Causal relationship between immune cells and prostate cancer: a Mendelian randomization study.

Introduction: Despite the abundance of research indicating the participation of immune cells in prostate cancer development, establishing a definitive cause-and-effect relationship has proven to be a difficult undertaking. Methods: This study employs Mendelian randomization (MR), leveraging genetic variables related to immune cells from publicly available genome-wide association studies (GWAS), to investigate this association. The primary analytical method used in this study is inverse variance weighting (IVW) analysis. Comprehensive sensitivity analyses were conducted to assess the heterogeneity and horizontal pleiotropy of the results. Results: The study identifies four immune cell traits as causally contributing to prostate cancer risk, including CD127- CD8+ T cell %CD8+ T cell (OR = 1.0042, 95%CI:1.0011-1.0073, p = 0.0077), CD45RA on CD39+ resting CD4 regulatory T cell (OR = 1.0029, 95%CI:1.0008-1.0050, p = 0.0065), CD62L- Dendritic Cell Absolute Count (OR = 1.0016; 95%CI:1.0005-1.0026; p = 0.0039), CX3CR1 on CD14+ CD16- monocyte (OR = 1.0024, 95%CI:1.0007-1.0040, p = 0.0060). Additionally, two immune cell traits are identified as causally protective factors: CD4 on monocyte (OR = 0.9975, 95%CI:0.9958-0.9992, p = 0.0047), FSC-A on plasmacytoid Dendritic Cell (OR = 0.9983, 95%CI:0.9970-0.9995, p = 0.0070). Sensitivity analyses indicated no horizontal pleiotropy. Discussion: Our MR study provide evidence for a causal relationship between immune cells and prostate cancer, holding implications for clinical diagnosis and treatment.

Methylation of GPRC5A promotes liver metastasis and docetaxel resistance through activating mTOR signaling pathway in triple negative breast cancer.

AIMS: This study aims to explore the function and mechanism of G Protein-coupled receptor class C group 5 member A (GPRC5A) in docetaxel-resistance and liver metastasis of breast cancer. METHODS: Single-cell RNA transcriptomic analysis and bioinformatic analysis are used to screen relevant genes in breast cancer metastatic hepatic specimens. MeRIP, dual-luciferase analysis and bioinformation were used to detect m6A modulation. Mass spectrometry (MS), co-inmunoprecipitation (co-IP) and immunofluorescence colocalization were executed to explore the mechanism of GPRC5A in breast cancer cells. RESULT: GPRC5A was upregulated in triple-negative breast cancer (TNBC) and was associated with a poor prognosis. In vitro and in vivo experiments demonstrated that knockdown of GPRC5A alleviated metastasis and resistance to docetaxel in TNBC. Overexpression of GPRC5A had the opposite effects. The m6A methylation of GPRC5A mRNA was modulated by METTL3 and YTHDF1, which facilitates its translation. GPRC5A inhibited the ubiquitination-dependent degradation of LAMTOR1, resulting in the recruitment of mTORC1 to lysosomes and activating the mTORC1/p70s6k signaling pathway. CONCLUSION: METTL3/YTHDF1 axis up-regulates GPRC5A expression by m6A methylation. GPRC5A activates mTORC1/p70s6k signaling pathway by recruiting mTORC1 to lysosomes, consequently promotes docetaxel-resistance and liver metastasis.

Tumor-derived Exosomal ENO2 Modulates Polarization of Tumor-associated Macrophages through Reprogramming Glycolysis to Promote Progression of Diffuse Large B-cell Lymphoma.

Macrophages can be polarized into functional classically activated (M1) or alternatively activated (M2) phenotype. Tumor-associated macrophages (TAMs) mainly exhibit M2 phenotype. Previous works determined that up-regulation of enolase 2 (ENO2) in diffuse large B-cell lymphoma (DLBCL) cells can promote macrophages to an M2-like phenotype, thereby consequently promoting the progression of DLBCL. Exosomes are a subset of extracellular vesicles, carrying various bioactive molecules, mediate signals transduction and regulate immune cells. In our study, we investigated the role and related mechanisms of DLBCL-derived exosomal ENO2 in regulating macrophage polarization during DLBCL progression via bioinformatics analysis and a series of experiments. The results of bioinformatics analysis indicated that high expression of ENO2 was positively correlated with DLBCL progression and macrophages M2/M1 ratio. ENO2 protein levels were increased in the exosomes of the sera of DLBCL patients and DLBCL cells. Moreover, the DLBCL-derived exosomes were assimilated by macrophages and then regulated macrophage polarization. The results of in vitro and in vivo experiments showed that DLBCL-derived exosomal ENO2 modulated macrophages polarization (increased M2 phenotype and decreased M1 phenotype), thereby promoting DLBCL proliferation, migration, and invasion. We then revealed that the modulation of macrophages polarization by DLBCL-derived exosomal ENO2 depended on glycolysis and was promoted through GSK3ß/ß-catenin/c-Myc signaling pathway. These findings suggested that DLBCL-derived exosomal ENO2 accelerated glycolysis via GSK3ß/ß-catenin/c-Myc signaling pathway to ultimately promote macrophages to an M2-like phenotype, which can promote the proliferation, migration and invasion of DLBCL, suggesting that exosomal ENO2 may be a promising therapeutic target and prognostic biomarker for DLBCL.

