Assessing Long-Term Stored Tissues for Multi-Omics Data Quality and Proteogenomics Suitability

As research into cancer biology progresses, multiomics analyses have become essential for unraveling its molecular complexities. However, sample availability remains a challenge due to factors such as collection procedures and long-term storage effects. Archived samples present an opportunity to expand multiomics studies, but concerns persist regarding storage duration's impact on data reliability. This study examines the genomic, transcriptomic, and proteomic profiles of samples stored for over a decade. Transcriptomic analysis revealed a decline in read counts for protein-coding genes but preserved core gene expression patterns. Proteomic measurements remained stable, with minimal changes in post-translational modifications. While phosphorylation and acetylation rates were largely unaffected, a slight increase in modification frequencies was observed. Housekeeping genes and proteins exhibited consistent expression across samples, yet proteomic differences between the tumor and normal tissues were distinct. Despite technical variations in transcriptomic data, essential transcription factors and kinases retained functionality. These findings underscore the viability of archived samples for multiomics research, enabling broader investigations into cancer biology and providing insights into molecular mechanisms. By leveraging archived specimens, researchers can overcome sample limitations and advance precision oncology efforts, ultimately deepening our understanding of cancer at the systems level.

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Publication Details

Journal
Journal of Proteome Research
Published
2025-08-10
DOI
https://doi.org/10.1021/acs.jproteome.5c00289
Citations
1
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
0.81

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article

Assessing Long-Term Stored Tissues for Multi-Omics Data Quality and Proteogenomics Suitability

Jae-Won Oh, Kwoneel Kim, Hee Sang Hwang, Minsuh Kim et al.
1 citations
Journal of Proteome Research
Gene expression and cancer classification
0.81
article

Assessing Long-Term Stored Tissues for Multi-Omics Data Quality and Proteogenomics Suitability

Jae-Won Oh, Kwoneel Kim, Hee Sang Hwang, Minsuh Kim, Joon‐Yong An, Se Jin Jang, Kyu Jin Song, Kyunggon Kim, Yong Jin Heo, Min‐Sik Kim, Kyung-Cho Cho, Eunok Paek, Seungjin Na, Kwang Pyo Kim, Chanwoong Hwa, Seunghyuk Choi, Yeji Do, Dae Ho Kim
article en
1 citations

Abstract

As research into cancer biology progresses, multiomics analyses have become essential for unraveling its molecular complexities. However, sample availability remains a challenge due to factors such as collection procedures and long-term storage effects. Archived samples present an opportunity to expand multiomics studies, but concerns persist regarding storage duration's impact on data reliability. This study examines the genomic, transcriptomic, and proteomic profiles of samples stored for over a decade. Transcriptomic analysis revealed a decline in read counts for protein-coding genes but preserved core gene expression patterns. Proteomic measurements remained stable, with minimal changes in post-translational modifications. While phosphorylation and acetylation rates were largely unaffected, a slight increase in modification frequencies was observed. Housekeeping genes and proteins exhibited consistent expression across samples, yet proteomic differences between the tumor and normal tissues were distinct. Despite technical variations in transcriptomic data, essential transcription factors and kinases retained functionality. These findings underscore the viability of archived samples for multiomics research, enabling broader investigations into cancer biology and providing insights into molecular mechanisms. By leveraging archived specimens, researchers can overcome sample limitations and advance precision oncology efforts, ultimately deepening our understanding of cancer at the systems level.

Journal of Proteome ResearchVol. 24(9)
Ulsan College (KR), Daegu Gyeongbuk Institute of Science and Technology (KR), Korea University (KR), Asan Medical Center (KR), Kyung Hee University (KR), University of Ulsan (KR), Integrated Oncology (United States) (US), Digital Science (United States) (US), Kyung Hee University Medical Center (KR), Fundação de Amparo à Pesquisa do Estado do Amazonas (BR), BioSystem (Germany) (DE), Korea Basic Science Institute (KR), Convergence (US), Institute of Natural Science (KP), Hanyang University (KR), Induk University (KR)
National Research Foundation of Korea
Openalex Percentile: Top 25%
Gene expression and cancer classification
0.81
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