk among IBD, CRC, and PAAD remains incompletely understood.

Previous studies strongly suggest that IBD, CRC, and PAAD share common pathogenic processes; however, specific and sensitive diagnostic biomarkers remain lacking, and the shared pathogenic mechanisms have not been fully elucidated. Fortunately, with the rapid advancement and widespread availability of high-throughput sequencing technologies, numerous transcriptomic datasets from patients with IBD, CRC, and PAAD have become publicly available, enabling systematic investigation of the molecular interconnections among these diseases2,5,6.

This study employed high-throughput sequencing data from patients with IBD, CRC, and PAAD to identify disease-associated and shared genes using weighted gene co-expression network analysis (WGCNA) and differential expression analysis. These genes were further investigated to identify potential shared signaling pathways among IBD, CRC, and PAAD. Additionally, we evaluated the diagnostic value of these common genes. Single-cell RNA sequencing (scRNA-seq) analysis demonstrated that the key gene S100P was predominantly expressed in epithelial cells. Finally, we investigated the biological role of S100P in IBD.

In conclusion, this study aimed to identify common diagnostic biomarkers and biological pathways associated with IBD, CRC, and PAAD, thereby providing valuable clinical insights into the shared prevention and treatment of these diseases.

Protocol

This study used publicly available, de-identified datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), as well as established commercial cell lines. No newly recruited human participants, identifiable patient information, or patient-derived samples were involved. All analyses were performed in accordance with relevant institutional guidelines and the terms of use of the public databases. Therefore, additional institutional ethical approval and informed consent were not required for this study.

Data Source
RNA-seq data for the IBD cohorts (GSE179285; platform: GPL6480 and GSE24287; platform: GPL6480), CRC cohorts (TCGA-CRC; platform: Illumina HiSeq 2000 and GSE87211; platform: GPL13497), and PAAD cohorts (GSE128735; platform: GPL20301 and GSE62452; platform: GPL6244) were downloaded from TCGA and the GEO. All datasets were accessed on December 5, 2025.

For each dataset, samples were strictly divided into two subgroups, with disease lesion/tumor tissues serving as the case group and corresponding non-lesion normal tissues serving as the control group. Specifically, the IBD cohort contained 297 intestinal mucosal samples from patients with IBD and 56 normal intestinal mucosal samples from healthy individuals; the CRC cohort included 841 primary colorectal tumor tissues and 211 matched adjacent normal colorectal epithelial tissues; and the PAAD cohort consisted of 114 PAAD tumor tissues and 106 normal pancreatic parenchymal tissues.

All datasets within the same disease category were integrated uniformly. The limma package’s normalizeBetweenArrays function was applied to perform cross-sample quantile normalization, effectively eliminating inter-platform batch effects and standardizing gene expression values across different datasets for subsequent differential expression analysis.

Screening of IBD-, CRC-, and PAAD-Related Genes and Common Genes
First, differentially expressed genes (DEGs) were screened from the IBD, CRC, and PAAD cohorts using the limma package, and the original P values were corrected using the Benjamini-Hochberg false discovery rate (FDR) method. In the IBD, CRC, and PAAD cohorts, the screening criteria were set at |logFC| > 0.4 and P < 0.05. Additionally, WGCNA was performed on all genes, with a minimum module gene threshold of 100 (soft-threshold power = 0.90; network type = signed). Consequently, common DEGs and module genes were identified across the three cohorts. Genes consistently identified by both methods were defined as common genes, whereas the remaining genes were categorized as related genes.

PPI and Functional Enrichment Analysis
These analyses were performed on the disease-associated genes. Protein–protein interaction (PPI) analysis was conducted using the STRING database (interaction score > 0.40). Functional enrichment analysis included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, which were performed using the clusterProfiler, enrichplot, and org.Hs.eg.db packages (P < 0.05 and FDR-adjusted q value [Benjamini–Hochberg method] < 0.05).

Immune Microenvironment Profiling
CIBERSORT is a reliable algorithm for estimating immune cell infiltration levels from gene expression data using the default LM22 signature matrix7. In this study, the CIBERSORT algorithm was used to estimate the extent of immune cell infiltration in samples from the IBD, CRC, and PAAD cohorts to explore the shared characteristics of the immune microenvironment among the three diseases. The analysis was performed with 1,000 permutations to calculate P values for each sample, and quantile normalization (QN = TRUE) was applied to the mixture expression file. Only samples with a CIBERSORT P value < 0.05 were retained for subsequent analyses, ensuring the reliability of the deconvolution results.

