Identification of a prognostic chemoresistance-related gene signature associated with immune microenvironment in breast cancer

Inhibition of TNBC Cell Growth by Paroxetine: Induction of Apoptosis and Blockage of Autophagy Flux

The strategy of drug repurposing has gained traction in the field of cancer therapy as a means of discovering novel therapeutic uses for established pharmaceuticals. Paroxetine (PX), a selective serotonin reuptake inhibitor typically utilized in the treatment of depression, has demonstrated promise as an agent for combating cancer. Nevertheless, the specific functions and mechanisms by which PX operates in the context of triple-negative breast cancer (TNBC) remain ambiguous. This study aimed to examine the impact of PX on TNBC cells in vitro as both a standalone treatment and in conjunction with other pharmaceutical agents. Cell viability was measured using the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay, apoptosis was assessed through flow cytometry, and the effects on signaling pathways were analyzed using RNA sequencing and Western blot techniques. Furthermore, a subcutaneous tumor model was utilized to assess the in vivo efficacy of combination therapy on tumor growth. The results of our study suggest that PX may activate the Ca2+-dependent mitochondria-mediated intrinsic apoptosis pathway in TNBC by potentially influencing the PI3K/AKT/mTOR pathway as well as by inducing cytoprotective autophagy. Additionally, the combination of PX and chemotherapeutic agents demonstrated moderate inhibitory effects on 4T1 tumor growth in an in vivo model. These findings indicate that PX may exert its effects on TNBC through modulation of critical molecular pathways, offering important implications for improving chemosensitivity and identifying potential therapeutic combinations for clinical use.

Development of a Chemoresistant Risk Scoring Model for Prechemotherapy Osteosarcoma Using Single-Cell Sequencing

Background

Chemoresistance is one of the leading causes that severely limits the success of osteosarcoma treatment. Evaluating chemoresistance before chemotherapy poses a new challenge for researchers. We established an effective chemoresistance risk scoring model for prechemotherapy osteosarcoma using single-cell sequencing.

Methods

We comprehensively analyzed osteosarcoma data from the bulk mRNA sequencing dataset TARGET-OS and the single-cell RNA sequencing (scRNA-seq) dataset GSE162454. Chemoresistant tumor clusters were identified using enrichment analysis and AUCell scoring. Its differentiated trajectory was achieved with inferCNV and pseudotime analysis. Ligand-receptor interactions were annotated with iTALK. Furthermore, we established a chemoresistance risk scoring model using LASSO regression based on scRNA-seq-based markers of chemoresistant tumor clusters. The TARGET-OS dataset was used as the training group, and the bulk mRNA array dataset GSE33382 was used as the validation group. Finally, the performance was verified for its discriminatory ability and calibration.

Results

Using bulk RNA data, we found that osteogenic expression was upregulated in chemoresistant osteosarcoma as compared to chemosensitive osteosarcoma. Then, we transferred the bulk RNA findings to scRNA-seq and noticed osteosarcoma tumor clusters C14 and C25 showing osteogenic cancer stem cell expression patterns, which fit chemoresistant characteristics. C14 and C25 possessed bridge roles in interactions with other clusters. On the one hand, they received various growth factor stimulators and could potentially transform into a proliferative state. On the other hand, they promote local tumor angiogenesis, bone remodeling and immunosuppression. Next, we identified a ten-gene signature from the C14 and C25 markers and constructed a chemoresistant risk scoring model using LASSO regression model. Finally, we found that chemoresistant osteosarcoma had higher chemoresistance risk score and that the model showed good discriminatory ability and calibration in both the training and validation groups (AUCtrain = 0.82; AUCvalid = 0.84). Compared with that of the classic bulk RNA-based model, it showed more robust performance in validation environment (AUCvalid-scRNA = 0.84; AUCvalid-bulk DEGs = 0.54).

Conclusions

Our work provides insights into understanding chemoresistant osteosarcoma tumor cells and using single-cell sequencing to establish a chemoresistance risk scoring model. The model showed good discriminatory ability and calibration and provided us with a feasible way to evaluate chemoresistance in prechemotherapy osteosarcoma.

Exosome-Mediated Therapeutic Strategies for Management of Solid and Hematological Malignancies

Exosomes are small membrane vesicles of endocytic origin containing cytokines, RNAs, growth factors, proteins, lipids, and metabolites. They have been identified as fundamental intercellular communication controllers in several diseases and an enormous volume of data confirmed that exosomes could either sustain or inhibit tumor onset and diffusion in diverse solid and hematological malignancies by paracrine signaling. Thus, exosomes might constitute a promising cell-free tumor treatment alternative. This review focuses on the effects of exosomes in the treatment of tumors, by discussing the most recent and promising data from in vitro and experimental in vivo studies and the few existing clinical trials. Exosomes are extremely promising as transporters of drugs, antagomir, genes, and other therapeutic substances that can be integrated into their core via different procedures. Moreover, exosomes can augment or inhibit non-coding RNAs, change the metabolism of cancer cells, and modify the function of immunologic effectors thus modifying the tumor microenvironment transforming it from pro-tumor to antitumor milieu. Here, we report the development of currently realized exosome modifiers that offer indications for the forthcoming elaboration of other more effective methods capable of enhancing the activity of the exosomes.

Tumor microenvironment-related multigene prognostic prediction model for breast cancer

BACKGROUND

Breast cancer is an invasive disease with complex molecular mechanisms. Prognosis-related biomarkers are still urgently needed to predict outcomes of breast cancer patients.

METHODS

Original data were download from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The analyses were performed using perl-5.32 and R-x64-4.1.1.

RESULTS

In this study, 1086 differentially expressed genes (DEGs) were identified in the TCGA cohort; 523 shared DEGs were identified in the TCGA and GSE10886 cohorts. Eight subtypes were estimated using non-negative matrix factorization clustering with significant differences seen in overall survival (OS) and progression-free survival (PFS) (P < 0.01). Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to develop a related risk score related to the 17 DEGs; this score separated breast cancer into low- and high-risk groups with significant differences in survival (P < 0.01) and showed powerful effectiveness (TCGA all group: 1-year area under the curve [AUC] = 0.729, 3-year AUC = 0.778, 5-year AUC = 0.781). A nomogram prediction model was constructed using non-negative matrix factorization clustering, the risk score, and clinical characteristics. Our model was confirmed to be related with tumor microenvironment. Furthermore, DEGs in high-risk breast cancer were enriched in histidine metabolism (normalized enrichment score [NES] = 1.49, P < 0.05), protein export (NES = 1.58, P < 0.05), and steroid hormone biosynthesis signaling pathways (NES = 1.56, P < 0.05).

CONCLUSIONS

We established a comprehensive model that can predict prognosis and guide treatment.