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Received — 2 April 2026 ⏭

Omics in Hepatocellular
Curcumol Induces G1 Phase Arrest in SK-Hep-1 Cells by Targeting SKP2-Mediated p27 Degradation Yizhuang Yang · Riqiu Zhang · Tong Dou · Zhangchi Liu · Rui Ai · Yue Zhao · Zhi Cui · Xu Chen · Juan Wang
Molecules. 2026 Mar 16;31(6):997. doi: 10.3390/molecules31060997.ABSTRACTCONTEXT: S-phase kinase-associated protein 2 (SKP2) is an oncogene and cell cycle regulator that mediates the ubiquitination of cell cycle regulators. Curcumol, a sesquiterpene natural product, has been reported to regulate SKP2-mediated ubiquitination degradation to overcome drug resistance in cancer cells. However, whether the cell cycle arrest effect of curcumol is related to SKP2's function in cancer cells and its mecha
28 March 2026 at 18:00

Curcumol Induces G1 Phase Arrest in SK-Hep-1 Cells by Targeting SKP2-Mediated p27 Degradation

Omics in Hepatocellular

By: Yizhuang Yang · Riqiu Zhang · Tong Dou · Zhangchi Liu · Rui Ai · Yue Zhao · Zhi Cui · Xu Chen · Juan Wang

28 March 2026 at 18:00

Molecules. 2026 Mar 16;31(6):997. doi: 10.3390/molecules31060997.

ABSTRACT

CONTEXT: S-phase kinase-associated protein 2 (SKP2) is an oncogene and cell cycle regulator that mediates the ubiquitination of cell cycle regulators. Curcumol, a sesquiterpene natural product, has been reported to regulate SKP2-mediated ubiquitination degradation to overcome drug resistance in cancer cells. However, whether the cell cycle arrest effect of curcumol is related to SKP2's function in cancer cells and its mechanisms are still unclear.

OBJECTIVE: To investigate the role of SKP2 in curcumol-induced cell cycle arrest and its underlying mechanisms.

MATERIALS AND METHODS: Transcriptomic and proteomic analyses were used to screen the ubiquitination-related factors in curcumol treated hepatocellular carcinoma cells. Lentiviral overexpression, co-immunoprecipitation assays, ubiquitination analysis, and cell-line-derived xenograft (CDX) models were used to dissect the role and mechanisms of the identified ubiquitination-related factor in the cell cycle arrest effect of curcucmol.

RESULTS: Curcumol modulated the expression of CDK4, CDK6, Cyclin D1, p27 and SKP2. SKP2 was one candidate target of curcumol selected by multi-omics. Overexpressed SKP2 partially reversed curcumol-induced growth inhibition and G1-phase arrest. The increased expression of p27 induced by curcumol was attenuated by overexpressed SKP2. Curcumol impaired the interaction between SKP2 and p27, and led to the ubiquitination and degradation of p27. In vivo, curcumol effectively reduced tumor growth, and its antitumor effect was significantly mitigated by SKP2 overexpression.

DISCUSSION AND CONCLUSIONS: Curcumol reduced SKP2 expression, weakened the interaction between SKP2 and p27, inhibited degradation of p27, and then induced G1 phase cell-cycle arrest in SK-Hep-1 cells.

PMID:41900096 | PMC:PMC13029316 | DOI:10.3390/molecules31060997

Received — 5 March 2026 ⏭

cs.AI, q-bio.NC updates on arXiv.org
Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs Wuyue Zhang · Chongdong Huang · Chunbo You · Cheng Gu · Fengjuan Wang · Mou Sun
arXiv:2603.02731v1 Announce Type: cross Abstract: Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4 or NVFP4 support. In this work, we present a training recipe that enables MXFP4 efficiency for MoE models on Hopper architectures without native 4-bit computation support. A central challenge is to integrate FP4 into an existing BF16/FP8 hybrid
4 March 2026 at 13:00

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

cs.AI, q-bio.NC updates on arXiv.org

By: Wuyue Zhang · Chongdong Huang · Chunbo You · Cheng Gu · Fengjuan Wang · Mou Sun

4 March 2026 at 13:00

arXiv:2603.02731v1 Announce Type: cross Abstract: Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4 or NVFP4 support. In this work, we present a training recipe that enables MXFP4 efficiency for MoE models on Hopper architectures without native 4-bit computation support. A central challenge is to integrate FP4 into an existing BF16/FP8 hybrid training pipeline without incurring costly precision round-trips (e.g., FP4 $\leftrightarrow$ BF16 $\leftrightarrow$ FP8). We address this challenge by introducing direct FP8-to-FP4 quantization and de-quantization, together with scaling-aware FP4 row-wise to column-wise conversion, enabling FP4 activations and expert-parallel communication with minimal overhead. Core MoE computations are executed in FP8, while activations and expert-parallel communication are compressed using MXFP4, achieving substantial memory and bandwidth savings without degrading convergence. At the 671B parameter scale, our method achieves end-to-end training performance comparable to strong FP8 baselines, while reducing peak activation memory by 14.8\% (11.8 GB) and improving training throughput by 12.5\%, from 1157 to 1302 tokens per GPU per second. These results show that FP4 efficiency can be practically realized for large-scale MoE training through careful software-hardware co-design, even without native FP4 Tensor Core support.