Prognosis and Characterization of Immune Microenvironment in Acute Myeloid Leukemia Through Identification of an Autophagy-Related Signature

A CD8+ T cell related immune score predicts survival and refines the risk assessment in acute myeloid leukemia

Although advancements in genomic and epigenetic research have deepened our understanding of acute myeloid leukemia (AML), only one-third of patients can achieve durable remission. Growing evidence suggests that the immune microenvironment in bone marrow influences prognosis and survival in AML. There is a specific association between CD8+ T cells and the prognosis of AML patients. To develop a CD8+ T cell-related immune risk score for AML, we first evaluated the accuracy of CIBERSORTx in predicting the abundance of CD8+ T cells in bulk RNA-seq and found it significantly correlated with observed single-cell RNA sequencing data and the proportions of CD8+ T cells derived from flow cytometry. Next, we constructed the CTCG15, a 15-gene prognostic signature, using univariate and LASSO regression on the differentially expressed genes between CD8+ THigh and CD8+ TLow groups. The CTCG15 was further validated across six datasets in different platforms. The CTCG15 has been shown to be independent of established prognostic markers, and can distill transcriptomic consequences of several genetic abnormalities closely related to prognosis in AML patients. Finally, integrating this model into the 2022 European LeukemiaNet contributed to a higher predictive power for prognosis prediction. Collectively, our study demonstrates that CD8+ T cell-related signature could improve the comprehensive risk stratification and prognosis prediction in AML.

Identification and immunoassay of prognostic genes associated with the complement system in acute myeloid leukemia

Studies have associated the development of pulmonary leukemia with the activation of the complement system. However, the roles and mechanisms of complement system-related genes (CSRGs) in acute myeloid leukemia (AML) have not been investigated extensively. This study used The Cancer Genome Atlas (TCGA)-AML and GSE37642 datasets. Differentially expressed CSRGs (CSRDEGs) were identified by overlapping genes differentially expressed between the high and low CSRG score groups and key module genes identified in a weighted gene co-expression network analysis. Univariate and multivariate Cox analyses identified CSRG-related biomarkers, which were used to build a prognostic model. After gene set enrichment analysis (GSEA), immune-related and drug-sensitivity analyses were performed in the high- and low-risk groups. Four prognosis-related biomarkers were identified and used to develop a prognostic model: MEOX2, IGFBP5, CH25H, and RAB3B. The model's performance was verified in a test cohort (a subset of samples from the TCGA-AML dataset) and a validation cohort (GSE37642). The GSEA revealed that the high-risk group was mainly enriched for Golgi organization and cytokine-cytokine receptor interactions, and the low-risk group was mainly enriched in the hedgehog signaling pathway and spliceosome. Lastly, two immune cells were found to show differential infiltration between risk groups, which correlated with the risk scores. M1 macrophage infiltration was significantly positively correlated with RAB3B expression. Sensitivity to 36 drugs differed significantly between risk groups. This study screened four CSRG-related biomarkers (MEOX2, IGFBP5, CH25H, and RAB3B) to provide a basis for predicting AML prognosis.

Increased Immune Infiltration and Improved Prognosis of Head and Neck Squamous Cell Carcinoma Associated with Reduced Ancient Ubiquitous Protein 1 Gene Expression

This study aimed to explore the molecular mechanism underlying the prognostic role of ancient ubiquitous protein 1 (AUP1) in head and neck squamous cell carcinoma (HNSCC) and its relationship with the tumor immune microenvironment. Various web resources were used to analyze the differential expression of AUP1 and its role in the HNSCC pathogenesis. A nomogram aimed at predicting 1-, 3-, and 5-year survival rates was developed based on the patient's clinicopathological characteristics and AUP1 expression pattern. Several algorithms and analytical tools were used to explore the correlation between AUP1 expression and sensitivity to immune checkpoint gene therapy by evaluating infiltrating immune cells in patients with HNSCC. Higher AUP1 mRNA and protein expression levels were observed in most tumors and HNSCC than in the normal tissues. High AUP1 expression was an independent predictive risk factor for the overall survival of patients as it was closely associated with the patients' T, M, clinical, and pathological stages and lymphovascular invasion in HNSCC. In conclusion, AUP1 is involved in the occurrence and progression of HNSCC, may be used as an independent prognostic factor in patients with HNSCC, and could serve as a potential intervention target to improve immunotherapy sensitivity in HNSCC.

AMPK-a key factor in crosstalk between tumor cell energy metabolism and immune microenvironment?

