Identification of prognostic genes in the acute myeloid leukemia immune microenvironment based on TCGA data analysis

ITGAM sustains MAPK signaling and serves as an adverse prognostic factor and therapeutic target in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is the second most frequently occurring type of leukemia in adults. Despite breakthroughs in genetics, the prognosis of AML patients remains dismal. The aim of this study is to find new therapeutic targets and diagnostic markers for AML and to explore their mechanisms of action.

Methods

The expression patterns of integrin subunit alpha M (ITGAM) were investigated across different cell types using the Human Protein Atlas (HPA) database. The ITGAM levels across cancer types were analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Prognostic correlations in AML individuals were evaluated using The Cancer Genome Atlas (TGCA) database. ITGAM-associated functions were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The AML cells were transfected with short-hairpin RNA targeting ITGAM or a control, and subsequently subjected to analysis in order to ascertain the impact of ITGAM on proliferation and apoptosis.

Results

The expression of ITGAM was significantly higher in the AML patient samples compared to the control samples. High ITGAM expression was significantly associated with poor overall survival (OS). The knockdown of ITGAM in the AML cells resulted in a decrease in proliferation and an increase in apoptosis. This was accompanied by cell cycle arrest at the G1 phase and a downregulation of protein production for cyclin D1, cyclin E1, cyclin-dependent kinase 2 (CDK2), and cyclin-dependent kinase 4 (CDK4). A pathway analysis and a western blot analysis revealed that ITGAM positively regulated mitogen-activated protein kinase (MAPK) signaling by silencing attenuated p38 MAPK (P38), c-Jun N-terminal kinase (JNK), and extracellular signal-regulated kinase (ERK) phosphorylation, while the total protein levels remained unchanged.

Conclusions

ITGAM can serve as a potential prognostic biomarker and therapeutic target for AML. ITGAM production was elevated in AML and indicated poor survival. Silencing ITGAM suppressed AML cell viability and induced apoptosis by blocking cell cycle progression, likely by impeding the activation of the MAPK pathway. Further investigations that directly target the ITGAM-MAPK axis may offer novel strategies for mitigating AML pathogenesis and overcoming chemotherapy resistance.

Construction of an immune-related prognostic signature and lncRNA-miRNA-mRNA ceRNA network in acute myeloid leukemia

The progression of acute myeloid leukemia (AML) is influenced by the immune microenvironment in the bone marrow and dysregulated intracellular competing endogenous RNA (ceRNA) networks. Our study utilized data from UCSC Xena, The Cancer Genome Atlas Program, the Gene Expression Omnibus, and the Immunology Database and Analysis Portal. Using Cox regression analysis, we identified an immune-related prognostic signature. Genomic analysis of prognostic messenger RNA (mRNA) was conducted through Gene Set Cancer Analysis (GSCA), and a prognostic ceRNA network was constructed using the Encyclopedia of RNA Interactomes. Correlations between signature mRNAs and immune cell infiltration, checkpoints, and drug sensitivity were assessed using R software, gene expression profiling interactive analysis (GEPIA), and CellMiner, respectively. Adhering to the ceRNA hypothesis, we established a potential long noncoding RNA (lncRNA)/microRNA (miRNA)/mRNA regulatory axis. Our findings pinpointed 9 immune-related prognostic mRNAs (KIR2DL1, CSRP1, APOBEC3G, CKLF, PLXNC1, PNOC, ANGPT1, IL1R2, and IL3RA). GSCA analysis revealed the impact of copy number variations and methylation on AML. The ceRNA network comprised 14 prognostic differentially expressed lncRNAs (DE-lncRNAs), 6 prognostic DE-miRNAs, and 3 prognostic immune-related DE-mRNAs. Correlation analyses linked these mRNAs' expression to 22 immune cell types and 6 immune checkpoints, with potential sensitivity to 27 antitumor drugs. Finally, we identified a potential LINC00963/hsa-miR-431-5p/CSRP1 axis. This study offers innovative insights for AML diagnosis and treatment through a novel immune-related signature and ceRNA axis. Identified novel biomarkers, including 2 mRNAs (CKLF, PNOC), 1 miRNA (hsa-miR-323a-3p), and 10 lncRNAs (SNHG25, LINC01857, AL390728.6, AC127024.5, Z83843.1, AP002884.1, AC007038.1, AC112512, AC020659.1, AC005921.3) present promising candidates as potential targets for precision medicine, contributing to the ongoing advancements in the field.

Identification of Prognostic Genes in Acute Myeloid Leukemia Microenvironment: A Bioinformatic and Experimental Analysis

Acute myeloid leukemia (AML) is a lethal hematologic malignancy with a variable prognosis that is highly dependent on the bone marrow microenvironment. Consequently, a better understanding of the AML microenvironment is crucial for early diagnosis, risk stratification, and personalized therapy. In recent years, the role of bioinformatics as a powerful tool in clarifying the complexities of cancer has become more prominent. Gene expression profile and clinical data of 173 AML patients were downloaded from the TCGA database, and the xCell algorithm was applied to calculate the microenvironment score (MS). Then, the correlation of MS with FAB classification, and CALGB cytogenetic risk category was investigated. Differentially expressed genes (DEGs) were identified, and the correlation analysis of DEGs with patient survival was done using univariate cox. The prognostic value of candidate prognostic DEGs was confirmed based on the GEO database. In the last step, real-time PCR was used to compare the expression of the top three prognostic genes between patients and the control group. During TCGA data analysis, 716 DEGs were identified, and survival analysis results showed that 152 DEGs had survival-related changes. In addition, the prognostic value of 31 candidate prognostic genes was confirmed by GEO data analysis. Finally, the expression analysis of FLVCR2, SMO, and CREB5 genes, the most related genes to patients' survival, was significantly different between patients and control groups. In summary, we identified key microenvironment-related genes that influence the survival of AML patients and may serve as prognostic and therapeutic targets.

Comprehensive characterization of immunogenic cell death in acute myeloid leukemia revealing the association with prognosis and tumor immune microenvironment

BACKGROUND

This study aimed to explore the clinical significance of immunogenic cell death (ICD) in acute myeloid leukemia (AML) and its relationship with the tumor immune microenvironment characteristics. It also aimed to provide a potential perspective for bridging the pathogenesis of AML and immunological research, and to provide a theoretical basis for precise individualized treatment of AML patients.

METHODS

Firstly, we identified two subtypes associated with ICD by consensus clustering and explored the biological enrichment pathways, somatic mutations, and tumor microenvironment landscape between the ICD subtypes. Additionally, we developed and validated a prognostic model associated with ICD-related genes. Finally, we conducted a preliminary exploration of the construction of disease regulatory networks and prediction of small molecule drugs based on five signature genes.

RESULTS

Differentially expressed ICD-related genes can distinguish AML into subgroups with significant differences in clinical characteristics and survival prognosis. The relationship between the ICD- high subgroup and the immune microenvironment was tight, showing significant enrichment in immune-related pathways such as antibody production in the intestinal immune environment, allograft rejection, and Leishmaniasis infection. Additionally, the ICD- high subtype showed significant upregulation in a variety of immune cells such as B_cells, Macrophages_M2, Monocytes, and T_cells_CD4. We constructed a prognostic risk feature based on five signature genes (TNF, CXCR3, CD4, PIK3CA and CALR), and the time-dependent ROC curve confirmed the high accuracy in predicting the clinical outcomes.

CONCLUSION

There is a strong close relationship between the ICD- high subgroup and the immune microenvironment. Immunogenicity-related genes have the potential to be a prognostic biomarker for AML.

