High-grade gliomas integrate into neural circuits through functional synapses with neurons, raising the question of which noncoding elements shape synaptogenic gene expression in tumor cells. The regulatory program written across the dark genome, what we call the $\textit{dark regulome}$, is the natural substrate to probe, and sequence foundation models offer a zero-shot route through in-silico mutagenesis (ISM); yet likelihood-based scoring is tautologically coupled to local sequence predictability, leaving the regulatory interpretation underdetermined. Across three architecturally distinct foundation models (Caduceus-Ph, HyenaDNA, Enformer) and 30,448 dark genome elements at 92 glioma-relevant loci, we introduce a residualization-and-permutation diagnostic that separates predictability-driven from regulation-driven RIS variance. A sharp 10kb proximal-regulatory horizon survives every control we apply, but the LM-derived element-class hierarchy does not: a six-feature linear baseline matches Caduceus top-decile membership at AUC $= 0.985$. Cross-architecture decomposition cleanly separates a sequence-predictability layer (the two language models co-rank long well-predicted transposable elements) from a regulatory-output layer (Enformer alone retains residual cCRE-discriminative signal), with literally zero overlap between the two top-100 lists. Conservation, brain cis-eQTL, and STRING-PPI cross-checks then anchor what biology survives: top-100 elements across all three models are $3.3\times$ enriched per model for matching brain eQTLs ($p_\mathrm{emp} < 5\times 10^{-3}$), while a tempting transposable-element regulatory layer and a striking NRXN1+NLGN1 protein-pair convergence both fail proper permutation tests once those tests are constructed. We deliver the diagnostic as a general methodological tool for any ISM-based regulatory study.

翻译：高级胶质瘤通过与神经元的功能性突触整合到神经回路中，这引发了一个问题：哪些非编码元件塑造了肿瘤细胞中的突触形成基因表达。隐藏在暗基因组中的调控程序，即我们所说的“暗调控组”，是探索的自然基质，而序列基础模型通过计算机模拟诱变提供了一条零样本途径；然而，基于似然性的评分在逻辑上与局部序列的可预测性耦合，导致调控解释不确定。针对三个架构不同的基础模型（Caduceus-Ph、HyenaDNA、Enformer）及92个胶质瘤相关基因座上的30,448个暗基因组元件，我们引入了一种残差化和置换诊断方法，用于分离由可预测性驱动和由调控驱动的RIS方差。一个清晰的10kb近端调控边界在所有控制条件下均成立，但LM导出的元件类别层级并不成立：一个六特征线性基线以AUC = 0.985匹配Caduceus最高十分位成员资格。跨架构分解清晰地分离出序列可预测性层（两个语言模型共同对长期可预测的转座元件进行排序）和调控输出层（仅Enformer保留残留的cCRE区分信号），两个前100列表之间完全没有重叠。保守性、脑cis-eQTL和STRING-PPI交叉验证随后锚定了生物学存活的部分：所有三个模型的前100元件在匹配脑eQTL方面每个模型富集了3.3倍（p_emp < 5×10^{-3}），而一个诱人的转座元件调控层和显著的NRXN1+NLGN1蛋白对汇聚，在构建适当的置换测试后均未能通过这些测试。我们提供了这一诊断方法，作为任何基于ISM的调控研究的一般方法论工具。