Identification of an Immune-Related Gene Signature Based on Immunogenomic Landscape Analysis to Predict the Prognosis of Adult Acute Myeloid Leukemia Patients

Characterization of a predictive signature for tumor microenvironment and immunotherapy response in hepatocellular carcinoma involving neutrophil extracellular traps

Neutrophil extracellular traps (NETs) and other factors play a significant role in impacting the prognosis of patients with Hepatocellular carcinoma (HCC). Nevertheless, further research is warranted to fully elucidate the prognostic implications of NETs in patients with HCC. We employed a hierarchical clustering technique to examine the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) data and identified subtypes associated with NETs. Subsequently, we utilized LASSO regression analysis to identify a distinct gene expression pattern within these subtypes. The strength of this signature was further validated through analysis of TCGA-LIHC and International Cancer Genome Consortium-Liver Cancer (ICGC-LIRI-JP) data. Our findings resulted in the construction of a six-gene signature related to NETs, which can predict survival outcomes in HCC patients. To enhance the predictive accuracy of our tool, we developed a nomogram that integrates the NETs signature with clinicopathological characteristics. We validated the significance of NETs in HCC patients using qRT-PCR and immunohistochemistry assays, along with in vitro experiments targeting high-risk genes. Furthermore, our exploration of the immune microenvironment uncovered augmented immune-specific metrics within the low-risk cohort, implying potential disparities in immune-related attributes between the high-risk and low-risk contingents. In summary, the NETs signature we discovered serves as a valuable biomarker and provides guidance for personalized therapy in HCC patients.

Pan-cancer analysis revealing that PTPN2 is an indicator of risk stratification for acute myeloid leukemia

The non-receptor protein tyrosine phosphatases gene family (PTPNs) is involved in the tumorigenesis and development of many cancers, but the role of PTPNs in acute myeloid leukemia (AML) remains unclear. After a comprehensive evaluation on the expression patterns and immunological effects of PTPNs using a pan-cancer analysis based on RNA sequencing data obtained from The Cancer Genome Atlas, the most valuable gene PTPN2 was discovered. Further investigation of the expression patterns of PTPN2 in different tissues and cells showed a robust correlation with AML. PTPN2 was then systematically correlated with immunological signatures in the AML tumor microenvironment and its differential expression was verified using clinical samples. In addition, a prediction model, being validated and compared with other models, was developed in our research. The systematic analysis of PTPN family reveals that the effect of PTPNs on cancer may be correlated to mediating cell cycle-related pathways. It was then found that PTPN2 was highly expressed in hematologic diseases and bone marrow tissues, and its differential expression in AML patients and normal humans was verified by clinical samples. Based on its correlation with immune infiltrates, immunomodulators, and immune checkpoint, PTPN2 was found to be a reliable biomarker in the immunotherapy cohort and a prognostic predictor of AML. And PTPN2'riskscore can accurately predict the prognosis and response of cancer immunotherapy. These findings revealed the correlation between PTPNs and immunophenotype, which may be related to cell cycle. PTPN2 was differentially expressed between clinical AML patients and normal people. It is a diagnostic biomarker and potentially therapeutic target, providing targeted guidance for clinical treatment.

Towards In Silico Identification of Genes Contributing to Similarity of Patients' Multi-Omics Profiles: A Case Study of Acute Myeloid Leukemia

We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients' similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software. For each cluster, we rank multi-omics features, ensuring the best separation between clusters, and select the top-ranked features that preserve clustering. To find genes targeted by DNA methylation and miRNAs found in the top-ranked features, we use chromosome-conformation capture data and miRNet2.0 software, respectively. To identify informative genes, these combined sets of target genes are analyzed in terms of their enrichment in somatic/germline mutations, GO biological processes/pathways terms and known sets of genes considered to be important in relation to a given disease, as recorded in the Molecular Signature Database from GSEA. The protein-protein interaction (PPI) networks were analyzed to identify genes that are hubs of PPI networks. We used data recorded in The Cancer Genome Atlas for patients with acute myeloid leukemia to demonstrate our approach, and discuss our findings in the context of results in the literature.

