Nuclear receptor subfamily group H member 4 (NR1H4), also known as farnesoid X receptor, has been implicated in several cellular processes in the liver and intestine. Preclinical and clinical studies have suggested a role of NR1H4 in colon cancer development; however, how NR1H4 regulates colon cancer cell growth and survival remains unclear. We generated NR1H4 knockout (KO) colon cancer cells using clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein-9 nuclease (CAS9) technology and explored the effects of NR1H4 KO in colon cancer cell proliferation, survival, and apoptosis. Interestingly, NR1H4 KO cells showed impaired cell proliferation, reduced colony formation, and increased apoptotic cell death compared to control colon cancer cells. We identified MYC as an important mediator of the signaling pathway alterations induced by NR1H4 KO. NR1H4 silencing in colon cancer cells resulted in reduced MYC protein levels, while NR1H4 activation using an NR1H4 ligand, chenodeoxycholic acid, resulted in time- and dose-dependent MYC induction. Moreover, NR1H4 KO enhanced the anti-cancer effects of doxorubicin and cisplatin, supporting the role of MYC in the enhanced apoptosis observed in NR1H4 KO cells. Taken together, our findings suggest that modulating NR1H4 activity in colon cancer cells might be a promising alternative approach to treat cancer using MYC-targeting agents.

Background and Objectives: As modulators of the tumor microenvironment, macrophages have been extensively studied for their potential in developing anticancer strategies, particularly in regulating macrophage polarization towards an antitumorigenic (M1) phenotype rather than a protumorigenic (M2) one in various experimental models. Here, we evaluated the effect of PD98059, a mitogen-activated protein kinase kinase MAPKK MEK1-linked pathway inhibitor, on the differentiation and polarization of THP-1 monocytes in response to phorbol-12-myristate-13-acetate (PMA) under various culture conditions for tumor microenvironmental application. Materials and Methods: Differentiation and polarization of THP-1 were analyzed by flow cytometry and RT-PCR. Polarized THP-1 subsets with different treatment were compared by motility, phagocytosis, and so on. Results: Clearly, PMA induced THP-1 differentiation occurs in adherent culture conditions more than nonadherent culture conditions by increasing CD11b expression up to 90%, which was not affected by PD98059 when cells were exposed to PMA first (post-PD) but inhibited when PD98059 was treated prior to PMA treatment (pre-PD). CD11b^high THP-1 cells treated with PMA and PMA-post-PD were categorized into M0 (HLA-DR^low and CD206^low), M1 (HLA-DR^high and CD206^low), and M2 (HLA-DR^low and CD206^high), resulting in an increased population of M1 macrophages. The transcription levels of markers of macrophage differentiation and polarization confirmed the increased M1 polarization of THP-1 cells with post-PD treatment rather than with PMA-only treatment. The motility and cytotoxicity of THP-1 cells with post-PD treatment were higher than THP-1 cells with PMA, suggesting that post-PD treatment enhanced the anti-tumorigenicity of THP-1 cells. Confocal microscopy and flow cytometry showed the effect of post-PD treatment on phagocytosis by THP-1 cells. Conclusions: We have developed an experimental model of macrophage polarization with THP-1 cells which will be useful for further studies related to the tumor microenvironment.

Eun-Young Lee, Minjeong Kim, Beom K. Choi, Dae Hong Kim, Inho Choi, and Hye Jin You. Mol. Cells 2021;44:784-94. https://doi.org/10.14348/molcells.2021.0130

Technological advances in information-processing capacity have enabled integrated analyses (multi-omics) of different omics data types, improving target discovery and clinical diagnosis. This study proposes novel artificial intelligence (AI) learning strategies for incomplete datasets, common in omics research. The model comprises (1) a multi-omics generative model based on a variational auto-encoder that learns tumor genetic patterns based on different omics data types and (2) an expanded classification model that predicts cancer phenotypes. Padding was applied to replace missing data with virtual data. The embedding data generated by the model accurately classified cancer phenotypes, addressing the class imbalance issue (weighted F1 score: cancer type > 0.95, primary site > 0.92, sample type > 0.97). The classification performance was maintained in the absence of omics data, and the virtual data resembled actual omics data (cosine similarity mRNA gene expression > 0.96, mRNA isoform expression > 0.95, DNA methylation > 0.96). Meanwhile, in the presence of omics data, high-quality, non-existent omics data were generated (cosine similarity mRNA gene expression: 0.9702, mRNA isoform expression: 0.9546, DNA methylation: 0.9687). This model can effectively classify cancer phenotypes based on incomplete omics data with data sparsity robustness, generating omics data through deep learning and enabling precision medicine.

As a high-grade soft-tissue sarcoma (STS), undifferentiated pleo-morphic sarcoma (UPS) is highly recurrent and malignant. UPS is categorized as a tumor of uncertain differentiation and has few options for treatment due to its lack of targetable genetic alterations. There are also few cell lines that provide a repre-sentative model for UPS, leading to a dearth of experimental research. Here, we established and characterized new cell lines derived from two recurrent UPS tissues. Cells were obtained from UPS tissues by mincing, followed by extraction or disso-ciation using enzymes and culture in a standard culture environ-ment. Cells were maintained for several months without artifi-cial treatment, and some cell clones were found to be tumori-genic in an immunodeficient mouse model. Interestingly, some cells formed tumors in vivo when injected after aggregation in a non-adherent culture system for 24 h. The tissues from in vivo study and tissues from patients shared common histological characteristics. Pathways related to the cell cycle, such as DNA replication, were enriched in both cell clones. Pathways related to cell-cell adhesion and cell-cell signaling were also enriched, suggesting a role of the mesenchymal-to-epithelial transition for tumorigenicity in vivo. These new UPS cell lines may fa-cilitate research to identify therapeutic strategies for UPS. [BMB Reports 2023; 56(4): 258-264]

Undifferentiated sarcomas (USs), including undifferentiated pleomorphic sarcoma (UPS), are aggressive mesenchymal malignancies with limited molecular biomarkers for prognostic assessment and therapeutic stratification. Expression-based markers may provide insight into tumor aggressiveness and clinical outcomes. Here, we performed integrative transcriptomic and spatial analyses to identify differentially expressed genes (DEGs). By comparing normal tissues with sarcoma tumors and sarcoma tumors with cell lines. Intersection and clustering analyses were conducted to define shared expression programs, which revealed a subset of DEGs enriched in epithelial-mesenchymal transition (EMT)-related pathways. CosMx spatial transcriptomics was applied to xenograft tumors derived from two UPS cell lines to resolve tumor-intrinsic signatures. The National Cancer Center Cohort samples were used for validation, and immunohistochemistry confirmed the expression in thirty US tissues. Spatial transcriptomic profiling identified mesenchymal tissue–driven gene expression programs in UPS xenografts. Across bulk RNA-seq and spatial data, epithelial membrane protein 3 (EMP3) consistently emerged as highly expressed in US tissues and cell lines. EMP3 is a robust mesenchymal-associated biomarker linked to EMT, tumor progression, and clinical outcomes in USs, supporting its potential utility as a prognostic indicator and therapeutic target.