Immunotherapy has now garnered significant attention as an essential component in cancer therapy during this new era. However, due to immune tolerance, immunosuppressive environment, tumor heterogeneity, immune escape, and other factors, the efficacy of tumor immunotherapy has been limited with its application to very small population size. Energy metabolism not only affects tumor progression but also plays a crucial role in immune escape. Tumor cells are more metabolically active and need more energy and nutrients to maintain their growth, which causes the surrounding immune cells to lack glucose, oxygen, and other nutrients, with the result of decreased immune cell activity and increased immunosuppressive cells. On the other hand, immune cells need to utilize multiple metabolic pathways, for instance, cellular respiration, and oxidative phosphorylation pathways to maintain their activity and normal function. Studies have shown that there is a significant difference in the energy expenditure of immune cells in the resting and activated states. Notably, competitive uptake of glucose is the main cause of impaired T cell function. Conversely, glutamine competition often affects the activation of most immune cells and the transformation of CD4+T cells into inflammatory subtypes. Excessive metabolite lactate often impairs the function of NK cells. Furthermore, the metabolite PGE2 also often inhibits the immune response by inhibiting Th1 differentiation, B cell function, and T cell activation. Additionally, the transformation of tumor-suppressive M1 macrophages into cancer-promoting M2 macrophages is influenced by energy metabolism. Therefore, energy metabolism is a vital factor and component involved in the reconstruction of the tumor immune microenvironment. Noteworthy and vital is that not only does the metabolic program of tumor cells affect the antigen presentation and recognition of immune cells, but also the metabolic program of immune cells affects their own functions, ultimately leading to changes in tumor immune function. Metabolic intervention can not only improve the response of immune cells to tumors, but also increase the immunogenicity of tumors, thereby expanding the population who benefit from immunotherapy. Consequently, identifying metabolic crosstalk molecules that link tumor energy metabolism and immune microenvironment would be a promising anti-tumor immune strategy. AMPK (AMP-activated protein kinase) is a ubiquitous serine/threonine kinase in eukaryotes, serving as the central regulator of metabolic pathways. The sequential activation of AMPK and its associated signaling cascades profoundly impacts the dynamic alterations in tumor cell bioenergetics. By modulating energy metabolism and inflammatory responses, AMPK exerts significant influence on tumor cell development, while also playing a pivotal role in tumor immunotherapy by regulating immune cell activity and function. Furthermore, AMPK-mediated inflammatory response facilitates the recruitment of immune cells to the tumor microenvironment (TIME), thereby impeding tumorigenesis, progression, and metastasis. AMPK, as the link between cell energy homeostasis, tumor bioenergetics, and anti-tumor immunity, will have a significant impact on the treatment and management of oncology patients. That being summarized, the main objective of this review is to pinpoint the efficacy of tumor immunotherapy by regulating the energy metabolism of the tumor immune microenvironment and to provide guidance for the development of new immunotherapy strategies.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers

Purpose

The objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis.

Methods

AML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs.

Results

816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups.

Conclusion

LncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Prognostic insights and immune microenvironment delineation in acute myeloid leukemia by ferroptosis-derived signature

Acute myeloid leukemia (AML) represents as a prevalent and formidable hematological malignancy, characterized by notably low 5-year survival rates. Ferroptosis has been found to be correlated with cancer initiation, therapeutic response, and clinical outcome. Nevertheless, the involvement of Ferroptosis-related genes (FRGs) in AML remains ambiguous. Five independent AML cohorts totaling 1,470 (GSE37642, GSE12417, GSE10358, Beat-AML, and TCGA-AML) patients with clinical information were used to systematically investigated the influence of these FRGs expression on outcome and tumor microenvironment. The integration of these datasets led to the subdivision into training and validation sets. Nineteen FRGs were identified as correlated with the overall survival (OS) of AML patients, primarily enriched in ferroptosis, fatty acid metabolism, and leukemia-related signaling pathways. The prognostic signature, consisting of 11 FRGs, was formulated using LASSO-Cox stepwise regression analysis. Patients with high-risk scores exhibited reduced survival compared to those in the low-risk group. The receiver operating characteristic (ROC) analysis underscored the signature's robust predictive accuracy. The high predictive efficacy was confirmed by both internal and external validation datasets. Leukemia and signaling related to immune regulation were mainly enriched pathways of the differentially expressed genes by comparing high- and low-risk groups. The immune composition deconvolution might indicate an immunosuppressive niche in the high-risk patients. The pRRophetic algorithm exploration unveiled chemical drugs with potentially sensitivity among patients in both groups. Collectively, our study developed a ferroptosis-derived prognostic signature that provides the OS prediction and identifies the immune microenvironment for AML patients on large-scale AML cohorts.

Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC.

Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking

Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

Global, national, and regional burden of acute myeloid leukemia among 60-89 years-old individuals: insights from a study covering the period 1990 to 2019

Background

Our study examined the global, national, and regional trends in the incidence, mortality, and disability-adjusted life years (DALYs) associated with older people's acute myeloid leukemia (AML) over a 30 years period. AML, which predominantly affects individuals aged 60-89, is known for its severity and unfavorable prognosis. By providing insights into the growing burden of AML, our research highlights the urgent need for effective interventions and support at various levels.