The KIR2DL family serves as prognostic biomarkers and correlates with immune infiltrates in acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a prevalent haematological malignancy in which various immune and stromal cells in the bone marrow microenvironment have instrumental roles and substantially influence its progression. KIR2DL is a member of the immunoglobulin-like receptor family and a natural killer (NK) cell surface-specific receptor. However, its impact on immune infiltration regarding AML has not been addressed. We aimed to explore molecular markers associated with the immune microenvironment and prognosis of AML with a particular focus on KIR2DL family members. Analysis of data from The Cancer Genome Atlas and Genotype-Tissue Expression databases revealed that KIR2DL1, KIR2DL3 and KIR2DL4 expression were significantly upregulated in AML and associated with decreased overall survival (OS). Moreover, univariate Cox analysis implicated KIR2DL genes as independent prognostic markers of OS. Functional enrichment analysis revealed that KIR2DL genes were associated with immune cells, the immune microenvironment and NK cell-mediated cytotoxicity. Additionally, immune infiltration analyses revealed that KIR2DL upregulation was associated with stronger immune infiltration. Finally, we performed drug sensitivity profiling of KIR2DL genes using the Cellminer database. Collectively, our findings suggest that KIR2DL1, KIR2DL3 and KIR2DL4 have critical roles in AML and may represent novel biomarker genes for disease prognosis and immune infiltration.

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.

Transcriptomic analysis identifies CYP27A1 as a diagnostic marker for the prognosis and immunity in lung adenocarcinoma

BACKGROUND

The association between lipid metabolism disorder and carcinogenesis is well-established, but there is limited research on the connection between lipid metabolism-related genes (LRGs) and lung adenocarcinoma (LUAD). The objective of our research was to identify LRGs as the potential biomarkers for prognosis and assess their impact on immune cell infiltration in LUAD.

METHODS

We identified novel prognostic LRGs for LUAD patients via the bioinformatics analysis. CYP27A1 expression level was systematically evaluated via various databases, such as TCGA, UALCAN, and TIMER. Subsequently, LinkedOmics was utilized to perform the CYP27A1 co-expression network and GSEA. ssGSEA was conducted to assess the association between infiltration of immune cells and CYP27A1 expression. CYP27A1's expression level was validated by qRT-PCR analysis.

RESULTS

CYP27A1 expression was decreased in LUAD. Reduced CYP27A1 expression was linked to unfavorable prognosis in LUAD. Univariate and multivariate analyses indicated that CYP27A1 was an independent prognostic biomarker for LUAD patients. GSEA results revealed a positive correlation between CYP27A1 expression and immune-related pathways. Furthermore, CYP27A1 expression was positively correlated with the infiltration levels of most immune cells.

CONCLUSION

CYP27A1 is a potential biomarker for LUAD patients, and our findings provided a novel perspective to develop the prognostic marker for LUAD patients.

VSIG2 promotes malignant progression of pancreatic ductal adenocarcinoma by enhancing LAMTOR2-mediated mTOR activation

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most intractable malignancies to overcome clinically due to its insidious onset as well as rapid progression. It is urgent to seek new diagnostic markers and therapeutic targets in order to furthest ameliorate the prognosis of patients with PDAC. V-set and immunoglobulin domain containing 2 (VSIG2) belongs to immunoglobulin superfamily (IgSF), which function as coinhibitory molecule to mediate immune evasion of tumors. Nevertheless, the role of VSIG2 in PDAC and related mechanism still keep unclear.

METHODS

Different expression of VSIG2 in PDAC tissues and cells were detected by bioinformatic analysis, immunohistochemistry, real-time quantitative PCR as well as western blotting. CCK-8, colony formation, Transwell assay, and scratch experiment were utilized to assess proliferation, invasion and migration properties of PDAC cells. The relationship of VSIG2 with late endosomal/lysosomal adaptor, MAPK and MTOR activator 2 (LAMTOR2) and mechanistic target of rapamycin (mTOR) was identified using mass spectrometry, co-immunoprecipitation and immunofluorescence. GO and KEGG enrichment analysis were performed for further pathway verification using western blotting. Additionally, subcutaneous xenograft tumor model and clinical samples analysis were implemented to further elucidate the oncogenic effect of VSIG2 on PDAC in vivo and clinically.

RESULTS

VSIG2 was highly expressed in PDAC tissues and cells. Overexpression of VSIG2 facilitated the proliferation, invasion and migration abilities of PDAC cells, while VSIG2-inhibition exerted opposite effects. Mechanistically, VSIG2 could simultaneously bind to LAMTOR2 and mTOR, thereby enhancing interaction between two molecules, which resulted in elevated phosphorylation-modificatory activation of mTOR and downstream key molecules. Clinically, up-regulation of VSIG2 was positively associated with advanced stage, overall survival and disease-free survival of PDAC patients.

CONCLUSIONS

Our study disclosed that VSIG2 was overexpressed in PDAC, which promoted the proliferation, invasion and metastasis. Mechanically, VSIG2 acted as a scaffold to recruit LAMTOR2 and mTOR simultaneously, stabilize the interaction between them, thus enhancing LAMTOR2-mediated mTOR phosphorylated activation. Collectively, VSIG2 could be exploited as a biomarker for diagnosis and prognosis monitor of PDAC in the future, meanwhile, targeting VSIG2 in PDAC management is expected to be a novel strategy. Video Abstract. Video Abstract.

Novel scheme for defining the clinical implications of TP53 mutations in myeloid neoplasia

BACKGROUND

TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis.

METHODS

We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model by identifying an optimal VAF cutoff using a statistically robust strategy of sampling-based regression on survival data to accurately classify the TP53 allelic configuration and assess prognosis more precisely.

RESULTS

Overall, TP53MT were found in 1010 patients. Following the traditional criteria, 36% of the cases were classified as single hits, while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53MT. Interestingly, our method was able to upstage 192 out of 352 (54.5%) traditionally single hit lesions into a probable biallelic category. Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. In addition, single-cell DNA studies unveiled the biallelic nature of previously considered monoallelic cases.

CONCLUSION

Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Identification and validation of a 7-genes prognostic signature for adult acute myeloid leukemia based on aging-related genes

To explore effects of aging-related genes (ARGs) on the prognosis of Acute Myeloid Leukemia (AML), a seven-ARGs signature was developed and validated in AML patients. The numbers of seven-ARG sequences were selected to construct the survival prognostic signature in TCGA-LAML cohort, and two GEO datasets were used independently to verify the prognostic values of signature. According to seven-ARGs signature, patients were categorized into two subgroups. Patients with high-risk prognostic score were defined as HRPS-group/high-risk group, while others were set as LRPS-group/low-risk group. HRPS-group presented adverse overall survival (OS) than LRPS-group in TCGA-AML cohort (HR=3.39, P<0.001). In validation, the results emphasized a satisfactory discrimination in different time points, and confirmed the poor OS of HRPS-group both in GSE37642 (HR=1.96, P=0.001) and GSE106291 (HR=1.88, P<0.001). Many signal pathways, including immune- and tumor-related processes, especially NF-κB signaling, were highly enriched in HRPS-group. Coupled with high immune-inflamed infiltration, the HRPS-group was highly associated with the driver gene and oncogenic signaling pathway of TP53. Prediction of blockade therapy targeting immune checkpoint indicated varied benefits base on the different ARGs signature score, and the results of predicted drug response suggested that Pevonedistat, an inhibitor of NEDD8-activating enzyme, targeting NF-κB signaling, may have potential therapeutic value for HRPS-group. Compared with clinical factors alone, the signature had an independent value and more predictive power of AML prognosis. The 7-ARGs signature may help to guide clinical-decision making to predict drug response, and survival in AML patients.

TLR8 agonist Motolimod-induced inflammatory death for treatment of acute myeloid leukemia

The clinical treatment of AML is dominated by "7 + 3" therapy, but it often shows great toxicity and limited therapeutic efficacy in application. Therefore, it is urgent to develop novel therapeutic strategies to achieve safe and efficient treatment of AML. Small-molecule inhibitors have the characteristics of high specificity, low off-target toxicity and remarkable therapeutic effect, and are receiving more and more attention in tumor therapy. In this study, we screened a library of 1972 FDA-approved small molecular compounds for those that induced the inflammatory death of AML cells, among which the TLR8 agonist Motolimod (MTL) showed stronger anti-AML activity in the animal model but slight affection on normal lymphocytes in control mice. In terms of mechanism, cellular experiments in AML cell lines proved that TLR8 and LKB1/AMPK are the key distinct mechanisms for MTL triggered caspase-3-dependent cell death and the expression of a large number of inflammatory factors. In conclusion, our findings identified the immunoactivator MTL as a single agent exerting significant anti-AML activity in vitro and in vivo, with strong potential for clinical translation.

