Tissue gene expression analysis approach in a context of high technical and biological heterogeneity
Abstract Background Immune expression profiling in colorectal lesions may provide insights into the origins of antitumor immunity and senescence. Optimal approaches for analyzing samples with lower quality RNA from molecularly diverse lesions are lacking. Therefore, we developed a NanoString nCounte...
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BMC
2025-07-01
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| Online Access: | https://doi.org/10.1186/s13104-025-07383-0 |
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| author | Evan Bagley Souvik Seal Lauren R. Fanning Jean-Sebastien Anoma Tami Crawford Benjamin G. Vincent Elizabeth L. Barry Martha J. Shrubsole Erin Kirk John A. Baron Dale C. Snover David N. Lewin Todd A. Mackenzie Xiaohua Gao Melissa A. Troester Alexander V. Alekseyenko Kristin Wallace |
| author_facet | Evan Bagley Souvik Seal Lauren R. Fanning Jean-Sebastien Anoma Tami Crawford Benjamin G. Vincent Elizabeth L. Barry Martha J. Shrubsole Erin Kirk John A. Baron Dale C. Snover David N. Lewin Todd A. Mackenzie Xiaohua Gao Melissa A. Troester Alexander V. Alekseyenko Kristin Wallace |
| author_sort | Evan Bagley |
| collection | DOAJ |
| description | Abstract Background Immune expression profiling in colorectal lesions may provide insights into the origins of antitumor immunity and senescence. Optimal approaches for analyzing samples with lower quality RNA from molecularly diverse lesions are lacking. Therefore, we developed a NanoString nCounter-based approach for quality control (QC), normalization, and differential expression (DE) analysis, optimized for FFPE samples in contexts of high biologic heterogeneity. Methods The approach incorporates a colon specific positive control gene set (11 genes) to minimize sample exclusions. We evaluated three normalization methods Removal of Unwanted Variation (RUVg), NanoStringDiff (NSDiff), and nSolver using a 277 gene immune panel to compare 100 samples, including sessile serrated lesions (SSLs) (n = 25), tubulovillous and villous adenomas (TVs) (n = 27), and tubular adenomas (TAs) (n = 48) We assessed Type I error rates, computational efficiency, and gene significance via FDR-corrected q-values. Results Incorporating the colon-specific QC set reduced sample exclusions by 63% compared to standard methods (13 vs. 35 sample exclusions). All three normalization approaches identified DE genes between SSLs and TAs (e.g., TFF1, MUC5AC, MUC6). For TVs vs. TAs, only RUVg and NSDiff detected significant DE genes, revealing wide-spread under-expression of innate and adaptive genes. While NSDiff labeled twice as many significant genes as RUVg, suggesting greater sensitivity, it also exhibited higher Type I error rates and increased computational demand. Conclusions RUVg achieved a balance between computational efficiency and low Type I error, while NSDiff was more sensitive but computationally demanding and exhibited higher Type I error. Our approach provides a robust framework for profiling immune genes in heterogeneous lesions. |
| format | Article |
| id | doaj-art-3d2e7e2e4501476998106ad848c243f7 |
| institution | Kabale University |
| issn | 1756-0500 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | BMC Research Notes |
| spelling | doaj-art-3d2e7e2e4501476998106ad848c243f72025-08-20T03:45:40ZengBMCBMC Research Notes1756-05002025-07-0118111310.1186/s13104-025-07383-0Tissue gene expression analysis approach in a context of high technical and biological heterogeneityEvan Bagley0Souvik Seal1Lauren R. Fanning2Jean-Sebastien Anoma3Tami Crawford4Benjamin G. Vincent5Elizabeth L. Barry6Martha J. Shrubsole7Erin Kirk8John A. Baron9Dale C. Snover10David N. Lewin11Todd A. Mackenzie12Xiaohua Gao13Melissa A. Troester14Alexander V. Alekseyenko15Kristin Wallace16Hollings Cancer Center, Medical University of South CarolinaHollings Cancer Center, Medical University of South CarolinaDepartment of Public Health Sciences, Medical University of South CarolinaHollings Cancer Center, Medical University of South CarolinaBiomedical Informatics