An exosome-based specific transcriptomic signature for profiling regulation patterns and modifying tumor immune microenvironment infiltration in triple-negative breast cancer

Deciphering the prognostic landscape of triple-negative breast cancer: A focus on immune-related hub genes and therapeutic implications

Triple-negative breast cancer (TNBC), known for its hostile nature and limited treatment modalities, has spurred researchers to explore novel approaches for enhancing clinical outcomes. Here, the study aimed to analyze transcriptomics data to identify immune-related hub genes associated with TNBC that might serve as prognostic biomarkers. Initially, we determined genes that were differentially expressed between TNBC and normal tissues by integrating microarray and RNA sequencing data. Then, through protein-protein interaction and module analysis, we identified five putative hub genes: AURKA, CCNB1, CDCA8, GAPDH, and TOP2A. Subsequently, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that the hub genes were primarily involved in the progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis. Additionally, we observed that these five hub genes were significantly elevated at both protein and mRNA levels in TNBC tissues and contributed to worse survival. Furthermore, the expression of these hub genes exhibited a strong positive association with immune-invading cells such as CD8 T cells, CD4 T cells, and dendritic cells. The analysis of the regulatory network revealed three transcription factors (YBX-1, E2F1, and E2F3) and three posttranscriptional regulators (hsa-mir-25-3p, hsa-mir-92a-3p, and hsa-let-7b-5p) of hub genes. Finally, we explored potential drug candidates for the hub genes using Drug-Gene Interaction Database and discovered that there are no FDA-approved drugs for CCNB1 and CDCA8, highlighting a promising area for future research. Taken together, our results will be of immense importance in addressing the intricacies of TNBC.

Tumor-derived exosomes as promising tools for cancer diagnosis and therapy

Mounting evidences indicated that cancer cell-derived exosomes (TDEs) contribute to cancer progression and metastasis by reshaping the tumor microenvironment (TME) and interfering immunity response. TDEs contain unique biomolecular cargo, consisting of protein, nucleic acid, and lipids. In recent years, TDEs have been used as potential disease therapeutics and diagnosis biomarkers and prime candidates as delivery tools for cancer treatment. In the present review, we firstly summarized TDEs biogenesis and characteristic. Also, the role of TDEs in cancer cell metastasis and invasiveness, drug resistance, and immunosuppression was mentioned via cell-cell communication. Additionally, we concluded the current strategies for TDE-based cancer therapy, including TDEs inhibition and clearance, usage as therapeutic drug delivery vector and cancer vaccines. Furthermore, combination therapy with engineered TDEs were summarized, such as radiotherapy, photodynamic therapy, photothermal therapy, and sonodynamic therapy. Consequently, the above opens up novel and interesting opportunities for cancer diagnosis and prognosis based on TDEs, which is prospective to accelerate the clinical translation of TDEs for cancer therapy.

Identification of key exosomes-related genes in hepatitis B virus-related hepatocellular carcinoma

One of the primary risk factors for hepatocellular carcinoma (HCC) is the hepatitis B virus (HBV). Exosomes have a significant impact on the dissemination of HBV-infected HCC. This study aimed to screen HBV exosome-related hub genes in HCC for a better understanding of the HCC pathogenic mechanism. First, multiple HBV-induced HCC datasets were collected from the Gene Expression Omnibus (GEO) database, and the exosome-related gene set was obtained from relevant literature. Nine HBV-related HCC exosome hub genes (HP, C9, APOA1, PON1, TTR, LPA, FCN2, FCN3, and MBL2) were selected through differential analysis and network analysis. An analysis of the receiver operation characteristic (ROC) revealed that these genes had good diagnostic value. These hub genes were primarily enriched in biological processes such as the citrate cycle tca cycle, phenylalanine metabolism, and fatty acid metabolism, according to gene set enrichment analysis (GSEA). Furthermore, this study predicted the miRNA (hsa-miR-590-5p) targeting LPA, as well as 12 lncRNAs (AL121655, SAP30-DT, LINC00472, etc.) targeting hsa-miR-590-5p. Finally, nelarabine, methylprednisolone, and methylprednisolone were predicted to be possible medications that target the hub gene based on the CellMiner database. To sum up, this work was crucial for discovering new biomarkers and comprehending the function of exosome-related genes in the growth of HBV-infected HCC.

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistance mechanisms, to evaluate and predict drug resistance, and to develop innovative therapeutic strategies to reduce drug resistance. In this review, we proposed a feasible workflow for incorporating AI into tumor drug resistance research, highlighted current AI-driven tumor drug resistance applications, and discussed the opportunities and challenges encountered in the process. Based on a comprehensive literature analysis, we systematically summarized the role of AI in tumor drug resistance research, including drug development, resistance mechanism elucidation, drug sensitivity prediction, combination therapy optimization, resistance phenotype identification, and clinical biomarker discovery. With the continuous advancement of AI technology and rigorous validation of clinical data, AI models are expected to fuel the development of precision oncology by improving efficacy, guiding therapeutic decisions, and optimizing patient prognosis. In summary, by leveraging clinical and omics data, AI models are expected to pioneer new therapy strategies to mitigate tumor drug resistance, improve efficacy and patient survival, and provide novel perspectives and tools for oncology treatment.

Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response

Introduction

Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.

Method

To develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. Time-dependent receiver operating characteristics (ROC) curves and Kaplan-Meier analysis can effectively assess the model's predictive performance. Validation was performed through a nomogram, calibration curves, univariate and multivariate Cox analysis. Further analyses included immunological profiling and gene set enrichment analyses (GSEA). Additionally, the prediction of 50% inhibitory concentration (IC50) of potential drugs between groups was determined.

