Bagley Evan, Seal Souvik, Fanning Lauren R, Anoma Jean-Sebastien, Crawford Tami, Vincent Benjamin G, Barry Elizabeth L, Shrubsole Martha J, Kirk Erin, Baron John A, Snover Dale C, Lewin David N, Mackenzie Todd A, Gao Xiaohua, Troester Melissa A, Alekseyenko Alexander V, Wallace Kristin
Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, USA.
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
BMC Res Notes. 2025 Jul 22;18(1):315. doi: 10.1186/s13104-025-07383-0.
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.
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.
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.
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.
结直肠病变中的免疫表达谱分析可能有助于深入了解抗肿瘤免疫和衰老的起源。目前缺乏针对来自分子异质性病变的低质量RNA样本的最佳分析方法。因此，我们开发了一种基于NanoString nCounter的方法，用于质量控制（QC）、标准化和差异表达（DE）分析，该方法针对高生物学异质性背景下的福尔马林固定石蜡包埋（FFPE）样本进行了优化。
该方法纳入了一组结肠特异性阳性对照基因（11个基因），以尽量减少样本排除。我们使用一个包含277个基因的免疫panel评估了三种标准化方法——去除不必要变异（RUVg）、NanoStringDiff（NSDiff）和nSolver，以比较100个样本，包括无蒂锯齿状病变（SSLs）（n = 25）、管状绒毛状和绒毛状腺瘤（TVs）（n = 27）以及管状腺瘤（TAs）（n = 48）。我们通过错误发现率（FDR）校正的q值评估I型错误率、计算效率和基因显著性。
与标准方法相比，纳入结肠特异性QC集使样本排除率降低了63%（排除样本数从35个降至13个）。所有三种标准化方法均鉴定出SSLs和TAs之间的差异表达基因（例如，TFF1、MUC5AC、MUC6）。对于TVs与TAs，只有RUVg和NSDiff检测到显著的差异表达基因，揭示了先天性和适应性基因的广泛低表达。虽然NSDiff标记的显著基因数量是RUVg的两倍，表明其灵敏度更高，但它也表现出更高的I型错误率和更高的计算需求。
RUVg在计算效率和低I型错误之间取得了平衡，而NSDiff更敏感，但计算要求高且I型错误率更高。我们的方法为分析异质性病变中的免疫基因提供了一个强大的框架。