Assessment of the Diagnostic Value of Common Genes
The diagnostic value of the common genes in the IBD, CRC, and PAAD cohorts was evaluated using receiver operating characteristic (ROC) analysis with the pROC package in R. The optimal trade-off between sensitivity and specificity was visualized using ROC curves.

qRT-PCR, Cell Transfection, and Colony Formation Assay
qRT-PCR and cell transfection were conducted according to previous studies8,9,10. Transient transfection was performed using jetPRIME transfection reagent (Polyplus, China) according to the manufacturer's instructions. Cells were incubated with the transfection mixture for 6 h, after which the medium was replaced with complete DMEM. Subsequent experiments were performed 48 h after transfection.

Briefly, total cellular RNA was extracted using TRIzol reagent. RNA was reverse-transcribed into cDNA using PrimeScript RT Master Mix. Quantitative PCR was performed using TB Green qPCR. β-actin was used as the internal reference gene for expression normalization. Biological experiments were performed in triplicate. The primer sequences and the siS100P sequence can be found in a previous study 11.

NCM460, FHC, HCT116, SW116, PANC1, and BXPC2 cells were obtained as listed in the Table of Materials. All cell lines underwent cell identification and mycoplasma testing.

During the experiments, all cells were passaged for 3–5 generations. All cells were cultured in complete DMEM containing 10% fetal bovine serum and 1% penicillin–streptomycin.

The colony formation assay was performed as described in a previous study12. Briefly, 1,000 cells were seeded into each well of a 6-well plate and cultured for 10 days before the experiment was terminated. Cells were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and colonies were counted using ImageJ.

Analysis of Common Genes Based on scRNA-seq Data
scRNA-seq data from the IBD dataset (GSE214695), CRC dataset (GSE166555), and PAAD dataset (GSE154778) were preprocessed as described in previous studies8,13. Raw count matrices were collapsed by mean expression for duplicate gene symbols using limma::avereps. Initial filtering retained genes detected in at least three cells and cells containing at least 50 unique transcripts. Cells with a mitochondrial transcript fraction >5% or fewer than 50 detected genes were removed. Log normalization was performed with a scale factor of 10,000, followed by variance-stabilizing transformation to identify the top 1,500 highly variable genes, which were Z-score standardized prior to principal component analysis (PCA). Cluster-defining marker genes were filtered using log2(fold change) > 0.5, a detection fraction ≥0.25 in target clusters, and an adjusted P value <0.05.

Briefly, data preprocessing was conducted using the Seurat package, and cell type annotation was performed using the SingleR package (version 2.6.0). Cell clustering was performed in Seurat using k-nearest neighbor graph construction and t-SNE embedding based on PCA dimensions 1–20. The distribution and expression levels of the common genes across different cell types were then examined.

Construction of the IBD Model
According to previous studies14, lipopolysaccharide (LPS) was used to induce inflammation in normal human colonic epithelial cells (FHC and NCM460), thereby generating an inflammation-mimicking IBD model. Biological experiments were performed in triplicate. Cells were routinely cultured in a humidified incubator at 37°C with 5% CO2. When cell confluence reached approximately 50%–70%, the culture medium was replaced with fresh complete medium, and the cells were treated with 10 ng/mL LPS for 12 h. An equal volume of sterile phosphate-buffered saline (PBS) was used as the vehicle control. The culture volume was 2 mL per well in 6-well plates. After treatment, the medium was removed, the cells were washed twice with pre-cooled sterile PBS, and the cells were collected for subsequent analyses.

Statistical Analysis
All bioinformatics analyses were performed using R software (version 4.1.2). Comparisons between two groups were performed using Student's t-test, whereas comparisons among multiple groups were performed using one-way analysis of variance (ANOVA). Correlation analysis was performed using the Spearman method. All cellular experiments were repeated at least three times, and the data are presented as the mean ± standard deviation (SD). A P value or FDR < 0.05 was considered statistically significant. NS, not significant; P < 0.05 (*), P < 0.01 (**), and P < 0.001 (***).

