Brusatol (BRU) is an important compound extracted from Brucea javanica oil, whose pharmacological effects are able to induce a series of biological effects, including inhibition of tumor cell growth, anti-inflammatory, antiviral, and antitumor. Currently, there are so few studies about the brusatol effects on colorectal cancer that its anticancer mechanism has not been clearly defined. In this study, we made an in-depth investigation into the brusatol effect towards the proliferation and metastasis of colon cancer and the possible mechanism. The inhibitory effect of BRU on the proliferation of colorectal cancer cells was unveiled via CCK-8 method and colony formation assay, while the inhibitory effect of BRU on migration and invasion of colorectal cancer cells was revealed by scratch assay and transwell assay. In addition, Western blot results also revealed that BRU inhibited not only the expressions of RhoA and ROCK1 but also the protein expressions of EMT-related markers e-cadherin, N-cadherin, Vimentin, MMP2, and MMP9 in colon cancer cells. Through the xenotransplantation model, our in vivo experiment further verified the antitumor effect of BRU on colon cancer cells in vitro, and the results were consistent with the protein expression trend. In conclusion, BRU may inhibit the proliferation and metastasis of colorectal cancer by influencing EMT through RhoA/ROCK1 pathway.
Berberine hydrochloride (BBR) could inhibit the proliferation, migration, and invasion of various cancer cells. As the only enzyme for the de novo synthesis of ribonucleotides, RRM2 is closely related to the development of tumorigenesis. However, not much is currently known about the functional roles of RRM2 in breast cancer (BRCA), and whether BBR regulates the migration and invasion of BRCA cells by regulating the expression of RRM2 remains to be determined. We study the effects of BBR on BRCA cell proliferation in vitro and tumorigenesis in vivo by using colony formation assays, EdU assays, and xenograft models. Transcriptome sequencing, the random forest algorithm, and KEGG analysis were utilized to explore the therapeutic target genes and relative pathways. The expression of RRM2 in BRCA patients was analyzed with The Cancer Genome Atlas (TCGA) dataset, the GEPIA website tool, the Gene Expression Omnibus (GEO) database, and the UALCAN database. The survival probability of BRCA patients could be predicted by survival curve and nomogram analysis. Molecular docking was used to explore the affinity between BBR and potential targets. Gain- and loss-of-function methods were employed to explore the biological process in RRM2 participants. We comprehensively investigated the pharmacological characteristics of BBR on BRCA cell lines and discovered that BBR could inhibit the proliferation of BRCA cells in vitro and in vivo. Combining transcriptome sequencing and KEGG analysis, we found that BBR mainly affected the biological behavior of BRCA cells via HIF-1α and AMPK signal pathways. Additionally, by using bioinformatics and molecular docking, we demonstrated that RRM2 plays an oncogenic role in BRCA samples and that it acts as the hub gene of BBR on BRCA cells. Knockdown and overexpression studies indicated that RRM2 promoted BRCA cell migration as well as invasion in vitro by affecting the epithelial-to-mesenchymal transition (EMT). Our study demonstrated the significance of BBR regulating HIF-1α and AMPK signaling pathways in BRCA cells. Moreover, we revealed the carcinogenic role and potential mechanism of RRM2 as a core regulatory factor of BBR in BRCA in controlling BRCA invasion, migration, and EMT, suggesting that RRM2 may be a therapeutic target and prognostic biomarker for BRCA therapy.
Background To establish a pyroptosis-related gene (PRG) signature that could be utilized to predict hepatocellular carcinoma (HCC) survival and clinical features. Methods The Cancer Genome Atlas (TCGA) database was utilized to identify differentially expressed PRGs. Univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were utilized to establish the prognostic signature. The signature was verified in the International Cancer Genome Consortium cohort (ID: LIHC-US). Based on the medium-risk score, HCC samples were classified into high- or low-risk subgroups. For signature accuracy prediction, we utilized receiver operating characteristic (ROC) analysis and the Kaplan-Meier estimate (K-M). Molecular and immunological aspects were also reviewed using single-sample gene set enrichment analysis (ssGSEA). Finally, quantitative real-time PCR (qRT-PCR) was utilized to verify the expression of hub genes in vitro. Results On basis of the 33 PRGs, five PRGs ( CASP8, GSDMC, NLRP6, NOD2 , and PLCG1 ) were identified that could predict HCC prognosis. Individuals with high-risk scores had significantly lower overall survival (OS) compared to those with low-risk scores. To assess and confirm this signature’s prediction performance, the area under the curve (AUC) of ROC curves was utilized. In multivariate analysis, the risk score was proven to be a significant independent prognostic factor. Immunological status and tumor cell infiltration in high-risk groups were both significantly greater than in low-risk groups, indicating that the immune system was more activated. qRT-PCR analysis demonstrated that the five PRGs in HCC cell lines were differently expressed in the prognostic signature. Conclusions The signature could precisely predict survival outcomes and reveal immune microenvironment composition, as well as strengthen the argument for more credible clinical and functional research in HCC patients.
Purpose: Due to poor prognosis and immunotherapy failure of skin cutaneous melanoma (SKCM), this study sought to find necroptosis-related biomarkers to predict prognosis and improve the situation with predicted immunotherapy drugs. Experimental Design: The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression Program (GTEx) database were utilized to recognize the differential necroptosis-related genes (NRGs). Univariate Cox (uni-Cox) and least absolute shrinkage and selection operator (LASSO) Cox analysis were utilized for prognostic signature establishment. The signature was verified in the internal cohort. To assess the signature’s prediction performance, the area under the curve (AUC) of receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) analyses, multivariate Cox (multi-Cox) regression, nomogram, and calibration curves were performed. The molecular and immunological aspects were also reviewed using single-sample gene set enrichment analysis (ssGSEA). Cluster analysis was performed to identify the different types of SKCM. Finally, the expression of the signature gene was verified by immunohistochemical staining. Results: On basis of the 67 NRGs, 4 necroptosis-related genes (FASLG, PLK1, EGFR, and TNFRSF21) were constructed to predict SKCM prognosis. The area’s 1-, 3-, and 5-year OS under the AUC curve was 0.673, 0.649, and 0.677, respectively. High-risk individuals had significantly lower overall survival (OS) compared to low-risk patients. Immunological status and tumor cell infiltration in high-risk groups were significantly lower, indicating an immune system that was suppressed. In addition, hot and cold tumors could be obtained by cluster analysis, which is helpful for accurate treatment. Cluster 1 was considered a hot tumor and more susceptible to immunotherapy. Immunohistochemical results were consistent with positive and negative regulation of coefficients in signature. Conclusion: The results of this finding supported that NRGs could predict prognosis and help make a distinction between the cold and hot tumors for improving personalized therapy for SKCM.
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