A novel prognostic prediction model of cuprotosis-related genes signature in hepatocellular carcinoma.

Background: Cuprotosis is a recently discovered copper-dependent cell death mechanism that relies on mitochondrial respiration. However, the role of cuprotosis-related genes (CRGs) in hepatocellular carcinoma (HCC) and their prognostic significances remain unknown. Methods: Based on the recently published CRGs, the LASSO Cox regression analysis was applied to construct a CRGs risk model using the gene expression data from the International Cancer Genome Consortium as a training set, followed by validation with datasets from The Cancer Genome Atlas and the Gene Expression Omnibus (GSE14520). Functional enrichment analysis of the CRGs was performed by single-sample gene set enrichment analysis. Results: Five of the 13 previously published CRGs were identified to be associated with prognosis in HCC. Kaplan-Meier analysis suggested that patients with high-risk scores have a shorter overall survival time than patients with low-risk scores. ROC curves indicated that the average AUC was more than 0.7, even at 4 years, and at least 0.5 at 5 years. Moreover, addition of this CRG risk score can significantly improve the efficiency of predicting overall survival compared to using traditional factors alone. Functional analysis demonstrated increased presence of Treg cells in patients with high-risk scores, suggesting a suppressed immune state in these patients. Finally, we point to the possibility that novel immunotherapies such as inhibitors of PDCD1, TIGIT, IDO1, CD274, CTLA4, and LAG3 may have potential benefits in high-risk patients. Conclusion: We constructed a better prognostic model for liver cancer by using CRGs. The CRG risk score established in this study can serve as a potentially valuable tool for predicting clinical outcome of patients with HCC.

Biomarkers as targets for CAR-T/NK cell therapy in AML.

The most common kind of acute leukemia in adults is acute myeloid leukemia (AML), which is often treated with induction chemotherapy regimens followed by consolidation or allogeneic hematopoietic stem cell transplantation (HSCT). However, some patients continue to develop relapsed or refractory AML (R/R-AML). Small molecular targeted drugs require long-time administration. Not all the patients hold molecular targets. Novel medicines are therefore needed to enhance treatment outcomes. T cells and natural killer (NK) cells engineered with chimeric antigen receptors (CARs) that target antigens associated with AML have recently been produced and are currently being tested in both pre-clinical and clinical settings. This review provides an overview of CAR-T/NK treatments for AML.

Lipid profile as a novel prognostic predictor for patients with acute myeloid leukemia.

Purpose: This study investigated the relationship between serum lipid levels and clinical outcomes in acute myeloid leukemia (AML) by establishing a predictive risk classification model. Method: A total of 214 AML patients who were pathologically diagnosed and treated with standard induction chemotherapy at Sun Yat-Sen University Cancer Center were included. The patients were randomly divided into the training (n = 107) and validation (n=107) cohorts. Univariate and multivariate Cox analyses were used to assess the value of triglyceride (TG), Apolipoprotein B (Apo B), Apo Apolipoprotein A-I (Apo A-I), cholesterol (CHO), and high-density lipoprotein (HDL) as prognostic factors for AML. Results: After a series of data analyses, a five-factor model was established to divide the patients into high- and low-risk groups. Kaplan-Meier survival analysis showed that the high-risk group had a poor prognosis (P<0.05). The area under the curve of the novel model for five-year OS was 0.737. A nomogram was constructed to integrate the model with age and the 2017 ELN cytogenetic classification, with the merged model showing improved accuracy with an area under the curve of 0.987 for five-year OS. Conclusion: A novel model was constructed using a combination of the serum lipid profile and clinical characteristics of AML patients to enhance the predictive accuracy of clinical outcomes. The nomogram used the lipid profile which is routinely tested in clinical blood biochemistry and showed both specific prognostic and therapeutic potential.