Results

DEGs in IBD, CRC, and PAAD
First, the IBD cohorts (GSE179285 and GSE24287), CRC cohorts (TCGA-CRC and GSE87211), and PAAD cohorts (GSE128735 and GSE62452) were integrated, and the integration was evaluated using PCA and gene expression density plots. The results showed that batch effects among the different datasets were effectively eliminated after integration (Figure 1A–F).

Batch correction PCA plots and expression charts; pre/post-analysis comparison; gene expression normalization.
Figure 1. Normalization of inflammatory bowel disease (IBD), colorectal cancer (CRC), and pancreatic adenocarcinoma (PAAD) cohorts. (A) Principal component analysis (PCA) plots before and after normalization of the IBD cohorts (GSE179285 and GSE24287). (B) Gene expression distribution plots before and after normalization of the IBD cohorts. (C) PCA plots before and after normalization of the CRC cohorts (TCGA-CRC and GSE87211). (D) Gene expression distribution plots before and after normalization of the CRC cohorts. (E) PCA plots before and after normalization of the PAAD cohorts (GSE62452 and GSE128735). (F) Gene expression distribution plots before and after normalization of the PAAD cohorts. Please click here to view a larger version of this figure.

Following dataset integration and batch effect correction, differential expression analysis was performed on the IBD, CRC, and PAAD cohorts. Within each cohort, disease lesion or tumor tissues were compared with matched normal tissues to identify DEGs. In the IBD cohort, 183 DEGs were identified, including 64 downregulated and 119 upregulated genes (Figure 2A). In the CRC cohort, 5,064 DEGs were identified, including 2,477 downregulated and 2,587 upregulated genes (Figure 2B). In the PAAD cohort, 2,293 DEGs were identified, including 901 downregulated and 1,392 upregulated genes (Figure 2C). Ultimately, 40 overlapping DEGs were identified among the IBD, CRC, and PAAD cohorts (Figure 2D).

Gene expression heatmaps, volcano plots, and Venn diagram for differential analysis in RNA sequencing.
Figure 2. Differential expression analysis of IBD, CRC, and PAAD cohorts. (A) Heatmap and volcano plot of differentially expressed genes (DEGs) in the IBD cohort. (B) Heatmap and volcano plot of DEGs in the CRC cohort. (C) Heatmap and volcano plot of DEGs in the PAAD cohort. (D) Venn diagram showing overlapping DEGs among the IBD, CRC, and PAAD cohorts. Please click here to view a larger version of this figure.

WGCNA in IBD, CRC, and PAAD
WGCNA was performed on the IBD, CRC, and PAAD cohorts. In the IBD cohort, three modules were identified as being closely associated with clinical traits, with the MEbrown and MEturquoise modules showing the strongest correlations (Figure 3A). Similarly, in the CRC cohort, eight modules were identified as being closely associated with clinical traits, with the MEbrown and MEturquoise modules showing the strongest correlations (Figure 3B). In the PAAD cohort, one module was identified as being closely associated with clinical traits, with the MEblack and MEbrown modules showing the strongest correlations (Figure 3C).

Gene module-trait relationship diagrams and Venn diagram for IBD, CRC, PAAD analysis results.
Figure 3. Weighted gene co-expression network analysis (WGCNA) of IBD, CRC, and PAAD cohorts. (A) Heatmap showing correlations between gene co-expression modules and clinical traits in the IBD cohort. (B) Heatmap showing correlations between gene co-expression modules and clinical traits in the CRC cohort. (C) Heatmap showing correlations between gene co-expression modules and clinical traits in the PAAD cohort. (D) Venn diagram showing overlapping genes identified from key co-expression modules across the IBD, CRC, and PAAD cohorts. Please click here to view a larger version of this figure.

Based on genes from modules significantly correlated with clinical traits, 434 module-associated genes potentially involved in IBD, CRC, and PAAD were identified (Figure 3D).

Functional Enrichment Analysis of Common Genes in IBD, CRC, and PAAD
By combining the results of the differential expression analysis and WGCNA, 40 common DEGs and 122 common module-associated genes were identified. To investigate the shared molecular characteristics of IBD, CRC, and PAAD, these genes were integrated for further analysis, resulting in 158 disease-associated genes.