Center, Medical University of South CarolinaDepartment of Cell Biology & Physiology, University of North Carolina at Chapel HillDepartment of Epidemiology, Geisel School of Medicine at DartmouthDepartment of Medicine, Vanderbilt-Ingram Cancer CenterDepartment of Epidemiology, Gillings School of Public HealthDepartment of Epidemiology, University of North Carolina School of MedicineDepartment of Pathology, Fairview Southdale HospitalDepartment of Pathology and Laboratory Medicine, Medical University of South CarolinaDepartment of Biomedical Data Science, Geisel School of Medicine at DartmouthLineberger Comprehensive Cancer Center, University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, University of North Carolina at Chapel HillHollings Cancer Center, Medical University of South CarolinaHollings Cancer Center, Medical University of South CarolinaAbstract Background Immune expression profiling in colorectal lesions may provide insights into the origins of antitumor immunity and senescence. Optimal approaches for analyzing samples with lower quality RNA from molecularly diverse lesions are lacking. Therefore, we developed a NanoString nCounter-based approach for quality control (QC), normalization, and differential expression (DE) analysis, optimized for FFPE samples in contexts of high biologic heterogeneity. Methods The approach incorporates a colon specific positive control gene set (11 genes) to minimize sample exclusions. We evaluated three normalization methods Removal of Unwanted Variation (RUVg), NanoStringDiff (NSDiff), and nSolver using a 277 gene immune panel to compare 100 samples, including sessile serrated lesions (SSLs) (n = 25), tubulovillous and villous adenomas (TVs) (n = 27), and tubular adenomas (TAs) (n = 48) We assessed Type I error rates, computational efficiency, and gene significance via FDR-corrected q-values. Results Incorporating the colon-specific QC set reduced sample exclusions by 63% compared to standard methods (13 vs. 35 sample exclusions). All three normalization approaches identified DE genes between SSLs and TAs (e.g., TFF1, MUC5AC, MUC6). For TVs vs. TAs, only RUVg and NSDiff detected significant DE genes, revealing wide-spread under-expression of innate and adaptive genes. While NSDiff labeled twice as many significant genes as RUVg, suggesting greater sensitivity, it also exhibited higher Type I error rates and increased computational demand. Conclusions RUVg achieved a balance between computational efficiency and low Type I error, while NSDiff was more sensitive but computationally demanding and exhibited higher Type I error. Our approach provides a robust framework for profiling immune genes in heterogeneous lesions.https://doi.org/10.1186/s13104-025-07383-0Immune expressionNanoStringSessile serrated lesionRUV |
| spellingShingle | Evan Bagley Souvik Seal Lauren R. Fanning Jean-Sebastien Anoma Tami Crawford Benjamin G. Vincent Elizabeth L. Barry Martha J. Shrubsole Erin Kirk John A. Baron Dale C. Snover David N. Lewin Todd A. Mackenzie Xiaohua Gao Melissa A. Troester Alexander V. Alekseyenko Kristin Wallace Tissue gene expression analysis approach in a context of high technical and biological heterogeneity BMC Research Notes Immune expression NanoString Sessile serrated lesion RUV |
| title | Tissue gene expression analysis approach in a context of high technical and biological heterogeneity |
| title_full | Tissue gene expression analysis approach in a context of high technical and biological heterogeneity |
| title_fullStr | Tissue gene expression analysis approach in a context of high technical and biological heterogeneity |
| title_full_unstemmed | Tissue gene expression analysis approach in a context of high technical and biological heterogeneity |
| title_short | Tissue gene expression analysis approach in a context of high technical and biological heterogeneity |
| title_sort | tissue gene expression analysis approach in a context of high technical and biological heterogeneity |
| topic | Immune expression NanoString Sessile serrated lesion RUV |
| url | https://doi.org/10.1186/s13104-025-07383-0 |
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