AUTHOR=Nie Yuanhua , Xu Longwen , Bai Zilong , Liu Yaoyao , Wang Shilong , Zeng Qingnuo , Gao Xuan , Xia Xuefeng , Chang Dongmin 

TITLE=Prognostic utility of TME-associated genes in pancreatic cancer

JOURNAL=Frontiers in Genetics

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1218774

DOI=10.3389/fgene.2023.1218774

ISSN=1664-8021

ABSTRACT=Background. Pancreatic cancer (PC) is a deadly disease. The tumor microenvironment (TME) participates in PC oncogenesis. This study focused on the assessment of the prognostic and treat utility of TME-associated genes in pancreatic cancer.
Methods. After obtaining the differentially-expressed TME-related genes, univariate, multivariate cox analyses and least absolute shrinkage and selection operator (LASSO) were performed to identify genes related to prognosis, and a risk model was established to evaluate risk scores, based on TCGA dataset and it. The risk model  was validated by two external datasets from GEO and CPTAC. Genomic characteristics, gene set enrichment analysis (GSEA), immunocyte infiltration, protein–protein interaction and single-cell data Multi-omics analyses were adopted to explore the potential mechanisms. GDSC, GEP, CYT, TIDE and Imvigor210 were used to assess the, discover novel treatment targets and assess the treatment sensitivities of immunotherapy and chemotherapy. 
Results. Five TME-associated genes, including  FERMT1, CARD9, IL20RB, MET, and MMP3, were identified and constructed risk score formula. Next,  and their mRNA expressions was were verified in cancer and normal pancreatic cells. The risk score formula comprised FERMT1, CARD9, IL20RB, MET, and MMP3. Multiple algorithms confirmed that the risk model displayed reliable ability of prognosis prediction and was an independent prognostic factor that high-risk patients had poor outcomes. Immunocyte infiltration, GSEA and single-cell analysis showed strong relationship between immune mechanism and low-risk samples altogether. Risk score could predict the sensitivity of immunotherapy and some chemotherapy regimens, including oxaliplatin and irinotecan. Various latent treatment  targets (LAG3, TIGIT and ARID1A) were addressed by mutation landscape based on risk model. 
Conclusion. The risk model based on TME-related genes can reflect the prognosis of pancreatic cancer patients and functions as a novel set of biomarkers for pancreatic cancer therapy.