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pmid:37251940

Identification of a chemoresistance-related prognostic gene signature by comprehensive analysis and experimental validation in pancreatic cancer.

Junliang Chen · Zhihao Liu · Zhiyuan Wu · Wenjun Li · Xiaodong Tan

Frontiers in oncology · 2023 doi:10.3389/fonc.2023.1132424

Paper

complete

Experiment

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Data

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Analysis

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Execution

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title
Identification of a chemoresistance-related prognostic gene signature by comprehensive analysis and experimental validation in pancreatic cancer.
doi
10.3389/fonc.2023.1132424
pmid
37251940
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[0]
Junliang Chen
[1]
Zhihao Liu
[2]
Zhiyuan Wu
[3]
Wenjun Li
[4]
Xiaodong Tan
journal
Frontiers in oncology
year
2023
abstract
Background: Chemoresistance is a major hurdle to improving the prognosis of pancreatic cancer (PC). This study aimed to identify key genes regulating chemoresistance and develop a chemoresistance-related gene signature for prognosis prediction. Methods: A total of 30 PC cell lines were subtyped according to gemcitabine sensitivity data from the Cancer Therapeutics Response Portal (CTRP v2). Differentially expressed genes (DEGs) between gemcitabine-resistant and gemcitabine-sensitive cells were subsequently identified. These upregulated DEGs associated with prognostic values were incorporated to build a LASSO Cox risk model for The Cancer Genome Atlas (TCGA) cohort. Four datasets (GSE28735, GSE62452, GSE85916, and GSE102238) from the Gene Expression Omnibus (GEO) were used as an external validation cohort. Then, a nomogram was developed based on independent prognostic factors. The responses to multiple anti-PC chemotherapeutics were estimated by the "oncoPredict" method. Tumor mutation burden (TMB) was calculated using the "TCGAbiolinks" package. Analysis of the tumor microenvironment (TME) was performed using the "IOBR" package, while the TIDE and "easier" algorithms were employed to estimate immunotherapy efficacy. Finally, RT-qPCR, Western blot and CCK-8 assays were conducted to validate the expression and functions of ALDH3B1 and NCEH1. Results: A five-gene signature and a predictive nomogram were developed from six prognostic DEGs, including EGFR, MSLN, ERAP2, ALDH3B1, and NCEH1. Bulk and single-cell RNA sequencing analyses indicated that all five genes were highly expressed in tumor samples. This gene signature was not only an independent prognostic factor but also a biomarker forecasting chemoresistance, TMB, and immune cells. In vitro experiments suggested that ALDH3B1 and NCEH1 were involved in PC progression and gemcitabine chemoresistance. Conclusion: This chemoresistance-related gene signature links prognosis with chemoresistance, TMB, and immune features. ALDH3B1 and NCEH1 are two promising targets for treating PC.
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The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was applied to develop a risk model from the prognostic genes associated with chemoresistance in the TCGA cohort (16).
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The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was applied to develop a risk model from the prognostic genes associated with chemoresistance in the TCGA cohort (16).
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The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
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A five-gene signature and a predictive nomogram were developed from six prognostic DEGs, including EGFR, MSLN, ERAP2, ALDH3B1, and NCEH1.
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A five-gene signature and a predictive nomogram were developed from six prognostic DEGs, including EGFR, MSLN, ERAP2, ALDH3B1, and NCEH1.
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130
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6244
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title
Microarray gene-expression profiles of 69 pancreatic tumors and 61 adjacent non-tumor tissue from patients with pancreatic ductal adenocarcinoma
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2026-11-01T15:39:55Z
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The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was applied to develop a risk model from the prognostic genes associated with chemoresistance in the TCGA cohort (16).
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The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
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to estimate immunotherapy efficacy. Finally, RT-qPCR, Western blot and CCK-8 assays were conducte
[1]
estimate immunotherapy efficacy. Finally, RT-qPCR, Western blot and CCK-8 assays were conducte
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te immunotherapy efficacy. Finally, RT-qPCR, Western blot and CCK-8 assays were conducted to validate
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R, MSLN, ERAP2, ALDH3B1, and NCEH1. Bulk and single-cell RNA sequencing analyses indicated that all five genes were
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rials and methods Cell culture and transient transfection Three human pancreatic cancer cell lines (As
[1]
2. Cells in six-well plates were transiently transfected with siRNA using Lipofectamine™ 3000 transfe
[2]
sfected with siRNA using Lipofectamine™ 3000 transfection reagent (Invitrogen, Carlsbad, CA, USA) acco
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The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was applied to develop a risk model from the prognostic genes associated with chemoresistance in the TCGA cohort (16).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37251940", "kind": "science-record", "revision": 3, "content_hash": "sha256:07d3f6f9a5d1790134d8bb1499df1105526779ee74929158dac8488fee871f37", "created_at": "2026-11-01T19:01:58Z", "updated_at": "2026-11-01T22:09:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a chemoresistance-related prognostic gene signature by comprehensive analysis and experimental validation in pancreatic cancer.", "doi": "10.3389/fonc.2023.1132424", "pmid": "37251940", "authors": ["Junliang Chen", "Zhihao Liu", "Zhiyuan Wu", "Wenjun Li", "Xiaodong Tan"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Background: Chemoresistance is a major hurdle to improving the prognosis of pancreatic cancer (PC). This study aimed to identify key genes regulating chemoresistance and develop a chemoresistance-related gene signature for prognosis prediction.\nMethods: A total of 30 PC cell lines were subtyped according to gemcitabine sensitivity data from the Cancer Therapeutics Response Portal (CTRP v2). Differentially expressed genes (DEGs) between gemcitabine-resistant and gemcitabine-sensitive cells were subsequently identified. These upregulated DEGs associated with prognostic values were incorporated to build a LASSO Cox risk model for The Cancer Genome Atlas (TCGA) cohort. Four datasets (GSE28735, GSE62452, GSE85916, and GSE102238) from the Gene Expression Omnibus (GEO) were used as an external validation cohort. Then, a nomogram was developed based on independent prognostic factors. The responses to multiple anti-PC chemotherapeutics were estimated by the \"oncoPredict\" method. Tumor mutation burden (TMB) was calculated using the \"TCGAbiolinks\" package. Analysis of the tumor microenvironment (TME) was performed using the \"IOBR\" package, while the TIDE and \"easier\" algorithms were employed to estimate immunotherapy efficacy. Finally, RT-qPCR, Western blot and CCK-8 assays were conducted to validate the expression and functions of ALDH3B1 and NCEH1.\nResults: A five-gene signature and a predictive nomogram were developed from six prognostic DEGs, including EGFR, MSLN, ERAP2, ALDH3B1, and NCEH1. Bulk and single-cell RNA sequencing analyses indicated that all five genes were highly expressed in tumor samples. This gene signature was not only an independent prognostic factor but also a biomarker forecasting chemoresistance, TMB, and immune cells. In vitro experiments suggested that ALDH3B1 and NCEH1 were involved in PC progression and gemcitabine chemoresistance.\nConclusion: This chemoresistance-related gene signature links prognosis with chemoresistance, TMB, and immune features. ALDH3B1 and NCEH1 are two promising targets for treating PC.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was applied to develop a risk model from the prognostic genes associated with chemoresistance in the TCGA cohort (16).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37251940", "locator": "sections[5] \"Risk model development and evaluation\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was applied to develop a risk model from the prognostic genes associated with chemoresistance in the TCGA cohort (16)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . 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