  Cancer is one of the leading causes of human death. Many efforts have made to
understand its mechanism and have further identified many proteins and DNA
sequence variations as suspected targets for therapy. However, drugs targeting
these targets have low success rates, suggesting the basic mechanism still
remains unclear. Here, we develop a computational software combining Cox
proportional-hazards model and stability-selection to unearth an overlooked,
yet the most important cancer drivers hidden in massive data from The Cancer
Genome Atlas (TCGA), including 11,574 RNAseq samples and clinic data.
Generally, noncoding RNAs primarily regulate cancer deaths and work as the
deadliest cancer inducers and repressors, in contrast to proteins as
conventionally thought. Especially, processed-pseudogenes serve as the primary
cancer inducers, while lincRNA and antisense RNAs dominate the repressors.
Strikingly, noncoding RNAs serves as the universal strongest regulators for all
cancer types although personal clinic variables such as alcohol and smoking
significantly alter cancer genome. Furthermore, noncoding RNAs also work as
central hubs in cancer regulatory network and as biomarkers to discriminate
cancer types. Therefore, noncoding RNAs overall serve as the deadliest cancer
regulators, which refreshes the basic concept of cancer mechanism and builds a
novel basis for cancer research and therapy. Biological functions of
pseudogenes have rarely been recognized. Here we reveal them as the most
important cancer drivers for all cancer types from big data, breaking a wall to
explore their biological potentials.