A tumor microenvironment-based prognostic index for osteosarcoma

BACKGROUND

The tumor microenvironment (TME) has a central role in the oncogenesis of osteosarcomas. The composition of the TME is essential for the interaction between tumor and immune cells. The aim of this study was to establish a prognostic index (TMEindex) for osteosarcoma based on the TME, from which estimates about patient survival and individual response to immune checkpoint inhibitor (ICI) therapy can be deduced.

METHODS

Based on osteosarcoma samples from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, the ESTIMATE algorithm was used to estimate ImmuneScore and StromalScore. Combined differentially expressed gene analysis, weighted gene co-expression network analyses, the Least Absolute Shrinkage and Selection Operator regression and stepwise regression to construct the TMEindex. The prognostic role of TMEindex was validated in three independent datasets. The molecular and immune characteristics of TMEindex and the impact on immunotherapy were then comprehensively investigated. The expression of TMEindex genes in different cell types and its effects on osteosarcoma cells were explored by scRNA-Seq analysis and molecular biology experiments.

RESULTS

Fundamental is the expression of MYC, P4HA1, RAMP1 and TAC4. Patients with high TMEindex had worse overall survival, recurrence-free survival, and metastasis-free survival. TMEindex is an independent prognostic factor in osteosarcoma. TMEindex genes were mainly expressed in malignant cells. The knockdown of MYC and P4HA1 significantly inhibited the proliferation, invasion and migration of osteosarcoma cells. A high TME index is related to the MYC, mTOR, and DNA replication-related pathways. In contrast, a low TME index is related to immune-related signaling pathways such as the inflammatory response. The TMEindex was negatively correlated with ImmuneScore, StromalScore, immune cell infiltration, and various immune-related signature scores. Patients with a higher TMEindex had an immune-cold TME and higher invasiveness. Patients with a low TME index were more likely to respond to ICI therapy and achieve clinical benefit. In addition, the TME index correlated with response to 29 oncologic drugs.

CONCLUSIONS

The TMEindex is a promising biomarker to predict the prognosis of patients with osteosarcoma and their response to ICI therapy, and to distinguish the molecular and immune characteristics.

Novel Scheme for Defining the Clinical Implications of TP53 Mutations in Myeloid Neoplasia

Background: TP53 mutations ( TP53 MT ) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. Methods: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model to properly resolve the allelic configuration of TP53 MT and assess prognosis more precisely. Results: Overall, TP53 MT were found in 1010 patients. Following the traditional criteria, 36% of cases were classified as single hits while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53 MT . Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. These results were recapitulated by single-cell DNA studies, which unveiled the biallelic nature of previously considered monoallelic cases. Conclusion: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

A Novel Fatty Acid Metabolism-Associated Risk Model for Prognosis Prediction in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is the most common acute leukaemia in adults, with an unfavourable outcome and a high rate of recurrence due to its heterogeneity. Dysregulation of fatty acid metabolism plays a crucial role in the development of several tumours. However, the value of fatty acid metabolism (FAM) in the progression of AML remains unclear. In this study, we obtained RNA sequencing and corresponding clinicopathological information from the TCGA and GEO databases. Univariate Cox regression analysis and subsequent LASSO Cox regression analysis were utilized to identify prognostic FAM-related genes and develop a potential prognostic risk model. Kaplan-Meier analysis was used for prognostic significances. We also performed ROC curve to illustrate that the risk model in prognostic prediction has good performance. Moreover, significant differences in immune infiltration landscape were found between high-risk and low-risk groups using ESTIMATE and CIBERSOT algorithms. In the end, differential expressed genes (DEGs) were analyzed by gene set enrichment analysis (GSEA) to preliminarily explore the possible signaling pathways related to the prognosis of FAM and AML. The results of our study may provide potential prognostic biomarkers and therapeutic targets for AML patients, which is conducive to individualized precision therapy.

Hsa_circ_0015278 Regulates FLT3-ITD AML Progression via Ferroptosis-Related Genes

AML with the FLT3-ITD mutation seriously threatens human health. The mechanism by which circRNAs regulate the pathogenesis of FLT3-ITD mutant-type AML through ferroptosis-related genes (FerRGs) remains unclear. Differentially expressed circRNAs and mRNAs were identified from multiple integrated data sources. The target miRNAs and mRNAs of the circRNAs were predicted using various databases. The PPI network, ceRNA regulatory network, GO, and KEGG enrichment analyses were performed. The "survival" and the "pROC" R packages were used for K-M and ROC analysis, respectively. GSEA, immune infiltration analysis, and clinical subgroup analysis were performed. Finally, circRNAs were validated by Sanger sequencing and qRT-PCR. In our study, 77 DECircs-1 and 690 DECircs-2 were identified. Subsequently, 11 co-up-regulated DECircs were obtained by intersecting DECircs-1 and DECircs-2. The target miRNAs of the circRNAs were screened by CircInteractome, circbank, and circAtlas. Utilizing TargetScan, ENCORI, and miRWalk, the target mRNAs of the miRNAs were uncovered. Ultimately, 73 FerRGs were obtained, and the ceRNA regulatory network was constructed. Furthermore, MAPK3 and CD44 were significantly associated with prognosis. qRT-PCR results confirmed that has_circ_0015278 was significantly overexpressed in FLT3-ITD mutant-type AML. In summary, we constructed the hsa_circ_0015278/miRNAs/FerRGs signaling axis, which provides new insight into the pathogenesis and therapeutic targets of AML with FLT3-ITD mutation.

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.

The Expressions and Functions of lncRNA Related to m6A in Hepatocellular Carcinoma from a Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is one of the most common cancer in these days. Besides, N6-methyladenosine (m6A) plays an important role in the occurrence and development of hepatocellular carcinoma. Meanwhile, it is known to us that long noncoding RNAs (lncRNA) have the capability to control the expression of genes which means some lncRNA can adjust the expression of some m6A.Thus, it is indispensable to dig the m6A-related lncRNA in hepatocellular carcinoma about its potential regulatory mechanism and immune analysis as well as its potential drugs. In this experiment, expression profile and clinical information of lncRNA are obtained by downloading the liver cancer data set from The Cancer Genome Atlas (TCGA) database. GO enrichment analysis is used to predict potential regulatory mechanism of lncRNA. Correlation analysis of clinical parameters are calculated via chisq.test. The Cox regression model is used in univariate and multivariate analysis, and the difference is statistically significant when P < 0.05. The results show that many kinds of lncRNA have influence on the prognosis of patients with HCC, and enrichment analysis discloses some pathways that can be used to evaluate mechanism underlying in HCC. The screening of targeted drugs can provide new clues for further experiments and clinical treatment.

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.

LPCAT3 is a potential prognostic biomarker and may be correlated with immune infiltration and ferroptosis in acute myeloid leukemia: a pan-cancer analysis

BACKGROUND

Recent studies have highlighted the critical role of lysophosphatidylcholine acyltransferase 3 (LPCAT3) during cancer development. However, the abnormal expression and prognostic significance of pan-cancer have not been determined.

METHODS

We explored the expression level and prognostic value of LPCAT3 in 33 cancers by bioinformatics techniques, and comprehensively studied the biological function and immune infiltration based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases as well as many online websites.