Pan-cancer analysis of the PDE4DIP gene with potential prognostic and immunotherapeutic values in multiple cancers including acute myeloid leukemia

Phosphodiesterase 4D interacting protein (PDE4DIP) interacts with cAMP-specific phosphodiesterase 4D and its abnormal expression promotes the development of hematological malignancies, breast cancer, and pineal cell carcinoma. However, there is currently no systematic pan-cancer analysis of the association between PDE4DIP and various cancers. Thus, this study aimed to elucidate the potential functions of PDE4DIP in various cancers. Based on the multiple public databases and online websites, we conducted comprehensive analyses for PDE4DIP in various cancers, including differential expression, prognosis, genetic variation, DNA methylation, and immunity. We thoroughly analyzed the specific role of PDE4DIP in acute myeloid leukemia (LAML). The results indicated that there were differences in PDE4DIP expression in cancers, and in kidney chromophobe, LAML, pheochromocytoma and paraganglioma, thymoma, and uveal melanoma, PDE4DIP had potential prognostic value. PDE4DIP expression was also correlated with genetic variation, DNA methylation, immune cell infiltration, and immune-related genes in cancers. Functional enrichment analysis showed that PDE4DIP was mainly related to immune-related pathways in cancers, and in LAML, PDE4DIP was mainly related to immunoglobulin complexes, T-cell receptor complexes, and immune response regulatory signaling pathways. Our study systematically revealed for the first time the potential prognostic and immunotherapeutic value of PDE4DIP in various cancers, including LAML.

Prognostic value of NOX2 as a potential biomarker for lung adenocarcinoma using TCGA and clinical validation

Lung adenocarcinoma (LUAD) is associated with high morbidity and mortality; therefore, effective biomarkers are essential. In recent years, a rapid increase in the efficiency of high‑throughput sequencing technologies and the continuous improvement of comprehensive online databases have facilitated the study of the genomic changes that affect tumor progression, including the identification of tumor biomarkers. Therefore, the identification of genes that may affect the progression and prognosis of LUAD is necessary. In the present study, the CIBERSORT and ESTIMATE bioinformatics packages were used to evaluate data from The Cancer Genome Atlas, including assessment of the proportion of tumor‑infiltrating immune cells in the tumor microenvironment, Cox regression analysis of differentially expressed genes and cross analysis of protein‑protein interaction networks. Myeloid cell NADPH oxidase isoform 2 (NOX2), an indispensable gene in the immune system, was demonstrated to serve a vital role in LUAD pathogenesis. Western blotting and immunohistochemistry confirmed that, at the protein level, NOX2 expression was increased in normal cells compared with cancer cells. Furthermore, reverse transcription‑quantitative PCR results at the mRNA level were consistent with these results, which confirmed that the abundance of NOX2 was significantly reduced in LUAD patients. NOX2 may be used as a novel marker and an independent prognostic indicator of LUAD. Its potential function was enriched in tumor immune and metabolic signaling pathways, which could provide clues for the study of the signaling pathways and molecular networks related to the disease progression of LUAD, which would be helpful for the assessment of prognosis in the clinical setting.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia

Background

Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML).

Methods

RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated.

Results

Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents.

Conclusion

A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.

FABP6 Expression Correlates with Immune Infiltration and Immunogenicity in Colorectal Cancer Cells

Background

Immune checkpoint inhibitors (ICIs) have rapidly revolutionized colorectal cancer (CRC) treatment, but resistance caused by the heterogeneous tumor microenvironment (TME) still presents a challenge. Therefore, it is necessary to characterize TME immune infiltration and explore new targets to improve immunotherapy.

Methods

The compositions of 64 types of infiltrating immune cells and their relationships with CRC patient clinical characteristics were assessed. Differentially expressed genes (DEGs) between “hot” and “cold” tumors were used for functional analysis. A prediction model was constructed to explore the survival of CRC patients treated with and without immunotherapy. Finally, fatty acid-binding protein (FABP6) was selected for in vitro experiments, which revealed its roles in the proliferation, apoptosis, migration, and immunogenicity of CRC tissues and cell lines.

