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TMEM92 acts as an immune-resistance and prognostic marker in pancreatic cancer from the perspective of predictive, preventive, and personalized medicine

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Abstract

Background

Pancreatic cancer presents extremely poor prognosis due to the difficulty of early diagnosis, low resection rate, and high rates of recurrence and metastasis. Immune checkpoint blockades have been widely used in many cancer types but showed limited efficacy in pancreatic cancer. The current study aimed to evaluate the landscape of tumor microenvironment (TME) of pancreatic cancer and identify the potential markers of prognosis and immunotherapy efficacy which might contribute to improve the targeted therapy strategy and efficacy in pancreatic cancer in the context of predictive, preventive, and personalized medicine (PPPM).

Methods

In the current study, a total of 382 pancreatic samples from the datasets of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were selected. LM22 gene signature matrix was applied to quantify the fraction of immune cells based on “CIBERSORT” algorithm. Weighted Gene Co-expression Network Analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm was applied to confirm the hub-network of immune-resistance phenotype. A nomogram model based on COX and Logistic regression was constructed to evaluate the prognostic value and the predictive value of hub-gene in immune-response. The role of transmembrane protein 92 (TMEM92) in regulating cell proliferation was evaluated by MTS assay. Western blot and Real-time PCR were applied to assess the biological effects of PD-L1 inhibition by TMEM92. Moreover, the effect of TMEM92 in immunotherapy was evaluated with PBMC co-culture and by MTS assay.

Results

Two tumor-infiltrating immune cell (TIIC) phenotypes were identified and a weighted gene co-expression network was constructed to confirm the 167 gene signatures correlated with immune-resistance TIIC subtype. TMEM92 was further identified as a core gene of 167 gene signature network based on MCODE algorithm. High TMEM92 expression was significantly correlated with unfavorable prognosis, characterizing by immune resistance. A nomogram model and external validation confirmed that TMEM92 was an independent prognostic factor in pancreatic cancer. An elevated tumor mutation burden (TMB), mostly is consistent with commonly mutations of KRAS and TP53, was found in the high TMEM92 group. The predictive role of TMEM92 in immunotherapeutic response was also confirmed by IMvigor210 datasets. In addition, the specific biological roles of TMEM92 in cancer was explored in vitro. The results showed that abnormal overexpression of TMEM92 was significantly associated with the poor survival rate of pancreatic cancer. Moreover, we demonstrated that TMEM92 inhibit tumour immune responses of the anti-PD-1 antibody with PBMC co-culture.

Conclusion

The current study explored for the first time the immune-resistance phenotype of pancreatic cancer and identified TMEM92 as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy. These findings not only help to recognize high-risk and immune-resistance population which could be supplied targeted prevention, but also provide personalized medical services by intervening TMEM92 function to improve the prognosis of pancreatic cancer. In addition, the biological role of TMEM92 might reveal the potential molecular mechanisms of pancreatic cancer and lead to a novel sight for development of a PPPM approach for pancreatic cancer management.

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Data availability

The datasets generated and/or analyzed during the current study are available in the TCGA, GEO, and IMvigor210 repository: TCGA (https://portal.gdc.cancer.gov); GEO (GSE57495: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57495; GSE62452:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62452; GSE85916: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85916); IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies).

Code availability

All software applications used are included in this article.

Abbreviations

TME:

Tumor microenvironment

TIIC:

Tumor-infiltrating immune cells

WGCNA:

Weighted gene co-expression network analysis

MCODE:

Molecular complex detection

GEO:

Gene Expression Omnibus

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

PD-1:

Programmed cell death protein 1

PD-L1:

Programmed cell death ligand 1

TMEM92:

Transmembrane protein 92

TMB:

Tumor mutation burden

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Funding

This work was supported by Science and Technology Plan Project of Liaoning Province (2021-BS-102); National Natural Science Foundation of China (81801661).

Author information

Author notes

  1. Simeng Zhang and Xing Wan have contributed equally to this work

Authors and Affiliations

  1. Department of Medical Oncology, the First Hospital of China Medical University, Shenyang, 110001, China

    Simeng Zhang & Ce Li

  2. Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, the First Hospital of China Medical University, Shenyang, 110001, China

    Simeng Zhang & Ce Li

  3. Liaoning Province Clinical Research Center for Cancer, Shenyang, 110001, China

    Simeng Zhang & Ce Li

  4. Department of Medical Oncology, the First Hospital of Dalian Medical University, Dalian, 116000, China

    Xing Wan

  5. Department of Plastic Surgery, the First Hospital of China Medical University, Shenyang, 110001, China

    Mengzhu Lv

  6. School of Basic Medicine, Capital Medical University, Beijing, 100069, China

    Qiaoyun Chu

  7. Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang, 110001, China

    Guan Wang

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  1. Simeng Zhang
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  2. Xing Wan
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Contributions

Guan Wang and Simeng Zhang designed the experiments. Guan Wang, Simeng Zhang, and Qiaoyun Chu wrote the manuscript. Mengzhu Lv and Ce Li performed the experiments. Simeng Zhang and Xing Wan performed the bioinformatics analysis. All authors contributed to the study design and data interpretation and have reviewed the final version of the manuscript.

Corresponding authors

Correspondence to Qiaoyun Chu or Guan Wang.

Ethics declarations

Ethics approval and consent to participate

Data retrieved from the GEO and TCGA controlled-access database was collected using tumors from patients who provided informed consent based on guidelines laid out by the GEO and TCGA Ethics, Law and Policy Group. This study was approved by the Human Ethics Review Committee of China Medical University, and all procedures were conducted in accordance with ethical principles (protocol #: 2015PS63K).

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Not applicable.

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The authors declare no competing interests.

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Supplementary Information

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13167_2022_287_MOESM1_ESM.jpg

Supplementary file1 (JPG 3538 KB) Supplementary Figure 1. (A-D) Consensus matrixes of 382 pancreatic cancer samples for each k (k = 2–5) in classifying TIIC clusters, displaying the clustering stability using 1000 iterations of hierarchical clustering. (E) 2 pairs of gene modules were merged according to their similarity base on the threshold (Red line). (F) The genedendrogram was constructed by hierarchical clustering base on dissTOM of genes. (G) The correlation of epigengenes in green module with TIIC cluster trait. (H-I) Probability of survival at 1 (H) and 3 (I) years in the total cohort.

Supplementary file2 (XLSX 145 KB)

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Zhang, S., Wan, X., Lv, M. et al. TMEM92 acts as an immune-resistance and prognostic marker in pancreatic cancer from the perspective of predictive, preventive, and personalized medicine. EPMA Journal 13, 519–534 (2022). https://doi.org/10.1007/s13167-022-00287-0

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