Methods

In this study, we analyzed older people with AML aged 60-89 using the Global Burden of Disease (GBD) database for 2019. Our goal was to assess trends and characteristics by examining the incidence rate, mortality rate, DALYs, and estimated annual percentage change (EAPC). We aimed to provide a comprehensive understanding of the disease's trajectory and development.

Results

In 2019, the older age group of 60 to 89 years reported 61,559 new cases of AML, with the corresponding number of deaths being 53,620, and the estimated DALYs standing at 990,656. Over the last 30 years, the incidence rate of AML in this age bracket increased by 1.67 per 100,000 people, the mortality rate rose by 1.57 per 100,000 people, and the rate of DALYs, indicative of disease burden, climbed by 1.42 per 100,000 people. High Socio-demographic Index (SDI) regions, particularly high-income North America and Australia, had the highest incidence rates. Germany had the highest incidence rate among the 204 countries analyzed, while Monaco reported the highest mortality and DALY rates. Smoking, high body mass index, occupational exposure to benzene, and formaldehyde were identified as significant risk factors associated with mortality from older people with AML in 2019.

Conclusion

Our study showed that the incidence, mortality, and DALY rates of AML in the older population were strongly correlated with the SDI, and these rates have been steadily increasing. This had become an increasingly serious global health issue, particularly in areas with a high SDI. We highlighted the urgency to focus more on this disease and called for the prompt implementation of appropriate preventive and control measures.

Comprehensive analysis of the RSK gene family in acute myeloid leukemia determines a prognostic signature for the prediction of clinical prognosis and treatment responses

OBJECTIVE

The prognosis of acute myeloid leukemia (AML) remains poor although the basic and translational research has been highly productive in understanding the genetics and pathopoiesis of AML and a plethora of targeted therapies have been developed. Consequently, it is crucial to deepen the knowledge of molecular pathogenesis underlying AML for the advancement of new treatment options.

METHOD

A RSK gene family-related signature was constructed to investigate whether RSK gene family members were useful in predicting the prognosis of AML patients. The relationship between the RSK gene family-related signature and the infiltration of immune cells was further assessed using the CIBERSORT algorithm. The 'oncoPredict' package was used to analyze relationships between the RSK gene family-related signature and the sensitivity to drugs or small molecules.

RESULTS

Patients were classified into two groups using the RSK gene family-related signature following the median risk score. Overall survival (OS) was significantly longer in patients with low-risk scores than that in patients with high-risk scores as showed by both training and validation datasets. Moreover, the signature was helpful in predicting 1-year, 3-year, and 5-year OS in training and validation datasets. In addition, it was identified that low-risk patients exhibited greater sensitivity to 20 drugs or small molecules and that high-risk patients had higher sensitivity to 38 drugs or small molecules.

CONCLUSION

RSK gene family members, particularly RPS6KA1 and RPS6KA4, may help to predict prognosis for AML patients. Furthermore, RPS6KA1 may serve as a novel drug target for AML.

Identification of a venetoclax-resistance prognostic signature base on 6-senescence genes and its clinical significance for acute myeloid leukemia

Background

Satisfactory responses can be obtained for acute myeloid leukemia (AML) treated by Venetoclax (VEN)-based therapy. However, there are still quite a few AML patients (AMLs) resistant to VEN, and it is critical to understand whether VEN-resistance is regulated by senescence.

Methods

Here, we established and validated a signature for predicting AML prognosis based on VEN resistance-related senescence genes (VRSGs). In this study, 51 senescence genes were identified with VEN-resistance in AML. Using LASSO algorithms and multiple AML cohorts, a VEN-resistance senescence prognostic model (VRSP-M) was developed and validated based on 6-senescence genes.

Results

According to the median score of the signature, AMLs were classified into two subtypes. A worse prognosis and more adverse features occurred in the high-risk subtype, including older patients, non-de novo AML, poor cytogenetics, adverse risk of European LeukemiaNet (ELN) 2017 recommendation, and TP53 mutation. Patients in the high-risk subtype were mainly involved in monocyte differentiation, senescence, NADPH oxidases, and PD1 signaling pathway. The model's risk score was significantly associated with VEN-resistance, immune features, and immunotherapy response in AML. In vitro, the IC50 values of ABT-199 (VEN) rose progressively with increasing expression of G6PD and BAG3 in AML cell lines.

Conclusions

The 6-senescence genes prognostic model has significant meaning for the prediction of VEN-resistance, guiding personalized molecularly targeted therapies, and improving AML prognosis.

