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Identification of prognostic genes in the pancreatic adenocarcinoma immune microenvironment by integrated bioinformatics analysis

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Abstract

Purpose

Pancreatic adenocarcinoma (PAAD) is one of the most common causes of death among solid tumors, and its pathogenesis remains to be clarified. This study aims to elucidate the value of immune/stromal-related genes in the prognosis of PAAD through comprehensive bioinformatics analysis based on the immune microenvironment and validated in Chinese pancreatic cancer patients.

Methods

Gene expression profiles of pancreatic cancer patients were obtained from TCGA database. Differentially expressed genes (DEGs) were identified based on the ESTIMATE algorithm. Gene co-expression networks were constructed using WGCNA. In the key module, survival analysis was used to reveal the prognostic value. Subsequently, we performed functional enrichment analysis to construct a protein–protein interaction (PPI) network. The relationship between tumor immune infiltration and hub genes was analyzed by TIMER and CIBERSORT. Finally, it was validated in the GEO database and in tissues of Chinese pancreatic cancer patients.

Results

In the TCGA pancreatic cancer cohort, a low immune/stromal score was associated with a good prognosis. After bioinformatic analysis, 57 genes were identified to be significantly associated with pancreatic cancer prognosis. Among them, up-regulation of four genes (COL6A3, PLAU, MMP11 and MMP14) indicated poor prognosis and was associated with multiple immune cell infiltration. IHC results showed that PLAU protein levels from Chinese pancreatic cancer tissues were significantly higher than those from adjacent non-tumor tissues and were also associated with tumor TNM stage and lymph node metastasis.

Conclusion

In conclusion, this study demonstrates that PLAU may serve as a new diagnostic and therapeutic target, which is highly expressed in Chinese pancreatic cancer tissues and associated with lymph node metastasis.

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Data availability

Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/ and https://www.ncbi.nlm.nih.gov/geo/.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grants 81570537 and 81974074 to Y.C).

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Author notes

  1. Haolan Wang and Liqing Lu contributed equally to this work.

Authors and Affiliations

  1. NHC Key Laboratory of Cancer Proteomics, Laboratory of Structural Biology, Department of Oncology, National Clinical Research Center for Geriatric Disorders, **angya Hospital, Central South University, Changsha, 410008, Hunan, China

    Haolan Wang, Liqing Lu, Xujun Liang & Yongheng Chen

  2. National Clinical Research Center for Geriatric Disorders, **angya Hospital, Central South University, Changsha, 410008, Hunan, China

    Yongheng Chen

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  1. Haolan Wang
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Contributions

HW and YC contributed to conceptualization; LL and HW provided methodology; LL and HW performed formal analysis and writing. HW and XL performed writing—review and editing; YC contributed to funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yongheng Chen.

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Wang, H., Lu, L., Liang, X. et al. Identification of prognostic genes in the pancreatic adenocarcinoma immune microenvironment by integrated bioinformatics analysis. Cancer Immunol Immunother 71, 1757–1769 (2022). https://doi.org/10.1007/s00262-021-03110-3

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