Results

The infiltration levels of several immune cells were associated with CRC tumor stage and prognosis. Different cell types showed the synergistic or antagonism infiltration patterns. Enrichment analysis of DEGs revealed that immune-related signaling was significantly activated in hot tumors, while metabolic process pathways were altered in cold tumors. In addition, the constructed model effectively predicted the survival of CRC patients treated with and without immunotherapy. FABP6 knockdown did not significantly alter tumor cell proliferation, apoptosis, and migration. FABP6 was negatively correlated with immune infiltration, and knockdown of FABP6 increased major histocompatibility complex (MHC) class 1 expression and promoted immune-related chemokine secretion, indicating the immunogenicity enhancement of tumor cells. Finally, knockdown of FABP6 could promote the recruitment of CD8+ T cells.

Conclusion

Collectively, we described the landscape of immune infiltration in CRC and identified FABP6 as a potential immunotherapeutic target for treatment.

A novel immune prognostic model of non-M3 acute myeloid leukemia

Acute myeloid leukemia (AML) is a common hematological malignancy in adults. AML patients exhibit clinical heterogeneity with complications of molecular basis. The leukemogenesis of AML involves immune escape, and the immunosuppression status of the patient might have great impact on AML treatment outcome. In this study, we established an immune prognostic model of AML using bioinformatics tools. With the data in the TCGA and GTEx datasets, we analyzed differentially expressed genes (DEGs) in non-M3 AML and identified 420 immune-related DEGs. Among which, 49 genes' expression was found to be related to AML prognosis based on univariate Cox regression analysis. Next, we established a prognostic model with these 49 genes in AML by LASSO regression and multivariate Cox regression analyses. In our model, the expressions of 5 immune genes, MIF, DEF6, OSM, MPO, AVPR1B, were used to stratify non-M3 AML patients' treatment outcome. A patient's risk score could be calculated as Risk Score=0.40081 × MIF (MIF expression) - 0.15201 × MPO + 0.78073 × DEF6 - 0.45192 × AVPR1B + 0.25912 × OSM. The area under the curve of the risk score signature was 0.8, 0.8, and 0.96 at 1 year, 3 years, and 5 years, respectively. The prognostic model was then validated internally by TCGA data and externally by GEO data. At last, the result of single-sample gene-set enrichment analysis demonstrated that compared with healthy samples, the abundance of non-turmeric immune cells was significantly repressed in AML. To summarize, we presented an immune-related 5-gene signature prognostic model in AML.

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.

Upregulation of FHL1, SPNS3, and MPZL2 predicts poor prognosis in pediatric acute myeloid leukemia patients with FLT3-ITD mutation

Chromosomal translocations and gene mutations are characteristics of the genomic profile of acute myeloid leukemia (AML). We aim to identify a gene signature associated with poor prognosis in AML patients with FLT3-ITD compared to AML patients with NPM1/CEBPA mutations. RNA-sequencing (RNA-Seq) count data were downloaded from the UCSC Xena browser. Samples were grouped by their mutation status into high and low-risk groups. Differential gene expression (DGE), machine learning (ML) and survival analyses were performed. A total of 471 differentially expressed genes (DEGs) were identified, of which 16 DEGs were used as features for the prediction of mutation status. An accuracy of 92% was obtained from the ML model. FHL1, SPNS3, and MPZL2 were found to be associated with overall survival in FLT3-ITD samples. FLT3-ITD mutation confers an indicative gene expression profile different from NPM1/CEBPA mutation, and the expression of FHL1, SPSN3, and MPZL2 can serve as prognostic indicators of unfavorable disease.

Identification of the integrated prognostic signature associated with immuno-relevant genes and long non-coding RNAs in acute myeloid leukemia

BACKGROUND

Like other cancers, considerable effort has been made in acute myeloid leukemia (AML) to identify prognostic genes and long non-coding RNAs (lncRNAs) with their potential clinical applications. However, to date, no integrated prognostic model has been developed that combines both gene expression and lncRNAs as a singular approach in AML.

METHOD

Comprehensive bioinformatic approaches (Weighted gene co-expression network analysis, Univariate Cox regression analyses, Pearson correlation, LASSO-Cox regression, Wilcoxon test) were used to construct the signature and to define high- and low-risk groups in AML datasets. ESTIMATE and CIBERSORT algorithms were applied to investigate the potential impact of infiltrating immune cells based on the obtained signature in tumor microenvironment. In addition, gene ontology (GO) and KEGG enrichment were applied to explore the potential function of the signature.

