Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. METHOD: We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. RESULT: Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. CONCLUSION: The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

S100A6 inhibits MDM2 to suppress breast cancer growth and enhance sensitivity to chemotherapy.

BACKGROUND: S100A6 and murine double minute 2 (MDM2) are important cancer-related molecules. A previous study identified an interaction between S100A6 and MDM2 by size exclusion chromatography and surface plasmon resonance experiments. The present study investigated whether S100A6 could bind to MDM2 in vivo and further explored its functional implication. METHODS: Co-immunoprecipitation, glutathione-S-transferase pull-down assay, and immunofluorescence were performed to determine the in vivo interaction between S100A6 and MDM2. Cycloheximide pulse-chase assay and ubiquitination assay were performed to clarify the mechanism by which S100A6 downregulated MDM2. In addition, clonogenic assay, WST-1 assay, and flow cytometry of apoptosis and the cell cycle were performed and a xenograft model was established to evaluate the effects of the S100A6/MDM2 interaction on growth and paclitaxel-induced chemosensitivity of breast cancer. The expressions of S100A6 and MDM2 in patients with invasive breast cancer were analyzed by immunohistochemistry. In addition, the correlation between the expression of S100A6 and the response to neoadjuvant chemotherapy was statistically analyzed. RESULTS: S100A6 promoted the MDM2 translocation from nucleus to cytoplasm, in which the S100A6 bound to the binding site of the herpesvirus-associated ubiquitin-specific protease (HAUSP) in MDM2, disrupted the MDM2-HAUSP-DAXX interactions, and induced the MDM2 self-ubiquitination and degradation. Furthermore, the S100A6-mediated MDM2 degradation suppressed the growth of breast cancer and enhanced its sensitivity to paclitaxel both in vitro and in vivo. For patients with invasive breast cancer who received epirubicin and cyclophosphamide followed by docetaxel (EC-T), expressions of S100A6 and MDM2 were negatively correlated, and high expression of S100A6 suggested a higher rate of pathologic complete response (pCR). Univariate and multivariate analyses showed that the high expression of S100A6 was an independent predictor of pCR. CONCLUSION: These results reveal a novel function for S100A6 in downregulating MDM2, which directly enhances sensitivity to chemotherapy.

Honokiol Inhibits HIF-1α-Mediated Glycolysis to Halt Breast Cancer Growth.

Background: Hypoxia-inducible factor-1α (HIF-1α) induces the expression of glycolysis-related genes, which plays a direct and key role in Warburg effect. In a recent study, honokiol (HNK) was identified as one of the potential agents that inhibited the HIF-1α signaling pathway. Because the HIF- 1α pathway is closely associated with glycolysis, we investigated whether HNK inhibited HIF-1α-mediated glycolysis. Methods: The effects of HNK on HIF-1α-mediated glycolysis and other glycolysis-related genes' expressions, cancer cells apoptosis and tumor growth were studied in various human breast cancer models in vitro and in vivo. We performed the following tests: extracellular acidification and oxygen consumption rate assays, glucose uptake, lactate, and ATP assays for testing glycolysis; WST-1 assay for investigating cell viability; colony formation assay for determining clonogenicity; flow cytometry for assessing cell apoptosis; qPCR and Western blot for determining the expression of HIF-1α, GLUT1, HK2 and PDK1. The mechanisms of which HNK functions as a direct inhibitor of HIF-1α were verified through the ubiquitination assay, the Co-IP assay, and the cycloheximide (CHX) pulse-chase assay. Results: HNK increased the oxygen consumption rate while decreased the extracellular acidification rate in breast cancer cells; it further reduced glucose uptake, lactic acid production and ATP production in cancer cells. The inhibitory effect of HNK on glycolysis is HIF-1α-dependent. HNK also downregulated the expression of HIF-1α and its downstream regulators, including GLUT1, HK2 and PDK1. A mechanistic study demonstrated that HNK enhanced the self-ubiquitination of HIF-1α by recruiting two E3 ubiquitin ligases (UFL1 and BRE1B). In vitro, HNK inhibited cell proliferation and clonogenicity, as well as induced apoptosis of cancer cells. These effects were also HIF1α-dependent. In vivo, HNK inhibited tumor growth and HIF-1α-mediated glycolysis. Conclusion: HNK has an inhibitory effect on HIF-1α-mediated glycolysis in human breast cancer. Our research revealed a new mechanism of HNK as an anti-cancer drug, thus representing a novel strategy to improve the prognosis of cancer.

Honokiol antagonizes doxorubicin resistance in human breast cancer via miR-188-5p/FBXW7/c-Myc pathway.

BACKGROUND: Honokiol, a natural phenolic compound derived from Magnolia plants, is a promising anti-tumor compound that exerts a wide range of anti-cancer effects. Herein, we investigated the effect of honokiol on doxorubicin resistance in breast cancer. METHODS: Doxorubicin-sensitive (MCF-7 and MDA-MB-231) and doxorubicin-resistant (MCF-7/ADR and MDA-MB-231/ADR) breast cancer cell lines were treated with doxorubicin in the absence or presence of honokiol; then, the following tests were performed: flow cytometry for cell apoptosis, WST-1 assay for cell viability, qPCR and western blot for the expression of miR-188-5p, FBXW7, and c-Myc. MiR-188-5p mimic, miR-188-5p inhibitor, siFBXW7, and c-Myc plasmids were transfected into cancer cells to evaluate whether miR-188-5p and FBXW7/c-Myc signaling are involved in the effect of honokiol on doxorubicin resistance in breast cancer. A dual luciferase reporter system was used to study the direct interaction between miR-188-5p and FBXW7. RESULTS: Honokiol sensitized doxorubicin-resistant breast cancer cells to doxorubicin-induced apoptosis. Mechanically, upregulation of miR-188-5p was associated with doxorubicin resistance, and honokiol enhanced doxorubicin sensitivity by downregulating miR-188-5p. FBXW7 was confirmed to be a direct target gene of miR-188-5p. FBXW7/c-Myc signaling was involved in the chemosensitization effect of honokiol. Honokiol induced apoptosis in MCF-7/ADR and MDA-MB-231/ADR cells. However, FBXW7 silencing or c-Myc transfection resulted in resistance to the honokiol-induced apoptotic effect. CONCLUSION: These findings suggest that downregulation of miR-188-5p by honokiol enhances doxorubicin sensitivity through FBXW7/c-Myc signaling in human breast cancer. Our study finds an important role of miR-188-5p in the development of doxorubicin resistance in breast cancer, and enriches our understanding of the mechanism of action of honokiol in cancer therapy.