Integrated assessment of the clinical and biological value of ferroptosis-related genes in multiple myeloma.

BACKGROUND: Ferroptosis is an iron-dependent mode of cell death that could be induced by erastin and exert antitumor effects. However, the clinical and biological roles of ferroptosis-related gene (FRG) signature and the therapeutic value of erastin in multiple myeloma (MM) remained unknown. METHODS: Clinical and gene expression data of MM subjects were extracted from the Gene Expression Omnibus (GEO) public database. Univariable cox analysis was applied to determine FRGs related to survival and the least absolute shrinkage and selection operator (LASSO) regression analysis was used to develop a prognostic model. Prediction accuracy of the model was estimated by receiver operating characteristic (ROC) curves. Functional pathway enrichments and infiltrating immune status were also analyzed. We conducted in vitro experiments to investigate the combination therapy of erastin and doxorubicin. RESULTS: 17 FRGs were strongly associated with patient survival and 11 genes were identified to construct the prognostic model. ROC curves indicated great predictive sensitivity and specificity of the model in all cohorts. Patients were divided into low- and high-risk groups by median risk score in each cohort and the survival of the low-risk group was significantly superior than that of the high-risk group. We also observed a close relevance between functional pathways and immune infiltration with risk scores. Moreover, we combined erastin and doxorubicin in our in vitro experiments and found synergetic antitumor effects of the two agents, and the underlying mechanism is the overgeneration of intracellular Reactive Oxygen Species (ROS). CONCLUSIONS: We demonstrated the important value of ferroptosis in patient prognosis and as a potential antitumor target for MM.

Integrative analysis identifies CXCL11 as an immune-related prognostic biomarker correlated with cell proliferation and immune infiltration in multiple myeloma microenvironment.

PURPOSE: The interaction between tumor cells and tumor microenvironment (TME) has an important impact on progression and prognosis of multiple myeloma (MM), and has been proven to be promising therapeutic targets. This study intended to explore the relationship between TME and prognosis and identify valuable biomarkers of MM. METHODS: The transcriptomic and clinical information of MM retrieved from the Gene Expression Omnibus (GEO) were used to establish the model. The curve of Kaplan-Meier survival and the time-dependent receiver operating characteristic (ROC) were used to appraise the predictive ability. A nomogram was established for clinical application. Furthermore, the CIBERSORT algorithm was used to investigate the relation between IRGPI with the infiltration of immune cells. We also used histology, as well as in vitro and in vivo experiments to validate these findings. RESULTS: The results demonstrated an immune-related gene-based prognostic index (IRGPI) combined with clinical information. Patients were separated into high- and low-risk groups based on risk score, which had significantly difference in survival status and immune infiltrations. Furthermore, we identified CXCL11 as a key factor, which positively promotes the progression of MM and correlate with macrophage M2-like polarization and tumor immune cells infiltration. CONCLUSION: Our findings suggest the IRGPI significantly demonstrate the differential prognosis and prediction of immune cells infiltration. It provides some insights into the complex interaction between myeloma tumor cells and the TME, as well as in the development of a novel biomarker target for anti-MM therapy.

Comprehensive analysis of a pyroptosis-related gene signature of clinical and biological value in acute myeloid leukaemia.

Acutemyeloidleukaemia(AML) is an illness of varied origin and unpredictable prognosis. Pyroptosis, a novel class of gasdermin-mediated programmed cell death (PCD), serves a critical function in anti-leukemia. But, the correlation between pyroptosis-related genes (PRGs) and AML prognosis is undetermined. Here, we obtained the RNA profile and matched clinical information of AML patients from the TCGA and GEO databases. 6 PRGs were identified to be strongly related to AML prognosis via univariate COX analysis. Next, the LASSO regression analysis was used to develop a PRG signature for AML prognosis, which was then employed for the stratification of patients into a low- (LR) or high-risk (HR) group. Kaplan-Meier analysis revealed that the HR group, but not the LR group, had worse prognosis. In addition, ROC curve analysis revealed that our prognotic model had good predictive value. Functional enrichment analysis indicated that the immune status was remarkably different between the two risk groups. In vitro experiments demonstrated that pyroptosis serves an essential function in anti-leukemia treatment. In summary, our newly developed model has good predictive value and can offer guidance into the precise estimation of AML prognosis and targeting pyroptosis is a potential therapeutic alternative for AML.

