Gene expression signature predicts relapse in adult patients with cytogenetically normal acute myeloid leukemia

Identification of a distinctive immunogenomic gene signature in stage-matched colorectal cancer

BACKGROUND

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Despite advances in diagnosis and treatment, including surgery, chemotherapy, and immunotherapy, accurate clinical markers are still lacking. The development of prognostic and predictive indicators, particularly in the context of personalized medicine, could significantly improve CRC patient management.

METHOD

In this retrospective study, we used FFPE blocks of tissue samples from CRC patients at Augusta University (AU) to quantify a custom 15-gene panel. To differentiate the tumor and adjacent normal regions (NAT), H&E staining was utilized. For the quantification of transcripts, we used the NanoString nCounter platform. Kaplan-Meier and Log-rank tests were used to perform survival analyses. Several independent datasets were explored to validate the gene signature. Orthogonal analyses included single-cell profiling, differential gene expression, immune cell deconvolution, neoantigen prediction, and biological pathway assessment.

RESULTS

A 3-gene signature (GTF3A, PKM, and VEGFA) was found to be associated with overall survival in the AU cohort (HR = 2.26, 95% CI 1.05-4.84, p = 0.02, 93 patients), TCGA cohort (HR = 1.57, 95% CI 1.05-2.35, p < 0.02, 435 patients) and four other GEO datasets. Independent single-cell analysis identified relatively higher expression of the 3-gene signature in the tumor region. Differential analysis revealed dysregulated tissue inflammation, immune dysfunction, and neoantigen load of cell cycle processes among high-risk patients compared to low-risk patients.

CONCLUSION

We developed a 3-gene signature with the potential for prognostic and predictive clinical assessment of CRC patients. This gene-based stratification offers a cost-effective approach to personalized cancer management. Further research using similar methods could identify therapy-specific gene signatures to strengthen the development of personalized medicine for CRC patients.

Chemotherapy resistance in acute myeloid leukemia is mediated by A20 suppression of spontaneous necroptosis

Acute myeloid leukemia (AML) is a deadly hematopoietic malignancy. Although many patients achieve complete remission with standard induction therapy, a combination of cytarabine and anthracycline, ~40% of patients have induction failure. These refractory patients pose a treatment challenge, as they do not respond to salvage therapy or allogeneic stem cell transplant. Herein, we show that AML patients who experience induction failure have elevated expression of the NF-κB target gene tumor necrosis factor alpha-induced protein-3 (TNFAIP3/A20) and impaired necroptotic cell death. A20High AML are resistant to anthracyclines, while A20Low AML are sensitive. Loss of A20 in AML restores sensitivity to anthracycline treatment by inducing necroptosis. Moreover, A20 prevents necroptosis in AML by targeting the necroptosis effector RIPK1, and anthracycline-induced necroptosis is abrogated in A20High AML. These findings suggest that NF-κB-driven A20 overexpression plays a role in failed chemotherapy induction and highlights the potential of targeting an alternative cell death pathway in AML.

Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction.

METHOD

This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods.

RESULTS

Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques.

CONCLUSIONS

This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future.

Transcriptional Responses of Different Brain Cell Types to Oxygen Decline

Brain hypoxia is associated with a wide range of physiological and clinical conditions. Although oxygen is an essential constituent of maintaining brain functions, our understanding of how specific brain cell types globally respond and adapt to decreasing oxygen conditions is incomplete. In this study, we exposed mouse primary neurons, astrocytes, and microglia to normoxia and two hypoxic conditions and obtained genome-wide transcriptional profiles of the treated cells. Analysis of differentially expressed genes under conditions of reduced oxygen revealed a canonical hypoxic response shared among different brain cell types. In addition, we observed a higher sensitivity of neurons to oxygen decline, and dissected cell type-specific biological processes affected by hypoxia. Importantly, this study establishes novel gene modules associated with brain cells responding to oxygen deprivation and reveals a state of profound stress incurred by hypoxia.

Prediction of immune infiltration and prognosis for patients with cholangiocarcinoma based on a cuproptosis-related lncRNA signature

Objective

Cholangiocarcinoma (CHOL) is a malignant disease that affects the digestive tract, and it is characterized by a poor prognosis. This research sought to explore the involvement of cuproptosis-related lncRNAs (CRLs) in the prognostic prediction and immune infiltration of cholangiocarcinoma.