RESULTS

Herein, we focused on immune-related genes (IRGs) and immune-related long non-coding RNAs (IRlncRNAs) and constructed an integrated prognostic immunorelevant signature in AML. The obtained signature exhibit five IRGs (DAXX, PSMB8, CSRP1, RAC2 and PTPN6) and one IRlncRNA (AC080037.2), and is strictly associated with age and FAB (French-American-British classification). Importantly, the high-risk AML group (defined by the signature) correlated positively with three types of scores (immune score, stroma score, and ESTIMATE score). We also identified a few immune cells (resting mast cells and monocytes) potentially involved in the correlation between signature and survival of AML patients. The prognostic ability of the obtained signature was tested in the training cohort and then validated in both test and total cohorts. The pathway enrichment analysis confirmed the possible immune- related role of the signature.

CONCLUSION

We constructed an integrated prognostic signature comprising five immune-related protein-coding genes (IRPCG) (DAXX, PSMB8, CSRP1, RAC2, and PTPN6) and one immune-related lncRNA (AC080037.2) that may serve as potential biomarkers for predicting survival and further stratifying AML patients.

HIVEP3 cooperates with ferroptosis gene signatures to confer adverse prognosis in acute myeloid leukemia

BACKGROUND

The human immunodeficiency virus type I enhancer binding protein (HIVEP) family, which contains zinc finger and acid-rich (ZAS) domains, has been demonstrated to be implicated in vital biological processes, such as cell survival, tumor necrosis factor (TNF) signaling, and tumor formation. However, its expression patterns, prognostic relevance, and functional implications in acute myeloid leukemia (AML) remain elusive.

METHODS

We inspected HIVEP mRNA expression levels in datasets from The Cancer Genome Atlas (TCGA) and GSE24006. Survival analyses were orchestrated using the web-based bioinformatics platforms and R studio in two AML cohorts. Prognostic value and capacity were assessed by Cox regression analyses. Association of HIVEP3 expression levels with clinical characteristics were analyzed with R and UALCAN. Subsequentially, functional enrichment analyses were operated to interpret HIVEP3 co-expressed gene clusters. A prognostic gene signature was created by the least absolute shrinkage and selection operator (LASSO) regression algorithm. Moreover, bone marrow aspirate smears of AML patients were stained for HIVEP3 by immunohistochemistry (IHC). HIVEP3 expression was examined by qRT-PCR in leukemia cell lines treated with ferroptosis compounds in vitro.

RESULTS

Augmented transcriptional levels of HIVEP2 and 3 were noted in AML patients (p<0.001). HIVEP3 not only could confer adverse prognosis independently in AML patients, but also was associated with AML subtypes, age, cytogenetic risk, and disease-related molecules. Co-expressed gene clusters of HIVEP3 were enriched in functional pathways related to AML leukemogenesis, such as ribosome, metabolism, and calcium signaling. Combined with multiple tumorigenesis signaling pathways, we proposed an integrated LASSO model with HIVEP3 and ferroptosis regulators AIFM2 and LPCAT3, to predict the outcome for AML patients. Furthernore, altered HIVEP3 expression at the mRNA or protein level was confirmed in sorted leukemia cells and blast cells in bone marrow tissues. In vitro experiments authenticated the involvement of HIVEP3 in ferroptosis signaling pathways.

CONCLUSIONS

Our findings suggest that HIVEP3 is a de novo independent prognostic indicator, and the crosstalk between HIVEP3 and ferroptosis signaling pathways may inspire a specific perspective on the oncological network of AML.

[Prognostic implications and functional enrichment analysis of LTB4R in patients with acute myeloid leukemia]

OBJECTIVE

To explore the expression patterns, prognostic implications, and biological role of leukotriene B4 receptor (LTB4R) in patients with acute myeloid leukemia (AML).

