Construction of a TAN-associated risk score model with integrated multi-omics data analysis and clinical validation in gastric cancer.

AIMS: An increasing number of studies have highlighted the biological significance of neutrophil activation and polarization in tumor progression. However, the characterization of tumor-associated neutrophils (TANs) is inadequately investigated. MATERIALS AND METHODS: Patients' expression profiles were obtained from TCGA, GEO, and IMvigor210 databases. Six algorithms were used to assess immune cell infiltration. RNA sequencing was conducted to evaluate the differentially expressed genes between induced N1- and N2-like neutrophils. A TAN-associated risk score (TRS) model was established using a combination of weighted gene co-expression network analysis (WGCNA) and RNA-seq data and further assessed in pan-cancer. A clinical cohort of 117 GC patients was enrolled to assess the role of TANs in GC via immunohistochemistry (IHC). KEY FINDINGS: A TRS signature was built with 10 TAN-related genes (TRGs) and most TRGs were highly abundant in the TANs of the GC microenvironment. The TRS model could accurately predict patients' prognosis, as well as their responses to chemotherapy and immunotherapy. The TRS was positively correlated with pro-tumor immune cells and exhibited negative relationship with anti-tumor immune cells. Additional functional analyses revealed that the signature was positively related to pro-tumor and immunosuppression pathways, such as the hypoxia pathway, across pan-cancer. Furthermore, our clinical cohort demonstrated TANs as an independent prognostic factor for GC patients. SIGNIFICANCE: This study constructed and confirmed the value of a novel TRS model for prognostic prediction of GC and pan-cancer. Further evaluation of TRS and TANs will help strengthen the understanding of the tumor microenvironment and guide more effective therapeutic strategies.

Investigation of fatty acid metabolism in chronic lymphocytic leukemia to guide clinical outcome and therapy.

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the West. With CLL's heterogeneity, some people still develop disease refractory and relapse despite advances in treatment. Thus, early diagnosis and treatment of high-risk CLL patients is critical. Fatty acid (FA) metabolism contributes to tumorigenesis, progression, and therapy resistance through enhanced lipid synthesis, storage, and catabolism. In this study, we aimed to construct a prognostic model to improve the risk stratification of CLL and reveal the link between FA metabolism and CLL. The differentially expressed FA metabolism-related genes (FMGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Functional enrichment was examined using prognostic FA metabolism-related gene enrichment analysis. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) estimated immune infiltration score and immune-related pathways. Pearson's correlation analysis investigated FA metabolism-related genes and drug sensitivity. A novel prognostic model was built using least absolute shrinkage and selection operator (LASSO) Cox algorithms. This validation cohort included 36 CLL patients from our center. We obtained CLL RNA microarray profiles from public databases and identified 15 prognostic-related FMGs. CLL patients were divided into two molecular clusters based on the expression of FMGs. The Kaplan-Meier analysis revealed a significant difference in TFS (P < 0.001) and OS (P < 0.001) between the two clusters. KEGG functional analysis showed that several pathways were enriched, including the chemokine and immune-related signaling pathways. In the training and validation cohorts, patients with higher FA metabolism-related prognostic index (FAPI) levels had worse outcomes. Finally, a novel nomogram prognostic model including CLL international prognostic index (CLL-IPI) was constructed, exhibiting reliable effectiveness and accuracy. In conclusion, we established a reliable predictive signature based on FA metabolism-related genes and constructed a novel nomogram prognostic model, supporting the potential preclinical implications of FA metabolism in CLL research.

Identification and Validation of Ferroptosis-Related LncRNAs Signature as a Novel Prognostic Model for Chronic Lymphocytic Leukemia.

Background: Chronic lymphocytic leukemia (CLL) is a subtype of B-cell malignancy with high heterogeneity. Ferroptosis is a novel cell death induced by iron and lipid peroxidation and exhibits prognostic value in many cancers. Emerging studies on long non-coding RNAs (lncRNAs) and ferroptosis reveal the unique value in tumorigenesis. However, the prognostic value of ferroptosis-related lncRNAs (FRLs) remains unclear in CLL. Aim: We aimed to construct a FRLs risk model to predict prognosis and improve prognostic stratification for clinical practice. Methods: RNA-sequencing data and clinical characteristics of CLL patients were downloaded from the GEO database. Based on ferroptosis-related genes from FerrDb database, differentially expressed FRLs with prognostic significance were identified and used to generate the risk model. The capability of the risk model was assessed and evaluated. GO and KEGG analyses were performed to confirm biological roles and potential pathways. Results: A novel ferroptosis-related lncRNAs prognostic score (FPS) model containing six FRLs (PRKCQ, TRG.AS1, LNC00467, LNC01096, PCAT6 and SBF2.AS1) was identified. Patients in the training and validation cohort were evenly divided into high- and low-risk groups. Our results indicated that patients in the high-risk group had worse survival than those in the low-risk group. Functional enrichment analyses showed that the differently expressed genes (DEGs) between the two groups were enriched in the chemokine signaling pathway, hematopoietic cell lineage, T cell differentiation, TCR pathway and NF-κB pathway. Moreover, significant differences in immune cell infiltration were also observed. Surprisingly, FPS was proved to be an independent prognostic indicator for OS. Conclusion: We established and evaluated a novel prognostic risk model with 6 FRLs that could predict prognosis accurately and describe the distinct immune infiltration in CLL.

