Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia
BackgroundPancreatic cancer is one of the most fatal malignancies of the gastrointestinal cancer, with a challenging early diagnosis due to lack of distinctive symptoms and specific biomarkers. The exact etiology of pancreatic cancer is unknown, making the development of reliable biomarkers difficul...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.942774/full |
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| author | Yunju Jo Min-Kyung Yeo Tam Dao Jeongho Kwon Hyon‐Seung Yi Hyon‐Seung Yi Dongryeol Ryu |
| author_facet | Yunju Jo Min-Kyung Yeo Tam Dao Jeongho Kwon Hyon‐Seung Yi Hyon‐Seung Yi Dongryeol Ryu |
| author_sort | Yunju Jo |
| collection | DOAJ |
| description | BackgroundPancreatic cancer is one of the most fatal malignancies of the gastrointestinal cancer, with a challenging early diagnosis due to lack of distinctive symptoms and specific biomarkers. The exact etiology of pancreatic cancer is unknown, making the development of reliable biomarkers difficult. The accumulation of patient-derived omics data along with technological advances in artificial intelligence is giving way to a new era in the discovery of suitable biomarkers.MethodsWe performed machine learning (ML)-based modeling using four independent transcriptomic datasets, including GSE16515, GSE62165, GSE71729, and the pancreatic adenocarcinoma (PAC) dataset of the Cancer Genome Atlas. To find candidates for circulating biomarkers, we exported expression profiles of 1,703 genes encoding secretory proteins. Integrating three transcriptomic datasets into either a training or test set, ML-based modeling distinguishing PAC from normal was carried out. Another ML-model classifying long-lived and short-lived patients with PAC was also built to select prognosis-associated features. Finally, circulating level of SCG5 in the plasma was determined from the independent cohort (non-tumor = 25 and pancreatic cancer = 25). We also investigated the impact of SCG5 on adipocyte biology using recombinant protein.ResultsThree distinctive ML-classifiers selected 29-, 64- and 18-featured genes, recognizing the only common gene, SCG5. As per the prediction of ML-models, the SCG5 transcripts was significantly reduced in PAC and decreased further with the progression of the tumor, indicating its potential as a diagnostic as well as prognostic marker for PAC. External validation of SCG5 using plasma samples from patients with PAC confirmed that SCG5 was reduced significantly in patients with PAC when compared to controls. Interestingly, plasma SCG5 levels were correlated with the body mass index and age of donors, implying pancreas-originated SCG5 could regulate energy metabolism systemically. Additionally, analyses using publicly available Genotype-Tissue Expression datasets, including adipose tissue histology and pancreatic SCG5 expression, further validated the association between pancreatic SCG5 expression and the size of subcutaneous adipocytes in humans. However, we could not observe any definite effect of rSCG5 on the cultured adipocyte, in 2D in vitro culture.ConclusionCirculating SCG5, which may be associated with adipopenia, is a promising diagnostic biomarker for PAC. |
| first_indexed | 2024-04-13T13:10:26Z |
| format | Article |
| id | doaj.art-1772114118664689bff22b2c95921af8 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-13T13:10:26Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-1772114118664689bff22b2c95921af82022-12-22T02:45:37ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-08-011210.3389/fonc.2022.942774942774Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopeniaYunju Jo0Min-Kyung Yeo1Tam Dao2Jeongho Kwon3Hyon‐Seung Yi4Hyon‐Seung Yi5Dongryeol Ryu6Department of Molecular Cell Biology, Sungkyunkwan University (SKKU) School of Medicine, Suwon, South KoreaDepartment of Pathology, Chungnam National University School of Medicine, Daejeon, South KoreaDepartment of Molecular Cell Biology, Sungkyunkwan University (SKKU) School of Medicine, Suwon, South KoreaDepartment of Molecular Cell Biology, Sungkyunkwan University (SKKU) School of Medicine, Suwon, South KoreaDepartment of Medical Science, Chungnam National University School of Medicine, Daejeon, South KoreaLaboratory of Endocrinology and Immune System, Chungnam National University School of Medicine, Daejeon, South KoreaDepartment of Molecular Cell Biology, Sungkyunkwan University (SKKU) School of Medicine, Suwon, South KoreaBackgroundPancreatic cancer is one of the most fatal malignancies of the gastrointestinal cancer, with a challenging early diagnosis due to lack of distinctive symptoms and specific biomarkers. The exact etiology of pancreatic cancer is unknown, making the development of reliable biomarkers difficult. The accumulation of patient-derived omics data along with technological advances in artificial intelligence is giving way to a new era in the discovery of suitable biomarkers.MethodsWe performed machine learning (ML)-based modeling using four independent transcriptomic datasets, including GSE16515, GSE62165, GSE71729, and the pancreatic adenocarcinoma (PAC) dataset of the Cancer Genome Atlas. To find candidates for circulating biomarkers, we exported expression profiles of 1,703 genes encoding secretory proteins. Integrating three transcriptomic datasets into either a training or test set, ML-based modeling distinguishing PAC from normal was carried out. Another ML-model classifying long-lived and short-lived patients with PAC was also built to select prognosis-associated features. Finally, circulating level of SCG5 in the plasma was determined from the independent cohort (non-tumor = 25 and pancreatic cancer = 25). We also investigated the impact of SCG5 on adipocyte biology using recombinant protein.ResultsThree distinctive ML-classifiers selected 29-, 64- and 18-featured genes, recognizing the only common gene, SCG5. As per the prediction of ML-models, the SCG5 transcripts was significantly reduced in PAC and decreased further with the progression of the tumor, indicating its potential as a diagnostic as well as prognostic marker for PAC. External validation of SCG5 using plasma samples from patients with PAC confirmed that SCG5 was reduced significantly in patients with PAC when compared to controls. Interestingly, plasma SCG5 levels were correlated with the body mass index and age of donors, implying pancreas-originated SCG5 could regulate energy metabolism systemically. Additionally, analyses using publicly available Genotype-Tissue Expression datasets, including adipose tissue histology and pancreatic SCG5 expression, further validated the association between pancreatic SCG5 expression and the size of subcutaneous adipocytes in humans. However, we could not observe any definite effect of rSCG5 on the cultured adipocyte, in 2D in vitro culture.ConclusionCirculating SCG5, which may be associated with adipopenia, is a promising diagnostic biomarker for PAC.https://www.frontiersin.org/articles/10.3389/fonc.2022.942774/fullpancreatic cancerpancreatic adenocarcinomabiomarkerdiagnosisprognosismachine learning |
| spellingShingle | Yunju Jo Min-Kyung Yeo Tam Dao Jeongho Kwon Hyon‐Seung Yi Hyon‐Seung Yi Dongryeol Ryu Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia Frontiers in Oncology pancreatic cancer pancreatic adenocarcinoma biomarker diagnosis prognosis machine learning |
| title | Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia |
| title_full | Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia |
| title_fullStr | Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia |
| title_full_unstemmed | Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia |
| title_short | Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia |
| title_sort | machine learning featured secretogranin v is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia |
| topic | pancreatic cancer pancreatic adenocarcinoma biomarker diagnosis prognosis machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.942774/full |
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