First, the 158 genes were analyzed using the STRING database to construct a PPI network (Figure 4A). Subsequently, GO and KEGG enrichment analyses were performed. GO analysis revealed significant enrichment in biological processes including the hormone metabolic process (Figure 4B,C), whereas KEGG analysis showed enrichment in pathways including the interleukin-17 (IL-17) signaling pathway and peroxisome proliferator-activated receptor (PPAR) signaling pathway (Figure 4D,E).

Network analysis, gene interaction chart. Includes nodes connected by pathways, data visualizations.
Figure 4. Functional enrichment analysis of disease-associated genes identified in IBD, CRC, and PAAD. (A) Protein–protein interaction (PPI) network of disease-associated genes. (B) Gene Ontology (GO) enrichment analysis presented as a dot plot of enriched biological process, cellular component, and molecular function terms. (C) GO gene–concept network showing the relationships between enriched GO terms and associated genes. (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis presented as a dot plot. (E) KEGG gene–pathway network showing the relationships between enriched pathways and associated genes. Please click here to view a larger version of this figure.

Correlation Analysis of Common Genes with Immune Cell Infiltration in IBD, CRC, and PAAD
CIBERSORT was used to estimate immune cell infiltration levels in the IBD, CRC, and PAAD cohorts. In the IBD cohort, differences in immune cell infiltration levels were observed between normal and IBD samples (Figure 5A), and correlations between common genes and immune cell populations were evaluated (Figure 5B). Similarly, differences in immune cell infiltration levels were observed between normal and CRC samples in the CRC cohort (Figure 5C), and correlations between common genes and immune cell populations were evaluated (Figure 5D). In the PAAD cohort, differences in immune cell infiltration levels were also observed between normal and PAAD samples (Figure 5E), and correlations between common genes and immune cell populations were evaluated (Figure 5F).

Violin plots and correlation diagrams of protein expression in normal, IBD, CRC, and treatment groups.
Figure 5. Immune cell infiltration analysis in IBD, CRC, and PAAD cohorts. (A) Violin plots showing the estimated proportions of immune cell populations in the IBD cohort. (B) Correlation analysis between common genes and immune cell populations in the IBD cohort, including the immune cell correlation heatmap and gene–immune cell association network. (C) Violin plots showing the estimated proportions of immune cell populations in the CRC cohort. (D) Correlation analysis between common genes and immune cell populations in the CRC cohort, including the immune cell correlation heatmap and gene–immune cell association network. (E) Violin plots showing the estimated proportions of immune cell populations in the PAAD cohort. (F) Correlation analysis between common genes and immune cell populations in the PAAD cohort, including the immune cell correlation heatmap and gene–immune cell association network. Please click here to view a larger version of this figure.

Assessment of the Potential Value of Common Genes in IBD, CRC, and PAAD
The expression patterns of the common genes were further evaluated in the IBD, CRC, and PAAD cohorts. S100P was consistently overexpressed across all three cohorts (Figure 6A,D,G). In addition, the diagnostic performance of the common genes was evaluated using ROC analysis.

Gene expression analysis box plots, ROC curves; sensitivity, specificity in cancer biomarker study results.
Figure 6. Diagnostic performance of common genes in IBD, CRC, and PAAD cohorts. (A) Expression levels of FXYD3, S100P, PLA2G2A, and MUC1 in the IBD cohort. (B) Receiver operating characteristic (ROC) curves showing the diagnostic performance of individual common genes in the IBD cohort. (C) ROC curve showing the diagnostic performance of the combined diagnostic model in the IBD cohort. (D) Expression levels of FXYD3, S100P, PLA2G2A, and MUC1 in the CRC cohort. (E) ROC curves showing the diagnostic performance of individual common genes in the CRC cohort. (F) ROC curve showing the diagnostic performance of the combined diagnostic model in the CRC cohort. (G) Expression levels of FXYD3, S100P, PLA2G2A, and MUC1 in the PAAD cohort. (H) ROC curves showing the diagnostic performance of individual common genes in the PAAD cohort. (I) ROC curve showing the diagnostic performance of the combined diagnostic model in the PAAD cohort. Area under the curve (AUC) values and corresponding 95% confidence intervals are shown where applicable. Please click here to view a larger version of this figure.

