Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer
Pancreatic cancer (PC) is burdened with a low 5-year survival rate and high mortality due to a severe lack of early diagnosis methods and slow progress in treatment options. To improve clinical diagnosis and enhance the treatment effects, we applied metabolomics using ultra-high-performance liquid c...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.991051/full |
| _version_ | 1811184617351282688 |
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| author | Chang Liu Chang Liu Henan Qin Huiying Liu Huiying Liu Tianfu Wei Tianfu Wei Zeming Wu Mengxue Shang Haihua Liu Aman Wang Jiwei Liu Dong Shang Dong Shang Dong Shang Peiyuan Yin Peiyuan Yin |
| author_facet | Chang Liu Chang Liu Henan Qin Huiying Liu Huiying Liu Tianfu Wei Tianfu Wei Zeming Wu Mengxue Shang Haihua Liu Aman Wang Jiwei Liu Dong Shang Dong Shang Dong Shang Peiyuan Yin Peiyuan Yin |
| author_sort | Chang Liu |
| collection | DOAJ |
| description | Pancreatic cancer (PC) is burdened with a low 5-year survival rate and high mortality due to a severe lack of early diagnosis methods and slow progress in treatment options. To improve clinical diagnosis and enhance the treatment effects, we applied metabolomics using ultra-high-performance liquid chromatography with a high-resolution mass spectrometer (UHPLC-HRMS) to identify and validate metabolite biomarkers from paired tissue samples of PC patients. Results showed that the metabolic reprogramming of PC mainly featured enhanced amino acid metabolism and inhibited sphingolipid metabolism, which satisfied the energy and biomass requirements for tumorigenesis and progression. The altered metabolism results were confirmed by the significantly changed gene expressions in PC tissues from an online database. A metabolites biomarker panel (six metabolites) was identified for the differential diagnosis between PC tumors and normal pancreatic tissues. The panel biomarker distinguished tumors from normal pancreatic tissues in the discovery group with an area under the curve (AUC) of 1.0 (95%CI, 1.000−1.000). The biomarker panel cutoff was 0.776. In the validation group, an AUC of 0.9000 (95%CI = 0.782–1.000) using the same cutoff, successfully validated the biomarker signature. Moreover, this metabolites panel biomarker had a great capability to predict the overall survival (OS) of PC. Taken together, this metabolomics method identifies and validates metabolite biomarkers that can diagnose the onsite progression and prognosis of PC precisely and sensitively in a clinical setting. It may also help clinicians choose proper therapeutic interventions for different PC patients and improve the survival of PC patients. |
| first_indexed | 2024-04-11T13:16:30Z |
| format | Article |
| id | doaj.art-ebc043e307f4413c92ba58a4c8206858 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T13:16:30Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-ebc043e307f4413c92ba58a4c82068582022-12-22T04:22:24ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-09-011210.3389/fonc.2022.991051991051Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancerChang Liu0Chang Liu1Henan Qin2Huiying Liu3Huiying Liu4Tianfu Wei5Tianfu Wei6Zeming Wu7Mengxue Shang8Haihua Liu9Aman Wang10Jiwei Liu11Dong Shang12Dong Shang13Dong Shang14Peiyuan Yin15Peiyuan Yin16Key Laboratory of Integrative Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, ChinaInstitute of Integrative Medicine, Dalian Medical University, Dalian, ChinaDepartment of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaKey Laboratory of Integrative Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, ChinaInstitute of Integrative Medicine, Dalian Medical University, Dalian, ChinaKey Laboratory of Integrative Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, ChinaInstitute of Integrative Medicine, Dalian Medical University, Dalian, ChinaiPhenome biotechnology (Yun Pu Kang) Inc., Dalian, ChinaInstitute of Integrative Medicine, Dalian Medical University, Dalian, ChinaInstitute of Integrative Medicine, Dalian Medical University, Dalian, ChinaDepartment of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaDepartment of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaKey Laboratory of Integrative Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, ChinaInstitute of Integrative Medicine, Dalian Medical University, Dalian, ChinaDepartment of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaKey Laboratory of Integrative Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, ChinaInstitute of Integrative Medicine, Dalian Medical University, Dalian, ChinaPancreatic cancer (PC) is burdened with a low 5-year survival rate and high mortality due to a severe lack of early diagnosis methods and slow progress in treatment options. To improve clinical diagnosis and enhance the treatment effects, we applied metabolomics using ultra-high-performance liquid chromatography with a high-resolution mass spectrometer (UHPLC-HRMS) to identify and validate metabolite biomarkers from paired tissue samples of PC patients. Results showed that the metabolic reprogramming of PC mainly featured enhanced amino acid metabolism and inhibited sphingolipid metabolism, which satisfied the energy and biomass requirements for tumorigenesis and progression. The altered metabolism results were confirmed by the significantly changed gene expressions in PC tissues from an online database. A metabolites biomarker panel (six metabolites) was identified for the differential diagnosis between PC tumors and normal pancreatic tissues. The panel biomarker distinguished tumors from normal pancreatic tissues in the discovery group with an area under the curve (AUC) of 1.0 (95%CI, 1.000−1.000). The biomarker panel cutoff was 0.776. In the validation group, an AUC of 0.9000 (95%CI = 0.782–1.000) using the same cutoff, successfully validated the biomarker signature. Moreover, this metabolites panel biomarker had a great capability to predict the overall survival (OS) of PC. Taken together, this metabolomics method identifies and validates metabolite biomarkers that can diagnose the onsite progression and prognosis of PC precisely and sensitively in a clinical setting. It may also help clinicians choose proper therapeutic interventions for different PC patients and improve the survival of PC patients.https://www.frontiersin.org/articles/10.3389/fonc.2022.991051/fullpancreatic cancermetabolismbiomarkerprognosisThe Cancer Genome Atlas (TCGA)Genotype-Tissue Expression (GTEx) |
| spellingShingle | Chang Liu Chang Liu Henan Qin Huiying Liu Huiying Liu Tianfu Wei Tianfu Wei Zeming Wu Mengxue Shang Haihua Liu Aman Wang Jiwei Liu Dong Shang Dong Shang Dong Shang Peiyuan Yin Peiyuan Yin Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer Frontiers in Oncology pancreatic cancer metabolism biomarker prognosis The Cancer Genome Atlas (TCGA) Genotype-Tissue Expression (GTEx) |
| title | Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer |
| title_full | Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer |
| title_fullStr | Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer |
| title_full_unstemmed | Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer |
| title_short | Tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer |
| title_sort | tissue metabolomics identified new biomarkers for the diagnosis and prognosis prediction of pancreatic cancer |
| topic | pancreatic cancer metabolism biomarker prognosis The Cancer Genome Atlas (TCGA) Genotype-Tissue Expression (GTEx) |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.991051/full |
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