Journal: Cancer Management and Research

Article Title: High Expression of lncRNA HEIH is Helpful in the Diagnosis of Non-Small Cell Lung Cancer and Predicts Poor Prognosis

doi: 10.2147/CMAR.S320965

Figure Lengend Snippet: LncRNA HEIH has a high diagnostic efficacy in NSCLC patients. ( A ) The diagnostic efficacy of lncRNA HEIH and CEA in LUSC patients was evaluated by ROC curve analysis; ( B ) the diagnostic efficacy of lncRNA HEIH and CEA in LUAD patients was evaluated by ROC curve analysis. MedCalc-comparison of ROC curves was used to compare and analyze the area difference under the ROC curve.

Article Snippet: MedCalc-comparison of ROC curves showed that the area under ROC curve of lncRNA HEIH was significantly higher than that of CEA ( P = 0.0011; 95% CI = 0.057–0.228), indicating that lncRNA HEIH had a higher diagnostic efficacy than CEA for LUAD.

Techniques: Diagnostic Assay, Comparison

Journal: Frontiers in Oncology

Article Title: Practical nomogram based on comprehensive CT texture analysis to preoperatively predict peritoneal occult metastasis of gastric cancer patients

doi: 10.3389/fonc.2022.882584

Figure Lengend Snippet: ROC curves of every continuous variable. (A) , primary tumor CT signs. (B) , texture features. The area under the curve (AUC) represented the accuracy of predicting for occult PM.

Article Snippet: The efficiency of the models was tested by the AUC of the ROC curve and compared by the DeLong test in MedCalc.

Techniques:

Journal: Frontiers in Oncology

Article Title: Practical nomogram based on comprehensive CT texture analysis to preoperatively predict peritoneal occult metastasis of gastric cancer patients

doi: 10.3389/fonc.2022.882584

Figure Lengend Snippet: ROC curves parameters of different models.

Article Snippet: The efficiency of the models was tested by the AUC of the ROC curve and compared by the DeLong test in MedCalc.

Techniques:

Journal: Frontiers in Oncology

Article Title: Practical nomogram based on comprehensive CT texture analysis to preoperatively predict peritoneal occult metastasis of gastric cancer patients

doi: 10.3389/fonc.2022.882584

Figure Lengend Snippet: Comparison of ROC curves AUC areas of different models.

Article Snippet: The efficiency of the models was tested by the AUC of the ROC curve and compared by the DeLong test in MedCalc.

Techniques: Comparison

Journal: Frontiers in Immunology

Article Title: Identification and functional characterization of T-cell exhaustion-associated lncRNA AL031775.1 in osteosarcoma: a novel therapeutic target

doi: 10.3389/fimmu.2025.1517971

Figure Lengend Snippet: Identifying T cell exhaustion-associated genes in osteosarcoma and constructing a risk prognostic model. (A) The heatmap of DEGs between TARGET-OS and GTEx databases, with elevated expression depicted in red and diminished expression in blue. (B) The intersection of DEGs and TEXRGs yielded osteosarcoma-associated differentially expressed TEXRGs. (C) The heatmap of osteosarcoma-associated differentially expressed TEXRGs, with heightened expression shown in red and reduced expression in blue. (D) The chord diagram presents the functional enrichment analysis of osteosarcoma-associated differentially expressed TEXRGs. (E) The functional enrichment network and table of osteosarcoma-associated differentially expressed TEXRGs. (F) Univariate Cox regression analysis identified 37 potential prognostic TEXRLs for osteosarcoma, comprising 12 high-risk TEXRLs and 25 low-risk TEXRLs. (G) LASSO regression analysis and determining the optimal penalty parameter for LASSO regression. (H) The survival status map and risk heatmap of risk model TEXRLs in the total sample group. (I) The Kaplan-Meier survival curve effectively demonstrates that patients in the red high-risk group exhibited a substantially lower overall survival rate compared to those in the blue low-risk group. (J) The survival analysis of the complete sample cohort ( p < 0.001), as well as the training cohort ( p < 0.001) and test cohort ( p = 0.005), demonstrated significant disparities in survival outcomes between patients categorized as red high-risk and blue low-risk. (K) Time-dependent ROC curves, 1 year (AUC = 0.821), 3 years (AUC = 0.861), and 5 years (AUC = 0.814). (L) Clinical ROC curves, Risk score (AUC = 0.821), Age (AUC = 0.453), Gender (AUC = 0.464), and Met (AUC = 0.905). (M, N) Univariate and multivariate COX regression analyses in the total sample group.

