Thymic Stromal Lymphopoietin Induction Suppresses Lung Cancer Development

Guennoun, Ranya; Hojanazarova, Jennet; Trerice, Kathryn E.; Azin, Marjan; McGoldrick, Matthew T.; Schiferle, Erik; Stover, Michael P; Demehri, Shadmehr

doi:10.3390/cancers14092173

Cited by 9 publications

(4 citation statements)

References 38 publications

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“…(D) Known functional roles of the putative coding gene biomarkers (TSLP and SPRY2) in breast cancer. TSLP were shown to inhibit the breast tumorigenesis through Th2 (Boieri et al, 2022; Demehri et al, 2016b; Guennoun et al, 2022), and the repressing the expression of SPRY2 would lead to promote the breast cancer progression through the hyperactivation of the Ras/Raf/ERK pathway (Faratian et al, 2011; Hanafusa et al, 2002; Kawazoe & Taniguchi, 2019). (E-G) The five putative lncRNA-biomarkers are contained in three co-expression clusters (Cluster I, II and III).…”

Section: Resultsmentioning

confidence: 99%

“…The five lncRNA biomarkers are coexpressed with three groups of coding-genes (Fig 2E and F). Among these five lncRNA biomarkers: 1) the HSALNG002284 was co-expressed with coding genes involved in extracellular matrix structural constituent, collagen binding and integrin binding ( (Boieri et al, 2022;Demehri et al, 2016b;Guennoun et al, 2022), and the repressing the expression of SPRY2 would lead to promote the breast cancer progression through the hyperactivation of the Ras/Raf/ERK pathway (Faratian et al, 2011;Hanafusa et al, 2002;Kawazoe & Taniguchi, 2019).…”

Section: Bambi Identifies Diagnostic Biomarkers With High Prediction ...mentioning

confidence: 99%

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BAMBI: Integrativebiostatistical andartificial-intelligencemodels discover coding and non-coding RNA genes asbiomarkers

Zhou,

Li,

Liu

et al. 2024

Preprint

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Accurate disease diagnosis and prognosis are crucial for effective treatment management and improving patient outcomes. However, accurately detecting early signs of certain diseases or recurrence remains challenging. Existing machine-learning methods for identifying gene expression biomarkers have several limitations, including poor performance on independent test datasets, inability to directly process omics data, and difficulty in identifying noncoding RNA genes as biomarkers. Additionally, these methods may not provide sufficient biological interpretation of their results, and the panel biomarkers they identify may not be suitable for clinical application. To address these limitations, we have developed a new computational method called BAMBI, which integrates multiple machine-learning algorithms and statistical approaches to identify putative coding and noncoding genes as biomarkers for disease diagnosis and prognosis. We evaluated BAMBI ability to identify diagnostic and prognostic biomarkers by analyzing multiple RNA-seq datasets from cancerous and non-cancerous diseases at population levels. The results from BAMBI demonstrate significant biological interpretability and state-of-the-art prediction performance. When the singular gene identified by BAMBI is used as a diagnostic biomarker, it achieves a balance accuracy exceeding 95% in studies of both breast cancer and psoriasis. Additionally, the prognostic biomarkers that BAMBI identifies from RNA-seq data of Acute Myeloid Leukemia (AML) patients significantly correlate with the survival rates in an independent AML patient cohort. Additionally, BAMBI outperforms existing methods by delivering more robust results, identifying biomarkers with fewer genes, and simultaneously achieving superior prediction accuracy. We have implemented BAMBI into user-friendly software for the research community. In summary, BAMBI serves as a more reliable pipeline for identifying both coding and noncoding genes as biosignature markers, enhancing the accuracy of disease diagnosis and prognosis. BAMBI is available via https://github.com/CZhouLab/BAMBI.

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Section: Resultsmentioning

confidence: 99%

Section: Bambi Identifies Diagnostic Biomarkers With High Prediction ...mentioning

confidence: 99%

BAMBI: Integrativebiostatistical andartificial-intelligencemodels discover coding and non-coding RNA genes asbiomarkers

Zhou,

Li,

Liu

et al. 2024

Preprint

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“…51 Systemic TSLP induction has driven a significant Th2 immunity to prevent tumorigenesis progression from atypical alveolar hyperplasia to LUAD. 52 Moreover, serum IgE levels may be related to antitumor response in patients with atopic diseases. IgE plays an essential role in triggering immune responses to cancer cells, such as in the pancreas, colon, and rectum.…”

Section: Discussionmentioning

confidence: 99%

“…TSLP is an efficient activator of dendritic cells that promotes Th2‐mediated immune response 51 . Systemic TSLP induction has driven a significant Th2 immunity to prevent tumorigenesis progression from atypical alveolar hyperplasia to LUAD 52 . Moreover, serum IgE levels may be related to antitumor response in patients with atopic diseases.…”

Section: Discussionmentioning

confidence: 99%

The causal effect of atopic dermatitis on lung cancer: A Mendelian randomization study

Huang,

Wen,

et al. 2024

Skin Research and Technology

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BackgroundGrowing evidence has shown that atopic dermatitis (AD) may decrease lung cancer (LC) risk. However, the causality between the two diseases is inconsistent and controversial. Therefore, we explored the causal relationship between AD and different histological subtypes of LC by using the Mendelian randomization (MR) method.Materials and methodsWe conducted the MR study based on summary statistics from the genome‐wide association studies (GWAS) of AD (10,788 cases and 30,047 controls) and LC (29,266 cases and 56,450 controls). Instrumental variables (IVs) were obtained after removing SNPs associated with potential confounders. We employed inverse‐variance weighted (IVW), MR‐Egger, and weighted median methods to pool estimates, and performed a comprehensive sensitivity analysis.ResultsThe results of the IVW method suggested that AD may decrease the risk of developing lung adenocarcinoma (LUAD) (OR = 0.91, 95% CI: 0.85‐0.97, P = 0.007). Moreover, no causality was identified between AD and overall LC (OR = 0.96, 95% CI: 0.91–1.01, P = 0.101), lung squamous cell carcinoma (LUSC) (OR = 1.04, 95% CI: 0.96–1.036, P = 0.324), and small cell lung carcinoma (SCLC) (OR = 0.95, 95% CI: 0.82–1.10, P = 0.512). A comprehensive sensitivity test showed the robustness of our results.ConclusionThe present study indicates that AD may decrease the risk of LUAD in the European population, which needs additional investigations to identify the potential molecular mechanisms.

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