RESULTS

LPCAT3 is significantly upregulated in many cancers, and it is associated with prognosis. Pan-cancer Cox regression analysis indicated that the high expression of LPCAT3 was associated with poor prognosis in acute myeloid leukemia (AML), lower-grade glioma (LGG), ovarian cancer (OV), and uveal melanoma (UVM), while better prognosis in kidney renal clear cell carcinoma (KIRC) (all P<0.05). Further analysis indicated that higher LPCAT3 expression in most cancers markedly decreased the infiltration of immune cells, except diffuse large B-cell lymphoma (DLBC), AML, LGG, stomach adenocarcinoma (STAD), and UVM. In contrast, the expression level of LPCAT3 was positively correlated with most immune checkpoints in colon adenocarcinoma (COAD), DLBC, LGG, liver hepatocellular carcinoma (LIHC), and UVM. Additionally, LPCAT3 expression was associated with tumor mutational burden (TMB) in 4 cancer types, while microsatellite instability (MSI) was in 3 cancer types. Functional enrichment analysis showed LPCAT3 upregulation was highly associated with lipid metabolism and ferroptosis processes. In addition, the result of prediction drug response suggested that B-cell lymphoma 2 (BCL2) inhibitors and Midostaurin may be a potential treatment option for AML with low-LPCAT3 expression.

CONCLUSIONS

LPCAT3 expression is increased in multiple cancers. Overexpression of LPCAT3 is associated with poor prognosis and tumor immune microenvironment in many cancers, especially in AML. Our results showed that the oncogene of LPCAT3 may serve as a potential prognostic biomarker and/or therapeutic target in AML patients.

Identification of immune and stromal cell infiltration-related gene signature for prognosis prediction in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a common and life-threatening hematologic malignancy, its occurrence and progression are closely related to immune/stromal cell infiltration in the bone marrow (BM) microenvironment. However, no studies have described an immune/stromal cell infiltration-related gene (ISCIRG)-based prognostic signature for ALL. A total of 444 patients involving 437 bulk and 7 single-cell RNA-seq datasets were included in this study. Eligible datasets were searched and reviewed from the database of TCGA, TARGET project and GEO. Then an integrated bioinformatics analysis was performed to select optimal prognosis-related genes from ISCIRGs, construct a nomogram model for predicting prognosis, and assess the predictive power. After LASSO and multivariate Cox regression analyses, a seven ISCIRGs-based signature was proved to be able to significantly stratify patients into high- and low-risk groups in terms of OS. The seven genes were confirmed that directly related to the composition and status of immune/stromal cells in BM microenvironment by analyzing bulk and single-cell RNA-seq datasets. The calibration plot showed that the predicted results of the nomogram were consistent with the actual observation results of training/validation cohort. This study offers a reference for future research regarding the role of ISCIRGs in ALL and the clinical care of patients.

V-Set and immunoglobulin domain containing (VSIG) proteins as emerging immune checkpoint targets for cancer immunotherapy

The development of immune checkpoint inhibitors is becoming a promising approach to fight cancers. Antibodies targeting immune checkpoint proteins such as CTLA-4 and PD-1 can reinvigorate endogenous antitumor T-cell responses and bring durable advantages to several malignancies. However, only a small subset of patients benefit from these checkpoint inhibitors. Identification of new immune checkpoints with the aim of combination blockade of multiple immune inhibitory pathways is becoming necessary to improve efficiency. Recently, several B7 family-related proteins, TIGIT, VSIG4, and VSIG3, which belong to the VSIG family, have attracted substantial attention as coinhibitory receptors during T-cell activation. By interacting with their corresponding ligands, these VSIG proteins inhibit T-cell responses and maintain an immune suppressive microenvironment in tumors. These results indicated that VSIG family members are becoming putative immune checkpoints in cancer immunotherapy. In this review, we summarized the function of each VSIG protein in regulating immune responses and in tumor progression, thus providing an overview of our current understanding of VSIG family members.

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.

Construction of a solid Cox model for AML patients based on multiomics bioinformatic analysis

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The bone marrow (BM) microenvironment in AML plays an important role in leukemogenesis, drug resistance and leukemia relapse. In this study, we aimed to identify reliable immune-related biomarkers for AML prognosis by multiomics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and constructed a LASSO-Cox regression model to predict the prognosis of AML using multiomics bioinformatic analysis data. This was followed by independent validation of the model in the GSE106291 (n=251) data set and mutated genes in clinical samples for predicting overall survival (OS). Molecular docking was performed to predict the most optimal ligands to the genes in prognostic model. The single-cell RNA sequence dataset GSE116256 was used to clarify the expression of the hub genes in different immune cell types. According to their significant differences in immune gene signatures and survival trends, we concluded that the immune infiltration-lacking subtype (IL type) is associated with better prognosis than the immune infiltration-rich subtype (IR type). Using the LASSO model, we built a classifier based on 5 hub genes to predict the prognosis of AML (risk score = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3). In summary, we constructed a prognostic model of AML using integrated multiomics bioinformatic analysis that could serve as a therapeutic classifier.

Oncogenic signaling pathway mediated by Notch pathway-related genes induces immunosuppression and immunotherapy resistance in hepatocellular carcinoma

The Notch pathway is a highly conserved signaling pathway involved in the regulation of cell proliferation and differentiation. However, the relationships between Notch pathway-related genes (NPRGs), immunosuppression, and immunotherapy resistance of hepatocellular carcinoma (HCC) remain unclear. Gene expression data and clinical information were extracted from GSE14520, GSE36376, GSE76427, LIRI-JP, TCGA-LIHC, GSE20140, GSE27150, and IMvigor210 datasets. A consensus clustering analysis based on 10 NPRGs was performed to determine the molecular subtypes, and then a notchScore was constructed based on differentially expressed and prognostic genes between molecular subtypes. Two molecular subgroups with significantly distinct survival and immune cell infiltration were identified. Then, a notchScore was constructed to quantify the Notch index of each patient with HCC. Next, we investigated the correlations between the clinical characteristics and the notchScore using logistic regression. Furthermore, multivariate Cox analysis showed that a high notchScore was an independent predictor of poor overall survival (OS) in the TCGA and LIRI-JP datasets and was associated with higher pathological stages. Additionally, a high notchScore was associated with higher immune cells, higher ESTIMATE score, higher immune score, higher stromal score, higher immune checkpoint, and lower tumor purity, which was consistent with the "immunity tidal model theory." Importantly, a high notchScore was sensitive to immunotherapy. Additionally, GSEA indicated that several GO and KEGG items associated with apoptosis, immune-related pathways, and cell cycle signal pathways were significantly enriched in the high notchScore phenotype pathway. Our findings propose that a high notchScore is a prognostic biomarker and correlates with immune infiltration and sensitivity to immunotherapy in HCC.

Apoptosis-Related Gene-Mediated Cell Death Pattern Induces Immunosuppression and Immunotherapy Resistance in Gastric Cancer

Background: Apoptosis is a type of cell death, which can produce abundant mediators to modify the tumor microenvironment. However, relationships between apoptosis, immunosuppression, and immunotherapy resistance of gastric cancer (GC) remain unclear.

Methods: Gene expression data and matching clinical information were extracted from TCGA-STAD, GSE84437, GSE34942, GSE15459, GSE57303, ACRG/GSE62254, GSE29272, GSE26253, and IMvigor210 datasets. A consensus clustering analysis based on six apoptosis-related genes (ARGs) was performed to determine the molecular subtypes, and then an apoptosisScore was constructed based on differentially expressed and prognostic genes between molecular subtypes. Estimate R package was utilized to calculate the tumor microenvironment condition. Kaplan-Meier analysis and ROC curves were performed to further confirm the apoptosisScore efficacy.

Results: Based on six ARGs, two molecular subgroups with significantly distinct survival and immune cell infiltration were identified. Then, an apoptosisScore was built to quantify the apoptosis index of each GC patient. Next, we investigated the correlations between the clinical characteristics and apoptosisScore using logistic regression. Multivariate Cox analysis shows that low apoptosisScore was an independent predictor of poor overall survival in TCGA and ACRG datasets, and was associated with the higher pathological stage. Meanwhile, low apoptosisScore was associated with higher immune cell, higher ESTIMATEScore, higher immuneScore, higher stromalScore, higher immune checkpoint, and lower tumorpurity, which was consistent with the “immunity tidal model theory”. Importantly, low apoptosisScore was sensitive to immunotherapy. In addition, GSEA indicated that several gene ontology and Kyoto Encyclopedia of Genes and Genomes items associated with apoptosis, several immune-related pathways, and JAK–STAT signal pathway were considerably enriched in the low apoptosisScore phenotype pathway.

