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

Journal: PLoS ONE

Article Title: Plasma SerpinA5 in conjunction with uterine artery pulsatility index and clinical risk factor for the early prediction of preeclampsia

doi: 10.1371/journal.pone.0258541

Figure Lengend Snippet: a. Single Prediction analysis. Receiver operating characteristic (ROC) curve analysis. As a single predictor SerpinA5, the AUC = 0.881 (95% CI 0.805–0.956)], with 89.58% sensitivity and 81.25% specificity. b. Combinated Prediction analysis. A combination of plasma SerpinA5, maternal factors and abnormal UtA-PI (above the 95th percentile) enhanced the predictive value for preeclampsia (United AUC was 0.946 (95% CI 0.905–0.988). (Pre-BMI: Pre-pregnancy BMI).

Article Snippet: Receiver operating characteristic (ROC) curves, the sensitivity, specificity, and area under the ROC curve (AUC) were calculated by Medcalc v19.6.1 (MedCalc Software bvba, Ostend, Belgium).

Techniques: Clinical Proteomics

Journal: PLoS Computational Biology

Article Title: A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

doi: 10.1371/journal.pcbi.1006483

Figure Lengend Snippet: A-C) The ROC curves for residue- (A), surface- (B), and interaction-based (C) comparison methods. The name of the tool is colored according to its corresponding ROC curve. The binding site comparison tools are sorted in descending order with respect to the AUC. Thin lines represent the resulting ROC curve for the scoring scheme that yielded the highest AUC. (A) A slightly higher AUC for SiteAlign was obtained if distance d2 was applied for binding site pair ranking. (B) For the surface-based methods, the Tanimoto (color) for Shaper or VolSite/Shaper and the ColorTanimoto for SiteHopper led to the highest AUC. (C) The use of the Tanimoto coefficient as similarity measure led to the highest AUC for TIFP(PDB). D-F) EFs for residue- (D), surface- (E), and interaction-based (F) comparison methods. A linear color gradient ranging from white for the highest value to gray to black for the lowest value was applied for the EFs at different percentages of screened data set.

Article Snippet: The AUC values for the resulting ROC curves were also calculated using KNIME.

Techniques: Residue, Comparison, Binding Assay

Journal: PLoS Computational Biology

Article Title: A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

doi: 10.1371/journal.pcbi.1006483

Figure Lengend Snippet: A-C) The ROC curves for residue- (A), surface- (B), and interaction-based (C) comparison methods. The name of the tool is colored according to its corresponding ROC curve. The binding site comparison tools are sorted in descending order with respect to the AUC. (A) The highest AUC was obtained for SiteAlign when using distance d1. (B) All Shaper comparisons led to higher AUCs for the scoring measure Tanimoto (color). SiteEngine results slightly improved the AUC for the distance scoring scheme. D-F) EFs for residue- (D), surface- (E), and interaction-based (F) comparison methods. A linear color gradient ranging from white for the highest value to gray to black for the lowest value was applied for the EFs at different percentages of screened data set.

Article Snippet: The AUC values for the resulting ROC curves were also calculated using KNIME.

Techniques: Residue, Comparison, Binding Assay

Journal: PLoS Computational Biology

Article Title: A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

doi: 10.1371/journal.pcbi.1006483

Figure Lengend Snippet: A-C) The ROC curves for residue- (A), surface- (B), and interaction-based (C) comparison methods. The name of the tool is colored according to its corresponding ROC curve. The binding site comparison tools are sorted in descending order with respect to their AUC. (A) PocketMatch showed the best AUC for the score PMScore min (thin orange line). (B) The scores SVA, RefTversky (color), RefTversky (color), RefTversky (color), RefTversky (color), and ColorTanimoto led to the highest AUC values for ProBiS, Shaper, Shaper(PDB), VolSite/Shaper, VolSite/Shaper(PDB), and SiteHopper, respectively (thin lines). (C) The highest AUC was obtained for IsoMIF and TIFP(PDB) when using taniM and the Tanimoto coefficient as similarity measure (thin lines). D-F) EFs for residue- (D), surface- (E), and interaction-based (F) comparison methods. A linear color gradient ranging from white for the highest value to gray to black for the lowest value was applied for the EFs at different percentages of screened data set.

Article Snippet: The AUC values for the resulting ROC curves were also calculated using KNIME.

