PPP1R81 correlates with the survival and cell proliferation in lower-grade glioma

Abstract

Background: The specific functions of PPP1R81 has been elucidated in multiple cancers; however, its role in lower-grade glioma (LGG) remains unknown. In this research, we inspected the specific role of PPP1R81 in LGG.

Methods: We totally evaluated the expression pattern and prognostic role of PPP1R81 in multitudinous tumors. Subsequently, we systematically examined the connection between PPP1R81 expression and prognosis, clinical characteristics, biological functions, genetic variations, and immunological characteristics in LGG according to the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Altas (CGGA) databases. In vitro experiments were executed to inspect the expression level and specific roles of PPP1R81 in LGG.

Results: PPP1R81 was elevated in multiple tumors and was tightly linked to a poor prognosis. LGG with higher expression of PPP1R81 showed poorer prognosis compared with lower expression of PPP1R81. The results of univariate and multivariate Cox regression analyses confirmed that the expression of PPP1R81 was an independent prognostic biomarker of LGG. Immune cell infiltration, immune checkpoint genes (ICPGs), copy number alterations (CNA), and tumor mutation burden (TMB) were also closely associated with PPP1R81 expression in LGG. In vitro experiments demonstrated that PPP1R81 was up-regulated and closely interrelated with cell proliferation and cell cycle in LGG.

Conclusion: PPP1R81 was an independent prognostic signature and underlying therapeutic target for patients with LGG.

Keywords: PPP1R81; cell proliferation; immune infiltration; lower-grade glioma; prognostic signature.

Figure 1. Flowchart showing the processes used in this study

Figure 2. Pan-cancer analysis of PPP1R81 in 33 cancers

(A) Distinct expression of PPP1R81 in various tumor tissues and relevant normal tissues. (B) Univariate Cox regression analysis of PPP1R81 expression in multiple tumors. (C) Kaplan–Meier analysis of PPP1R81 in pan-LGG. (D) Differential TMB in different cancers. (E) Co-expression of ICPGs in different cancers (*P<0.05, **P<0.01, ***P<0.001).

Figure 3. Clinical relevance of PPP1R81 in LGG patients

(A) Connection between PPP1R81 expression and LGG clinical traits in TCGA cohort. (B) Analysis of the variance in PPP1R81 expression and clinical traits in TCGA dataset. Prognostic analysis of high-PPP1R81 and low-PPP1R81 subtypes in TCGA (C) and CGGA (D) cohorts. Distribution of the risk score, OS, and OS status of the high-PPP1R81 and low-PPP1R81 subtypes in TCGA (E) and CGGA (F) datasets. ROC curves reflecting the predictive capacity of the risk score in TCGA (G) and CGGA (H) cohorts (*P<0.05, **P<0.01, ***P<0.001).

Figure 4. Cox regression analysis and nomogram model for LGG patients

Univariate and multivariate Cox regression analysis of clinical traits and expression of PPP1R81 in TCGA (A) and CGGA (B) cohorts. Nomogram model created with WHO grade, IDH mutation, 1p/19q codel, and PPP1R81 expression in TCGA cohort (C). Calibration curves: confirming the accuracy of predicting 1/3/5-year OS in TCGA (D) and CGGA (E) datasets.

Figure 5. Biological functions of PPP1R81 in LGG in TCGA database

(A) Functional enrichment analyses for PPP1R81 expression in patients with LGG. (B) GSVA for PPP1R81 in LGG patients.

Figure 6. Comparisons of somatic variations between low-PPP1R81 and high-PPP1R81 expression subtypes in TCGA

(A) Circos plots of the low-PPP1R81 and high-PPP1R81 subtypes revealed chromosome amplifications and deletions and boxplots exhibited greater burdens of copy number amplifications and deletions in the high-PPP1R81 expression subtype. The waterfall plots showing the mutated genes in the low-PPP1R81 subtype (B) and the high-PPP1R81 subtype (C). (D,E) TMB levels were positively linked to the expression of PPP1R81 (*P<0.05, **P<0.01, ***P<0.001).

Figure 7. Different TIME and immunological patterns of the low-PPP1R81 and high-PPP1R81 expression subtypes in TCGA

(A) Comparisons of the ESTIMATE, stromal, immune scores, and tumor purity between the two subtypes. (B) Distribution and abundance of 22 immune cells between the two subtypes. (C) Distinct immune-associated functions between the two subtypes. (D) Differential analysis of 25 ICPG expression levels between the two subtypes. (E) Correlation analysis between PPP1R81 expression and six common ICPGs expression (*P<0.05, **P<0.01, ***P<0.001).

Figure 8. In vitro experiments of PPP1R81 in LGG

(A) qRT-PCR analysis of PPP1R81 expression in LGG and NHA cell lines. (B) The cell viability of si-PPP1R81-transfected and si-NC-transfected SW1088 cells by CCK-8 assays. (C,D) Effect of down-regulation of PPP1R81 on colony formation in SW1088 cells was assessed. (E,F) EdU assays were executed to evaluate the cell proliferation after PPP1R81 knockdown in SW1088 cells. (G,H) Cell cycle assays were conducted to ascertain the cell distribution of the SW1088 cell lines after knockdown PPP1R81 (*P<0.05, **P<0.01, ***P<0.001).