Nanomedicine Strategies for Management of Drug Resistance in Lung Cancer

Haider, Mohamed; Sherbeny, Amr El; Pittalà, Valeria; Fallica, Antonino N.; Alghamdi, Metib; Hussain, Zahid; Khoder, Ghalia; Greish, Khaled

doi:10.20944/preprints202201.0146.v1

Cited by 9 publications

(1 citation statement)

References 202 publications

(233 reference statements)

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“…Lung cancer is the most frequently occurring cancer and the leading cause of cancer death in men, and the morbidity and mortality are approximately twice that of women [1]. 80% of lung cancers belong to nonsmall-cell lung cancer (NSCLC), which can be subdivided into adenocarcinoma, squamous cell carcinoma, bronchiolar alveolar carcinoma, and large cell carcinoma [2]. About 2/3 of lung cancer deaths worldwide can be attributed to smoking [1].…”

Section: Introductionmentioning

confidence: 99%

A New Hypoxia-Related Prognostic Risk Score (HPRS) Model Was Developed to Indicate Prognosis and Response to Immunotherapy for Lung Adenocarcinoma

Yang

Liang

et al. 2022

Journal of Oncology

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Background. Hypoxia is a typical microenvironmental feature of most solid tumors, affecting a variety of physiological processes. We developed a hypoxia-related prognostic risk score (HPRS) model to reveal tumor microenvironment (TME) and predict prognosis of lung adenocarcinoma (LUAD). Methods. LUAD sample expression data were from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) Cox regression identified hypoxia-related genes (HRGs) to create HPRS. The prognostic value, genetic mutation and TME, and therapeutic response of distinct HPRS groups were analyzed. Univariate and multivariate Cox regression analysis identified independent factors associated with the prognosis of LUAD. A decision tree based on HPRS and clinicopathological variables was established using the classification system based on decision tree algorithm. A nomogram was constructed with important clinical features and HPRS by the RMS package. Results. A HPRS model with five HRGs was developed and verified in two separate cohorts of GEO. HPRS model divided patients with LUAD into two groups. High HPRS was related to high probability of genetic alterations. HPRS could predict the prognosis, TME, and sensitivity to immunotherapy/chemotherapy of LUAD. The decision tree defined four risk subgroups with significant OS differences. Nomogram with integrated HPRS and clinical features had acceptable accuracy in predicting LUAD prognosis. Conclusions. A HPRS model was developed to evaluate prognosis, genetic alterations, TME, and response to immunotherapy, which may provide theoretical reference for the study of molecular mechanism of hypoxia in LUAD.

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Section: Introductionmentioning

confidence: 99%

A New Hypoxia-Related Prognostic Risk Score (HPRS) Model Was Developed to Indicate Prognosis and Response to Immunotherapy for Lung Adenocarcinoma

Yang

Liang

et al. 2022

Journal of Oncology

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tRNA-derived fragments: mechanism of gene regulation and clinical application in lung cancer

Wu,

Yang,

Pan

et al. 2023

Cell Oncol.

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Nano-Enabled Strategies for the Treatment of Lung Cancer: Potential Bottlenecks and Future Perspectives

et al. 2023

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On a global scale, lung cancer is acknowledged to be the major driver of cancer death attributable to treatment challenges and poor prognosis. Classical cancer treatment regimens, such as chemotherapy or radiotherapy, can be used to treat lung cancer, but the appended adverse effects limit them. Because of the numerous side effects associated with these treatment modalities, it is crucial to strive to develop novel and better strategies for managing lung cancer. Attributes such as enhanced bioavailability, better in vivo stability, intestinal absorption pattern, solubility, prolonged and targeted distribution, and the superior therapeutic effectiveness of numerous anticancer drugs have all been boosted with the emergence of nano-based therapeutic systems. Lipid-based polymeric and inorganic nano-formulations are now being explored for the targeted delivery of chemotherapeutics for lung cancer treatment. Nano-based approaches are pioneering the route for primary and metastatic lung cancer diagnosis and treatment. The implementation and development of innovative nanocarriers for drug administration, particularly for developing cancer therapies, is an intriguing and challenging task in the scientific domain. The current article provides an overview of the delivery methods, such as passive and active targeting for chemotherapeutics to treat lung cancer. Combinatorial drug therapy and techniques to overcome drug resistance in lung cancer cells, as potential ways to increase treatment effectiveness, are also discussed. In addition, the clinical studies of the potential therapies at different stages and the associated challenges are also presented. A summary of patent literature has also been included to keep readers aware of the new and innovative nanotechnology-based ways to treat lung cancer.

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Nanomedicine Strategies for Management of Drug Resistance in Lung Cancer

Cited by 9 publications

References 202 publications

A New Hypoxia-Related Prognostic Risk Score (HPRS) Model Was Developed to Indicate Prognosis and Response to Immunotherapy for Lung Adenocarcinoma

A New Hypoxia-Related Prognostic Risk Score (HPRS) Model Was Developed to Indicate Prognosis and Response to Immunotherapy for Lung Adenocarcinoma

tRNA-derived fragments: mechanism of gene regulation and clinical application in lung cancer

Nano-Enabled Strategies for the Treatment of Lung Cancer: Potential Bottlenecks and Future Perspectives

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