Techniques: Residue, Comparison, Binding Assay

Journal: PLoS Computational Biology

Article Title: A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

doi: 10.1371/journal.pcbi.1006483

Figure Lengend Snippet: A-C) The ROC curves for residue- (A), surface- (B), and interaction-based (C) comparison methods. The name of the tool is colored according to its corresponding ROC curve. The binding site comparison tools are sorted in descending order with respect to their AUC. (A) PocketMatch showed the best AUC for the score PMScore min (thin orange line). (B) The scores SVA, Tanimoto (color), Tanimoto (color), RefTversky (color), RefTversky (color), and ColorTanimoto led to the highest AUC values for ProBiS, Shaper, Shaper(PDB), VolSite/Shaper, VolSite/Shaper(PDB), and SiteHopper, respectively (thin lines). (C) The highest AUC was obtained for TIFP(PDB) when using the Tanimoto coefficient as scoring measure (thin dark green line). D-F) EFs for residue- (D), surface- (E), and interaction-based (F) comparison methods. A linear color gradient ranging from white for the highest value to gray to black for the lowest value was applied for the EFs at different percentages of screened data set.

Article Snippet: The AUC values for the resulting ROC curves were also calculated using KNIME.

Techniques: Residue, Comparison, Binding Assay

Journal: PLoS Computational Biology

Article Title: A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

doi: 10.1371/journal.pcbi.1006483

Figure Lengend Snippet: A-C) The ROC curves for residue- (A), surface- (B), and interaction-based (C) comparison methods. The name of the tool is colored according to its corresponding ROC curve. The binding site comparison tools are sorted in descending order with respect to the AUC. (A) The best AUC for SiteAlign resulted from the d1 distance (thin red line). (B) For ProBiS, VolSite/Shaper, SiteEngine, and SiteHopper the scores SVA, Tanimoto (color), TotalScore, and ShapeTanimoto yielded the best AUC values (thin lines). (C) For TIFP(PDB), the use of the Hamming distance led to the best results with respect to AUC (thin line). D-F) EFs for residue- (D), surface- (E), and interaction-based (F) comparison methods. A linear color gradient ranging from white for the highest value to gray to black for the lowest value was applied for the EFs at different percentages of screened data set.

Article Snippet: The AUC values for the resulting ROC curves were also calculated using KNIME.

Techniques: Residue, Comparison, Binding Assay

Journal: PLoS Computational Biology

Article Title: A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

doi: 10.1371/journal.pcbi.1006483

Figure Lengend Snippet: A-C) The ROC curves for residue- (A), surface- (B), and interaction-based (C) comparison methods. The name of the tool is colored according to its corresponding ROC curve. The binding site comparison tools are sorted in descending order with respect to the AUC. (A) The thin red line represents the resulting ROC curve for SiteAlign when using the distance d1. (B) Thin lines represent the ROC curves for ProBiS, Shaper, Shaper(PDB), VolSite/Shaper, VolSite/Shaper(PDB), SiteEngine and SiteHopper when using the scoring schemes SVA, FitTversky (color), FitTversky (color), RefTversky (color), Tanimoto (fit), distance, and ShapeTanimoto, respectively. (C) The thin line represents the resulting ROC curve for IsoMIF and the taniMW score. D-F) EFs for residue- (D), surface- (E), and interaction-based (F) comparison methods. A linear color gradient ranging from white for the highest value to gray to black for the lowest value was applied for the EFs at different percentages of screened data set.

Article Snippet: The AUC values for the resulting ROC curves were also calculated using KNIME.

Techniques: Residue, Comparison, Binding Assay

Journal: PLoS Computational Biology

Article Title: A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

doi: 10.1371/journal.pcbi.1006483

Figure Lengend Snippet: A-C) The ROC curves for residue- (A), surface- (B), and interaction-based (C) comparison methods. The name of the tool is colored according to its corresponding ROC curve. The binding site comparison tools are sorted in descending order with respect to the AUC. (A) SiteAlign yielded a slightly better AUC if the distance d1 was used (thin line). (B) The best AUC values for ProBis, Shaper, Shaper(PDB), VolSite/Shaper, VolSite/Shaper(PDB), SiteEngine, and SiteHopper resulted from the scoring measures Zscore, Tanimoto (color), Tanimoto (color), Tanimoto (color), Tanimoto (color), TotalScore, and ShapeTanimoto, respectively (thin lines). D-F) EFs for residue- (D), surface- (E), and interaction-based (F) comparison methods. A linear color gradient ranging from white for the highest value to gray to black for the lowest value was applied for the EFs at different percentages of screened data set.

Article Snippet: The AUC values for the resulting ROC curves were also calculated using KNIME.

Techniques: Residue, Comparison, Binding Assay