A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts

A 4-gene prognostic index for enhancing acute myeloid leukaemia survival prediction

Despite advancements in utilizing genetic markers to enhance acute myeloid leukaemia (AML) outcome prediction, significant disease heterogeneity persists, hindering clinical management. To refine survival predictions, we assessed the transcriptome of non-acute promyelocytic leukaemia chemotherapy-treated AML patients from five cohorts (n = 975). This led to the identification of a 4-gene prognostic index (4-PI) comprising CYP2E1, DHCR7, IL2RA and SQLE. The 4-PI effectively stratified patients into risk categories, with the high 4-PI group exhibiting TP53 mutations and cholesterol biosynthesis signatures. Single-cell RNA sequencing revealed enrichment for leukaemia stem cell signatures in high 4-PI cells. Validation across three cohorts (n = 671), including one with childhood AML, demonstrated the reproducibility and clinical utility of the 4-PI, even using cost-effective techniques like real-time quantitative polymerase chain reaction. Comparative analysis with 56 established prognostic indexes revealed the superior performance of the 4-PI, highlighting its potential to enhance AML risk stratification. Finally, the 4-PI demonstrated to be potential marker to reclassified patients from the intermediate ELN2017 category to the adverse category. In conclusion, the 4-PI emerges as a robust and straightforward prognostic tool to improve survival prediction in AML patients.

EMP1 correlated with cancer progression and immune characteristics in pan-cancer and ovarian cancer

This study examines the prognostic role and immunological relevance of EMP1 (epithelial membrane protein-1) in a pan-cancer analysis, with a focus on ovarian cancer. Utilizing data from TCGA, CCLE, and GTEx databases, we assessed EMP1 mRNA expression and its correlation with tumor progression, prognosis, and immune microenvironment across various cancers. Our results indicate that EMP1 expression is significantly associated with poor prognosis in multiple cancer types, including ovarian, bladder, testicular, pancreatic, breast, brain, and uveal melanoma. Immune-related analyses reveal a positive correlation between EMP1 and immune cell infiltration, particularly neutrophils, macrophages, and dendritic cells, as well as high expression of immune checkpoint such as CD274, HAVCR2, IL10, PDCD1LG2, and TGFB1 in most tumors. In vivo experiments confirm that EMP1 promotes ovarian cancer cell proliferation, metastasis, and invasion. In conclusion, EMP1 emerges as a potential prognostic biomarker and therapeutic target in various cancers, particularly ovarian cancer, due to its influence on tumor progression and immune cell dynamics. Further research is warranted to elucidate the precise mechanisms of EMP1 in cancer biology and to translate these findings into clinical applications.

Endoplasmic reticulum stress-related signature predicts prognosis and immune infiltration analysis in acute myeloid leukemia

OBJECTIVES

To construct an endoplasmic reticulum stress-related prognostic risk score (RS) model to predict prognosis and perform a preliminary analysis of immune infiltration in patients with acute myeloid leukemia (AML).

METHODS

The whole-genome expression data for AML and endoplasmic reticulum stress (ER stress)-related genes were downloaded from the GEO and GSEA databases, respectively. The samples were divided into death and survival groups, combined with clinical prognosis information. LASSO regression was used to construct a prognostic RS model. The Kaplan-Meier curve method was used to evaluate the association between different risk groups and actual survival prognosis information. A cox regression analysis was used to screen for independent survival prognostic clinical factors and construct a nomogram. CIBERSORT and ssGSEA was used for immune-related analysis.

RESULTS

Eighteen ER-stress related genes were identified and a comprehensive network was constructed. Further, 5 CC, 8 MF, 17 BP, and 2 KEGG pathways were enriched. Ten optimal DEGs were obtained and a prognostic risk model was constructed. Compared to the low RS group, the OS values of the high RS group were significantly lower. A significant correlation between the different risk groups and the actual prognosis was demonstrated. Ten immune cells with significantly different distributions in different risk groups were screened. KEGG enrichment analysis showed that there were 5 signaling pathways in the high-risk group.

CONCLUSIONS

The RS model can effectively predict the prognosis and has clinical implications for the prognosis of AML, combined with the correlation between different RS groups and the immune microenvironment.

Serum circRNA_100199 is a Prognostic Biomarker in Acute Myeloid Leukemia

Background

An aberrant level of serum microRNA expression has been demonstrated to be a prognostic marker for acute myeloid leukemia (AML). The therapeutic relevance of serum circRNA 100199 remained unknown, however. This research aimed to investigate the probable prognostic significance of serum circRNA_100199 for AML.

Methods

This study included a total of 200 participants consisting of 114 AML-diagnosed patients and 86 healthy people. Blood samples were taken, and the level of circRNA_100199 in the serum was measured using quantitative reverse transcription polymerase chain reaction (qRT-PCR) to explore its potential clinical significance.