Extracellular vesicle small RNA cargo discriminates non-cancer donors from pediatric B-lymphoblastic leukemia patients

Pediatric B-acute lymphoblastic leukemia (B-ALL) is a disease of abnormally growing B lymphoblasts. Here we hypothesized that extracellular vesicles (EVs), which are nanosized particles released by all cells (including cancer cells), could be used to monitor B-ALL severity and progression by sampling plasma instead of bone marrow. EVs are especially attractive as they are present throughout the circulation regardless of the location of the originating cell. First, we used nanoparticle tracking analysis to compare EVs between non-cancer donor (NCD) and B-ALL blood plasma; we found that B-ALL plasma contains more EVs than NCD plasma. We then isolated EVs from NCD and pediatric B-ALL peripheral blood plasma using a synthetic peptide-based isolation technique (Vn96), which is clinically amenable and isolates a broad spectrum of EVs. RNA-seq analysis of small RNAs contained within the isolated EVs revealed a signature of differentially packaged and exclusively packaged RNAs that distinguish NCD from B-ALL. The plasma EVs contain a heterogenous mixture of miRNAs and fragments of long non-coding RNA (lncRNA) and messenger RNA (mRNA). Transcripts packaged in B-ALL EVs include those involved in negative cell cycle regulation, potentially suggesting that B-ALL cells may use EVs to discard gene sequences that control growth. In contrast, NCD EVs carry sequences representative of multiple organs, including brain, muscle, and epithelial cells. This signature could potentially be used to monitor B-ALL disease burden in pediatric B-ALL patients via blood draws instead of invasive bone marrow aspirates.

Aging-related genes related to the prognosis and the immune microenvironment of acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML), one of the common malignancies of the hematologic system, has progressively increased in incidence. Aging is present in both normal tissues and the tumor microenvironment. However, the relationship between senescence and AML prognosis is still not elucidated.

METHODS

In this study, RNA sequencing data of AML were obtained from TCGA, and prognostic prediction models were established by LASSO-Cox analysis. Differences in immune infiltration between the different risk groups were calculated using the CIBERSORT and ESTIMATE scoring methods. The KEGG and GO gene enrichment and GSEA enrichment were also used to enrich for differential pathways between the two groups. Subsequently, this study collected bone marrow samples from patients and healthy individuals to verify the differential expression of uncoupling protein 2 (UCP2) in different populations. Genipin, a UCP2 protein inhibitor, was also used to examine its effects on proliferation, cell cycle, and apoptosis in AML cell lines in vitro.

RESULTS

It showed that aging-related genes (ARGs) expression was correlated with prognosis. And there was a significant difference in the abundance of immune microenvironment cells between the two groups of patients at high risk and low risk. Subsequently, UCP2 expression was found to be elevated in AML patients. Genipin inhibits UCP2 protein and suppresses the proliferation of AML cell lines in vitro.

CONCLUSION

ARGs can be used as a predictor of prognosis in AML patients. Moreover, suppressing UCP2 can reduce the proliferation of AML cell lines, alter their cell cycle, and promote apoptosis in vitro.

Molecular subtyping of acute myeloid leukemia through ferroptosis signatures predicts prognosis and deciphers the immune microenvironment

Acute myeloid leukemia (AML) is one of the most aggressive hematological malignancies with a low 5-year survival rate and high rate of relapse. Developing more efficient therapies is an urgent need for AML treatment. Accumulating evidence showed that ferroptosis, an iron-dependent form of programmed cell death, is closely correlated with cancer initiation and clinical outcome through reshaping the tumor microenvironment. However, understanding of AML heterogeneity based on extensive profiling of ferroptosis signatures remains to be investigated yet. Herein, five independent AML transcriptomic datasets (TCGA-AML, GSE37642, GSE12417, GSE10358, and GSE106291) were obtained from the GEO and TCGA databases. Then, we identified two ferroptosis-related molecular subtypes (C1 and C2) with distinct prognosis and tumor immune microenvironment (TIME) by consensus clustering. Patients in the C1 subtype were associated with favorable clinical outcomes and increased cytotoxic immune cell infiltration, including CD8+/central memory T cells, natural killer (NK) cells, and non-regulatory CD4+ T cells while showing decreased suppressive immune subsets such as M2 macrophages, neutrophils, and monocytes. Functional enrichment analysis of differentially expressed genes (DEGs) implied that cell activation involved in immune response, leukocyte cell-cell adhesion and migration, and cytokine production were the main biological processes. Phagosome, antigen processing and presentation, cytokine-cytokine receptor interaction, B-cell receptor, and chemokine were identified as the major pathways. To seize the distinct landscape in C1 vs. C2 subtypes, a 5-gene prognostic signature (LSP1, IL1R2, MPO, CRIP1, and SLC24A3) was developed using LASSO Cox stepwise regression analysis and further validated in independent AML cohorts. Patients were divided into high- and low-risk groups, and decreased survival rates were observed in high- vs. low-risk groups. The TIME between high- and low-risk groups has a similar scenery in C1 vs. C2 subtypes. Single-cell-level analysis verified that LSP1 and CRIP1 were upregulated in AML and exhausted CD8+ T cells. Dual targeting of these two markers might present a promising immunotherapeutic for AML. In addition, potential effective chemical drugs for AML were predicted. Thus, we concluded that molecular subtyping using ferroptosis signatures could characterize the TIME and provide implications for monitoring clinical outcomes and predicting novel therapies.