Prognostic value and therapeutic targeting of XPO1 in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is a subtype of B-cell malignancy with high heterogeneity. XPO1 is highly expressed in many hematological malignancies, which predicts poor prognosis. In the study, we aimed to explore the prognostic role of XPO1 and the therapeutic effect of Selinexor, a selective inhibitor of nuclear export, which targets XPO1. We collected 200 CLL samples in our center to confirm XPO1 mRNA expression and analyzed the correlation between XPO1 expression and prognosis. Then, we decreased XPO1 expression with Selinexor to explore the effect of proliferation inhibition, cell cycle arrest, and apoptosis in CLL cell lines. RNA-Seq was performed to explore potential mechanisms. We analyzed XPO1 expression in a cohort of 150 treatment naive patients and another cohort of 50 relapsed and refractory (R/R) patients and found that XPO1 expression was upregulated in 76% of CLL patients compared with healthy donors. Survival analysis suggested that patients with increased XPO1 expression had inferior treatment-free survival (P = 0.022) and overall survival (P = 0.032). The inhibitor of XPO1, Selinexor, induced apoptosis in primary CLL cells. We showed the effects of Selinexor on proliferation inhibition, cell cycle arrest, and apoptosis in CLL cell lines with JVM3, MEC1, and ibrutinib-resistant (MR) cells via nuclear retention of cargo proteins of IκBα, p65, p50, and FOXO3a. Moreover, downregulation of the NF-κB and FOXO pathways was a common feature of the three CLL cell lines responding to Selinexor, indicating the potential application of XPO1 inhibitor even in the high-risk CLL cells. We identified XPO1 as an unfavorable prognostic factor for CLL patients and provided a rationale for further investigation of the clinically XPO1 targeted therapeutic strategy against CLL.

Identifying a novel ferroptosis-related prognostic score for predicting prognosis in chronic lymphocytic leukemia.

Background: Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western world. Although the treatment landscape for CLL is rapidly evolving, there are still some patients who develop drug resistance or disease refractory. Ferroptosis is a type of lipid peroxidation-induced cell death and has been suggested to have prognostic value in several cancers. Our research aims to build a prognostic model to improve risk stratification in CLL patients and facilitate more accurate assessment for clinical management. Methods: The differentially expressed ferroptosis-related genes (FRGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Least absolute shrinkage and selection operator (LASSO) Cox algorithms were performed to construct a prognostic risk model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were performed to estimate the immune infiltration score and immune-related pathways. A total of 36 CLL patients in our center were enrolled in this study as a validation cohort. Moreover, a nomogram model was established to predict the prognosis. Results: A total of 15 differentially expressed FRGs with prognostic significance were screened out. After minimizing the potential risk of overfitting, we constructed a novel ferroptosis-related prognostic score (FPS) model with nine FRGs (AKR1C3, BECN1, CAV1, CDKN2A, CXCL2, JDP2, SIRT1, SLC1A5, and SP1) and stratified patients into low- and high-risk groups. Kaplan-Meier analysis showed that patients with high FPS had worse overall survival (OS) (P<0.0001) and treatment-free survival (TFS) (P<0.0001). ROC curves evaluated the prognostic prediction ability of the FPS model. Additionally, the immune cell types and immune-related pathways were correlated with the risk scores in CLL patients. In the validation cohort, the results confirmed that the high-risk group was related to worse OS (P<0.0001), progress-free survival (PFS) (P=0.0140), and TFS (P=0.0072). In the multivariate analysis, only FPS (P=0.011) and CLL-IPI (P=0.010) were independent risk indicators for OS. Furthermore, we established a nomogram including FPS and CLL-IPI that could strongly and reliably predict individual prognosis. Conclusion: A novel FPS model can be used in CLL for prognostic prediction. The model index may also facilitate the development of new clinical ferroptosis-targeted therapies in patients with CLL.