In the IBD cohort, the area under the curve (AUC) values were 0.626 for FXYD3, 0.597 for S100P, 0.670 for PLA2G2A, and 0.697 for MUC1, whereas the combined diagnostic model yielded an AUC of 0.815 (Figure 6B,C). Similarly, in the CRC cohort, the AUC values were 0.839 for FXYD3, 0.738 for S100P, 0.716 for PLA2G2A, and 0.672 for MUC1, whereas the combined diagnostic model yielded an AUC of 0.925 (Figure 6E,F). In the PAAD cohort, the AUC values were 0.852 for FXYD3, 0.896 for S100P, 0.637 for PLA2G2A, and 0.733 for MUC1, whereas the combined diagnostic model yielded an AUC of 0.901 (Figure 6H,I).

scRNA-seq Analysis Based on Common Genes
Following preprocessing of the scRNA-seq data from the IBD dataset, 17 cell clusters and 8 cell types were identified. The distribution of the common genes across different cell populations was then evaluated, and the common genes were found to be predominantly expressed in epithelial cells (Figure 7A). Similarly, preprocessing of the CRC dataset identified 20 cell clusters and 8 cell types, with the common genes also predominantly expressed in epithelial cells (Figure 7B). Finally, preprocessing of the PAAD dataset identified 19 cell clusters and 7 cell types, and the common genes were likewise predominantly expressed in epithelial cells (Figure 7C).

UMAP plots showing cell type clustering; panels A, B, C illustrate IBD, CRC, PAAD gene expression.
Figure 7. Single-cell RNA sequencing (scRNA-seq) analysis of common genes in IBD, CRC, and PAAD tissues. (A) Uniform manifold approximation and projection (UMAP) visualization of cell clusters in the IBD dataset (GSE214695), corresponding cell-type annotations, and feature plots showing the expression of FXYD3, S100P, PLA2G2A, and MUC1 across cell populations. (B) UMAP visualization of cell clusters in the CRC dataset (GSE166555), corresponding cell-type annotations, and feature plots showing the expression of FXYD3, S100P, PLA2G2A, and MUC1 across cell populations. (C) UMAP visualization of cell clusters in the PAAD dataset (GSE154778), corresponding cell-type annotations, and feature plots showing the expression of FXYD3, S100P, PLA2G2A, and MUC1 across cell populations. Color scales indicate relative gene expression levels. Please click here to view a larger version of this figure.

Biological Function of S100P in IBD
Given the consistent overexpression of S100P across the IBD, CRC, and PAAD cohorts, together with previous reports describing its role in CRC and PAAD15,16, this study further investigated the biological function of S100P in IBD.

First, S100P expression was significantly increased in the LPS-induced IBD model, and the knockdown efficiency of siS100P was confirmed (Figure 8A,B). In addition, LPS treatment reduced cell proliferation, whereas inhibition of S100P expression partially restored cell proliferation (Figure 8C). Furthermore, S100P knockdown significantly reduced the expression of IL-1β, IL-6, and TNF-α in the IBD model cell lines FHC and NCM460 (Figure 8D,E). Finally, downregulation of S100P reduced IL17RA expression in colonic epithelial cells, CRC cells, and PAAD cells (Figure 8F).
 

Bar charts, colony assays, cytokine concentration graphs for siRNA impact on cells, inflammatory response.
Figure 8. Downregulation of S100P attenuates lipopolysaccharide (LPS)-induced inflammatory responses in colonic epithelial cells. (A) Relative S100P mRNA expression in FHC cells following LPS stimulation and S100P knockdown. (B) Relative S100P mRNA expression in NCM460 cells following LPS stimulation and S100P knockdown. (C) Representative colony formation images and quantification of cell proliferation in FHC and NCM460 cells following LPS stimulation and S100P knockdown. (D) ELISA measurement of IL-1β, IL-6, and TNF-α levels in FHC cells following LPS stimulation and S100P knockdown. (E) ELISA measurement of IL-1β, IL-6, and TNF-α levels in NCM460 cells following LPS stimulation and S100P knockdown. (F) Relative IL-17RA mRNA expression following S100P knockdown in FHC, NCM460, HCT116, SW1116, PANC-1, and BxPC-3 cells. Please click here to view a larger version of this figure.