Conclusion: Our findings propose that low apoptosisScore is a prognostic biomarker, correlated with immune infiltrates, and sensitivity to immunotherapy in GC.

Identification of prognostic genes in the pancreatic adenocarcinoma immune microenvironment by integrated bioinformatics analysis

PURPOSE

Pancreatic adenocarcinoma (PAAD) is one of the most common causes of death among solid tumors, and its pathogenesis remains to be clarified. This study aims to elucidate the value of immune/stromal-related genes in the prognosis of PAAD through comprehensive bioinformatics analysis based on the immune microenvironment and validated in Chinese pancreatic cancer patients.

METHODS

Gene expression profiles of pancreatic cancer patients were obtained from TCGA database. Differentially expressed genes (DEGs) were identified based on the ESTIMATE algorithm. Gene co-expression networks were constructed using WGCNA. In the key module, survival analysis was used to reveal the prognostic value. Subsequently, we performed functional enrichment analysis to construct a protein-protein interaction (PPI) network. The relationship between tumor immune infiltration and hub genes was analyzed by TIMER and CIBERSORT. Finally, it was validated in the GEO database and in tissues of Chinese pancreatic cancer patients.

RESULTS

In the TCGA pancreatic cancer cohort, a low immune/stromal score was associated with a good prognosis. After bioinformatic analysis, 57 genes were identified to be significantly associated with pancreatic cancer prognosis. Among them, up-regulation of four genes (COL6A3, PLAU, MMP11 and MMP14) indicated poor prognosis and was associated with multiple immune cell infiltration. IHC results showed that PLAU protein levels from Chinese pancreatic cancer tissues were significantly higher than those from adjacent non-tumor tissues and were also associated with tumor TNM stage and lymph node metastasis.

CONCLUSION

In conclusion, this study demonstrates that PLAU may serve as a new diagnostic and therapeutic target, which is highly expressed in Chinese pancreatic cancer tissues and associated with lymph node metastasis.

Characteristics of Pyroptosis-Related Subtypes and Novel Scoring Tool for the Prognosis and Chemotherapy Response in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is usually associated with poor prognosis and low complete remission (CR) rate due to individual biological heterogeneity. Pyroptosis is a special form of inflammatory programmed cell death related to the progression, treatment response, and prognosis of multiple tumors. However, the potential connection of pyroptosis-related genes (PRGs) and AML still remains unclear. We described the genetic and transcriptional alterations of PRGs in 151 AML samples and presented a consensus clustering of these patients into two subtypes with distinct immunological and prognostic characteristics. Cluster A, associated with better prognosis, was characterized by relatively lower PRG expression, activated immune cells, higher immune scores in the tumor microenvironment (TME), and downregulation of immunotherapy checkpoints. Subsequently, a PRG score was constructed to predict overall survival (OS) of AML patients by using univariate and multivariate Cox regression analysis, and its immunological characteristics and predictive capability were further validated by 1,054 AML samples in external datasets. Besides an immune-activated status, low-PRG score cohorts exhibited higher chemotherapeutic drug sensitivity and significant positive correlation with the cancer stem cell (CSC) index. Combined with age, clinical French-American-British (FAB) subtypes, and PRG score, we successfully constructed a nomogram to effectively predict the 1-/3-/5-year survival rate of AML patients, and the predictive capability was further validated in multiple external datasets with a high area under the curve (AUC) value. The various transcriptomic analysis helps us screen significant pyroptosis-related signatures of AML and provide a new clinical application of PRG scores in predicting the prognosis and benefits of treatment for AML patients.

Development and validation of a leukocyte-associated immunoglobulin-like receptor-1 prognostic signature for lower-grade gliomas

OBJECTIVE

Leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1), is an immunosuppressive receptor, widely expressed by immune cells, but the part of LAIR-1 in glioma progression remains unclear. The purpose of this study was to explore the relationship between LAIR-1 expression and the development of lower-grade glioma (LGG) using publicly available data sets.

METHODS

We took advantage of The Cancer Genome Atlas (TCGA) to analyze the expression of LAIR-1 in patients with LGG. Second, Kaplan-Meier methods and univariate and multivariate Cox regression analyses were used to examine the clinical significance of LAIR-1 expression in combination with CGGA databases, and then receiver operating characteristic curve analysis was used to verify the prognostic utility of LAIR-1. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) were used to explore the function of LAIR-1. Analysis of the correlation with immune infiltration was conducted using the ESTIMATE algorithm and single sample gene set enrichment analysis.

RESULTS

Our results showed that LAIR-1 expression to be positively correlated with malignant clinicopathologic features of LGG. Univariate analysis and multivariate analysis revealed that overexpression of LAIR-1 was correlated with a worse prognosis in patients. A nomogram model combining LAIR-1 was more useful in guiding clinical diagnosis, and functional enrichment analysis showed that malignant development of glioma was closely affiliated with the tumor immune microenvironment.

CONCLUSION

These results indicate that LAIR 1 is a latent marker for determining the prognosis of LGG patients. LAIR 1 may also participate a critical part in TIME of LGG by regulating the infiltration of immune cells, suggesting that LAIR 1 might be used as a therapeutic target to regulate the antitumor immune response.

CSF2RB Is a Unique Biomarker and Correlated With Immune Infiltrates in Lung Adenocarcinoma

Background

The tumor microenvironment plays an important role in the occurrence and development of tumors. However, there are gaps in understanding the molecular and cellular interactions between tumor cells and the immune tumor microenvironment (TME). The aim of this study was to identify a novel gene that played an important role in the tumor microenvironment of lung adenocarcinoma (LUAD).

Methods

The gene expression profile and clinical data for LUAD were downloaded from TCGA database. First, we used the ESTIMATE algorithm to evaluate the immune and stromal scores accordingly. Also, we analyzed differentially expressed immune-related genes (IRGs) in the high and low immune/stromal score groups. Then, we used the protein–protein interaction network (PPI network) and a univariate Cox regression analysis to identify the hub gene. After that, we analyzed the relationship between CSF2RB expression and TNM stage/prognosis. Furthermore, gene set enrichment analysis (GSEA) was used to analyze the pathway regulated by CSF2RB and the Pearson correlation analysis method was used to analyze the correlation between the CSF2RB and immune cells. Finally, we used Western blot, real-time quantitative PCR (RT-qPCR), and immunohistochemistry (IHC) to validate CSF2RB expression in cancer and para-cancerous tissues.

Results

We identified that CSF2RB played an important role in the tumor microenvironment of LUAD. The expression of CSF2RB in tumor tissues was lower than that in normal tissues. Furthermore, the Kaplan–Meier plotter showed that a low CSF2RB expression was associated with poor survival and multivariate COX regression analysis revealed that the CSF2RB gene was an independent risk factor for prognosis, independent of whether patients received chemotherapy or radiotherapy. More importantly, a high expression of CSF2RB was related to early T, N, and clinical stages. GSEA analysis revealed that CSF2RB associated with diverse immune-related pathways, including T-cell receptor signaling pathway, Toll-like receptor signaling pathway, and B-cell receptor signaling pathway. CSF2RB expression levels were also positively related with the levels of infiltrating CD4+ T cells, macrophages, NK cells, and monocytes in LUAD. Finally, tumor tissues from LUAD patients were used for the assessment of CSF2RB expression. It was significantly lower in tumor sites than in adjacent normal tissues, which was consistent with data analysis.

Conclusion

CSF2RB effectively predicted the prognosis of patients with lung adenocarcinoma which could also be a potential target for cancer treatment and prevention. However, further studies are required to elucidate the function and regulatory mechanisms of CSF2RB and to develop some novel treatment strategies.

Prognostic value and immune characteristics of RUNX gene family in human cancers: a pan-cancer analysis

Background: Runt-related transcription factors (RUNX) are involved in numerous fundamental biological processes and play crucial parts in tumorigenesis and metastasis both directly and indirectly. However, the pan-cancer evidence of the RUNX gene family is not available.