METHODS

We collected the data of mRNA expression levels and clinical information of patients with AML from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database for mRNA expression analyses, survival analyses, Cox regression analyses and correlation analyses using R studio to assess the expression patterns and prognostic value of LTB4R. The correlation of LTB4R expression levels with clinical characteristics of the patients were analyzed using UALCAN. The co-expressed genes LTB4R were screened from Linkedomics and subjected to functional enrichment analysis. A protein-protein interaction network was constructed using STRING. GSEA analyses of the differentially expressed genes (DEGs) were performed based on datasets from TCGA-LAML stratified by LTB4R expression level. We also collected peripheral blood mononuclear cells (PBMCs) from AML patients and healthy donors for examination of the mRNA expression levels of LTB4R and immune checkpoint genes using qRT-PCR. We also examined serum LTB4R protein levels in the patients using ELISA.

RESULTS

The mRNA expression level of LTB4R was significantly increased in AML patients (4.898±1.220 vs 2.252±0.215, P < 0.001), and an elevated LTB4R expression level was correlated with a poor overall survival (OS) of the patients (P=0.004, HR=1.74). LTB4R was identified as an independent prognostic factor for OS (P=0.019, HR=1.66) and was associated with FAB subtypes, cytogenetic risk, karyotype abnormalities and NPM1 mutations. The co- expressed genes of LTB4R were enriched in the functional pathways closely associated with AML leukemogenesis, including neutrophil inflammation, lymphocyte activation, signal transduction, and metabolism. The DEGs were enriched in differentiation, activation of immune cells, and cytokine signaling. Examination of the clinical serum samples also demonstrated significantly increased expressions of LTB4R mRNA (P=0.044) and protein (P=0.008) in AML patients, and LTB4R mRNA expression was positively correlated with the expression of the immune checkpoint HAVCR2 (r= 0.466, P=0.040).

CONCLUSION

LTB4R can serve as a novel biomarker and independent prognostic indicator of AML and its expression patterns provide insights into the crosstalk of leukemogenesis signaling pathways involving tumor immunity and metabolism.

Bioinformatics analysis and identification of hub genes and immune-related molecular mechanisms in chronic myeloid leukemia

BACKGROUND

Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor of the bone marrow originating from pluripotent hematopoietic stem cells. The advent of tyrosine kinase inhibitors (TKIs) has greatly improved the survival rate of patients with CML. However, TKI-resistance leads to the disease recurrence and progression. This study aimed to identify immune-related genes (IRGs) associated with CML progression.

METHODS

We extracted the gene's expression profiles from the Gene Expression Omnibus (GEO). Bioinformatics analysis was used to determine the differentially expressed IRGs of CML and normal peripheral blood mononuclear cells (PBMCs). Functional enrichment and gene set enrichment analysis (GSEA) were used to explore its potential mechanism. Hub genes were identified using Molecular Complex Detection (MCODE) and the CytoHubba plugin. The hub genes' diagnostic value was evaluated using the receiver operating characteristic (ROC). The relative proportions of infiltrating immune cells in each CML sample were evaluated using CIBERSORT. Quantitative real-time PCR (RT-qPCR) was used to validate the hub gene expression in clinical samples.

RESULTS

A total of 31 differentially expressed IRGs were identified. GO analyses revealed that the modules were typically enriched in the receptor ligand activity, cytokine activity, and endopeptidase activity. KEGG enrichment analysis of IRGs revealed that CML involved Th17 cell differentiation, the NF-kappa B signaling pathway, and cytokine-cytokine receptor interaction. A total of 10 hub genes were selected using the PPI network. GSEA showed that these hub genes were related to the gamma-interferon immune response, inflammatory response, and allograft rejection. ROC curve analysis suggested that six hub genes may be potential biomarkers for CML diagnosis. Further analysis indicated that immune cells were associated with the pathogenesis of CML. The RT-qPCR results showed that proteinase 3 (PRTN3), cathepsin G (CTSG), matrix metalloproteinase 9 (MMP9), resistin (RETN), eosinophil derived neurotoxin (RNase2), eosinophil cationic protein (ECP, RNase3) were significantly elevated in CML patients' PBMCs compared with healthy controls.

CONCLUSION

These results improved our understanding of the functional characteristics and immune-related molecular mechanisms involved in CML progression and provided potential diagnostic biomarkers and therapeutic targets.

