Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research
Abstract Gene expression profiling is an effective method for identifying predictive and prognostic biomarkers. However, measurements are prone to uncertainty and errors due to various pre-analytical variables. Systematic evaluating effects of these variables on gene expression measurements and rela...
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Nature Portfolio
2025-02-01
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| author | Tian Tian Guie Lai Ming He Xiaofang Liu Yun Luo You Guo Guini Hong Hongdong Li Kai Song Hao Cai |
| author_facet | Tian Tian Guie Lai Ming He Xiaofang Liu Yun Luo You Guo Guini Hong Hongdong Li Kai Song Hao Cai |
| author_sort | Tian Tian |
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| description | Abstract Gene expression profiling is an effective method for identifying predictive and prognostic biomarkers. However, measurements are prone to uncertainty and errors due to various pre-analytical variables. Systematic evaluating effects of these variables on gene expression measurements and relative expression orderings (REOs) of gene pairs, is necessary. A total of 18 datasets were collected, comprising over 800 paired samples. These paired samples were utilized to assess the impact of pre-analytical variables on gene expression measurements and REOs, including sampling methods, tumor sample heterogeneity, fixed time delays, preservation conditions, degradation levels, library preparation kits, amplification kits, RNA quantity, measuring platforms, and laboratory sites at single and multi-variable level. Low-quality samples served as the case group, while paired high-quality samples constituted the control group. In both single and multiple variable analyses, comparing each case sample to paired control sample revealed thousands of genes exhibited a twofold change in expression values. In contrast, on average, 82% and 76% of gene pairs keep consistent REO pattern between paired samples in single-variable and multi-variable analyses, respectively. Notably, the rate steadily increased after excluding gene pairs with the closest expression levels. Statistical analyses shown a higher proportion of differentially expressed genes (DEGs) than that of reversed gene pairs between case and control groups in both single-variable and multi-variable analyses. Furthermore, the proportion of reversal gene pairs among all gene pairs involving DEGs remained below 20% in the majority of comparisons. Our research demonstrates that REOs exhibit higher robustness under the influence of pre-analytical variables. These findings indicate the potential of the REOs-based approach in transcriptomics research and its applicability for biomarker studies. |
| format | Article |
| id | doaj-art-81bafb04cf90411e8f61ca21e66dfa28 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-81bafb04cf90411e8f61ca21e66dfa282025-02-09T12:36:02ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-88756-0Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer researchTian Tian0Guie Lai1Ming He2Xiaofang Liu3Yun Luo4You Guo5Guini Hong6Hongdong Li7Kai Song8Hao Cai9Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical UniversityBreast Disease Comprehesive Center, First Affiliated Hospital of Gannan Medical UniversityMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical UniversityMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical UniversityMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical UniversityMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical UniversitySchool of Medical Information Engineering, Gannan Medical UniversitySchool of Medical Information Engineering, Gannan Medical UniversitySchool of Medical Information Engineering, Gannan Medical UniversityMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical UniversityAbstract Gene expression profiling is an effective method for identifying predictive and prognostic biomarkers. However, measurements are prone to uncertainty and errors due to various pre-analytical variables. Systematic evaluating effects of these variables on gene expression measurements and relative expression orderings (REOs) of gene pairs, is necessary. A total of 18 datasets were collected, comprising over 800 paired samples. These paired samples were utilized to assess the impact of pre-analytical variables on gene expression measurements and REOs, including sampling methods, tumor sample heterogeneity, fixed time delays, preservation conditions, degradation levels, library preparation kits, amplification kits, RNA quantity, measuring platforms, and laboratory sites at single and multi-variable level. Low-quality samples served as the case group, while paired high-quality samples constituted the control group. In both single and multiple variable analyses, comparing each case sample to paired control sample revealed thousands of genes exhibited a twofold change in expression values. In contrast, on average, 82% and 76% of gene pairs keep consistent REO pattern between paired samples in single-variable and multi-variable analyses, respectively. Notably, the rate steadily increased after excluding gene pairs with the closest expression levels. Statistical analyses shown a higher proportion of differentially expressed genes (DEGs) than that of reversed gene pairs between case and control groups in both single-variable and multi-variable analyses. Furthermore, the proportion of reversal gene pairs among all gene pairs involving DEGs remained below 20% in the majority of comparisons. Our research demonstrates that REOs exhibit higher robustness under the influence of pre-analytical variables. These findings indicate the potential of the REOs-based approach in transcriptomics research and its applicability for biomarker studies.https://doi.org/10.1038/s41598-025-88756-0Relative expression orderingsGene expressionPre-analytical variablesBiomarkerRobustnessMeasurement variation |
| spellingShingle | Tian Tian Guie Lai Ming He Xiaofang Liu Yun Luo You Guo Guini Hong Hongdong Li Kai Song Hao Cai Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research Scientific Reports Relative expression orderings Gene expression Pre-analytical variables Biomarker Robustness Measurement variation |
| title | Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research |
| title_full | Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research |
| title_fullStr | Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research |
| title_full_unstemmed | Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research |
| title_short | Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research |
| title_sort | exploring the influence of pre analytical variables on gene expression measurements and relative expression orderings in cancer research |
| topic | Relative expression orderings Gene expression Pre-analytical variables Biomarker Robustness Measurement variation |
| url | https://doi.org/10.1038/s41598-025-88756-0 |
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