Advances in Understanding the Links between Metabolism and Autophagy in Acute Myeloid Leukemia: From Biology to Therapeutic Targeting

Autophagy is a highly conserved cellular degradation process that regulates cellular metabolism and homeostasis under normal and pathophysiological conditions. Autophagy and metabolism are linked in the hematopoietic system, playing a fundamental role in the self-renewal, survival, and differentiation of hematopoietic stem and progenitor cells, and in cell death, particularly affecting the cellular fate of the hematopoietic stem cell pool. In leukemia, autophagy sustains leukemic cell growth, contributes to survival of leukemic stem cells and chemotherapy resistance. The high frequency of disease relapse caused by relapse-initiating leukemic cells resistant to therapy occurs in acute myeloid leukemia (AML), and depends on the AML subtypes and treatments used. Targeting autophagy may represent a promising strategy to overcome therapeutic resistance in AML, for which prognosis remains poor. In this review, we illustrate the role of autophagy and the impact of its deregulation on the metabolism of normal and leukemic hematopoietic cells. We report updates on the contribution of autophagy to AML development and relapse, and the latest evidence indicating autophagy-related genes as potential prognostic predictors and drivers of AML. We review the recent advances in autophagy manipulation, combined with various anti-leukemia therapies, for an effective autophagy-targeted therapy for AML.

A meta-analysis and bioinformatics analysis of P4HB expression levels in the prognosis of cancer patients

BACKGROUND

P4HB (prolyl 4-hydroxylase, beta polypeptide) is a human chromosomal gene that encodes an endoplasmic reticulum (ER) molecular chaperone protein with oxidoreductase, chaperone and isomerase activities. Recent studies indicated that P4HB may have clinical significance, with elevated P4HB expression reported in cancer patients, but its impact on tumor prognosis is not yet clear. To our knowledge, this is the first meta-analysis to show an association between P4HB expression and the prognosis of various cancers.

METHODS

We conducted a systematic literature search in the PubMed, PubMed Central, Web of Science, Embase, CNKI, Wanfang and Weipu databases, followed by a quantitative meta-analysis using Stata SE14.0 and R statistics software 4.2.1. The hazard ratio (HR) and relative risk (RR) were analyzed to evaluate the relationships of P4HB expression levels with overall survival (OS), disease-free survival (DFS), and clinicopathological parameters of cancer patients. Subsequently, P4HB expression in various cancer types was validated using the Gene Expression Profiling Interactive Analysis (GEPIA) online database.

RESULTS

Ten articles containing the data of 4121 cancer patients were included in the analysis, and a significant correlation of high P4HB expression with apparently shorter OS was found (HR, 1.90; 95% CI, 1.50-2.40; P < 0.01), while there was no significant correlation with gender (RR, 1.06; 95% CI, 0.91-1.22; P = 0.084), or age. Additionally, GEPIA online analysis revealed significant upregulation of P4HB in 13 types of cancer. Among them, P4HB overexpression was associated with shorter OS in 9 and worse DFS in 11 cancer types.

CONCLUSIONS

Upregulation of P4HB is correlated with worse prognosis in various cancers, which could provide new ideas for the development of P4HB-related diagnostic biomarkers and new therapeutic targets.

Cystatin B increases autophagic flux by sustaining proteolytic activity of cathepsin B and fuels glycolysis in pancreatic cancer: CSTB orchestrates autophagy and glycolysis in PDAC

BACKGROUND

Both autophagy and glycolysis are essential for pancreatic ductal adenocarcinoma (PDAC) survival due to desmoplasia. We investigated whether targeting a hub gene which participates in both processes could be an efficient strategy for PDAC treatment.

METHODS

The expression pattern of glycolysis signatures (GS) and autophagy signatures (AS) and their correlation with cystatin B (CSTB) in PDAC were analysed. It was discovered how CSTB affected the growth, glycolysis, and autophagy of PDAC cells. We assessed competitive binding to cathepsin B (CTSB) between CSTB and cystatin C (CSTC) via immunoprecipitation (IP) and immunofluorescence (IF). Chromatin immunoprecipitation quantitative polymerase chain reaction (ChIP-qPCR) and luciferase reporter gene assays were used to unveil the mechanism underlying CSTB upregulation. The expression pattern of CSTB was examined in clinical samples and KrasG12D/+, Trp53R172H/+, Pdx1-Cre (KPC) mice.

RESULTS

GS and AS were enriched and closely associated in PDAC tissues. CSTB increased autophagic flux and provided substrates for glycolysis. CSTB knockdown attenuated the proliferation of PDAC cells and patient-derived xenografts. The liquid chromatography-tandem mass spectrometry assay indicated CSTB interacted with CTSB and contributed to the proteolytic activity of CTSB in lysosomes. IF and IP assays demonstrated that CSTB competed with CSTC to bind to CTSB. Mutation of the key sites of CSTB abolished the interaction between CSTB and CTSB. CSTB was highly expressed in PDAC due to H3K27acetylation and SP1 expression. High expression of CSTB in PDAC was observed in tissue microarray and patients' serum samples.