A novel immune-related genes signature after bariatric surgery is histologically associated with non-alcoholic fatty liver disease

Increasing evidence shows that immune-related genes (IRGs) play an important role in bariatric surgery (BS). We identified differentially expressed immune-related genes (DEIRGs) of adipose tissue after BS by analysing the two expression profiles of GEO (GSE59034 and GSE29409). Subsequently, enrichment analysis, GSEA and PPI networks were examined to identify the hub IRGs and related pathways. The performance of the signature was evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). CIBERSORT algorithm was used to evaluate the relative abundance of infiltrated immune cells.42 DEIRGs were found between the GSE59034 and GSE29409 datasets. The AUC of the signature was 0.904 and 0.865 in the GSE58979 and GSE48452, respectively. Interestingly, the signature also showed good performance in diagnosing non-alcoholic fatty liver disease (NAFLD) (AUC was 0.834 and 0.800, respectively). The number of neutrophils, macrophages M2, macrophages M0 and dendritic cells activated decreased significantly. After BS, the infiltration of T cells regulatory, monocytes, mast cells resting and plasma cells in adipose tissue increased. The novel proposed IRGs signature reveals the underlying immune mechanism of BS and is a promising biomarker for distinguishing the severity of NAFLD. This will provide new insights into strategies for treating obesity and NAFLD.

Higher TOX Genes Expression Is Associated With Poor Overall Survival for Patients With Acute Myeloid Leukemia

Thymocyte selection-associated HMG box (TOX) is a transcription factor that belongs to the high mobility group box (HMG-box) superfamily, which includes four subfamily members: TOX, TOX2, TOX3, and TOX4. TOX is related to the formation of multiple malignancies and contributes to CD8+ T cell exhaustion in solid tumors. However, little is known about the role of TOX genes in hematological malignancies. In this study, we explored the prognostic value of TOX genes from 40 patients with de novo acute myeloid leukemia (AML) by quantitative real-time PCR (qRT-PCR) in a training cohort and validated the results using transcriptome data from 167 de novo AML patients from the Cancer Genome Atlas (TCGA) database. In the training cohort, higher expression of TOX and TOX4 was detected in the AML samples, whereas lower TOX3 expression was found. Moreover, both the training and validation results indicated that higher TOX2, TOX3, and TOX4 expression of AML patients (3-year OS: 0% vs. 37%, P = 0.036; 3-year OS: 4% vs. 61%, P < 0.001; 3-year OS: 0% vs. 32%, P = 0.010) and the AML patients with highly co-expressed TOX, TOX2, TOX4 genes (3-year OS: 0% vs. 25% vs. 75%, P = 0.001) were associated with poor overall survival (OS). Interestingly, TOX2 was positively correlated with CTLA-4, PD-1, TIGIT, and PDL-2 (rs = 0.43, P = 0.006; rs = 0.43, P = 0.006; rs = 0.56, P < 0.001; rs = 0.54, P < 0.001). In conclusion, higher expression of TOX genes was associated with poor OS for AML patients, which was related to the up-regulation of immune checkpoint genes. These data might provide novel predictors for AML outcome and direction for further investigation of the possibility of using TOX genes in novel targeted therapies for AML.

A novel prognostic signature based on immune-related genes of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) presents a great clinical challenge and has a poor prognosis, with immune-related genes playing a crucial role. We aimed to develop an immune-related prognostic signature for improving prognosis prediction in DLBCL. Samples from the GSE31312 dataset were randomly allocated to discovery and internal validation cohorts. Univariate Cox, random forest, LASSO regression and multivariate Cox analyses were utilized to develop a prognostic signature, which was verified in the internal validation cohort, entire validation cohort and external validation cohort (GSE10846). The tumor microenvironment was investigated using the CIBERSORT and ESTIMATE tools. Gene set enrichment analysis (GSEA) was further applied to analyze the entire GSE31312 cohort. We identified four immune-related genes (CD48, IL1RL, PSDM3, RXFP3) significantly associated with overall survival. Based on discovery and validation cohort analyses, this four-gene signature could classify patients into high- and low-risk groups, with significantly different prognoses. Activated memory CD4 T cells and activated dendritic cells were significantly decreased in the high-risk group, and these patients had lower immune scores. GSEA revealed enrichment of signaling pathways, such as T cell receptor, antigen receptor-mediated, antigen processing and presentation of peptide antigen via MHC class I, in the low-risk group. In conclusion, a robust signature based on four immune-related genes was successfully constructed for predicting prognosis in DLBCL patients.