Targeting the Tumor-Tumor Microenvironment Crosstalk

Badve, Sunil; Gökmen–Polar, Yesim

doi:10.1080/14728222.2023.2230362

Cited by 13 publications

(3 citation statements)

References 122 publications

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“…[80] The TME exhibits different physiological environments from normal tissues and provides the "soil" for supporting tumor growth, proliferation, invasion, and metastasis, as well as avoiding immunological recognition. [81] Therefore, the abnormal TME, including hypoxia, low pH, high redox, and overexpressed enzymes, offers opportunities for developing multiple tumorspecific therapy platforms (Figure 6). Some represented modalities were summarized in Table 1.…”

Section: Tumor-responsive Therapy Modalitiesmentioning

confidence: 99%

Nano‐Engineering Strategies for Tumor‐Specific Therapy

Li,

Xie,

Nie.

2024

ChemMedChem

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Nanodelivery systems (NDSs) provide promising prospects for decreasing drug doses, reducing side effects, and improving therapeutic effects. However, the bioapplications of NDSs are still compromised by their fast clearance, indiscriminate biodistribution, and limited tumor accumulation. Hence, engineering modification of NDSs aiming at promoting tumor‐specific therapy and avoiding systemic toxicity is usually needed. An NDS integrating various functionalities, including flexible camouflage, specific biorecognition, and sensitive stimuli‐responsiveness, into one sequence would be "smart" and highly effective. Herein, we systematically summarize the related principles, methods, and progress. At the end of the review, we predict the obstacles to precise nanoengineering and prospects for the future application of NDSs.

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Section: Tumor-responsive Therapy Modalitiesmentioning

confidence: 99%

Nano‐Engineering Strategies for Tumor‐Specific Therapy

Li,

Xie,

Nie.

2024

ChemMedChem

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“…The tumor microenvironment is a key factor in the formation of tumors and contains a variety of potential biomarkers. 14 , 15 Exosomes, which are actively secreted by both normal and cancer cells, are small extracellular vesicles composed of a lipid bilayer membrane structure. They contain proteins, nucleic acids, lipids, and other bioactive substances, with various functions in the tumor microenvironment.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exosome‐related lncRNA score: A value‐based individual treatment strategy for predicting the response to immunotherapy in clear cell renal cell carcinoma

Yang,

Zhang,

Zhan

et al. 2024

Cancer Medicine

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BackgroundExosomes play a crucial role in intercellular communication in clear cell renal cell carcinoma (ccRCC), while the long non‐coding RNAs (lncRNAs) are implicated in tumorigenesis and progression.AimsThe purpose of this study is to construction a exosomes‐related lncRNA score and a ceRNA network to predict the response to immunotherapy and potential targeted drug in ccRCC.MethodsData of ccRCC patients were obtained from the TCGA database. Pearson correlation analysis was used to identify eExosomes‐related lncRNAs (ERLRs) from Top10 exosomes‐related genes that have been screened. The entire cohort was randomly divided into a training cohort and a validation cohort in equal scale. LASSO regression and multivariate cox regression was used to construct the ERLRs‐based score. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and drug susceptibility between the high‐ and low‐risk groups were also investigated. Finally, the relevant ceRNA network was constructed by machine learning to analyze their potential targets in immunotherapy and drug use of ccRCC patients.ResultsA score consisting of 4ERLRs was identified, and patients with higher ERLRs‐based score tended to have a worse prognosis than those with lower ERLRs‐based score. ROC curves and multivariate Cox regression analysis demonstrated that the score could be considered as a risk factor for prognosis in both training and validation cohorts. Moreover, patients with high scores are predisposed to experience poor overall survival, a larger prevalence of advanced stage (III‐IV), a greater tumor mutational burden, a higher infiltration of immunosuppressive cells, and a greater likelihood of responding favorably to immunotherapy. The importance of EMX2OS was determined by mechanical learning, and the ceRNA network was constructed, and EMX2OS may be a potential therapeutic target, possibly exerting its function through the EMX2OS/hsa‐miR‐31‐5p/TLN2 axis.ConclusionsBased on machine learning, a novel ERLRs‐based score was constructed for predicting the survival of ccRCC patients. The ERLRs‐based score is a promising potential independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics. Meanwhile, we screened out key lncRNAEMX2OS and identified the EMX2OS/hsa‐miR‐31‐5p/TLN2 axis, which may provide new clues for the targeted therapy of ccRCC.

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