Three human regulatory networks

(1)lncRNA network

Human endogenous un_annotated lncRNA network, which was derived from our paper entitled "Distinctive functional regime of endogenous lncRNAs in dark regions of human genome" as referred below. Please click the link for detail lncRNA network

(2)Normal network

(3)Cancer network

(2) and (3) normal and cancer networks were based on our recent study entitled

"Big-data analysis unearths the general regulatory regime in normal human genome and cancer"

by Anyou Wang and Rong Hai

All cancers share a commonality in genome activation regulated by a systems endogenous network distinct from normal, but such a network remains elusive. Here, we unearth a systems regulatory network endogenous for all cancers and normal human respectively from massive data including all RNAseq data available from SRA and TCGA, and reveal systems endogenous rulers governing cancerous and normal regulatory realm. Noncoding RNAs, especially processed-pseudogenes, serve as cancerous network centrality and the strongest systems inducers. They also dominate cancerous network modules. In addition, these noncoding RNAs primarily regulate their targets via cis-regulations. Thus noncoding RNAs endogenously cis-regulate the cancerous realm. In contrast, proteins endogenously trans-regulate the normal realm, which is centrally controlled by ribosomal proteins. Our finding establishes a systems picture of endogenous mechanism overlooking the cancerous and normal realm. Noncoding RNAs ruling the overall cancer realm refreshes the conventional concept, in which proteins serve as key rulers.
A turnover breakthrough was made by these two studies (combai.org/network and combai.org/database) 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.

This study generated two regulatory networks, normal human network and cancer network, which can be searched by listed below

Normal network Cancer network

References

Anyou Wang and Rong Hai .2019. Big-data analysis unearths the general regulatory regime in normal human genome and cancer.Full txt and figures in pdf Wang, A. & Hai, R. FINET: Fast Inferring NETwork. BMC Res Notes 13, 521 (2020). https://doi.org/10.1186/s13104-020-05371-0 Anyou Wang and Rong Hai .2020. Distinctive functional regime of endogenous lncRNAs in dark regions of human genome.bioRxiv 2020.12.06.413880