GABARAP, a microtuble-associated protein, is identified to interact with GABAA receptor. Anchoring of the GABAA receptor to GABARAP helps to cluster the receptor at the synaptic termini and to mediate fast synaptic transmission. GABARAP may mediate interaction of gephyrin with the GABAA receptor to stabilize clusters by forming multimeric structures. Furthermore, GABARAP and gephyrin may play more of a role in receptor sorting and transport to the cell surface than in anchoring to the cytoplasm, because at inhibitory synapses GABARAP appears to associate with transport vesicles rather than the cell surface. The association of GABARAP with NSF (N-ethylmaleimide sensitive factor), a protein involved in intracellular vesicle transport, supports this hypothesis. We cloned cDNA encoding full-length human GABARAP by nested PCR and inserted it into eukaryon expression vector pcDNA6HA and GST fusion protein expression vector pGEX4T2. The recombinant plasmid pGEX4T2-hGABARAP was transformed into E. coli BL21, from which GST-hGABARAP fusion protein was purified after IPTG induction by affinity chromatography with glutathione Sepharose-4B column. The antiserum against GABARAP was generated by immunizing rabbits with the purified GST-hGABARAP and was purified with GST-hGABARAP coupled NHS-activated Sepherose 4 column. The purified polyclonal antibody was effective for Western blotting and immunostaining. The hGABARAP was located both in the cytoplasm and nucleus with an abundant distribution around the peripheral nucleus.
Adaptor Proteins, Signal Transducing ; biosynthesis ; genetics ; immunology ; Animals ; Antibodies ; blood ; immunology ; Antibodies, Monoclonal ; biosynthesis ; genetics ; immunology ; Cloning, Molecular ; Escherichia coli ; genetics ; metabolism ; Glutathione Transferase ; biosynthesis ; genetics ; Humans ; Immunization ; Microtubule-Associated Proteins ; biosynthesis ; genetics ; immunology ; Rabbits ; Recombinant Fusion Proteins ; biosynthesis ; genetics ; immunology

Non-small cell lung cancer is one of the cancers with the highest incidence and mortality rate in the world, and precise prognostic models can guide clinical treatment plans. With the continuous upgrading of computer technology, deep learning as a breakthrough technology of artificial intelligence has shown good performance and great potential in the application of non-small cell lung cancer prognosis model. The research on the application of deep learning in survival and recurrence prediction, efficacy prediction, distant metastasis prediction, and complication prediction of non-small cell lung cancer has made some progress, and it shows a trend of multi-omics and multi-modal joint, but there are still shortcomings, which should be further explored in the future to strengthen model verification and solve practical problems in clinical practice.