COMBAI computational biology and artificial intelligence

Deadliest cancer gene database

The database was based on our recent study entitled

"Noncoding RNAs Serve as the Deadliest Universal Regulators of all Cancers"

by Anyou Wang and Rong Hai

All cancerous genome activation share a certain commonality controlled by regulators universal for all cancers, but such universal cancerous regulators remain largely unknown. Here we develop algorithms to reveal the deadliest regulators universal for all cancers from big data, including all RNAseqs and clinical data available from The Cancer Genome Atlas (TCGA). Generally, noncoding RNAs primarily serve as the deadliest inducers and suppressors for all types of cancers. Especially, pseudogenes dominate the primary cancer inducers, but one antisense RNA, RP11−335K5.2, serves as the most universal cancerous inducer, independent on any cancer types and conditions. Therefore, noncoding RNAs, instead of proteins as conventionally thought, primarily drive tumorigenesis, which establishes a novel universal foundation for future cancer research and therapy.
A turnover breakthrough was made by these two studies ( and regarding cancer mechanism research: pseudogenes are the most important oncogenes and cancer drivers for all types of cancer, replacing current protein theory. Current researches mostly focus on proteins, and pseudogenes and non coding RNAs are thought as supplemental. These current theories are derived from individual experiments and specific cancer types, and are biased. Just looks like blind men and elephant. We reached this above conclusion from massive data including all conditions and cancer types, and got the picture of entire elephant. Every day, millions dollars was put into the cancer researches world-widely, but the mechanism is still unclear, and cancer is still an uncurable disease. This research conclusion may make cancer eventually curable, and have huge applications and huge market values