Killer cell lectin-like receptor G1 (KLRG1) is an immune checkpoint receptor expressed predominantly in NK and T-cell subsets that downregulates the activation and proliferation of immune cells and participates in cell-mediated immune responses. Accumulating evidence has demonstrated the importance of KLRG1 as a noteworthy disease marker and therapeutic target that can influence disease onset, progression, and prognosis. Blocking KLRG1 has been shown to effectively mitigate the effects of downregulation in various mouse tumor models, including solid tumors and hematologic malignancies. However, KLRG1 inhibitors have not yet been approved for human use, and the understanding of KLRG1 expression and its mechanism of action in various diseases remains incomplete. In this review, we explore alterations in the distribution, structure, and signaling pathways of KLRG1 in immune cells and summarize its expression patterns and roles in the development and progression of autoimmune diseases, infectious diseases, and cancers. Additionally, we discuss the potential applications of KLRG1 as a tool for tumor immunotherapy.

Acute myeloid leukemia (AML) is characterized by abundant immature myeloid cells, relapse and refractory due to leukemia stem cells (LSCs). Bone marrow mesenchymal stem/ stromal cells (BMSCs) supported LSCs survival, meanwhile, chemotherapy improved connexin43 (CX43) expression. CX43, as the most intercellular gap junction, facilitated transmit mitochondria from BMSCs into AML. We hypothesized that increased mitochondria transferred from BMSCs supported metabolic remodeling in LSCs to sustain their stemness. Primary BMSCs from AML patients were isolated. CX43-BMSCs, overexpressing CX43, were cocultured with KG-1a cells. Fluorescence and confocal microscopy observed mitochondrial transfer. Flow cytometry, EdU assay, and clonogenicity evaluated cell cycle, proliferation, and clonogenic potential. Xenograft mouse models were used to evaluate the tumorigenicity of KG-1a in vivo. Seahorse, RNA-seq, and LC-MS assessed mitochondrial function, transcriptomes, and metabolites post-coculture. CX43-BMSCs promoted unidirectional mitochondrial transfer, enhancing KG-1a adhesion and proliferation to maintain LSCs stemness in vitro and vivo. RNA-seq revealed coculture with CX43-BMSCs upregulated genes related to adhesion, proliferation, and migration in KG-1a cells. Elevated CX43 expression strengthened BMSCs-KG-1a interaction, facilitating mitochondrial transfer and nucleoside metabolism, fueling KG-1a cells. This enhanced mitochondrial energy metabolism, promoting metabolic reprogramming and clonogenicity. CX43-mediated mitochondrial transfer from BMSCs to KG-1a enhances LSCs adhesion, proliferation, clonogenicity, and metabolic reprogramming. CX43 emerges as a potential therapeutic target for AML by sustaining LSCs stemness through metabolic remodeling.

Acute myeloid leukemia (AML) with a nucleophosmin 1 (NPM1) mutation is a unique subtype of adult leukemia. Recent studies show that NPM1-mutated AML has high autophagy activity. However, the mechanism for upholding the high autophagic level is still not fully elucidated. In this study, we first identified that tumor protein p53 inducible nuclear protein 2 (TP53INP2) was highly expressed and cytoplasmically localized in NPM1-mutated AML cells. Subsequent data showed that the expression of TP53INP2 was upregulated by fat mass and obesity-associated protein (FTO)-mediated m⁶A modification. Meanwhile, TP53INP2 was delocalized to the cytoplasm by interacting with NPM1 mutants. Functionally, cytoplasmic TP53INP2 enhanced autophagy activity by promoting the interaction of microtubule-associated protein 1 light chain 3 (LC3) - autophagy-related 7 (ATG7) and further facilitated the survival of leukemia cells. Taken together, our study indicates that TP53INP2 plays an oncogenic role in maintaining the high autophagy activity of NPM1-mutated AML and provides further insight into autophagy-targeted therapy of this leukemia subtype.