A new prediction model integrated serum lipid profile for patients with multiple myeloma.

Purpose: This study aimed to explore a predictive risk-stratification model combing clinical characteristics and lipid profiles in multiple myeloma (MM) patients. Methods: The data of 275 patients in Sun Yat-Sen University Cancer Center were retrospectively analyzed and randomly divided into the training (n = 138) and validation (n=137) cohorts. Triglyceride (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), lactate dehydrogenase (LDH), Apolipoprotein B (Apo B) and Apo B/Apolipoprotein A1 (Apo A1) ratio were the prognostic factors identified through univariate and multivariate Cox analysis. Results: A 6-prognostic factor model was constructed based on Lasso regression. Patients were divided into low- and high-risk groups and the former group showed longer overall survival (OS) time (p<0.05). The area under the curve (AUC) of the risk score model for 5-and 10-year OS were 0.756 [95% CI: 0.661-0.850] and 0.940 [95% CI: 0.883-0.997], which exhibited better accuracy than International Staging System (ISS) and Durie and Salmon (DS) stage. Conclusion: This study aims to combine the lipid metabolism profile with the clinical characteristics of MM patients to generate a prognostic model. The nomogram integrating ISS stage and risk score increased the prediction accuracy. This model can monitor lipid profile as a simple and effective method, which has certain clinical significance for improving the accuracy of the prognosis and exploring potential therapeutic targets.

Integrative Analysis of a Pyroptosis-Related Signature of Clinical and Biological Value in Multiple Myeloma.

Purpose: Pyroptosis is an inflammation-based programmed cell death that holds great potential as a novel cancer therapeutic target in patients with multiple myeloma (MM). However, thus far, the function of pyroptosis-related genes (PRGs) in MM and their prognostic relevance remains undetermined. Methods: The model was established by the LASSO analysis, based on the Gene Expression Omnibus (GEO) dabatase, and its efficacy was verified using two external datasets. The model's predictive ability was assessed by the Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) curves. Finally, a nomogram was established for clinical application. We also confirmed the validity of our model using specimens and in vitro experiments. Results: We established an 11-PRG signature profile, and verified its efficacy using two validation cohorts (VCs). In both cohorts, patients were separated into two subpopulations, according to their median risk scores (RS). Our analysis revealed that high-risk (HR) patients experienced considerably lower overall survival (OS), compared to the low-risk (LR) patients. Using functional enrichment and immune infiltration analyses, we demonstrated that the immunologic status was strongly related to RS. Furthermore, using a pyroptosis inhibitor Q-VD-OPh, we revealed that MM cell proliferation and progression was drastically suppressed and the doxorubicin (DOX)-induced apoptosis was reversed. Conclusion: Based on our analysis, pyroptosis not only serves as a measure of MM treatment efficiency and patient prognosis, but is also a possible target for anti-MM therapy.

Identification of the Pyroptosis-Related Gene Signature for Overall Survival Prediction in Patients With Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor worldwide, with an increasing incidence and mortality. Due to general resistance to antitumor drugs, only limited therapies are currently available for advanced HCC patients, leading to a poor prognosis with a 5-year survival rate less than 20%. Pyroptosis is a type of inflammation-related programmed cell death and may become a new potential target for cancer therapy. However, the function and prognostic value of pyroptosis-related genes (PRGs) in HCC remain unknown. Here, we identified a total of 58 PRGs reported before and conducted a six-PRG signature via the LASSO regression method in the GEO training cohort, and model efficacy was further validated in an external dataset. The HCC patients can be classified into two subgroups based on the median risk score. High-risk patients have significantly shorter overall survival (OS) than low-risk patients in both training and validation cohorts. Multivariable analysis indicated that the risk score was an independent prognostic factor for OS of HCC patients. Functional enrichment analysis and immune infiltration evaluation suggested that immune status was more activated in the low-risk group. In summary, PRGs can be a prediction factor for prognosis of HCC patients and targeting pyroptosis is a potential therapeutic alternative in HCC.

Establishment of a prognostic ferroptosis-related gene profile in acute myeloid leukaemia.