Results

From analyses in the HNSCC tissues and normal tissues, we found 536 differentially expressed genes (DEGs). Subsequent univariate-cox regression analysis narrowed this list to 18 genes. A robust risk model, outperforming other clinical signatures, was then constructed using machine learning techniques. The MLDPM indicated that high-risk scores showed a greater propensity for immune escape and reduced survival rates. Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.

Conclusions

The construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.

Platelet-derived microvesicles isolated from type-2 diabetes mellitus patients harbour an altered miRNA signature and drive MDA-MB-231 triple-negative breast cancer cell invasion

The underlying causes of breast cancer are diverse, however, there is a striking association between type 2 diabetes and poor patient outcomes. Platelet activation is a common feature of both type 2 diabetes and breast cancer and has been implicated in tumourigenesis through a multitude of pathways. Here transcriptomic analysis of type 2 diabetes patient-derived platelet microvesicles revealed an altered miRNA signature compared with normoglycaemic control patients. Interestingly, interrogation of these data identifies a shift towards an oncogenic signature in type 2 diabetes-derived platelet microvesicles, with increased levels of miRNAs implicated in breast cancer progression and poor prognosis. Functional studies demonstrate that platelet microvesicles isolated from type 2 diabetes patient blood are internalised by triple-negative breast cancer cells in vitro, and that co-incubation with type 2 diabetes patient-derived platelet microvesicles led to significantly increased expression of epithelial to mesenchymal transition markers and triple-negative breast cancer cell invasion compared with platelet microvesicles from healthy volunteers. Together, these data suggest that circulating PMVs in type 2 diabetes patients may contribute to the progression of triple-negative breast cancer.

Constructing a novel prognostic model for triple-negative breast cancer based on genes associated with vasculogenic mimicry

BACKGROUND

Research has shown a connection between vasculogenic mimicry (VM) and cancer progression. However, the functions of genes related to VM in the emergence and progression of TNBC have not been completely elucidated.

METHODS

A survival risk model was constructed by screening biomarkers using DESeq2 and WGCNA based on public TNBC transcriptome data. Furthermore, gene set enrichment analysis was performed, and tumor microenvironment and drug sensitivity were analyzed. The selected biomarkers were validated via quantitative PCR detection, immunohistochemical staining, and protein detection in breast cancer cell lines. Biomarkers related to the proliferation and migration of TNBC cells were validated via in vitro experiments.

RESULTS

The findings revealed that 235 target genes were connected to the complement and coagulation cascade pathways. The risk score was constructed using KCND2, NRP1, and VSTM4. The prognosis model using the risk score and pathological T stage yielded good validation results. The clinical risk of TNBC was associated with the angiogenesis signaling pathway, and the low-risk group exhibited better sensitivity to immunotherapy. Quantitative PCR and immunohistochemistry indicated that the expression levels of KCND2 in TNBC tissues were higher than those in adjacent nontumor tissues. In the TNBC cell line, the protein expression of KCND2 was increased. Knockdown of KCND2 and VSTM4 inhibited the proliferation and migration of TNBC cells in vitro.

CONCLUSIONS

In this study, three VM-related biomarkers were identified, including KCND2, NRP1, and VSTM4. These findings are likely to aid in deepening our understanding of the regulatory mechanism of VM in TNBC.

Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer

Background

Exosomes are promising tools for the development of new diagnostic and therapeutic approaches. Exosomes possess the ability to activate signaling pathways that contribute to the remodeling of the tumor microenvironment, angiogenesis, and the regulation of immune responses. We aimed to develop a prognostic score based on exosomes derived from breast cancer.

Materials and methods

Training was conducted on the TCGA-BRCA dataset, while validation was conducted on GSE20685, GSE5764, GSE7904, and GSE29431. A total of 121 genes related to exosomes were retrieved from the ExoBCD database. The Cox proportional hazards model is used to develop risk score model. The GSVA package was utilized to analyze single-sample gene sets and identify exosome signatures, while the WGCNA package was utilized to identify gene modules associated with clinical outcomes. The clusterProfiler and GSVA R packages facilitated gene set enrichment and variation analyses. Furthermore, CIBERSORT quantified immune infiltration, and a correlation between gene expression and drug sensitivity was assessed using the TIDE algorithm.

Results

An exosome-related prognostic score was established using the following selected genes: ABCC9, PIGR, CXCL13, DOK7, CD24, and IVL. Various immune cells that promote cancer immune evasion were associated with a high-risk prognostic score, which was an independent predictor of outcome. High-risk and low-risk groups exhibited significantly different infiltration abundances (p < 0.05). By conducting a sensitivity comparison, we found that patients with high-risk scores exhibited more favorable responses to immunotherapy than those with low-risk scores.

Conclusion

The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy.

Biological Roles and Clinical Applications of Exosomes in Breast Cancer: A Brief Review

Breast cancer (BC) is a global health risk for women and has a high prevalence rate. The drug resistance, recurrence, and metastasis of BC affect patient prognosis, thus posing a challenge to scientists. Exosomes are extracellular vesicles (EVs) that originate from various cells; they have a double-layered lipid membrane structure and contain rich biological information. They mediate intercellular communication and have pivotal roles in tumor development, progression, and metastasis and drug resistance. Exosomes are important cell communication mediators in the tumor microenvironment (TME). Exosomes are utilized as diagnostic and prognostic biomarkers for estimating the treatment efficacy of BC and have the potential to function as tools to enable the targeted delivery of antitumor drugs. This review introduces recent progress in research on how exosomes influence tumor development and the TME. We also present the research progress on the application of exosomes as prognostic and diagnostic biomarkers and drug delivery tools.