Multifaceted Roles of Caveolin-1 in Lung Cancer: A New Investigation Focused on Tumor Occurrence, Development and Therapy

Shi, Yubo; Li, Jun; Lai, Xing-Ning; Jiang, Rui; Zhao, Ruichen; Xiong, Likuan

doi:10.3390/cancers12020291

Cited by 31 publications

(28 citation statements)

References 149 publications

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“…The top five proteins predicted by two models, NKX2-1, CAV1, CDH3, FN1 and YBX1, were exactly the same. The relationship between the expression of these five proteins and the prognosis of the cancer patients was almost consistent with that described in previous reports [ 23 , 24 , 25 , 26 , 27 , 28 ]. CAV-1 and YBX-1 are generally negative predictors of NSCLC outcomes [ 24 , 25 ].…”

Section: Resultssupporting

confidence: 90%

“…The relationship between the expression of these five proteins and the prognosis of the cancer patients was almost consistent with that described in previous reports [ 23 , 24 , 25 , 26 , 27 , 28 ]. CAV-1 and YBX-1 are generally negative predictors of NSCLC outcomes [ 24 , 25 ]. FN1 seems to be associated with cisplatin resistance and over-expression of CDH3 correlates to a poor prognosis in carcinomas of the breast, prostate, ovary, colon and stomach [ 29 , 30 ].…”

Section: Resultssupporting

confidence: 90%

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Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

et al. 2020

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Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

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Section: Resultssupporting

confidence: 90%

Section: Resultssupporting

confidence: 90%

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

et al. 2020

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“…Caveolin-1 has been shown as a lipid transporter in a variety of cell types [ 35 , 36 , 37 ]. Changes in the expression of this protein have been related to lung cancer prognosis [ 38 ], although some studies suggest that this relationship may be context-dependent [ 39 ]. This protein may constitute the entry point for Macrocybin into the cell and the fact that its expression is affected in tumor cells but not in nontumor cells may partially explain the preferential toxicity of the triglyceride for cancer cells.…”

Section: Discussionmentioning

confidence: 99%

Macrocybin, a Natural Mushroom Triglyceride, Reduces Tumor Growth In Vitro and In Vivo through Caveolin-Mediated Interference with the Actin Cytoskeleton

et al. 2020

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Mushrooms have been used for millennia as cancer remedies. Our goal was to screen several mushroom species from the rainforests of Costa Rica, looking for new antitumor molecules. Mushroom extracts were screened using two human cell lines: A549 (lung adenocarcinoma) and NL20 (immortalized normal lung epithelium). Extracts able to kill tumor cells while preserving non-tumor cells were considered “anticancer”. The mushroom with better properties was Macrocybe titans. Positive extracts were fractionated further and tested for biological activity on the cell lines. The chemical structure of the active compound was partially elucidated through nuclear magnetic resonance, mass spectrometry, and other ancillary techniques. Chemical analysis showed that the active molecule was a triglyceride containing oleic acid, palmitic acid, and a more complex fatty acid with two double bonds. The synthesis of all possible triglycerides and biological testing identified the natural compound, which was named Macrocybin. A xenograft study showed that Macrocybin significantly reduces A549 tumor growth. In addition, Macrocybin treatment resulted in the upregulation of Caveolin-1 expression and the disassembly of the actin cytoskeleton in tumor cells (but not in normal cells). In conclusion, we have shown that Macrocybin constitutes a new biologically active compound that may be taken into consideration for cancer treatment.

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“…Lung cancer is one of the most common malignant cancer types in both males and females [ 1 , 2 , 3 ]. It causes very high rates of morbidity and mortality in both sexes and is ranked as the most common cause of cancer-related deaths in the United States and other countries [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

et al. 2021

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The incidence and mortality rates of lung cancers are different between females and males. Therefore, sex information should be an important part of how to train and optimize a diagnostic model. However, most of the existing studies do not fully utilize this information. This study carried out a comparative investigation between sex-specific models and sex-independent models. Three feature selection algorithms and five classifiers were utilized to evaluate the contribution of the sex information to the detection of early-stage lung cancers. Both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) showed that the sex-specific models outperformed the sex-independent detection of early-stage lung cancers. The Venn plots suggested that females and males shared only a few transcriptomic biomarkers of early-stage lung cancers. Our experimental data suggested that sex information should be included in optimizing disease diagnosis models.

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Multifaceted Roles of Caveolin-1 in Lung Cancer: A New Investigation Focused on Tumor Occurrence, Development and Therapy

Cited by 31 publications

References 149 publications

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Macrocybin, a Natural Mushroom Triglyceride, Reduces Tumor Growth In Vitro and In Vivo through Caveolin-Mediated Interference with the Actin Cytoskeleton

Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

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