A Nomogram Model Incorporating PVALB Expression for Individualized Prognostic Prediction in Glioma

Technical Summary: A Nomogram Model Incorporating PVALB Expression for Individualized Prognostic Prediction in Glioma

Problem Statement
Glioma remains a highly malignant brain tumor with a poor prognosis, characterized by diffuse invasion and therapeutic resistance. The median overall survival (OS) is limited to 12–18 months, and the five-year OS rate is approximately 36%. A critical driver of glioma progression is the immunosuppressive tumor microenvironment (TME), particularly the polarization of tumor-associated macrophages/microglia (TAMs/GAMs) into a pro-tumor M2 phenotype. While physical exercise has been shown to modulate the immune system and delay tumor progression in various cancers, the specific molecular mechanisms linking exercise, the TME, and glioma outcomes remain unclear. Furthermore, there is a need for improved prognostic tools that integrate molecular biomarkers reflecting TME interactions, beyond traditional tumor-intrinsic markers.

Methodology
This study employed a multi-faceted approach combining in vivo animal experiments, retrospective clinical cohort analysis, and bioinformatics:

1. In Vivo Validation: A murine glioma model was established using GL261 cells subcutaneously injected into athymic nude mice. The study evaluated two interventions:

   • Exercise Intervention: Mice underwent a one-month exercise regimen starting one week prior to tumor inoculation.
   • PVALB Treatment: Tumor-bearing mice received intraperitoneal injections of Parvalbumin (PVALB) (20 µg/mouse/day) for 14 days.
   • Tumor volume, EMT biomarkers, and PVALB expression were measured to assess anti-tumor efficacy.

2. Bioinformatics Analysis:

   • Data Sources: RNA-seq data from 1,987 glioma samples were analyzed from three public databases: CGGA (n=979), TCGA (n=666), and Rembrandt (n=342).
   • Immune Profiling: Single-sample Gene Set Enrichment Analysis (ssGSEA) was used to quantify the infiltration of 28 immune cell types.
   • Correlation Analysis: Spearman correlation was used to link PVALB expression with immune cell abundance and immune checkpoint markers (PD-1, CTLA-4).

3. Clinical Cohort and Validation:

   • Patient Data: A retrospective cohort of 210 glioma patients from two independent hospitals (Shanghai East Hospital and Wenzhou Medical University) was analyzed. Samples were collected between 2021 and 2023.
   • Immunohistochemistry (IHC): PVALB protein expression was validated in FFPE tissue sections.
   • Statistical Modeling: Patients were split into a training cohort (Hospital A, n=117) and a validation cohort (Hospital B, n=93). Univariate and multivariate Cox proportional hazards regression were performed to identify independent prognostic factors.
   • Nomogram Development: A prognostic nomogram was constructed integrating PVALB expression, IDH1 mutation status, and WHO grade. Model performance was evaluated using the Concordance Index (C-index), calibration curves, time-dependent ROC analysis, and Decision Curve Analysis (DCA).

Key Results

• Anti-Tumor Effects of Exercise and PVALB:

  • Exercise intervention resulted in an approximately 33% reduction in tumor volume compared to controls.
  • Direct PVALB treatment significantly inhibited tumor growth and slowed progression even after treatment cessation.
  • Both exercise and PVALB treatment suppressed Epithelial-Mesenchymal Transition (EMT) biomarkers in tumor tissues.

• PVALB Expression and Glioma Grade:

  • PVALB mRNA and protein expression levels decreased significantly as glioma grade increased (from Grade II to Grade IV) across all public datasets and the clinical cohort.
  • High PVALB expression was significantly associated with lower WHO grades and IDH1 mutant status.

• Prognostic Significance:

  • Kaplan-Meier survival analysis consistently showed that patients with high PVALB expression had significantly better Overall Survival (OS) across all datasets.
  • Multivariate Cox regression identified low PVALB expression as an independent risk factor for poor OS (HR = 0.31 for high vs. low in univariate; HR = 0.41 in multivariate), alongside high WHO grade and wild-type IDH1.

• Immune Microenvironment Association:

  • High PVALB expression was inversely correlated with the infiltration of immunosuppressive cells, specifically Myeloid-Derived Suppressor Cells (MDSCs) and Regulatory T cells (Tregs).
  • High PVALB expression was associated with lower expression levels of immune checkpoint proteins (PD-1, CTLA-4), suggesting a less immunosuppressive TME.

• Nomogram Performance:

  • The developed nomogram (incorporating PVALB, IDH1, and WHO grade) demonstrated strong predictive accuracy.
  • Time-dependent AUC values for 3-year OS were 0.90 in the training set and 0.89 in the validation set.
  • Calibration curves indicated good agreement between predicted and observed probabilities, and DCA confirmed the clinical utility of the model over single factors.

Significance and Claims
The paper claims that PVALB is a novel, independent favorable prognostic biomarker in glioma. Its significance lies in three main areas:

1. Mechanistic Insight: The study provides evidence that PVALB, an exercise-induced myokine, exerts anti-tumor effects potentially by suppressing M2 macrophage polarization via binding to CSF1R. This links physical activity to a specific molecular mechanism that modulates the glioma TME.
2. Clinical Utility: The validated nomogram offers a practical, individualized tool for risk stratification. By integrating a TME-related biomarker (PVALB) with established clinical markers (IDH1, WHO grade), the model bridges the gap between tumor biology and clinical decision-making, potentially improving the accuracy of survival predictions compared to traditional models.
3. Therapeutic Potential: The findings suggest that PVALB or exercise-induced PVALB elevation could represent a potential therapeutic strategy to modulate the immunosuppressive TME in glioma, although the paper notes that further functional validation is required.

The authors acknowledge limitations, including the retrospective nature of the clinical study, potential selection bias, and the need for longer follow-up to capture long-term survival outcomes. However, they conclude that the integration of PVALB into prognostic models represents a significant step toward personalized management strategies for glioma patients.