Results

Our study demonstrated that circRNA_100199 expression in the serum was substantially higher in AML subjects than in healthy persons. This increase in serum circRNA_100199 levels was particularly noticeable in M4/M5 subtype AML patients, and those with poor cytogenetic risk or higher white blood cell counts. Using receiver operating characteristic (ROC) analysis, AML cases were effectively differentiated from healthy persons based on the level of serum circRNA_100199. Furthermore, it was found that high serum circRNA_100199 expression was strongly linked with shorter survival times and more severe clinical features. Our study also confirmed that high serum circRNA_100199 expression was an independent predictor of relapse-free survival (RFS) and overall survival (OS) in AML patients. Interestingly, the serum expression level of circRNA_100199 was significantly reduced following treatment, and its levels were substantially lower in AML patients who achieved complete remission (CR) than those who did not.

Conclusion

Overall, these findings suggest that serum circRNA_100199 has the potential to be a favorable prognostic biomarker for AML.

Serum hsa_circ_0079480 is a novel prognostic marker for acute myeloid leukemia

Background

The dysregulated expression of serum circular RNAs (circRNAs) has previously been linked to the prognosis of acute myeloid leukemia (AML) patients, but the clinical and prognostic relevance of serum hsa_circ_0079480 levels in this oncogenic setting have yet to be established. Herein, we assessed the putative prognostic relevance of circulating hsa_circ_0079480 levels in AML patient serum.

Methods

Serum was prepared from blood samples collected from 236 AML patients and 160 healthy controls, with hsa_circ_0079480 levels therein being quantified by quantitative real‐time reverse transcription‐polymerase chain reaction (qRT‐PCR) after which the clinical value of these levels was assessed.

Results

Acute myeloid leukemia patients were found to exhibit significant hsa_circ_0079480 upregulation in their serum as compared to serum from healthy controls, with such upregulation being most profound in individuals with M4/M5 type disease and to be more common in patients with poor cytogenic risk or high white blood cell counts. Receiver operating characteristic (ROC) curves demonstrated that serum hsa_circ_0079480 levels were able to effectively differentiate between patients with AML and healthy controls. Moreover, the upregulation of serum hsa_circ_0079480 was found to be closely related to clinicopathological findings and to be an independent predictor of reduced overall and relapse‐free survival among individuals diagnosed with AML. Furthermore, serum hsa_circ_0079480 levels were markedly decreased after treatment in this patient population, with these levels being lower in patients that achieved complete remission as compared to those patients that did not.

Conclusion

Levels of hsa_circ_0079480 in patient serum may offer value as a prognostic biomarker in AML.

A novel 4-mRNA signature predicts the overall survival in acute myeloid leukemia

Acute myeloid leukemia (AML) is an aggressive cancer of myeloid cells with high levels of heterogeneity and great variability in prognostic behaviors. Cytogenetic abnormalities and genetic mutations have been widely used in the prognostic stratification of AML to assign patients into different risk categories. Nevertheless, nearly half of AML patients assigned to intermediate risk need more precise prognostic schemes. Here, 336 differentially expressed genes (DEGs) between AML and control samples and 206 genes representing the intratumor heterogeneity of AML were identified. By applying a LASSO Cox regression model, we generated a 4-mRNA prognostic signature comprising KLF9, ENPP4, TUBA4A and CD247. Higher risk scores were significantly associated with shorter overall survival, complex karyotype, and adverse mutations. We then validated the prognostic value of this 4-mRNA signature in two independent cohorts. We also proved that incorporation of the 4-mRNA-based signature in the 2017 European LeukemiaNet (ELN) risk classification could enhance the predictive accuracy of survival in patients with AML. Univariate and multivariate analyses showed that this signature was independent of traditional prognostic factors such as age, WBC count, and unfavorable cytogenetics. Finally, the molecular mechanisms underlying disparate outcomes in high-risk and low-risk AML patients were explored. Therefore, our findings suggest that the 4-mRNA signature refines the risk stratification and prognostic prediction of AML patients.

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.

Prognostic Prediction of Cytogenetically Normal Acute Myeloid Leukemia Based on a Gene Expression Model

Acute myeloid leukemia (AML) refers to a heterogeneous group of hematopoietic malignancies. The well-known European Leukemia Network (ELN) stratifies AML patients into three risk groups, based primarily on the detection of cytogenetic abnormalities. However, the prognosis of cytogenetically normal AML (CN-AML), which is the largest AML subset, can be hard to define. Moreover, the clinical outcomes associated with this subgroup are diverse. In this study, using transcriptome profiles collected from CN-AML patients in the BeatAML cohort, we constructed a robust prognostic Cox model named NEST (Nine-gEne SignaTure). The validity of NEST was confirmed in four external independent cohorts. Moreover, the risk score predicted by the NEST model remained an independent prognostic factor in multivariate analyses. Further analysis revealed that the NEST model was suitable for bone marrow mononuclear cell (BMMC) samples but not peripheral blood mononuclear cell (PBMC) samples, which indirectly indicated subtle differences between BMMCs and PBMCs. Our data demonstrated the robustness and accuracy of the NEST model and implied the importance of the immune dysfunction in the leukemogenesis that occurs in CN-AML, which shed new light on the further exploration of molecular mechanisms and treatment guidance for CN-AML.

