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. 2024 Apr;7(4):e2059.
doi: 10.1002/cnr2.2059.

Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies

Md Roman Mogal  1 , Jasmin Akter Jame  1 , Md Sohel  2 , Md Mozibullah  1 , Md Rashel Mahmod  1 , Asadullah Junayed  1 , Newton Kar  1 , Lubatul Arbia  2 , Abdullah Al Mamun  1 , Md Asaduzzaman Sikder  1
Affiliations

Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies

Md Roman Mogal et al. Cancer Rep (Hoboken). 2024 Apr.

Abstract

Background: Pancreatic cancer (PC) stands out as one of the most formidable malignancies and exhibits an exceptionally unfavorable clinical prognosis due to the absence of well-defined diagnostic indicators and its tendency to develop resistance to therapeutic interventions. The primary objective of this present study was to identify extracellular matrix (ECM)-related hub genes (HGs) and their corresponding molecular signatures, with the intent of potentially utilizing them as biomarkers for diagnostic, prognostic, and therapeutic applications.

Methods: Three microarray datasets were sourced from the NCBI database to acquire upregulated differentially expressed genes (DEGs), while MatrisomeDB was employed for filtering ECM-related genes. Subsequently, a protein-protein interaction (PPI) network was established using the STRING database. The created network was visually inspected through Cytoscape, and HGs were identified using the CytoHubba plugin tool. Furthermore, enrichment analysis, expression pattern analysis, clinicopathological correlation, survival analysis, immune cell infiltration analysis, and examination of chemical compounds were carried out using Enrichr, GEPIA2, ULCAN, Kaplan Meier plotter, TIMER2.0, and CTD web platforms, respectively. The diagnostic and prognostic significance of HGs was evaluated through the ROC curve analysis.

Results: Ten genes associated with ECM functions were identified as HGs among 131 DEGs obtained from microarray datasets. Notably, the expression of these HGs exhibited significantly (p < 0.05) higher in PC, demonstrating a clear association with tumor advancement. Remarkably, higher expression levels of these HGs were inversely correlated with the likelihood of patient survival. Moreover, ROC curve analysis revealed that identified HGs are promising biomarkers for both diagnostic (AUC > 0.75) and prognostic (AUC > 0.64) purposes. Furthermore, we observed a positive correlation between immune cell infiltration and the expression of most HGs. Lastly, our study identified nine compounds with significant interaction profiles that could potentially act as effective chemical agents targeting the identified HGs.

Conclusion: Taken together, our findings suggest that COL1A1, KRT19, MMP1, COL11A1, SDC1, ITGA2, COL1A2, POSTN, FN1, and COL5A1 hold promise as innovative biomarkers for both the diagnosis and prognosis of PC, and they present as prospective targets for therapeutic interventions aimed at impeding the progression PC.

Keywords: collagen proteins; extracellular matrix; hub genes; immune cells infiltration; pancreatic cancer.

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Conflict of interest statement

The authors have stated explicitly that there are no conflicts of interest in connection with this article.

Figures

FIGURE 1
FIGURE 1
Schematic representation of the overall workflow of this study.
FIGURE 2
FIGURE 2
A Venn diagram of differentially expressed upregulated genes. One hundred and thirty‐one common upregulated genes were identified from GSE16515, GSE62165, and GSE71989 datasets.
FIGURE 3
FIGURE 3
(A) Protein–protein interaction network consisting of 99 nodes and 344 edges. (B) Hub genes network with 10 hub genes.
FIGURE 4
FIGURE 4
Signaling pathways (KEGG, BioPlanet, WikiPathway and Reactome) and gene ontologies (biological process, molecular function, and cellular component) for hub genes based on combined score.
FIGURE 5
FIGURE 5
Comparison of the hub genes expression in PC and normal tissue. The boxplot shows the hub gene expression in normal tissue (right) and PC (left) (*indicates p ≤ 0.05).
FIGURE 6
FIGURE 6
Protein expression of hub genes in PC based on cancer stages.
FIGURE 7
FIGURE 7
Genomic mutations determination of Hub genes in PC.
FIGURE 8
FIGURE 8
Correlation between hub genes expression and overall survival probability of PC patients. Red lines indicate hub gene overexpression, and blue lines indicate low hub gene expression.
FIGURE 9
FIGURE 9
Correlation between hub genes expression and relapse‐free survival probability of PC patients. Red lines indicate hub gene overexpression, and blue lines indicate low hub gene expression.
FIGURE 10
FIGURE 10
ROC curve analysis for determining hub genes as a diagnostic marker.
FIGURE 11
FIGURE 11
ROC curve analysis for determining hub genes as a prognostic marker.
FIGURE 12
FIGURE 12
Correlation between six hub gene expressions and infiltration levels of immune cells in PC.
FIGURE 13
FIGURE 13
Interaction network of hub genes with TFs and miRNAs. (A) TF network constructed from the JASPAR database through the NetworkAnalyst platform. (B) miRNAs network constructed from the TarBase database using the NetworkAnalyst platform.
See this image and copyright information in PMC

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