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Войти в исследования Pediatric dermatology · 2004
Onychomycosis in children: a brief overview with treatment strategies.
СтатьяОбзорНазвание и аннотация: Перевод пока не подготовлен
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De Marco Gabriele, Marchesoni Antonio, Manara Maria, Gisondi Paolo, Idolazzi Luca, Ramonda Roberta, Piaserico Stefano, Cauli Alberto, Cimmino Marco Amedeo, Tomatis Veronica, Salvarani Carlo, Scrivo Rossana, Zanetti Anna, Carrara Greta, Scirè Carlo Alberto, Cattaneo Angelo
Аннотация
BACKGROUND: Despite expanding therapeutic options, the prognosis of lung squamous cell carcinoma (LUSC) remains poor. Immune checkpoint inhibitors benefit only a subset of patients, and epithelial-mesenchymal transition (EMT) has been implicated in invasion, metastasis, treatment resistance, and immune heterogeneity. Therefore, EMT-related biomarkers may offer improved risk stratification.
AIM: To identify differentially expressed EMT-related genes (DEEMTGs) in LUSC, construct an EMT-based prognostic signature, and evaluate its associations with the tumor microenvironment (TME), tumor mutational burden (TMB), and tissue-level expression patterns.
METHODS: The Cancer Genome Atlas (TCGA) RNA-seq and clinical data were analyzed to obtain DEEMTGs. A prognostic model was built using LASSO and multivariable Cox regression. Survival performance was assessed via Kaplan-Meier, ROC, and Cox analyses. Immune infiltration (CIBERSORT), stromal/immune scores (ESTIMATE), and TMB were compared between risk groups. Exploratory immunohistochemistry (IHC; n = 8) provided orthogonal expression validation.
RESULTS: A total of 1,651 DEEMTGs were identified, and a six-gene signature (GAB2, ALDOA, PCDHA3, TMEM92, ERH, IRS4) was established. The risk score independently predicted overall survival and corresponded to distinct TME patterns: low-risk tumors showed higher CD8+ T cells, activated CD4+ memory T cells, and naïve B cells, whereas high-risk tumors had more resting CD4+ memory T cells and M0 macrophages. TMB differences were nonsignificant. IHC provided directional protein-level support while acknowledging transcript-protein variability.
CONCLUSION: We developed a biologically interpretable EMT-based prognostic model that stratifies survival and reflects immune-microenvironment heterogeneity in LUSC. Larger, stage-balanced and immunotherapy-treated cohorts are needed to further validate its clinical utility.
AIM: To identify differentially expressed EMT-related genes (DEEMTGs) in LUSC, construct an EMT-based prognostic signature, and evaluate its associations with the tumor microenvironment (TME), tumor mutational burden (TMB), and tissue-level expression patterns.
METHODS: The Cancer Genome Atlas (TCGA) RNA-seq and clinical data were analyzed to obtain DEEMTGs. A prognostic model was built using LASSO and multivariable Cox regression. Survival performance was assessed via Kaplan-Meier, ROC, and Cox analyses. Immune infiltration (CIBERSORT), stromal/immune scores (ESTIMATE), and TMB were compared between risk groups. Exploratory immunohistochemistry (IHC; n = 8) provided orthogonal expression validation.
RESULTS: A total of 1,651 DEEMTGs were identified, and a six-gene signature (GAB2, ALDOA, PCDHA3, TMEM92, ERH, IRS4) was established. The risk score independently predicted overall survival and corresponded to distinct TME patterns: low-risk tumors showed higher CD8+ T cells, activated CD4+ memory T cells, and naïve B cells, whereas high-risk tumors had more resting CD4+ memory T cells and M0 macrophages. TMB differences were nonsignificant. IHC provided directional protein-level support while acknowledging transcript-protein variability.
CONCLUSION: We developed a biologically interpretable EMT-based prognostic model that stratifies survival and reflects immune-microenvironment heterogeneity in LUSC. Larger, stage-balanced and immunotherapy-treated cohorts are needed to further validate its clinical utility.