Identification of a Prognostic Immune Signature for Esophageal Squamous Cell Carcinoma to Predict Survival and Inflammatory Landscapes

Immunotherapy has achieved success in the treatment of esophageal squamous cell carcinoma (ESCC). However, studies concerning immune phenotypes within the ESCC microenvironment and their relationship with prognostic outcomes are limited. We constructed and validated an individual immune-related risk signature for patients with ESCC. We collected 196 ESCC cases, including 119 samples from our previous public data (GSE53624) to use as a training set and an independent cohort with 77 quantitative real-time polymerase chain reaction (qRT-PCR) data, which we used for validation. Head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LUSC) cohorts were also collected for validation. A least absolute shrinkage and selection operator (LASSO) model and a stepwise Cox proportional hazards regression model were used to construct the immune-specific signature. The potential mechanism and inflammatory landscapes of the signature were explored using bioinformatics and immunofluorescence assay methods. This signature predicted different prognoses in clinical subgroups and the independent cohort, as well as in patients with HNSCC and LUSC. Further exploration revealed that the signature was associated with specific inflammatory activities (activation of macrophages and T-cell signaling transduction). Additionally, high-risk patients exhibited distinctive immune checkpoints panel and higher regulatory T cell and fibroblast infiltration. This signature served as an independent prognostic factor in ESCC. This was the first applicable immune-related risk signature for ESCC. Our results furnished new hints of immune profiling of ESCC, which may provide some clues to further optimize associated cancer immunotherapies.

Comprehensive Analysis of Prognostic Markers for Acute Myeloid Leukemia Based on Four Metabolic Genes

Background: Metabolic reprogramming is the core characteristic of tumors during the development of tumors, and cancer cells can rely on metabolic changes to support their rapid growth. Nevertheless, an overall analysis of metabolic markers in acute myeloid leukemia (AML) is absent and urgently needed. Methods: Within this work, genetic expression, mutation data and clinical data of AML were queried from Genotype-Tissue Expression (GTEx) database, The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The tumor samples of TCGA were randomly divided into a training group (64 samples) and an internal validation group (64 samples) at one time, and the tumor samples of GEO served as two external validation groups (99 samples, 374 samples). According to the expression levels of survival-associated metabolic genes, we divided all TCGA tumor samples into high, medium and low metabolism groups, and evaluated the immune cell activity in the tumor microenvironment of the three metabolism groups by single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, we examined the mutations and prognostic effects of each model gene. Results: Four metabolism-related genes were screened and applied to construct a prognostic model for AML, giving excellent results. As for the area under the curve (AUC) value of receiver operating characteristic (ROC) curve, the training group was up to 0.902 (1-year), 0.81 (3-year), and 0.877 (5-year); and the internal and external validation groups also met the expected standards, showing high potency in predicting patient outcome. Univariate and multivariate prognostic analyses indicated that the riskScore obtained from our prognostic model was an independent prognostic factor. ssGSEA analysis revealed the high metabolism group had higher immune activity. Single and multiple gene survival analysis validated that each model gene had significant effects on the overall survival of AML patients. Conclusions: In our study, a high-efficiency prognostic prediction model was built and validated for AML patients. The results showed that metabolism-related genes could become potential prognostic biomarkers for AML.

Serum miR-22 is a novel prognostic marker for acute myeloid leukemia

Background

It has been demonstrated that aberrant expression of serum microRNAs is potential markers for the prognostic prediction of acute myeloid leukemia (AML). However, the clinical significance of serum miR‐22 remained uncovered. In this study, we aimed to explore the potential prognostic value of serum miR‐22 for AML.

Methods

Blood samples were collected from 124 patients with AML and 60 healthy individuals. Serum miR‐22 level was detected by quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR), and its potential clinical value was investigated.

Results

Our results showed that serum miR‐22 expression was significantly downregulated in AML subjects compared to healthy controls. Serum miR‐22 levels were lowest in AML patients with M4/M5 subtypes, and low serum miR‐22 expression occurred more frequently in AML patients with higher white blood cell counts or poor cytogenetic risk. Receiver operating characteristic (ROC) analysis revealed that serum miR‐22 well differentiated AML cases from healthy controls. In addition, serum miR‐22 downregulation was closely associated with worse clinical features and shorter survival. Low serum miR‐22 expression was confirmed to be an independent predictor for overall survival and relapse‐free survival in AML patients. Moreover, the expression level of serum miR‐22 was dramatically increased following treatment. In addition, serum miR‐22 levels were significantly higher in AML patients achieving complete remission (CR) than those without CR.

Conclusion

Collectively, serum miR‐22 might serve as a novel and promising prognostic biomarker for AML.