Background/Objectives: Immune checkpoint blockade (ICB) has emerged as a promising therapeutic option for hepatocellular carcinoma (HCC), yet reliable biomarkers to predict clinical outcomes remain limited. Tumor-associated macrophages (TAMs) are increasingly recognized as key regulators of the tumor immune microenvironment. Methods: We interrogated a publicly available HCC single-cell RNA sequencing (scRNA-seq) dataset to characterize intratumoral immune cell subpopulations. Through unsupervised clustering and gene signature analysis, we identified a distinct subset of SPP1 (secreted phosphoprotein 1, also known as osteopontin) and BCL2A1 (Bcl-2-related protein A1) double-positive TAMs. Their abundance was quantified and associated with patient outcomes. Further independent HCC transcriptomic datasets with annotated PD1-based ICB response status were used for examination. Results: Across the discovery (GSE149614; n = 10) cohort, elevated expression of intratumoral SPP1⁺BCL2A1⁺ TAMs was identified in HCC. In the ICB datasets (GSE151530; n = 4), patients with high SPP1⁺BCL2A1⁺ TAM expression further exhibited significantly poorer responses to ICB therapy. Further, the validation cohort (GSE206325; n = 18) confirmed these findings accordingly. Notably, these TAMs were expressed thoroughly within the immunosuppressive T-cell microenvironment in non-responders but were distinctly expressed among the cytotoxic T-cell responses in responders. Conclusions: Our findings identify SPP1⁺BCL2A1⁺ TAMs as a poor prognostic biomarker in HCC patients undergoing ICB therapy. By promoting an immunosuppressive microenvironment, SPP1⁺BCL2A1⁺ TAMs, which are survival-advantaged, may represent both a predictive marker and a potential therapeutic target to enhance the efficacy of immunotherapy.

Sepsis is a major cause of acute kidney injury, progressing to sepsis-associated acute kidney disease (SA-AKD). This study explores SA-AKD prediction by combining immune cell profiling. Peripheral immune cell expression and phenotypes were analyzed in sepsis patients without ( n = 97) and with (n = 41) SA-AKD, admitted to a hospital (2020–2022). Blood urea nitrogen and creatinine levels were measured, and a decision tree (DT)-based model was used to evaluate their predictive power in the training ( n = 106) and validation ( n = 32) cohorts. The DT model, incorporating naïve Treg and CD56 ^dim NK cells along with clinical parameters, showed high accuracy in predicting SA-AKD. The model using blood urea nitrogen as the first node reached 89.62% accuracy (sensitivity: 94.4% and specificity: 87.14%; area under the curve = 0.91). The model starting with creatinine showed 89.62% accuracy. Validation results confirmed an 81.25% accuracy. Profiling specific immune cells may enable pre-evaluation of SA-AKD.