Article Snippet: ROC curves and area under the ROC curve (AUC) were calculated using MedCalc for Windows version 19.3.0 (MedCalc Software, Ostend, Belgium).

Techniques: Expressing, Functional Assay

Journal: Frontiers in Immunology

Article Title: Identification and functional characterization of T-cell exhaustion-associated lncRNA AL031775.1 in osteosarcoma: a novel therapeutic target

doi: 10.3389/fimmu.2025.1517971

Figure Lengend Snippet: Survival prediction validation of risk models in training and testing groups. (A, B) The survival status map and risk heatmap of risk model TEXRLs in the training group. (C) In the training group, the Kaplan-Meier survival curve effectively demonstrates that patients in the red high-risk group exhibited a substantially lower overall survival rate compared to those in the blue low-risk group. (D) Time-dependent ROC curves in the training group, 1 year (AUC = 0.966), 3 years (AUC = 0.993), and 5 years (AUC = 0.994). (E) Clinical ROC curves in the training group, Risk score (AUC = 0.966), Age (AUC = 0.325), Gender (AUC = 0.359), and Met (AUC = 0.856). (F, G) Univariate and multivariate COX regression analyses in the training group. (H, I) The survival status map and risk heatmap of risk model TEXRLs in the test group. (J) In the test group, the Kaplan-Meier survival curve effectively demonstrates that patients in the red high-risk group exhibited a substantially lower overall survival rate compared to those in the blue low-risk group. (K) Time-dependent ROC curves in the test group, 1 year (AUC = 0.667), 3 years (AUC = 0.741), and 5 years (AUC = 0.694). (L) Clinical ROC curves in the test group, Risk score (AUC = 0.667), Age (AUC = 0.603), Gender (AUC = 0.570), and Met (AUC = 0.956). (M, N) Univariate and multivariate COX regression analyses in the test group.

Article Snippet: ROC curves and area under the ROC curve (AUC) were calculated using MedCalc for Windows version 19.3.0 (MedCalc Software, Ostend, Belgium).

Techniques: Biomarker Discovery

Journal: Frontiers in Immunology

Article Title: Identification and functional characterization of T-cell exhaustion-associated lncRNA AL031775.1 in osteosarcoma: a novel therapeutic target

doi: 10.3389/fimmu.2025.1517971

Figure Lengend Snippet: Analysis of the prognostic prediction ability of single genes from the risk model. (A) The effect of high AC090559.1 expression on the prognosis of osteosarcoma overall survival is statistically significant. (B) The effect of high AC135178.4 expression on the prognosis of osteosarcoma overall survival is statistically significant. (C) Kaplan-Meier survival curve analysis indicates that the expression level of AL031775.1cannot be used to predict the survival prognosis of osteosarcoma patients. (D) The effect of low LINC01060 expression on the prognosis of osteosarcoma overall survival is statistically significant. (E) The effect of high LINC02777 expression on the prognosis of osteosarcoma overall survival is statistically significant. (F) Kaplan-Meier survival curve analysis indicates that the expression level of PSMB8-AS1 cannot be used to predict the survival prognosis of osteosarcoma patients. (G) Time-dependent ROC curves of AC090559.1, 1 year (AUC = 0.802), 3 years (AUC = 0.693), and 5 years (AUC = 0.607). (H) Time-dependent ROC curves of AC135178.4, 1 year (AUC = 0.680), 3 years (AUC = 0.593), and 5 years (AUC = 0.579). (I) Time-dependent ROC curves of AL031775.1, 1 year (AUC = 0.671), 3 years (AUC = 0.735), and 5 years (AUC = 0.712). (J) Time-dependent ROC curves of LINC01060, 1 year (AUC = 0.522), 3 years (AUC = 0.681), and 5 years (AUC = 0.678). (K) Time-dependent ROC curves of LINC02777, 1 year (AUC = 0.676), 3 years (AUC = 0.709), and 5 years (AUC = 0.663). (L) Time-dependent ROC curves of PSMB8-AS1, 1 year (AUC = 0.698), 3 years (AUC = 0.655), and 5 years (AUC = 0.521).

Article Snippet: ROC curves and area under the ROC curve (AUC) were calculated using MedCalc for Windows version 19.3.0 (MedCalc Software, Ostend, Belgium).

Techniques: Expressing