   

Data Availability
The datasets analyzed during the current study are publicly available in TCGA and the GEO repository under the accession numbers TCGA-CRC, GSE179285, GSE24287, GSE87211, GSE128735, GSE62452, GSE214695, GSE166555, and GSE154778. No new sequencing datasets were generated during this study.

Discussion

Previous studies have highlighted the association of IBD, an immune-related gastrointestinal disorder, with several diseases, including neurodegenerative diseases17, multiple sclerosis18, amyotrophic lateral sclerosis19, endometriosis20, rheumatoid arthritis2, Hodgkin’s lymphoma21, CRC22, and PAAD23. IBD is generally considered to increase the risk of developing both CRC24 and PAAD23. Although epidemiological associations among these diseases have been reported, their shared molecular characteristics remain incompletely understood. In this study, we integrated bulk transcriptomic datasets, scRNA-seq data, and cellular functional experiments to investigate shared molecular features among IBD, CRC, and PAAD. Our findings provide additional insight into potential molecular and immune-related characteristics shared across these disease settings.

In this study, we identified 158 disease-associated genes through differential expression analysis and WGCNA, comprising 40 common DEGs and 122 common module-associated genes. Functional enrichment analysis demonstrated significant enrichment of pathways including the IL-17 signaling pathway. Previous studies have reported important roles for IL-17 signaling in the progression of IBD, CRC, and PAAD25,26,27. These observations suggest that IL-17-related processes may represent a common biological feature across these diseases. However, the present study did not directly investigate the mechanistic relationship between S100P and IL-17 signaling, and additional functional studies are needed to clarify this association. Four common genes (FXYD3, S100P, PLA2G2A, and MUC1) were identified across the analyzed datasets. Previous studies have demonstrated that FXYD3 regulates the growth of PAAD cells28, S100P has been implicated in CRC and PAAD progression15,16, and MUC1 participates in the progression of IBD, CRC, and PAAD29,30,31. In contrast, PLA2G2A has been less extensively investigated across all three diseases. ROC analysis demonstrated that each of the four genes showed measurable diagnostic performance, whereas the combined diagnostic model achieved higher diagnostic performance than the individual genes. Our findings therefore extend previous observations by identifying these genes as shared molecular features across IBD, CRC, and PAAD.

Immune cell infiltration analysis demonstrated similar infiltration patterns for naïve B cells, resting natural killer (NK) cells, and M0 macrophages across the IBD, CRC, and PAAD cohorts. Furthermore, scRNA-seq analysis showed that FXYD3, S100P, PLA2G2A, and MUC1 were predominantly expressed in epithelial cells. These findings provide additional information regarding the cellular distribution of the identified genes and their potential association with immune-related characteristics. However, the present analyses do not establish direct interactions between epithelial cells and the immune microenvironment, and further mechanistic studies are required.

Finally, we investigated the biological role of S100P in an LPS-induced inflammation-mimicking IBD model. S100P expression was significantly increased following LPS stimulation, and siRNA-mediated knockdown effectively reduced its expression. Under the experimental conditions examined, S100P knockdown partially restored cell proliferation and reduced the expression of the inflammatory cytokines IL-1β, IL-6, and TNF-α. In addition, S100P downregulation was associated with reduced IL17RA expression. These findings support further investigation of S100P in inflammation-related cellular responses.

This study has several limitations. First, only loss-of-function experiments were performed, and overexpression or rescue experiments were not conducted; therefore, a direct causal role of S100P cannot be established. Second, S100P expression was evaluated primarily at the mRNA level, and protein-level validation was not performed. Third, the functional experiments were limited to an inflammation-mimicking cell model, whereas CRC and PAAD models were not experimentally investigated. Finally, although multiomics analyses strengthened the identification of shared molecular features, additional mechanistic studies and in vivo validation are required to further clarify the biological roles of S100P and the other shared genes.

In conclusion, this study identified shared molecular features, biological pathways, and immune-related characteristics across IBD, CRC, and PAAD. Four common genes (FXYD3, S100P, PLA2G2A, and MUC1) demonstrated diagnostic potential across the analyzed datasets, and S100P was further investigated in an inflammation-mimicking IBD model. These findings provide a foundation for future studies investigating the shared molecular mechanisms linking IBD, CRC, and PAAD.

