Long intergenic noncoding RNAs (lincRNAs) have risen to prominence in cancer biology as new biomarkers of disease. Those lincRNAs transcribed from active cis-regulatory elements (enhancers) have provided mechanistic insight into cis-acting regulation, however, in the absence of an enhancer hallmark, computational prediction of cis-acting transcription of lincRNAs remains challenging. Here, we introduce a novel transcriptomic method: a cis-regulatory lincRNA-gene associating metric, termed CisPi. CisPi quantifies the mutual information between lincRNAs and local gene expression regarding their response to perturbation, such as disease risk-dependence. To predict risk-dependent lincRNAs in neuroblastoma, an aggressive pediatric cancer, we advance this scoring scheme to measure lincRNAs that represent the minority of reads in RNA-Seq libraries by a novel side-by-side analytical pipeline.