ArticleJournal of gastrointestinal oncology2024
Single-cell analysis of chemotherapy-resistant microenvironment identifies a chemo-response biomarker for pancreatic cancer.
Article in Journal of gastrointestinal oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Exploratory single-nucleus multiomics analysis of myeloid cell states associated with neoadjuvant chemotherapy response in pancreatic ductal adenocarcinoma.Clinical and translational medicine · 2026Article
- Molecular Oncodiagnostics in Precision Oncology: Integrating Tumor Transcriptomics, Patient Pharmacogenetics, and Ex Vivo Chemoresistance Testing to Improve Individual Chemotherapy Response.Journal of personalized medicine · 2026Review
- Single-cell RNA sequencing reveals cellular and molecular heterogeneity in extensive-stage small cell lung cancer with different chemotherapy responses.Cancer cell international · 2025Article
- NIPAL1 as a prognostic biomarker associated with pancreatic adenocarcinoma progression and immune infiltration.BMC cancer · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal, aggressive cancer due to limited response chemotherapy. The tumor microenvironment (TME) has emerged as a key player in the development of chemoresistance and in malignant progression. In this study, we hypothesized that chemotherapy response is predictable by chemotherapy-related cell types and their differentially expressed genes (DEGs). Methods: DEGs of chemoresistance cell types were identified via single cell analysis and Wilcoxon test. A chemotherapy response signature was established using a random forest model and validated with public datasets. Bulk cell fraction was analyzed using BayesPrism algorithms. Log-rank test was used to analyze survival of PDAC patients. Results: We found that natural killer (NK) cells, myeloid cells, and erythroid cells were highly infiltrated in chemo-resistant TME. A total of 36 chemoresistance-related DEGs of chemo-resistant cells were identified in chemo-resistant PDAC. Functional enrichment analysis showed that chemoresistance upregulated various inflammation-related pathways, including TGF-β signaling. Based on these features, we constructed a random forest model to predict the response and survival for PDAC patients, which accurately distinguished high-risk and chemoresistant patients with significantly poorer prognosis in both the training and independent validation datasets. Cox regression analysis indicated that predicted labels were an independent prognostic factor in PDAC. Moreover, deconvolution of TME confirmed higher infiltration levels of M2 macrophage and NK cells in predicted chemoresistance. When combined with chemotherapy response related tumor mutations, the model showed excellent ability in predicting chemotherapy response and survival. Conclusions: The TME was closely associated with the chemotherapy response and prognosis of PDAC. Our TME-based random forest model predicted chemotherapy response with complementary knowledge to the response-related genetic mutations.
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