SF3B3-regulated mTOR alternative splicing promotes colorectal cancer progression and metastasis.

BACKGROUND: Aberrant alternative splicing (AS) is a pervasive event during colorectal cancer (CRC) development. SF3B3 is a splicing factor component of U2 small nuclear ribonucleoproteins which are crucial for early stages of spliceosome assembly. The role of SF3B3 in CRC remains unknown. METHODS: SF3B3 expression in human CRCs was analyzed using publicly available CRC datasets, immunohistochemistry, qRT-PCR, and western blot. RNA-seq, RNA immunoprecipitation, and lipidomics were performed in SF3B3 knockdown or overexpressing CRC cell lines. CRC cell xenografts, patient-derived xenografts, patient-derived organoids, and orthotopic metastasis mouse models were utilized to determine the in vivo role of SF3B3 in CRC progression and metastasis. RESULTS: SF3B3 was upregulated in CRC samples and associated with poor survival. Inhibition of SF3B3 by RNA silencing suppressed the proliferation and metastasis of CRC cells in vitro and in vivo, characterized by mitochondria injury, increased reactive oxygen species (ROS), and apoptosis. Mechanistically, silencing of SF3B3 increased mTOR exon-skipped splicing, leading to the suppression of lipogenesis via mTOR-SREBF1-FASN signaling. The combination of SF3B3 shRNAs and mTOR inhibitors showed synergistic antitumor activity in patient-derived CRC organoids and xenografts. Importantly, we identified SF3B3 as a critical regulator of mTOR splicing and autophagy in multiple cancers. CONCLUSIONS: Our findings revealed that SF3B3 promoted CRC progression and metastasis by regulating mTOR alternative splicing and SREBF1-FASN-mediated lipogenesis, providing strong evidence to support SF3B3 as a druggable target for CRC therapy.

Serological Biomarker-Based Machine Learning Models for Predicting the Relapse of Ulcerative Colitis.

Purpose: To explore whether machine learning models using serological markers can predict the relapse of Ulcerative colitis (UC). Patients and Methods: This clinical cohort study included 292 UC patients, and serological markers were obtained when patients were discharged from the hospital. Subsequently, four machine learning models including the random forest (RF) model, the logistic regression model, the decision tree, and the neural network were compared to predict the relapse of UC. A nomogram was constructed, and the performance of these models was evaluated by accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results: Based on the patients' characteristics and serological markers, we selected the relevant variables associated with relapse and developed a LR model. The novel model including gender, white blood cell count, percentage of leukomonocyte, percentage of monocyte, absolute value of neutrophilic granulocyte, and erythrocyte sedimentation rate was established for predicting the relapse. In addition, the average AUC of the four machine learning models was 0.828, of which the RF model was the best. The AUC of the test group was 0.889, the accuracy was 76.4%, the sensitivity was 78.5%, and the specificity was 76.4%. There were 45 variables in the RF models, and the relative weight coefficients of these variables were determined. Age has the greatest impact on classification results, followed by hemoglobin concentration, white blood cell count, and platelet distribution width. Conclusion: Machine learning models based on serological markers had high accuracy in predicting the relapse of UC. The model can be used to noninvasively predict patient outcomes and can be an effective tool for determining personalized treatment plans.

Zanthoxylum bungeanum seed oil inhibits tumorigenesis of human melanoma A375 by regulating CDC25A/CyclinB1/CDK1 signaling pathways in vitro and in vivo.

Background: Zanthoxylum bungeanum seed oil (ZBSO) is extracted from the seeds of the traditional Chinese medicine Z. bungeanum Maxim, which has been shown to have anti-melanoma effects. However, the specific mechanisms are not illustrated adequately. Aims: To further investigate the mechanism by which ZBSO inhibits melanoma and to provide scientific evidence to support ZBSO as a potential melanoma therapeutic candidate. Methods: CCK-8 assays were used to detect the function of ZBSO on A375 cells. Based on transcriptomics analyses, Western blot analysis was applied to determine whether an association existed in ZBSO with the CDC25A/CyclinB1/CDK1 signaling pathway. In addition, RT-qPCR and immunohistochemistry analysis validated that ZBSO has the anti-melanoma effect in a nude mouse xenograft model of human melanoma. Then, 16S rRNA sequencing was used to detect the regulation of gut microbes. Results: Cellular assays revealed that ZBSO could inhibit A375 cell viability by regulating the cell cycle pathway. Further studies presented that ZBSO could constrain CDC25A/CyclinB1/CDK1 signaling pathway in vitro and in vivo models of melanoma. ZBSO did not produce toxicity in mice, and significantly reduced tumor volume in xenotransplants of A375 cells. Genome analysis indicated that ZBSO successfully altered specific gut microbes. Conclusion: ZBSO inhibited the growth of A375 cells by regulating CDC25A/cyclinB1/CDK1 signaling pathway both in vitro and in vivo, suggesting that ZBSO may be a novel potential therapeutic agent.

Shunaoxin dropping pill improves cognitive functions of rats with chronic cerebral hypoperfusion via the microbiota-gut-brain axis.

Chronic cerebral hypoperfusion (CCH) is a major risk factor for cognitive decline and degenerative processes. Shunaoxin dropping pill (SNX) has been clinically used to treat cerebrovascular diseases. However, the effect and mechanism of SNX in treating CCH-induced cognitive impairment remain unclear. In this study, CCH was induced in rats using permanent bilateral common carotid artery ligation (2-VO). CCH rats were characterized by impaired spatial learning and memory ability, as well as increased oxidative stress and inflammation in the hippocampus. Additionally, CCH rats had reduced richness and biodiversity of fecal microbiota, which showed a strong correlation with altered serum metabolites. SNX significantly improved the cognitive impairment and restored the dysbiosis of fecal microbiota and serum metabolites in CCH rats. Notably, SNX did not prevent cognitive impairment in antibiotics-treated CCH rats. Our findings suggest that the microbiota-gut-brain axis is a promising therapeutic target for the treatment of CCH-induced cognitive impairment.

Plasmonic Au nanocube enhanced SERS biosensor based on heated electrode and strand displacement amplification for highly sensitive detection of Dam methyltransferase activity.

In this work, a novel "turn-on" mode Au nanocubes (AuNCs) enhanced surface-enhanced Raman scattering (SERS) biosensing platform coupled with heated Au electrode (HAuE) and strand displacement amplification (SDA) strategy was proposed for highly sensitive detection of DNA adenine methylation (Dam) Methyltransferase (MTase) activity. The Dam MTase and DpnI enzyme activities were significantly increased by elevating the HAuE surface temperature, resulting in the rapid production of template DNA for later SDA. During the SDA process, the released single-stranded DNA (ssDNA) could be amplified exponentially, and its concentration was positively related to the Dam MTase activity. The plasmonic AuNCs in SERS tags could provide significant SERS enhancement due to their "lightning rod" effect resulting from the sharp feature of the edges and corners of AuNCs. Because of these factors, the proposed biosensors exhibited high sensitivity in detecting the Dam MTase activity. The limit of detection was estimated to be 8.65 × 10-5 U mL-1, which was lower than that in most of the sensors for detection of Dam MTase activity in the literature. This SERS biosensor could also be used to screen inhibitors of Dam MTase and had the potential for detecting Dam MTase activity in real biological samples.