Fig 1.
Differential Expression Characteristics of Lysosome Signatures in PTC and Construction of the Prognostic Scoring System.
(A) Differential expression analysis of lysosomal genes between PTC samples and adjacent normal samples, with blue representing downregulation and red representing upregulation. The thresholds for differential expression are |Fold change| > 1 and p.adjust <0.05. (B) Univariate Cox analysis identifying LAG signatures associated with PTC prognosis. (C) LASSO analysis selecting prognostic LAG signature variables. (D) Construction of the LAG prognostic scoring system in PTC samples. (E) Clinical prognostic curve analysis for the LAG scoring subgroups.
Fig 2.
Identification of LAG Molecular Subtype Characteristics and Prognostic Analysis.
(A-C) Molecular subtype analysis of LAG in PTC samples. (D) PCA plot revealing the distribution characteristics of LAG molecular subtypes. (E) Clinical prognostic curve analysis of LAG molecular subtypes. (F) GSVA analysis showing the differential regulation of KEGG signaling pathways between LAG molecular subtypes.
Fig 3.
Immune Infiltration Landscape and Immunotherapy Response Evaluation of LAG Molecular Subtypes.
(A-C) Quantitative assessment of immune infiltration status. (D) Proportion calculation of 23 immune cell types based on the ssGSEA algorithm. (E-G) Prediction of immunotherapy response in LAG molecular subtypes.
Fig 4.
Validation of the Consistency and Stability of the LAG Scoring System Model.
(A) Differential analysis of LAG scores within LAG molecular subtypes. (B) Sankey diagram analysis of the relationship between LAG molecular subtypes, LAG scoring system, and PTC survival status. (C, D) Clinical survival curve analysis of LAG scoring subgroups in independent cohorts. (E, F) Time-dependent ROC curve analysis for 1-year, 3-year, and 5-year survival in independent cohorts.
Fig 5.
Construction of a Nomogram Diagnostic Model Based on Clinical Pathological Features and LAG Scoring System, and Clinical Pathological Subgroup Analysis.
(A) Nomogram diagnostic model predicting survival probabilities of PTC at different time points based on clinical pathological variables and LAG scores. (B) Time-dependent ROC curve analysis. (C) Assessment of the diagnostic ability of the LAG scoring system. (D) Distribution of LAG scores across clinical pathological features in PTC. (E) Clinical prognostic curve analysis of LAG scoring subgroups across different clinical pathological subgroups.
Fig 6.
Immune Microenvironment Landscape Characteristics and Immunotherapy Response Prediction in LAG Scoring Subgroups.
(A) Assessment of the proportion of 23 immune cell types based on the ssGSEA algorithm. (B-D) Prediction of immunotherapy response. (E) Correlation analysis between LAG prognostic signatures and immune infiltration characteristics.
Fig 7.
Potential Mechanism Analysis and Drug Sensitivity Prediction in LAG Scoring Subgroups.
(A) Differential expression analysis of genes between LAG scoring subgroups. (B, C) KEGG and GO enrichment analysis of differentially expressed genes. (D-H) Prediction of drug sensitivity in LAG scoring subgroups.
Fig 8.
Single-Cell Sequencing Analysis Reveals the Distribution Characteristics of LAG Prognostic Signatures in Cell Subpopulations.
(A, B) Quality control and normalization of single-cell sequencing data from 7 PTC samples. (C, D) t-SNE and UMAP dimensionality reduction model plots revealing the distribution characteristics of 22 cell subpopulations in PTC. (E) Heatmap analysis of marker gene expression across the 22 cell subpopulations. (F, G) Cell type annotation based on the singleR algorithm and dimensionality reduction model plot analysis (t-SNE/UMAP). (H) Violin plot analysis of LAG signatures across 8 cell types.
Fig 9.
DNASE2B regulated the proliferation and invasion of thyroid carcinoma cell.
(A, B) DNASE2B expression in Nthy-ori 3−1 and TPC-1 cells were analyzed by Western blot. (C, D) DNASE2B expression in TPC-1 cells was analyzed by Western blot after treatment with DNASE2B siRNA. (E, F) Colony formation assay was performed to detect the proliferation ability of cells after interfering with DNASE2B siRNA. (G, H) Cell invasion ability was decreased when transfected with DNASE2B siRNA. Cell invasion abilities were measured by a Transwell assay method. (I) MTT assay revealed that knockout of DNASE2B reduced the growth rate. *P < 0.05; **P < 0.01; ***P < 0.001.