Exosomal Plasma Gelsolin Is an Immunosuppressive Mediator in the Ovarian Tumor Microenvironment and a Determinant of Chemoresistance

Onuma, Toshimichi; Asare-Werehene, Meshach; Yoshida, Yoshio; Tsang, Benjamin K.

doi:10.3390/cells11203305

Cited by 6 publications

(4 citation statements)

References 115 publications

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“…Interestingly, another recent study contradicts our findings and the findings of Arentz et al (2023), demonstrating that the expression and secretion of GSN were higher in chemo-resistant OC cells than in chemo-sensitive OC cells [80]. The supporting study by Onuma et al (2022) [81] suggested that higher levels of GSN prevent cisplatin from dissociating GSN from the FLIP-ITCH complex, thus preventing caspase-3 and -8 activation and caspase-mediated GSN cleavage and thereby inhibiting apoptosis in chemo-resistant OC cells. However, further analysis is needed to understand the role of GSN in OC.…”

Section: Pathogenic Role Of Genes Identified In Platinum-paclitaxel S...contrasting

confidence: 85%

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning

Amniouel,

Yalamanchili,

Sankararaman

et al. 2024

BioMedInformatics

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Background: Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis. Methods: Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models. Results: The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum–paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved. Conclusions: In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.

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Section: Pathogenic Role Of Genes Identified In Platinum-paclitaxel S...contrasting

confidence: 85%

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning

Amniouel,

Yalamanchili,

Sankararaman

et al. 2024

BioMedInformatics

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“…An escalating number of studies underscore the tumor‐related ECM as a significant impediment to achieving enhanced success in cancer immunotherapy cases 31,32 . Multiple investigations have delineated that the tumor‐related extracellular matrix (ECM) actively propels the processes of tumor cell growth, invasion, metastasis, and angiogenesis, while simultaneously thwarting programmed cell death and impeding drug diffusion 33,34 . Consequently, gaining an in‐depth understanding of the intricate interplay between ECM and the tumor immune response is essential for realizing the potential of targeting the tumor‐associated ECM to enhance the efficacy of cancer immunotherapy.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of basement membrane‐related gene based on single‐cell and bulk RNA sequencing data to predict prognosis and evaluate immune characteristics in colorectal cancer

Han,

Wang,

et al. 2024

Environmental Toxicology

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AimsBasement membrane‐related genes (BMs) participate in regulating cell polarity, invasion, metastasis, and survival across different tumor types. Nevertheless, the specific functions of BMs in colorectal cancer (CRC) remain uncertain.MethodsTo investigate the clinical relevance of BMs in CRC, we retrieved both gene expression and clinical data from The Cancer Genome Atlas (TCGA) datasets for subsequent analysis. The Kaplan–Meier (K–M) survival curve was employed to evaluate prognosis in high‐ and low‐risk groups. Furthermore, additional analyses, including nomogram construction, functional enrichment, examination of the tumor immune microenvironment, prediction of small‐molecule drugs, and more, were conducted to delve into the significance of BM‐related signatures in CRC. Single‐cell data from seven CRC patients were obtained from the TISCH2 database, and expression validation and cell source exploration of BM‐related signatures were performed. Lastly, the expression and function of TIMP1, a key gene in BMs that may play a role in the progression of CRC, was validated in vitro through a series of basic experiments.ResultsWe constructed a seven BMs‐based model to categorize CRC patients into high‐risk and low‐risk groups. K–M survival analysis indicated a poorer prognosis for high‐risk CRC patients. Cox regression analysis further identified the risk score as an independent prognostic factor for CRC patients. The nomogram model exhibited superior discrimination and calibration abilities of CRC patients. Based on the results from GO/KEGG and GSEA, genes in the high‐risk subgroup were implicated in immune‐related pathways and exhibited a positive correlation with immune checkpoints. In single‐cell data, we found that TIMP1 is highly expressed in many cells, especially in malignant tumor cells. We also observed up‐regulation of TIMP1 in CRC cell lines, promoting cancer invasion and migration in vitro.ConclusionsOur study has discovered a novel prognostic index derived from BM‐related genes in CRC patients. Specifically, the new model enables patient stratification, improving the selection of individuals likely to benefit from immunotherapy.

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“…The role of these proteins in cancer has remained unclear due to conflicting results: while some studies have classified them as oncogenes, others have proposed that they predominantly encompass tumor suppressor functions [ 89 , 90 ]. In addition, only a limited number of studies have investigated the potential role of these proteins in the development of resistance in cancer, particularly in ovarian cancer and gliomas [ 91 , 92 ]. Therefore, it would be necessary to investigate the potential role of all these under-expressed proteins and their various complexes in the development of resistance to anti-HER2 therapies.…”

Section: Discussionmentioning

confidence: 99%

Generation and Characterization of Trastuzumab/Pertuzumab-Resistant HER2-Positive Breast Cancer Cell Lines

Sanz-Álvarez,

Luque,

Morales-Gallego

et al. 2023

IJMS

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The combination of trastuzumab and pertuzumab as first-line therapy in patients with HER2-positive breast cancer has shown significant clinical benefits compared to trastuzumab alone. However, despite initial therapeutic success, most patients eventually progress, and tumors develop acquired resistance and invariably relapse. Therefore, there is an urgent need to improve our understanding of the mechanisms governing resistance in order to develop targeted therapeutic strategies with improved efficacy. We generated four novel HER2-positive cell lines via prolonged exposure to trastuzumab and pertuzumab and determined their resistance rates. Long-term resistance was confirmed by a significant increase in the colony-forming capacity of the derived cells. We authenticated the molecular identity of the new lines via both immunohistochemistry for the clinical phenotype and molecular profiling of point mutations. HER2 overexpression was confirmed in all resistant cell lines, and acquisition of resistance to trastuzumab and pertuzumab did not translate into differences in ER, PR, and HER2 receptor expression. In contrast, changes in the expression and activity of other HER family members, particularly HER4, were observed. In the same vein, analyses of the receptor and effector kinase status of different cellular pathways revealed that the MAPK pathway may be involved in the acquisition of resistance to trastuzumab and pertuzumab. Finally, proteomic analysis confirmed a significant change in the abundance patterns of more than 600 proteins with implications in key biological processes, such as ribosome formation, mitochondrial activity, and metabolism, which could be relevant mechanisms in the generation of resistance in HER2-positive breast cancer. We concluded that these resistant BCCLs may be a valuable tool to better understand the mechanisms of acquisition of resistance to trastuzumab and pertuzumab-based anti-HER2 therapy.

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Exosomal Plasma Gelsolin Is an Immunosuppressive Mediator in the Ovarian Tumor Microenvironment and a Determinant of Chemoresistance

Cited by 6 publications

References 115 publications

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning

Comprehensive analysis of basement membrane‐related gene based on single‐cell and bulk RNA sequencing data to predict prognosis and evaluate immune characteristics in colorectal cancer

Generation and Characterization of Trastuzumab/Pertuzumab-Resistant HER2-Positive Breast Cancer Cell Lines

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