Methods: In this study, we analyzed the potential association between RUNX gene family expression and patient’s prognosis, immune cell infiltration, drug response, and genetic mutation data across different types of tumors using based on The Cancer Genome Atlas, Gene Expression Omnibus, and Oncomine database.

Results: The results showed that the expression of the RUNX gene family varied among different cancer types, revealing its heterogeneity in cancers and that expression of RUNX2 was lower than that of RUNX1 and RUNX3 across all cancer types. RUNX gene family gene expression was related to prognosis in several cancers. Furthermore, our study revealed a clear association between RUNX gene family expression and ESTIMATE score, RNA stemness, and DNA stemness scores. Compared with RUNX1 and RUNX2, RUNX3 showed relatively low levels of genetic alterations. RUNX gene family genes had clear associations with immune infiltrate subtypes, and their expression was positively related to immune checkpoint genes and drug sensitivity in most cases. Two immunotherapy cohorts confirm that the expression of RUNX was correlated with the clinical response of immunotherapy.

Conclusions: These findings will help to elucidate the potential oncogenic roles of RUNX gene family genes in different types of cancer and it can function as a prognostic marker in various malignant tumors.

eRNAs Identify Immune Microenvironment Patterns and Provide a Novel Prognostic Tool in Acute Myeloid Leukemia

Background: Enhancer RNAs (eRNAs) play an essential role in tumorigenesis as non-coding RNAs transcribed from enhancer regions. However, the landscape of eRNAs in acute myeloid leukemia (AML) and the potential roles of eRNAs in the tumor microenvironment (TME) remain unclear.

Method: Gene expression data collected from The Cancer Genome Atlas (TCGA) project were combined with Histone ChIP-seq so as to reveal the comprehensive landscape of eRNAs. Single-sample gene set enrichment analysis algorithm (ssGSEA) and ESTIMATE were employed to enumerate immune cell infiltration and tumor purity.

Results: Most prognostic eRNAs were enriched in immune-related pathways. Two distinct immune microenvironment patterns, the immune-active subtype and the immune-resistant subtype, were identified in AML. We further developed an eRNA-derived score (E-score) that could quantify immune microenvironment patterns and predict the response to immune checkpoint inhibitor (ICI) treatment. Finally, we established a prognostic nomogram combining E-score and other clinical features, which showed great discriminative power in both the training set [Harrell’s concordance index (C index): 0.714 (0.651–0.777), p < 0.0001] and validation set [C index: 0.684 (0.614–0.755), p < 0.0001]. Calibration of the nomogram was also validated independently.

Conclusion: In this study, we systematically understood the roles of eRNAs in regulating TME diversity and complexity. Moreover, our E-score model provided the first predictive model for ICI treatment in AML.

A Six-Gene Risk Model Based on the Immune Score Reveals Prognosis in Intermediate-Risk Acute Myeloid Leukemia

Tumor microenvironment (TME) has been revealed as an important determinant of diagnosis and treatment response in AML patients. The scores of immune and stromal cell scores of AML in the intermediate-risk group from The Cancer Genome Atlas (TCGA) database were calculated using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Differentially expressed genes were identified between high and low scores. Gene set enrichment and pathway analyses were performed. A risk score model based on TME for six immune-related genes was established and validated. Patients with a lower immune score had a longer overall survival than those with a higher score (P = 0.044). A total of 805 intersected genes as differentially expressed genes were identified and selected according to the comparison of both immune and stromal scores. The functional enrichment analysis shows that these genes are mainly associated with the immune/inflammatory response. The risk score model based on TME for six immune-related genes (including MEF2C, ENPP2, FAM107A, CD37, TNFAIP8L2, and CASS4) was established and validated in the TCGA database and well validated in the TARGET database (P = 0.005). A key microenvironment-related gene signature was identified that affects the outcomes of AML patients in the intermediate-risk group and might serve as therapeutic targets.

Negative Regulation of the IL-1 System by IL-1R2 and IL-1R8: Relevance in Pathophysiology and Disease

Interleukin-1 (IL-1) is a primary cytokine of innate immunity and inflammation. IL-1 belongs to a complex family including ligands with agonist activity, receptor antagonists, and an anti-inflammatory cytokine. The receptors for these ligands, the IL-1 Receptor (IL-1R) family, include signaling receptor complexes, decoy receptors, and negative regulators. Agonists and regulatory molecules co-evolved, suggesting the evolutionary relevance of a tight control of inflammatory responses, which ensures a balance between amplification of innate immunity and uncontrolled inflammation. IL-1 family members interact with innate immunity cells promoting innate immunity, as well as with innate and adaptive lymphoid cells, contributing to their differentiation and functional polarization and plasticity. Here we will review the properties of two key regulatory receptors of the IL-1 system, IL-1R2, the first decoy receptor identified, and IL-1R8, a pleiotropic regulator of different IL-1 family members and co-receptor for IL-37, the anti-inflammatory member of the IL-1 family. Their complex impact in pathology, ranging from infections and inflammatory responses, to cancer and neurologic disorders, as well as clinical implications and potential therapeutic exploitation will be presented.

A Prognostic Model for Acute Myeloid Leukemia Based on IL-2/STAT5 Pathway-Related Genes

Accurate prognostic stratification of patients can provide guidance for personalized therapy. Many prognostic models for acute myeloid leukemia (AML) have been reported, but most have considerable inaccuracies due to contained variables with insufficient capacity of predicting survival and lack of adequate verification. Here, 235 genes strongly related to survival in AML were systematically identified through univariate Cox regression analysis of eight independent AML datasets. Pathway enrichment analysis of these 235 genes revealed that the IL-2/STAT5 signaling pathway was the most highly enriched. Through Cox proportional-hazards regression model and stepwise algorithm, we constructed a six-gene STAT5-associated signature based on the most robustly survival-related genes related to the IL-2/STAT5 signaling pathway. Good prognostic performance was observed in the training cohort (GSE37642-GPL96), and the signature was validated in seven other validation cohorts. As an independent prognostic factor, the STAT5-associated signature was positively correlated with patient age and ELN2017 risk levels. An integrated score based on these three prognostic factors had higher prognostic accuracy than the ELN2017 risk category. Characterization of immune cell infiltration indicated that impaired B-cell adaptive immunity, immunosuppressive effects, serious infection, and weakened anti-inflammatory function tended to accompany high-risk patients. Analysis of in-house clinical samples revealed that the STAT5-assocaited signature risk scores of AML patients were significantly higher than those of healthy people. Five chemotherapeutic drugs that were effective in these high-risk patients were screened in silico. Among the five drugs, MS.275, a known HDAC inhibitor, selectively suppressed the proliferation of cancer cells with high STAT5 phosphorylation levels in vitro. Taken together, the data indicate that the STAT5-associated signature is a reliable prognostic model that can be used to optimize prognostic stratification and guide personalized AML treatments.

Identification of prognostic genes and tumor-infiltrating immune cells in the tumor microenvironment of esophageal squamous cell carcinoma and esophageal adenocarcinoma

BACKGROUND

Esophageal cancer (EC) is a highly aggressive malignancy that is classified as esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Infiltrating stromal/immune cells, a major component of the tumor immune microenvironment (TIME), have prognostic significance in various cancers.

METHODS

In this study we investigated genes and immune factors in the tumor microenvironment (TME) of ESCC and EAC that can serve as prognostic biomarkers. Stromal and immune scores were calculated using the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) algorithm based on gene expression profiles of patient-derived tumor tissues in The Cancer Genome Atlas database. The correlation between ESTIMATE scores and survival rates in EC were analyzed. A comparison of high and low stromal/immune score groups revealed multiple differentially expressed genes (DEGs) as candidate prognostic genes; their role in immune-related biological processes was evaluated by functional and protein-protein interaction (PPI) network analyses, and the genes were validated using Gene Expression Omnibus datasets. Additionally, 22 tumor-infiltrating immune cell (TIIC) subsets were analyzed using the CIBERSORT algorithm.