CONCLUSIONS

Our work demonstrated the tumorigenic roles of autophagy and glycolysis in PDAC. CSTB is a key role in orchestrating these processes to ensure energy supply of PDAC cells.

A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis

Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 ( $P<0.001$ ). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort ( $P<0.001$ ) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.

A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis

Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 ( $P<0.001$ ). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort ( $P<0.001$ ) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.

Systematic analysis of prognostic and immunologic characteristics associated with coronavirus disease 2019 regulators in acute myeloid leukemia

The coronavirus disease 2019 (COVID-19) pandemic has so far damaged the health of millions and has made the treatment of cancer patients more complicated, and so did acute myeloid leukemia (AML). The current problem is the lack of understanding of their interactions and suggestions of evidence-based guidelines or historical experience for the treatment of such patients. Here, we first identified the COVID-19-related differentially expressed genes (C-DEGs) in AML patients by analyzing RNA-seq from public databases and explored their enrichment pathways and candidate drugs. A total of 76 C-DEGs associated with the progress of AML and COVID-19 infection were ultimately identified, and the functional analysis suggested that there are some shared links between them. Their protein–protein interactions (PPIs) and protein–drug interactions were then recognized by multiple bioinformatics algorithms. Moreover, a COVID-19 gene-associated prognostic model (C-GPM) with riskScore was constructed, patients with a high riskScore had poor survival and apparently immune-activated phenotypes, such as stronger monocyte and neutrophil cell infiltrations and higher immunosuppressants targeting expressions, meaning which may be one of the common denominators between COVID-19 and AML and the reason what complicates the treatment of the latter. Among the study’s drawbacks is that these results relied heavily on publicly available datasets rather than being clinically confirmed. Yet, these findings visualized those C-DEGs’ enrichment pathways and inner associations, and the C-GPM based on them could accurately predict survival outcomes in AML patients, which will be helpful for further optimizing therapies for AML patients with COVID-19 infections.

Unraveling the mystery: How bad is BAG3 in hematological malignancies?

BAG3, also known as BIS and CAIR-1, interacts with Hsp70 via its BAG domain and with other molecules through its WW domain, PXXP repeats and IPV motifs. BAG3 can participate in major cellular pathways including apoptosis, autophagy, cytoskeleton structure, and motility by regulating the expression, location, and activity of its chaperone proteins. As a multifunctional protein, BAG3 is highly expressed in skeletal muscle, cardiomyocytes and multiple tumors, and its intracellular expression can be stimulated by stress. The functions and mechanisms of BAG3 in hematological malignancies have recently been a topic of interest. BAG3 has been confirmed to be involved in the development and chemoresistance of hematological malignancies and to act as a prognostic indicator. Modulation of BAG3 and its corresponding proteins has thus emerged as a promising therapeutic and experimental target. In this review, we consider the characteristics of BAG3 in hematological malignancies as a reference for further clinical and fundamental investigations.

Risk stratification of acute myeloid leukemia: Assessment using a novel prediction model based on ferroptosis-immune related genes

In acute myeloid leukemia (AML), the link between ferroptosis and the immune microenvironment has profound clinical significance. The objective of this study was to investigate the role of ferroptosis-immune related genes (FIRGs) in predicting the prognosis and therapeutic sensitivity in patients with AML. Using The Cancer Genome Atlas dataset, single sample gene set enrichment analysis was performed to calculate the ferroptosis score of AML samples. To search for FIRGs, differentially expressed genes between the high- and low-ferroptosis score groups were identified and then cross-screened with immune related genes. Univariate Cox and LASSO regression analyses were performed on the FIRGs to establish a prognostic risk score model with five signature FIRGs (BMP2, CCL3, EBI3, ELANE, and S100A6). The prognostic risk score model was then used to divide the patients into high- and low-risk groups. For external validation, two Gene Expression Omnibus cohorts were employed. Overall survival was poorer in the high-risk group than in the low-risk group. The novel risk score model was an independent prognostic factor for overall survival in patients with AML. Infiltrating immune cells were also linked to high-risk scores. Treatment targeting programmed cell death protein 1 may be more effective in high-risk patients. This FIRG-based prognostic risk model may aid in optimizing prognostic risk stratification and treatment of AML.

