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Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer

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Abstract

Purpose

Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer

Methods

Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC

Results

High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids

Conclusion

The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC

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Data availability

All data generated or analyzed during this study can be downloaded from TCGA (https://portal.gdc.cancer.gov/), GTEx (https://www.gtexportal.org/home), and ICGC (https://icgc.org).

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Acknowledgements

Thanks to all who have contributed to the process of my writing this article. Especially, thanks to my girlfriend NR P who supported me in my life. At last, I’d like to thank those leaders, teachers, and my families who have encouraged me.

Funding

This study was supported by grants from the National Natural Science Foundation of China (No.81972258, No.81974376, No. 82103016, No. 82172836, No.82272917); Natural Science Foundation of Beijing (No. 7192157); CAMS Innovation Fund for Medical Sciences (CIFMS) (2021-1-I2M-002); National Key R&D Program of China (2018YFE0118600); National High Level Hospital Clinical Research Funding 2022-PUMCH-D-001; China Postdoctoral Science Foundation (2021T140071 and 2021M690462), Youth Research Fund of Peking Union Medical College Hospital (pumch201911710, pumch201910819), National High Level Hospital Clinical Research Funding 2022-PUMCH-A-245, National High Level Hospital Clinical Research Funding 2022-PUMCH-A-056, and Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2018PT32014).

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Author notes

  1. Guangyu Chen, Yueze Liu and Dan Su contributed equally.

Authors and Affiliations

  1. General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Guangyu Chen, Yueze Liu, Dan Su, Jiangdong Qiu, Fangyu Zhao, Jinxin Tao, Gang Yang, Hua Huang, Jianchun Xiao, Taiping Zhang & Yupei Zhao

  2. Department of Breast Surgery, The Affiliated Hospital of Guizhou Medical University, Guizhou Medical University, Guiyang, China

    Guangyu Chen

  3. Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, People’s Republic of China

    Guangyu Chen, Yueze Liu, Dan Su, Jiangdong Qiu, Fangyu Zhao, Jinxin Tao, Gang Yang, Hua Huang, Jianchun Xiao, Taiping Zhang & Yupei Zhao

  4. National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, People’s Republic of China

    Guangyu Chen, Yueze Liu, Dan Su, Jiangdong Qiu, Junyu Long, Fangyu Zhao, Jinxin Tao, Gang Yang, Hua Huang, Jianchun Xiao, Taiping Zhang & Yupei Zhao

  5. Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Junyu Long

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Contributions

GC analyzed the data and wrote the manuscript. TZ and YZ conceived the study and obtained financial support. , YL, DS, JL applied guiding suggestions, GY, JQ, FZ, JT,HH,XJ prepared the dataset. All authors read and approved the final manuscript.

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Correspondence to Taiping Zhang or Yupei Zhao.

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Chen, G., Liu, Y., Su, D. et al. Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer. Cell Oncol. 46, 1691–1708 (2023). https://doi.org/10.1007/s13402-023-00836-3

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