RESULTS

Median stromal score was higher whereas immune score was lower in ESCC than in EAC (both P<0.01). Stromal score was lower in female as compared to male ESCC patients (P<0.05), and was significantly correlated with T stage (P<0.05). In EAC, median immune score was higher in female as compared to male patients (P<0.05) and was correlated with tumor-node-metastasis stage (P<0.05). The identified DEGs were mainly involved in lymphocyte (especially T-lymphocyte) activation and carbohydrate binding. Moreover, the levels of infiltrating resting-stage dendritic cells, CD8+ T cells, naïve B cells, activated mast cells, and resting memory CD4+ T cells were significantly correlated with EC prognosis (P<0.05).

CONCLUSIONS

The immune microenvironment of ESCC and EAC are quite different. We have found genes with prognostic value in multiple tumor databases.

CX3CR1 Acts as a Protective Biomarker in the Tumor Microenvironment of Colorectal Cancer

The tumor microenvironment (TME) plays an important role in the pathogenesis of many cancers. We aimed to screen the TME-related hub genes of colorectal adenoma (CRAD) and identify possible prognostic biomarkers. The gene expression profiles and clinical data of 464 CRAD patients in The Cancer Genome Atlas (TCGA) database were downloaded. The Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was performed to calculate the ImmuneScore, StromalScore, and EstimateScore. Thereafter, differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein–protein interaction (PPI) analysis were performed to explore the roles of DEGs. Furthermore, univariate and multivariate Cox analyses were accomplished to identify independent prognostic factors of CRAD. CX3CR1 was selected as a hub gene, and the expression was confirmed in colorectal cancer (CRC) patients and cell lines. The correlations between CX3CR1 and tumor-infiltrating immune cells were estimated by Tumor IMmune Estimation Resource database (TIMER) and CIBERSORT analysis. Besides, we investigated the effects of coculture with THP-1-derived macrophages with HCT8 cells with low CX3CR1 expression on immune marker expression, cell viability, and migration. There were significant differences in the ImmuneScore and EstimateScore among different stages. Patients with low scores presented significantly lower lifetimes than those in the high-score group. Moreover, we recognized 1,578 intersection genes in ImmuneScore and StromalScore, and these genes were mainly enriched in numerous immune-related biological processes. CX3CR1 was found to be associated with immune cell infiltration levels, immune marker expression, and macrophage polarization. Simultaneous silencing of CX3CR1 and coculture with THP-1 cells further regulated macrophage polarization and promoted the cell proliferation and migration of CRC cells. CX3CR1 was decreased in CRAD tissues and cell lines and was related to T and N stages, tumor differentiation, and prognosis. Our results suggest that CX3CR1 contributes to the recruitment and regulation of immune-infiltrating cells and macrophage polarization in CRC and TAM-induced CRC progression. CX3CR1 may act as a prognostic biomarker in CRC.

The prognosis biomarkers based on m6A-related lncRNAs for myeloid leukemia patients

Background

Chronic myeloid leukemia (CML) and acute myeloid leukemia (AML) are two common malignant disorders in leukemia. Although potent drugs are emerging, CML and AML may still relapse after the drug treatment is stopped. N6-methyladenosine (m6A) and lncRNAs play certain roles in the occurrence and development of tumors, but m6A-modified LncRNAs in ML remain to be further investigated.

Methods

In this study, we extracted and analyzed the TCGA gene expression profile of 151 ML patients and the clinical data. On this basis, we then evaluated the immune infiltration capacity of ML and LASSO-penalized Cox analysis was applied to construct the prognostic model based on m6A related lncRNAs to verify the prognostic risk in clinical features of ML. Quantitative reverse transcription PCR was used to detect the expression level of LncRNA in in ML cell lines K562, MOLM13 and acute monocytic leukemia cell line THP-1.

Results

We found 70 m6A-related lncRNAs that were related to prognosis, and speculated that the content of stromal cells and immune cells would correlate with the survival of patients with ML. Next, Prognostic risk model of m6A-related lncRNAs was validated to have excellent consistency in clinical features of ML. Finally, we verified the expression levels of CRNDE, CHROMR and NARF-IT1 in ML cell lines K562, MOLM13 and acute monocytic leukemia cell line THP-1, which were significant.

Conclusions

The research provides clues for the prognosis prediction of ML patients by using the m6A-related lncRNAs model we have created, and clarifies the accuracy and authenticity of it.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02428-3.

Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature

Background

Advanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.

Methods

Single-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.

Results

Three immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.

Conclusions

We identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09065-z.

Effect of unfolded protein response on the immune infiltration and prognosis of transitional cell bladder cancer

Background: Bladder cancer (BC) is one of the most common human malignancies worldwide. Previous researches have shown that the unfolded protein response (UPR) pathway could contribute to the tumorigenesis of BC. However, the role of UPR in the immune infiltration, progression, and prognosis of BC is unclear.Methods: The GSVA and ssGSEA methods were used for assessing the UPR score and immune cells infiltration score in three BC public datasets, respectively. The relationship between the UPR pathway and clinicopathological characteristics was analyzed by the Kruskal-Wallis, Wilcox test, and log-rank test. The association of the UPR pathway with various tumor-infiltrating immune cells was evaluated with the correlation analysis. Univariate Cox regression analysis was performed to identify risk factors significantly associated with prognosis. The predictive models were built based on risk factors and visualized with nomograms. The performance of our models was evaluated with the calibration curve, Harrell's concordance index (c-index), and receiver operating characteristic (ROC) analysis.Results: We found that the UPR pathway and many UPR-related genes were significantly associated with the pathologic grade, tumor type, and invasive progression of transitional cell bladder cancer (TCBC), and a high UPR score predicted a poor prognosis in patients. The UPR score was positively correlated with the infiltration abundance of many tumor immune cells in TCBC. Besides, we constructed predictive models based on the UPR score, and good performance was observed, with c-indexes ranging from 0.74 to 0.87.Conclusions: Our study proved that the UPR pathway may have an important impact on the progression, prognosis, and tumor immune infiltration in TCBC, and the models we built may provide effective and reliable guides for prognosis assessment and treatment decision-making for TCBC patients.

Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer

Background

The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC.

Methods

The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort from GEO database were used for external validation.

Results

A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan–Meier analysis, age, T stage, N stage, AJCC stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-year, 2-year, and 3-year survival rates of PC patients. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves. 1-year, 2-year and 3-year AUC of nomogram model was 0.899, 0.765 and 0.776, respectively.

Conclusions

In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08097-w.

CSF1R Inhibition Combined with GM-CSF Reprograms Macrophages and Disrupts Protumoral Interplays with AML Cells

Relapse is a major issue in acute myeloid leukemia (AML) and while the contribution of gene mutations in developing drug resistance is well established, little is known on the role of macrophages (MΦs) in an AML cell microenvironment. We examined whether myeloblasts could educate MΦs to adopt a protumoral orientation supporting myeloblast survival and resistance to therapy. Flow cytometry analyses demonstrated that M2-like CD163⁺ MΦs are abundantly present, at diagnosis, in the bone marrow of AML patients. We showed that myeloblasts, or their conditioned medium, polarize monocytes to M2-like CD163⁺ MΦs, induce the secretion of many protumoral factors, and promote myeloblast survival and proliferation as long as close intercellular contacts are maintained. Importantly, pharmacologic inhibition of the CSF1 receptor (CSF1R), in the presence of GM-CSF, reprogrammed MΦ polarization to an M1-like orientation, induced the secretion of soluble factors with antitumoral activities, reduced protumoral agonists, and promoted the apoptosis of myeloblasts interacting with MΦs. Furthermore, myeloblasts, which became resistant to venetoclax or midostaurin during their interplay with protumoral CD163⁺ MΦs, regained sensitivity to these targeted therapies following CSF1R inhibition in the presence of GM-CSF. These data reveal a crucial role of CD163⁺ MΦ interactions with myeloblasts that promote myeloblast survival and identify CSF1R inhibition as a novel target for AML therapy.