Disclosures

Conflict of Interest:
The authors declare no competing interests.

Acknowledgements

The authors acknowledge financial support from the National Key R&D Plan of China (Grant No. 2023YFB3210400).

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
BXPC2 cell lineCell Bank of the Chinese Academy of SciencesN/AHuman pancreatic adenocarcinoma cell line
CIBERSORTN/AN/AImmune cell infiltration analysis
clusterProfiler packageBioconductorv4.8.0Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses
Dulbecco's Modified Eagle Medium (DMEM)Thermo Fisher Scientific11965092Cell culture medium
Fetal bovine serum (FBS)Thermo Fisher ScientificA5256701Cell culture supplement
FHC cell lineCell Bank of the Chinese Academy of SciencesN/AHuman normal colonic epithelial cell line
GEO dataset (GSE128735)Gene Expression Omnibus (GEO)N/APublic gene expression dataset for pancreatic adenocarcinoma
GEO dataset (GSE154778)Gene Expression Omnibus (GEO)N/APublic single-cell RNA sequencing dataset for pancreatic adenocarcinoma
GEO dataset (GSE166555)Gene Expression Omnibus (GEO)N/APublic single-cell RNA sequencing dataset for colorectal cancer
GEO dataset (GSE179285)Gene Expression Omnibus (GEO)N/APublic gene expression dataset for inflammatory bowel disease
GEO dataset (GSE214695)Gene Expression Omnibus (GEO)N/APublic single-cell RNA sequencing dataset for inflammatory bowel disease
GEO dataset (GSE24287)Gene Expression Omnibus (GEO)N/APublic gene expression dataset for inflammatory bowel disease
GEO dataset (GSE62452)Gene Expression Omnibus (GEO)N/APublic gene expression dataset for pancreatic adenocarcinoma
GEO dataset (GSE87211)Gene Expression Omnibus (GEO)N/APublic gene expression dataset for colorectal cancer
ggplot2 packageCRANv3.4.2Data visualization
HCT116 cell lineCell Bank of the Chinese Academy of SciencesN/AHuman colorectal cancer cell line
ImageJNational Institutes of Health (NIH)v1.8.0Colony counting
jetPRIME Transfection ReagentPolyplus101000046Cell transfection
Lipopolysaccharide (LPS)Beyotime Co., LtdS1735Induction of an inflammation-mimicking IBD cell model
limma packageBioconductorv3.54.0Differential expression analysis
NCM460 cell lineCell Bank of the Chinese Academy of SciencesN/AHuman normal colonic epithelial cell line
org.Hs.eg.db packageBioconductorv3.23.1Gene annotation for enrichment analysis
PANC1 cell lineCell Bank of the Chinese Academy of SciencesN/AHuman pancreatic adenocarcinoma cell line
Penicillin–streptomycinThermo Fisher Scientific15140-122Antibiotic supplement for cell culture
pheatmap packageCRANv1.0.12Heatmap visualization
pROC packageCRANv1.19.0.1Receiver operating characteristic (ROC) analysis
PrimeScript RT Master MixTakara BioRR036AReverse transcription of RNA into cDNA
Primer sets for qRT-PCRTsingke Biotech Co., LtdN/APrimer sequences reported in Reference 11
R softwareR Foundation for Statistical Computingv4.1.2Statistical and bioinformatics analyses
Seurat packageCRANv4.0Single-cell RNA sequencing data preprocessing and analysis
siS100PTsingke Biotech Co., LtdN/ASmall interfering RNA targeting S100P
SingleR packageBioconductorv2.6.0Cell type annotation for single-cell RNA sequencing
STRING databaseSTRING ConsortiumN/AProtein-protein interaction analysis
SW1116 cell lineCell Bank of the Chinese Academy of SciencesN/AHuman colorectal cancer cell line
TB Green qPCR MixTakara BioRR430BQuantitative real-time PCR
TCGA-CRC datasetThe Cancer Genome Atlas (TCGA)N/APublic colorectal cancer transcriptomic dataset
TRIzol reagentInvitrogen15596-026Total RNA extraction
WGCNA packageCRANv1.73Weighted gene co-expression network analysis

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