Molecular and clinical insights into HIV-associated and HIV-negative aggressive B-cell lymphomas: prognostic quantitative biomarker analysis and therapeutic implications

Abstract

Background: HIV-associated lymphomas (HALs) exhibit aggressive features and poorer prognosis compared to HIV-negative lymphomas. However, their molecular and clinicopathological characteristics remain unclear in the antiretroviral therapy (ART) era.

Methods: We retrospectively analyzed 208 lymphoma patients (57 HALs, 151 HIV-negative lymphomas) diagnosed between July 2019 and March 2024. Quantitative immunohistochemistry evaluated expression levels of Ki67, CD10, BCL6, MUM1, BCL2, and MYC. Independent prognostic factors were identified using multivariate Cox regression analysis, and a survival prediction model was validated by receiver operating characteristic (ROC) curve analysis.

Results: HALs exhibited significantly higher proliferative activity (Ki67 AOD: 0.92 vs. 0.82, P < 0.001), more advanced disease stages (Ann Arbor stage III/IV: 77.2% vs. 60.0%, P = 0.022), and increased Epstein-Barr virus (EBV) positivity (51.1% vs. 17.9%, P < 0.001). Immunophenotyping revealed a GCB-like phenotype in HALs, characterized by elevated CD10 and BCL6 expression and decreased MUM1 and BCL2 expression. Patients with HALs had significantly shorter survival (median: 32.1 vs. 46.1 months, P < 0.001). Multivariate analysis identified Ki67 AOD (hazard ratio [HR] = 3.04, 95% confidence interval [CI]: 3.85-10.85), International Prognostic Index (IPI) (HR = 9.35, 95% CI: 4.20-20.82), and ART duration (protective, HR = 0.29/year, 95% CI: 0.19-0.45) as independent prognostic factors. The survival model demonstrated strong predictive accuracy (1-year area under the curve [AUC] = 0.831).

Conclusions: HALs exhibit distinct molecular profiles-including elevated EBV infection, a GCB-like phenotype, increased Ki67 AOD, and decreased BCL2 expression-that contribute to significantly poorer survival compared to HIV-negative lymphomas. Integrating Ki67 AOD and IPI scores into prognostic models may enhance individualized prognosis and optimize treatment strategies for HAL patients.

Keywords: Epstein-Barr virus; HIV-associated lymphomas; aggressive B-cell lymphoma; prognostic modelling; quantitative immunophenotyping.

Figure 1

Flowchart illustrating the inclusion and exclusion criteria for patient selection.

Figure 2

Kaplan-Meier survival analysis of lymphoma patients stratified by HIV status. (A) One-year survival curves for the HIV⁺ and HIV^– groups (log-rank test, p<0.001). (B) Overall survival curves for the HIV⁺ and HIV^– groups (log-rank test, p<0.001). (C) One-year survival curves for the HIV⁺ and HIV^– groups in the aggressive B-cell lymphoma subgroup (log-rank test, p<0.001). (D) Overall survival curves for the HIV⁺ and HIV^– groups in the aggressive B-cell lymphoma subgroup (log-rank test, p=0.003).

Figure 3

Histopathological analysis of lymphoma tissue samples. Comparison of tissue morphology between HIV⁺ and HIV⁻ patients. The images show lymphoma tissues at two magnifications: 200× (top row) and 400× (bottom row).

Figure 4

Immunohistochemical staining of lymphoma tissue samples. Immunohistochemical analysis of lymphoma tissues from HIV⁺ and HIV⁻ patients, showing expression of the following markets: Top rows: CD10 and BCL6 at 200× magnification and 400× magnification. Middle rows: MUM1 and BCL2 at 200×and 400×. Bottom rows: Ki67 and MYC at 200×and 400×.

Figure 5

Prognostic modeling and analysis for lymphoma survival outcomes. (A) Nomogram based on clinical features and IHC markers for predicting 1-, 2-, and 3-year survival probabilities in lymphoma patients. (B) ROC curves showing the performance of the nomogram at different time points (1-, 2-, and 3-year survival). (C) Forest plot showing Hazard Ratios (HR) with their respective 95% confidence intervals (CI) for various prognostic factors associated with survival outcomes in HIV⁺ lymphomas.