With the advancement in big data and artificial intelligence technologies, the extensive application of omics technologies in traditional Chinese medicine(TCM) research has generated large experimental datasets, enabling the exploration of cross-scale correlations among massive data and thereby resulting in the shift toward a data-intensive research paradigm. The emerging approach of multi-omics data fusion analysis, emphasizing technical and computational tools, presents a potential breakthrough in this field. The holistic perspective of TCM aligns with the concept of multi-omics data fusion, yet the data types encountered exhibit high dimensionality with small sample sizes, necessitating data processing techniques such as dimensionality reduction. The current challenge lies in selecting suitable analytical methods for these data to enhance the systematic understanding of physiological functions and disease diagnosis/treatment processes. This paper explores the theories and frameworks of multi-omics data fusion, analyzes methods for fusing high-dimensional, small-sample multi-omics data in TCM, and aims to provide insights for advancing TCM research.
Abstract Selective inhibition of the transcription elongation factor (P‐TEFb) complex represents a promising approach in cancer therapy, yet CDK9 inhibitors (CDK9i) are currently limited primarily to certain hematological malignancies. Herein, while initial responses to CDK9‐targeted therapies are observed in vitro across various KRAS‐mutant cancer types, their efficacy is far from satisfactory in nude mouse xenograft models. Mechanistically, CDK9 inhibition leads to compensatory activation of ERK‐MYC signaling, accompanied by the recovery of proto‐oncogenes, upregulation of immediate early genes (IEGs), stimulation of the complement C1r‐C3‐C3a cascade, and induction of tumor immunosuppression. The “paradoxical” regulation of PP2Ac activity involving the CDK9/Src interplay contributes to ERK phosphorylation and pause‐release of RNA polymerase II (Pol II). Co‐targeting of CDK9 and KRAS/MAPK signaling pathways eliminates ERK‐MYC activation and prevents feedback activation mediated by receptor tyrosine kinases, leading to more effective control of KRAS‐mutant cancers and overcoming KRASi resistance. Moreover, modulating the tumor microenvironment (TME) by complement system intervention enhances the response to CDK9i and potently suppresses tumor growth. Overall, the preclinical investigations establish a robust framework for conducting clinical trials employing KRASi/SOS1i/MEKi or immunomodifiers in combination with CDK9i to simultaneously target cancer cells and their crosstalk with the TME, thereby yielding improved responses in KRAS‐mutant patients.
Despite initial success with FLT3 inhibitors (FLT3is), outcomes for FLT3-ITD acute myeloid leukemia (AML) patients remain unsatisfactory, underscoring the need for more effective treatment options. Epigenetic modifications, such as histone acetylation, contribute to AML's onset and persistence, advocating the potential for epigenetic therapies. However, the poor specificity of pan-histone deacetylase inhibitors (HDACis) leads to undesirable adverse effects, prompting the need for isoform-specific HDACis. This study aims to explore the antileukemic activities and mechanisms of IHCH9033, a novel class I HDACi, alone or combined with FLT3i in FLT3-ITD AML. The viability of AML cell lines and primary AML cells treated with HDACis alone or in combination with FLT3i was detected by MTT or CCK8 assay. Flow cytometry was utilized to examine cell apoptosis, cell cycle progression and ROS production. RNA sequencing analysis, RT-qPCR, western blotting, and co-immunoprecipitation assays were employed to elucidate the molecule mechanisms. The in vivo anti-leukemia efficacy was tested in xenografted mice models derived from FLT3-ITD cell lines and primary AML patients. Here, we identified IHCH9033, a novel selective class I HDACi, which exhibited an increased antitumor effect in FLT3-ITD AML through effectively eliminating leukemia burden and overcoming resistance to FLT3i. Mechanically, IHCH9033 selectively inhibited DNA repair in FLT3-ITD AML cells, leading to the accumulation of DNA damage that eventually resulted in cell cycle arrest and apoptosis. Additionally, IHCH9033 induced HSP90 acetylation, FLT3 ubiquitination, and proteasomal degradation of FLT3, thereby inhibiting FLT3 downstream signaling. Notably, IHCH9033 maintained its potency in both FLT3i-resistant AML cell lines and primary-resistant patient samples, and exerted strong synergy with the FLT3i quizartinib, leading to tumor regression in FLT3-ITD/TKD AML xenografts. In patient-derived xenografts, the treatment with IHCH9033, both alone and in combination, led to nearly complete eradication of the AML burden, without significant adverse effects. Our study shows that IHCH9033, a novel class I HDACi with a desirable pharmacological profile, is a promising drug candidate for FLT3-ITD AML, and suggests a strategy of combining class I HDACis and FLT3is in AML clinical trials to increase efficacy and overcome resistance, thus potentially providing a curative treatment option.
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In response to the issue that particle swarm optimization algorithms tend to fall into local optima when dealing with multi-objective optimization tasks, a multi-objective optimization algorithm based on particle swarm is proposed. This algorithm is based on the relationship between the position vectors of particles, changing the selection and movement strategies of particles to find the true Pareto front. Firstly, two additional position vectors are added around the iterating particles to enhance their search capability; then, a non-dominated vector archive is established to record the non-dominated solutions of the iterating particles and the additional position vectors, increasing particle diversity. Finally, additional position vectors with high fitness are selected to produce a shift in the iterating particle's position, accelerating particle convergence. Comparing this algorithm with dMOPSO, SMPSO, NMPSO, and MOPSOCD algorithms, simulation experiments show that the proposed PVSPSO algorithm has stronger optimization ability.
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