Fukui, T. | Search bioRxiv

bioRxiv2024

Discovering cancer stem-like cells using Spatial transcriptomic analysis: Nuclear factor I X as a novel therapeutic target for gastric cancer

BackgroundGastric cancer (GC) is characterized by significant intratumoral heterogeneity and stem cells presenting as promising therapeutic targets. Despite advancements in spatial transcriptome analysis, unexplored targets for addressing cancer stemness remain unknown. This study aims to identify Nuclear Factor IX (NFIX) as a critical regulator of cancer stemness in GC and evaluate its clinicopathological significance and function. MethodsSpatial transcriptome analysis was conducted on GC. NFIX expressions correlation with clinicopathological factors and prognosis was assessed through immunostaining in 127 GC cases. Functional analyses in cancer cell lines validated these findings. ResultsSpatial transcriptome analysis stratified GC tissues based on genetic profiles, pinpointing CSC-like cells and further refined the classification to identify and highlight the NFIXs significance, validated by Monocle 3 and CytoTRACE analyses. Knockdown experiments in cancer cell lines demonstrated the involvement of NFIX in cancer cell proliferation and kinase activity. ConclusionsThis study underscores spatial transcriptome analysiss role in refining GC tissue classification and identifying therapeutic targets, highlighting NFIX as pivotal. NFIX expression correlates with poor prognosis and drives GC progression, suggesting its potential as a novel therapeutic target for personalized GC therapies.

pathology↗

bioRxiv2024

Ribonanza: deep learning of RNA structure through dual crowdsourcing

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.

biophysics↗