The red blood cell (RBC) lifespan is a crucial indicator used in clinical diagnostics, treatment, and disease monitoring. This biomarker quantifies the duration that red blood cells (RBCs) circulate within the bloodstream after being released from the bone marrow, serving as a sensitive and direct indicator of red blood cell turnover. Conventional techniques for RBC lifespan measurement, including differential agglutination, ⁵¹Cr labeling, and ¹⁵N glycine labeling, each present their own set of challenges, such as complexity, radioactive exposure, and potential allergic reaction. The carbon monoxide (CO) breath test has emerged as an advanced and non-invasive alternative, indirectly assessing RBC lifespan through hemoglobin (Hb) renewal rates. This method is convenient, rapid, and lacks the drawbacks of traditional approaches. The CO breath test for RBC lifespan is widely utilized in benign anemia, malignant hematological disorders, neonatal hyperbilirubinemia, and diabetes mellitus, offering valuable insights into disease mechanisms, progression, and treatment outcomes.

HIV-associated diffuse large B-cell lymphoma (DLBCL) is a rare type of lymphoma with poor prognosis. β2-microglobulin (β2-M) is elevated in people living with HIV, but its prognostic significance in HIV-associated DLBCL remains unclear. We retrospectively analyzed 89 HIV-associated DLBCL patients treated at Chongqing University Cancer Hospital between October 2012 and December 2023. The primary outcome was the clinical overall survival (OS) rate. The optimal cut-off value of β2-M was determined to be 5 mg/L. Compared to the low serum β2-M group (< 5 mg/L), patients in the high serum β2-M group (≥ 5 mg/L) had a higher International Prognostic Index (IPI)/ age adjusted IPI (aaIPI) score, more B symptoms, lower CD4⁺T cell counts, and higher lactate dehydrogenase (LDH) level. Patients in high group exhibited poorer OS, with 1-, 3-, and 5-year OS rates of 33.4%, 22.8%, and 18.2%, respectively. Multivariate Cox regression analysis identified serum β2-M ≥ 5 mg/L as an independent risk factor influencing OS of this patient group. Moreover, CD8⁺T cell count < 392 cells/µL, LDH ≥ 375 U/L, and non-receipt of standard treatment were also independent risk factors. Receiver operating characteristic curve analysis demonstrated that these four independent risk factors accurately predicted survival in HIV-associated DLBCL. 5 mg/L threshold for serum β2-M was associated with poor OS in HIV-associated DLBCL, indicating it could serve as a novel biomarker for assessing their prognosis.

Background: Differential cell counts (DCCs) on bone marrow aspirate (BMA) smear is a critical step in the diagnosis and treatment of blood and bone marrow diseases. However, manual counts relies on the experience of pathologists and is very time-consuming. In recent years, deep learning-based intelligent cell detection models have achieved high detection accuracy on datasets of specific diseases and medical centers, but these models depend on a large amount of annotated data and have poor generalization. When the detection task changes or model is applied in different medical centers, we need to re-annotate a large amount of data and retrain the model to ensure detection accuracy.Methods: To address the above issues, we designed an automated framework for whole-slide bone marrow aspirate smear differential cell counts (BMADCC), called VFM-SSL-BMADCC-Framework. This framework only requires whole-slide images (WSIs) as input to generate DCCs. The vision foundation model SAM, known for its strong generalization ability, precisely segments cells within the countable regions of the BMA. The MAE, pre-trained on a large unlabeled cell dataset, excels in generalized feature extraction, enabling accurate classification of cells for counting. Additionally, TextureUnet and TCNet, with their powerful texture feature extraction capabilities, effectively segment the body-tail junction areas from WSIs and classify suitable tiles for DCCs. The framework was trained and validated on 40 WSIs from Chongqing Cancer Hospital. To assess its generalization ability across different medical centers and diseases, correlation tests were conducted using 13 WSIs from Chongqing Cancer Hospital and 5 WSIs from Southwest Hospital.Results: The framework demonstrated high accuracy across all stages: The IoU for region of interest (ROI) segmentation was 46.19%, and the accuracy for tile of interest (TOI) classification was 90.45%, the Recall75 for cell segmentation was 99.01%, and the accuracy for cell classification was 77.92%. Experimental results indicated that the automated framework had excellent cell classification and counts performance, suitable for BMADCC across different medical centers and diseases. The differential cell counts results from all centers were highly consistent with manual analysis.Conclusion: The proposed VFM-SSL-BMADCC-Framework effectively automates differential cell counts on bone marrow aspirate smears, reducing reliance on extensive annotations and improving generalization across medical centers.