ZNF768: controlling cellular senescence and proliferation with ten fingers.

We recently identified Zinc-finger protein 768 (ZNF768) as a novel transcription factor controlling cell fate decision downstream of Rat sarcoma virus (RAS). We showed that ZNF768 depletion impairs cell cycle progression and triggers cellular senescence, while its overexpression allows cells to bypass oncogene-induced senescence. Elevated ZNF768 levels is common in tumors, suggesting that ZNF768 may help to escape cellular senescence, sustain proliferation and promote malignant transformation.

ZNF768 Expression Associates with High Proliferative Clinicopathological Features in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and a leading cause of cancer-related deaths worldwide. Despite important recent advances, the prognosis for LUAD patients is still unfavourable, with a 5 year-survival rate close to 15%. Improving the characterization of lung tumors is important to develop alternative options for the diagnosis and the treatment of this disease.

ZNF768 links oncogenic RAS to cellular senescence.

RAS proteins are GTPases that lie upstream of a signaling network impacting cell fate determination. How cells integrate RAS activity to balance proliferation and cellular senescence is still incompletely characterized. Here, we identify ZNF768 as a phosphoprotein destabilized upon RAS activation.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.