Acute myeloid leukaemia (AML) is a heterogeneous disease with a difficult to predict prognosis. Ferroptosis, an iron-induced programmed cell death, is a promising target for cancer therapy. Nevertheless, not much is known about the relationship between ferroptosis-related genes and AML prognosis. Herein, we retrieved RNA profile and corresponding clinical data of AML patients from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate Cox analysis was employed to identify ferroptosis-related genes significantly associated with AML prognosis. Next, the least absolute shrinkage and selection operator (LASSO) regression was employed to establish a prognostic ferroptosis-related gene profile. 12 ferroptosis-related genes were screened to generate a prognostic model, which stratified patients into a low- (LR) or high-risk (HR) group. Using Kaplan-Meier analysis, we demonstrated that the LR patients exhibited better prognosis than HR patients. Moreover, receiver operating characteristic (ROC) curve analysis confirmed that the prognostic model showed good predictability. Functional enrichment analysis indicated that the infiltration of regulatory T cells (Treg) differed vastly between the LR and HR groups. Our prognostic model can offer guidance into the accurate prediction of AML prognosis and selection of personalized therapy in clinical practice.

Identification of a prognostic metabolic gene signature in diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is a clinically diverse disease. Given the numerous genetic mutations and variations associated with it, a prognostic gene signature that can be related to the overall survival (OS) is a clinical implication. We used the mRNA expression profiles and clinicopathological data of patients with DLBCL from the Gene Expression Omnibus (GEO) database to identify a metabolism-related gene signature. Using LASSO regression analysis, a novel 13-metabolic gene signature was identified to evaluate prognosis. The information gathered was used to construct the nomogram model to improve risk stratification and quantify risk factors for individual patients. We performed gene set enrichment analysis to identify the enriched signalling axes to further understand the underlying biological pathways. The receiver operating characteristic (ROC) curve revealed a satisfactory performance in the training cohorts. The model also showed clinical benefit when compared to the standard prognostic factors (P < .05) in validation cohorts. This study aimed to combine metabolic dysregulation with clinical features of patients with DLBCL to generate a prognostic model that might not only indicate the value of the metabolic microenvironment for prognostic stratification but also improve the decision-making during individual therapy.

Cladribine with Granulocyte Colony-Stimulating Factor, Cytarabine, and Aclarubicin Regimen in Refractory/Relapsed Acute Myeloid Leukemia: A Phase II Multicenter Study.

LESSONS LEARNED: Studies targeting cladribine in combination with granulocyte colony-stimulating factor, low-dose cytarabine, and aclarubicin (C-CAG) regimen in relapsed and refractory acute myeloid leukemia (R/R AML) are limited. The complete remission rate after two cycles of C-CAG regimen was 67.6%, and 1-year overall survival and disease-free survival rates were 59.7% and 72.9%, respectively. The C-CAG regimen is significantly effective against R/R AML with a low hematological toxicity and thus serves as an alternative treatment for R/R AML. BACKGROUND: The optimal salvage chemotherapy regimen for relapsed and refractory acute myeloid leukemia (R/R AML) remains uncertain. Therefore, a phase II study was conducted for the prospective evaluation of the efficacy and safety of the purine analog cladribine in combination with granulocyte colony-stimulating factor (G-CSF), low-dose cytarabine, and aclarubicin (C-CAG) regimen for patients with R/R AML. METHODS: A total of 34 patients received C-CAG regimen for salvage treatment as follows: cladribine 5 mg/m2 , days 1-5; G-CSF 300 µg, days 0-9; aclarubicin 10 mg, days 3-6; cytarabine 10 mg/m2 every 12 hours, subcutaneously, days 3-9; 4 weeks per cycle. Patients were allowed to withdraw from the study if complete remission (CR) was not achieved after two courses of chemotherapy. If conditions were right, the patients achieving CR were recommended to receive allogeneic hematopoietic stem cell transplantation. Otherwise, they were treated for a total of six cycles unless disease progression or unacceptable side effects were observed or they withdrew their consent. RESULTS: All patients received at least two cycles of C-CAG regimen chemotherapy. After two cycles of C-CAG, 23 patients (67.6%) achieved CR, and 5 patients had partial remission (14.7%). At a median follow-up of 15 months (range, 3-38 months), the 1-year overall survival (OS) and disease-free survival (DFS) rates were 59.7% (95% confidence interval [CI], 42.6%-76.8%) and 72.9% (95% CI, 54.3%-91.5%), respectively. The most common adverse effect was myelosuppression. Nonhematological toxicities were mild, and no treatment-related deaths occurred. CONCLUSION: Preliminary data indicate that the C-CAG regimen chemotherapy is significantly effective against R/R AML with a high remission rate and a low hematological toxicity. Thus, it may serve as an alternative treatment for R/R AML.