A novel defined cuproptosis-related gene signature for predicting the prognosis of lung adenocarcinoma

Lung adenocarcinoma (LUAD) has become the most prevalent histologic subset of primary lung cancer, and effective innovative prognostic models are needed to enhance the feasibility of targeted therapies for the disease. Programmed cell death (PCD) performs an integral function in the origin and treatment of cancer. Some PCD-related effective signatures for predicting prognosis in LUAD patients could provide potential therapeutic options in LUAD. A copper-dependent cell death referred to as cuproptosis is distinct from known PCD. However, whether cuproptosis is associated with LUAD patients' prognoses and the potential roles of cuproptosis-related genes involved is still unknown. For the prediction of LUAD prognosis, we developed a unique cuproptosis-associated gene signature. In The Cancer Genome Atlas (TCGA) cohort, the score derived from the risk signature on the basis of six cuproptosis-related genes was found to independently serve as a risk factor for anticipating lung cancer-related death. The differentially expressed genes between the high- and low-risk groups were linked to the cilium-related function. LUAD patients’ prognoses may now be predicted by a unique gene signature identified in this work. This discovery also provides a substantial foundation for future research into the links between cuproptosis-associated genes and cilium-related function in LUAD patients.

The pyroptosis-related gene signature predicts prognosis and reveals characterization of the tumor immune microenvironment in acute myeloid leukemia

Background: Pyroptosis is a novel inflammatory form of programmed cell death and a prospective target for cancer therapy. Nevertheless, little is known about the association between pyroptosis-related genes (PRGs) and acute myeloid leukemia (AML) prognosis. Herein, we systematically investigated the specific functions and clinical prognostic value of multiple PRGs in AML.

Methods: Univariate and LASSO Cox regression analyses based on TCGA and GTEx databases were used to generate the PRG signature, whose predictive efficacy of survival was evaluated using survival analysis, ROC, univariate and multivariate Cox analyses as well as subgroup analysis. The BeatAML cohort was used for data validation. The association between risk score and immune cell infiltration, HLA, immune checkpoints, cancer stem cell (CSC), tumor mutation burden (TMB), and therapeutic drug sensitivity were also analyzed.

Results: Six -PRG signatures, namely, CASP3, ELANE, GSDMA, NOD1, PYCARD, and VDR were generated. The high-risk score represented a poorer prognosis and the PRG risk score was also validated as an independent predictor of prognosis. A nomogram including the cytogenetic risk, age, and risk score was constructed for accurate prediction of 1-, 3-, and 5-year survival probabilities. Meanwhile, this risk score was significantly associated with the tumor immune microenvironment (TIME). A high-risk score is characterized by high immune cell infiltration, HLA, and immune checkpoints, as well as low CSC and TMB. In addition, patients with low-risk scores presented significantly lower IC50 values for ATRA, cytarabine, midostaurin, doxorubicin, and etoposide.

Conclusion: Our findings might contribute to further understanding of PRGs in the prognosis and development of AML and provide novel and reliable biomarkers for its precise prevention and treatment.

An immunity and pyroptosis gene-pair signature predicts overall survival in acute myeloid leukemia

Treatment responses of patients with acute myeloid leukemia (AML) are known to be heterogeneous, posing challenges for risk scoring and treatment stratification. In this retrospective multi-cohort study, we investigated whether combining pyroptosis- and immune-related genes improves prognostic classification of AML patients. Using a robust gene pairing approach, which effectively eliminates batch effects across heterogeneous patient cohorts and transcriptomic data, we developed an immunity and pyroptosis-related prognostic (IPRP) signature that consists of 15 genes. Using 5 AML cohorts (n = 1327 patients total), we demonstrate that the IPRP score leads to more consistent and accurate survival prediction performance, compared with 10 existing signatures, and that IPRP scoring is widely applicable to various patient cohorts, treatment procedures and transcriptomic technologies. Compared to current standards for AML patient stratification, such as age or ELN2017 risk classification, we demonstrate an added prognostic value of the IPRP risk score for providing improved prediction of AML patients. Our web-tool implementation of the IPRP score and a simple 4-factor nomogram enables practical and robust risk scoring for AML patients. Even though developed for AML patients, our pan-cancer analyses demonstrate a wider application of the IPRP signature for prognostic prediction and analysis of tumor-immune interplay also in multiple solid tumors.

Function and regulation of ULK1: From physiology to pathology

The expression of ULK1, a core protein of autophagy, is closely related to autophagic activity. Numerous studies have shown that pathological abnormal expression of ULK1 is associated with various human diseases such as neurological disorders, infections, cardiovascular diseases, liver diseases and cancers. In addition, new advances in the regulation of ULK1 have been identified. Furthermore, targeting ULK1 as a therapeutic strategy for diseases is gaining attention as new corresponding activators or inhibitors are being developed. In this review, we describe the structure and regulation of ULK1 as well as the current targeted activators and inhibitors. Moreover, we highlight the pathological disorders of ULK1 expression and its critical role in human diseases.

An inflammatory response-related gene signature associated with immune status and prognosis of acute myeloid leukemia

OBJECTIVE

To determine the prognostic significance of inflammatory response-associated genes in acute myeloid leukemia (AML).