The Multi-Omic Prognostic Model of Oxidative Stress-Related Genes in Acute Myeloid Leukemia

Background: Acute myeloid leukemia (AML) is one of the most common cancers in the world, and oxidative stress is closely related to leukemia. A lot of effort has been made to improve the prognosis of AML. However, the situation remains serious. Hence, we focused on the study of prognostic genes in AML. Materials and Methods: Prognostic oxidative stress genes were screened out. The gene expression profile of AML patients was downloaded from the The Cancer Genome Atlas (TCGA) database. The oxidative stress-related model was constructed, by which the prognosis of AML patients was predicted using the two GEO GSE23143 datasets and the stability of the GSE71014 authentication model. Results: The prognostic oxidative stress genes were screened out in AML, and the prognostic genes were significantly enriched in a large number of pathways based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. There was a complex interaction between prognostic genes and transcription factors. After constructing the prediction model, the clinical predictive value of the model was discussed in a multi-omic study. We investigated the sensitivity of risk score to common chemotherapeutic agents, the influence of signaling pathways on the prognosis of AML patients, and the correlation of multiple genes with immune score and immune dysfunction. Conclusions: A highly effective prognostic risk model for AML patients was established and validated. The association of prognostic oxidative stress genes with drug sensitivity, signaling pathways, and immune infiltration was explored. The results suggested that oxidative stress genes promised to be potential prognostic biomarkers for AML, which may provide a new basis for disease management.

Pan-Cancer Analysis of the Associations of TGFBI Expression With Prognosis and Immune Characteristics

Transforming growth factor-beta-induced (TGFBI) protein has important roles in tumor growth, metastasis, and immunity. However, there is currently no pan-cancer evidence regarding TGFBI. In this study, we conducted a pan-cancer analysis of TGFBI mRNA and protein expression and prognoses of various cancer types using public databases. We also investigated the associations of TGFBI expression with tumor microenvironment (TME) components, immune cell infiltration, tumor mutational burden (TMB), and microsatellite instability (MSI), along with the TGFBI genetic alteration types. The results showed that TGFBI expression varied among different cancer types, and it was positively or negatively related to prognosis in various cancers. TGFBI expression was also significantly correlated with TME components, TMB, MSI, immune cell infiltration, and immunoinhibitory and immunostimulatory gene subsets. These findings indicate that TGFBI participates in various immune responses and it may function as a prognostic marker in various cancers. The findings may be useful for developing immunotherapies that target TGFBI.

Development of a Prognostic Nomogram for Acute Myeloid Leukemia on IGHD Gene Family

Purpose

Acute myeloid leukaemia (AML) is a common haematological disease in adults. The overall survival (OS) remains unsatisfactory. It is critical to identify potential prognostic biomarkers and develop a nomogram that predicts overall survival in patients with AML.

Patients and Methods

We used gene expression dataset and clinical data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to identify differential expression analysis, survival analysis, and prognostic value of IGHD gene family (IGHDs) in AML patients. A risk score model was built through Lasso analysis and multivariate Cox regression. We also developed a nomogram and evaluated its accuracy with Harrell’s Harmony Index (C-index) and calibration curve. Last, the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database was used for external validation.

Results

IGHD1-20 mRNA expression level was an independent prognostic factor for patients with AML by multivariate analysis. After Lasso analysis and multivariate Cox regression, we constructed a 3-gene model (IGHD1-1, IGHD1-20, IGHD3-16) associated with OS in AML. Risk score and age were validated as independent risk factors for prognosis and were used to build a nomogram. The C index and calibration curve results show that its ability to predict 1-year, 3-year and 5-year overall survival is accurate.

Conclusion

The mRNA level of IGHDs was increased in AML patients. IGHD1-20 was an independent risk factor for OS in AML patients. The IGHDs risk model (IGHD1-1, IGHD1-20, IGHD3-16) relates to the OS of AML patients. The nomogram, including risk score and age, can conveniently and effectively predict the overall survival rate of patients.

Antibody-Drug Conjugates for the Treatment of Acute Pediatric Leukemia

The clinical development of antibody-drug conjugates (ADCs) has gained momentum in recent years and these agents are gradually moving into frontline regimens for pediatric acute leukemias. ADCs consist of a monoclonal antibody attached to a cytotoxic payload by a cleavable linker. This structure allows for highly cytotoxic agents to be directly delivered to leukemia cells leading to cell death and avoids excessive off-tumor toxicity. Near universal expression on B-cell acute lymphoblastic leukemia (ALL) blasts and the ability of rapid internalization has rendered CD22 an ideal target for ADC in B-ALL. Inotuzumab ozogamicin, the anti-CD22 antibody linked to calicheamicin led to complete remission rates of 60-80% in patients with relapsed/refractory B-ALL. In acute myeloid leukemia (AML), the CD33 targeting gemtuzumab ozogamicin has demonstrated modest improvements in survival and is the only ADC currently licensed in the United States for pediatric patients with de novo AML. Several other ADCs have been developed and tested clinically for leukemia but have achieved limited success to date. The search for additional leukemia-specific targets and optimization of ADC structure and specificity are ongoing efforts to improve their therapeutic window. This review provides a comprehensive overview of ADCs in acute leukemias, with a focus on pediatric ALL and AML.

NLRP3-activated bone marrow dendritic cells play antileukemic roles via IL-1β/Th1/IFN-γ in acute myeloid leukemia

The bone marrow microenvironment of acute myeloid leukemia (AML) characterized by immunosuppressive features fosters leukemia immune escape. Elucidating the immunosuppressive mechanism and developing effective immunotherapeutic strategies are necessary. Here, we found that the Th1% and IFN-γ level were downregulated in bone marrow of AML and NLRP3-activated BMDCs promoted CD4⁺ T cell differentiation into Th1 cells via IL-1β secretion. However, IFN-γ-producing Th1 cells were not induced by NLRP3-activated BMDCs in the presence of the NLRP3 inflammasome inhibitor MCC950 or anti-IL-1β antibody in vitro unless exogenous IL-1β was replenished. This inhibitory effect on Th1 differentiation was also observed in Nlrp3^-/- mice or anti-IL-1β antibody-treated mice. Notably, elevated Th1 cell levels promoted apoptosis and inhibited proliferation in leukemia cells via IFN-γ secretion in vitro and in vivo. Thus, NLRP3-activated BMDCs promote the proliferation of IFN-γ-producing Th1 cells with antileukemic effects and may provide insight into the basis for leukemia immunotherapy in patients with AML.

Bioinformatics-Based Identification of Tumor Microenvironment-Related Prognostic Genes in Pancreatic Cancer

Objective

Growing evidence has highlighted that the immune and stromal cells that infiltrate in pancreatic cancer microenvironment significantly influence tumor progression. However, reliable microenvironment-related prognostic gene signatures are yet to be established. The present study aimed to elucidate tumor microenvironment-related prognostic genes in pancreatic cancer.

Methods

We applied the ESTIMATE algorithm to categorize patients with pancreatic cancer from TCGA dataset into high and low immune/stromal score groups and determined their differentially expressed genes. Then, univariate and LASSO Cox regression was performed to identify overall survival-related differentially expressed genes (DEGs). And multivariate Cox regression analysis was used to screen independent prognostic genes and construct a risk score model. Finally, the performance of the risk score model was evaluated by Kaplan-Meier curve, time-dependent receiver operating characteristic and Harrell’s concordance index.

Results

The overall survival analysis demonstrated that high immune/stromal score groups were closely associated with poor prognosis. The multivariate Cox regression analysis indicated that the signatures of four genes, including TRPC7, CXCL10, CUX2, and COL2A1, were independent prognostic factors. Subsequently, the risk prediction model constructed by those genes was superior to AJCC staging as evaluated by time-dependent receiver operating characteristic and Harrell’s concordance index, and both KRAS and TP53 mutations were closely associated with high risk scores. In addition, CXCL10 was predominantly expressed by tumor associated macrophages and its receptor CXCR3 was highly expressed in T cells at the single-cell level.

Conclusions

This study comprehensively investigated the tumor microenvironment and verified immune/stromal-related biomarkers for pancreatic cancer.