Fig 1.
Pan-cancer expression characteristics of SEPN1 and its association with the TME.
(A) Differential expression of SEPN1 in cancer and adjacent normal tissues across pan-cancer. (B) Prognostic impact of SEPN1 on OS, DSS, and PFI in pan-cancer. (C) Forest plot of SEPN1’s effect on OS in pan-cancer based on CoxPHs. (D-F) Correlations of SEPN1 expression with immune, stromal, and ESTIMATE scores in pan-cancer. (G) Correlations of SEPN1 expression with various immune cells across pan-cancer using multiple TME algorithms.
Fig 2.
The role of SEPN1 in glioma prognosis.
(A-D) KM curves of high and low SEPN1 expression groups in TCGA-LGG, CGGA-693, CGGA-325, and GSE16011 cohorts. (E-H) Forest plots of SEPN1 expression groups and other clinical indicators based on univariate and multivariate CoxPHs in TCGA-LGG, CGGA-693, CGGA-325, and GSE16011 cohorts. (I) Representative IHC staining images of high and low SEPN1 expression groups in the ZN-Glioma cohort. (J) KM curves of high and low SEPN1 expression groups in the ZN-Glioma cohort. (K) Forest plot of SEPN1 expression groups and other clinical indicators based on univariate and multivariate CoxPHs in the ZN-Glioma cohort.
Fig 3.
Expression characteristics of SEPN1 at the single-cell level in glioma.
(A) Single-cell clustering in the GSE131928 cohort using 10X Genomics technology. (B) Expression of SEPN1 in cell clusters identified in the GSE131928 cohort using 10X Genomics technology. (C) Single-cell clustering in the GSE131928 cohort using Smart-seq2 technology. (D) Expression of SEPN1 in cell clusters identified in the GSE131928 cohort using Smart-seq2 technology.
Fig 4.
Functional enrichment analysis of SEPN1 in glioma.
(A-D) Partial results of GO-based BP analysis in TCGA-LGG, CGGA-693, CGGA-325, and GSE16011 cohorts. (E-H) Partial results of KEGG-based analysis in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG cohorts.
Fig 5.
SEPN1 deficiency promotes glioblastoma cell proliferation, G2M cycle arrest and apoptosis.
(A) The mRNA level of SEPN1 was weakened by si-SEPN1 transfection in U251 and U87 cell lines. (B) Knockdown of SEPN1 inhibited the proliferation of the U251 and U87 cell lines. (C) Clone formation assay showed the knockdown of SEPN1 could restrain the growth capacity of GBM cells. (D, E) The si-SEPN1 induced increased cell apoptosis and G2/M phase cycle arrest in U251 and U87 cell lines.
Fig 6.
(A) Association of SRS with prognosis in pan-cancer. (B) Construction of a nomogram based on SRS and other clinical indicators to comprehensively predict patient survival in the TCGA-LGG cohort. (C) Calibration curves plotted to evaluate the predictive performance of the nomogram. (D) Heatmaps showing the immune profiles in TCGA-LGG, CGGA-693, CGGA-325, and GSE16011 cohorts, including immune score, stromal score, and enrichment scores of 24 TME cell types. (E) Drugs with IC50s significantly correlated with SRS in the GDSC database for TCGA-LGG, CGGA-693, CGGA-325, and GSE16011 cohorts. (F-I) CMap analyses based on DEGs between high and low SRS groups, showing drugs with anti-glioma potential in TCGA-LGG, CGGA-693, CGGA-325, and GSE16011 cohorts.
Fig 7.
The pan-cancer analyses of selenoprotein family.
(A) The heatmap displaying FC and false discovery rate (FDR) values illustrates the expression of the selenoprotein family in pan-cancer. The histogram illustrates the number of genes significantly differentially expressed. (B) Associations between selenoprotein family expression and pan-cancer prognosis were assessed using CoxPHs. A hazard ratio (HR)>1 indicates unfavorable associations, while an HR<1 indicates favorable associations. Grey indicates p-values>0.05. (C) Amplification and deletion rates of selenoprotein families in pan-cancer calculated using CNV data (threshold set at 0.05).