METHODS

Transcriptomic profiles and related clinical information of AML patients were acquired from a public database. To establish a multi-gene prognosis signature, we performed least absolute shrinkage and selection operator Cox analysis for the TCGA cohort and evaluated the ICGC cohort for verification. Subsequently, Kaplan-Meier analysis was carried out to compare the overall survival (OS) rates between high- and low-risk groups. Biological function and single-sample gene set enrichment (ssGSEA) analyses were employed to investigate the association of risk score with immune status and the tumor microenvironment. Prognostic gene expression levels in AML samples and normal controls were confirmed by qRT-PCR and immunofluorescence.

RESULTS

We identified a potential inflammatory response-related signature comprising 11 differentially expressed genes, including ACVR2A, CCL22, EBI3, EDN1, FFAR2, HRH1, ICOSLG, IL-10, INHBA, ITGB3, and LAMP3, and found that AML patients with high expression levels in the high-risk group had poor OS rates. Biological function analyses revealed that prognostic genes mainly participated in inflammation and immunity signaling pathways. Analyses of cancer-infiltrating immunocytes indicated that in high-risk patients, the immune suppressive microenvironment was significantly affected. The expression of the inflammation reaction-associated signature was found to be associated with susceptibility to chemotherapy. There was a significant difference in prognostic gene expression between AML and control tissues.

CONCLUSION

A novel inflammatory response-related signature was developed with 11 candidate genes to predict prognosis and immune status in AML patients.

The dual role of autophagy in acute myeloid leukemia

Acute myeloid leukemia (AML) is a severe hematologic malignancy prevalent in older patients, and the identification of potential therapeutic targets for AML is problematic. Autophagy is a lysosome-dependent catabolic pathway involved in the tumorigenesis and/or treatment of various cancers. Mounting evidence has suggested that autophagy plays a critical role in the initiation and progression of AML and anticancer responses. In this review, we describe recent updates on the multifaceted functions of autophagy linking to genetic alterations of AML. We also summarize the latest evidence for autophagy-related genes as potential prognostic predictors and drivers of AML tumorigenesis. We then discuss the crosstalk between autophagy and tumor cell metabolism into the impact on both AML progression and anti-leukemic treatment. Moreover, a series of autophagy regulators, i.e., the inhibitors and activators, are described as potential therapeutics for AML. Finally, we describe the translation of autophagy-modulating therapeutics into clinical practice. Autophagy in AML is a double-edged sword, necessitating a deeper understanding of how autophagy influences dual functions in AML tumorigenesis and anti-leukemic responses.

The role of E2A in ATPR-induced cell differentiation and cycle arrest in acute myeloid leukaemia cells

Acute myeloid leukaemia (AML) is a biologically heterogeneous disease with an overall poor prognosis; thus, novel therapeutic approaches are needed. Our previous studies showed that 4-amino-2-trifluoromethyl-phenyl retinate (ATPR), a new derivative of all-trans retinoic acid (ATRA), could induce AML cell differentiation and cycle arrest. The current study aimed to determine the potential pharmacological mechanisms of ATPR therapies against AML. Our findings showed that E2A was overexpressed in AML specimens and cell lines, and mediate AML development by inactivating the P53 pathway. The findings indicated that E2A expression and activity decreased with ATPR treatment. Furthermore, we determined that E2A inhibition could enhance the effect of ATPR-induced AML cell differentiation and cycle arrest, whereas E2A overexpression could reverse this effect, suggesting that the E2A gene plays a crucial role in AML. We identified P53 and c-Myc were downstream pathways and targets for silencing E2A cells using RNA sequencing, which are involved in the progression of AML. Taken together, these results confirmed that ATPR inhibited the expression of E2A/c-Myc, which led to the activation of the P53 pathway, and induced cell differentiation and cycle arrest in AML.

Identification and Validation of 17-lncRNA Related to Regulatory T Cell Heterogeneity as a Prognostic Signature for Head and Neck Squamous Cell Carcinoma

Successful eradication of tumors by the immune system depends on generation of antigen-specific T cells that migrate to tumor sites and kill cancerous cells. However, presence of suppressive Treg populations inside tumor microenvironment hinders effector T cell function and decreases antitumor immunity. In this study we independently evaluated and confirmed prognostic signature of 17-Treg-related-lncRNA. Immune cell infiltration analysis using 17-lncRNA signature as a probe, accurately described Treg populations in tumor immune microenvironment. 17-lncRNA signature model predicted prognosis with excellent accuracy in all three cohorts: training cohort (AUC=0.82), testing cohort (AUC=0.61) and total cohort (AUC=0.72). The Kaplan-Meier analysis confirmed that the overall survival of patients in the low-risk group was significantly better than those in the high-risk group(P<0.001). CIBERSORT analysis confirmed that low risk group had higher infiltration of tumor killer CD8 T cells, memory activated CD4 T cells, follicular helper T cells and T cells regulatory (Tregs), and lower expression of M0 macrophages and Mast cells activated. These results indicate that the 17-lncRNA signature is a novel prognostic and support the use of lncRNA as a stratification tool to help guide the course of treatment and clinical decision making in patients at high risk of HNSCC.