Methods

The expression profiles and clinical data of CHOL patients were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and CRLs were defined via co-expression analysis. Two molecular clusters distinguished by cuproptosis-related genes (CRGs) were produced. Then a risk signature consisted by four CRLs was formed, and all samples were separated into low- and high-risk groups using a risk score. Kaplan-Meier survival analysis, principal component analysis, differentially expressed analysis, immune cell infiltration analysis, and sensitivities analysis of chemotherapy drugs were conducted between the two groups. Simultaneously, the expression values of four lncRNAs confirmed by real-time PCR in our own 20 CHOL samples were brought into the risk model.

Results

The CHOL samples could be differentiated into two molecular clusters, which displayed contrasting survival times. Additionally, patients with higher risk scores had significantly worse prognosis compared to those in the low-risk group. Furthermore, both immune infiltration and enrichment analysis revealed significant discrepancies in the tumor immune microenvironment (TIME) between different risk groups. Moreover, the predictive power and the correlation with CA19-9 and CEA of risk signature were validated in our own samples.

Conclusion

We developed a risk signature which could serve as an independent prognostic factor and offer a promising prediction for not only prognosis but also TIME in CHOL patients.

Dynamic alterations of the transcriptome-wide m6A methylome in cytogenetically normal acute myeloid leukaemia during initial diagnosis and relapse

Accumulating studies have indicated that N6-methyladenosine (m6A) plays an important role in acute myeloid leukaemia (AML). However, little is known about the m6A methylome at a transcriptome-wide scale in AML patients. We obtained three pairs of bone marrow (BM) samples from cytogenetically normal AML patients at the timepoints of diagnosis (AML) and relapse (R_AML) and three BM samples from healthy donors used as normal controls (NCs). Methylated RNA immunoprecipitation next-generation sequencing (MeRIP-Seq) was conducted to identify differences in the m6A methylomes between AML and NC and between R_AML and AML. We identified a total of 11,076 and 11,962 differential m6A peaks in AML and R_AML group, respectively. These dysregulated m6A peaks were detected on all chromosomes, especially chr1, chr19 and chr17, and were mainly enriched in 3' untranslated regions, stop codon and coding sequence regions. Moreover, GO and KEGG analyses indicated that m6A -modified genes were significantly enriched in cancer-related biological functions and pathways. Additionally, we identified a link between the m6A methylome and RNA transcriptome via combined analyses of MeRIP-seq and RNA-seq data. In addition, 5 genes, HSPG2, HOMER3, TSPO2, CXCL12 and FUT1 regulated by m6A modification potentially, were shown to be related to the prognosis of AML patients. Additionally, we detected the mRNA expression of major m6A regulators and potential target mRNA on the leukemogenesis and found that the expression of IGF2BP2, HSPG2 and HOMER3 were upregulated in AML at the time of diagnosis. Moreover, their expression became downregulated after remission and then elevated again at relapse. Our study provides the first data on the differential m6A methylome in AML patients during initial diagnosis and relapse. This study demonstrates a novel relationship between m6A modification and AML relapse and paves the way for further studies aimed at elucidating the epigenic mechanisms involved in the relapse of AML.

Performance evaluation and clinical impact of the Oncomine Myeloid Research Assay for gene expression analysis in myeloid haematologic malignancies

AIM

Gene expression analysis facilitates the detection of diagnostic and prognostic biomarkers for myeloid haematological malignancies. The Oncomine Myeloid Research Assay (OMA; Thermo Fisher Scientific, Massachusetts, USA) provides a comprehensive analysis of gene expression of five target genes, along with gene alteration and fusion. Here, we present the performance of the OMA for gene expression analysis.

METHODS

In total, 53 RNA samples from patients diagnosed with acute myeloid leukaemia (AML) or myelodysplastic syndrome were included. Of these 53 samples, 3 were evaluated for reproducibility and 50 were evaluated for comparison with RNA-sequencing (RNA-seq). The prognostic impact of the gene expression profile produced by both OMA and RNA-seq in AML was investigated using follow-up data from 33 patients with AML.

RESULTS

The OMA showed good intrarun and interrun reproducibility. Compared with the RNA-seq results, high correlations were found in BAALC, MECOM and WT1 (all r>0.9), with moderate correlations in MYC (r=0.75, p<0.001) and SMC1A (r=0.42, p=0.002). The agreement between OMA and RNA-seq in classifying the dysregulated expression group was almost perfect, except for SMC1A (κ=0.175). Among these five genes, only BAALC showed a significant clinical impact in patients with AML. Patients with high BAALC expression showed significantly shorter overall survival based on both OMA (p=0.037) and RNA-seq (p=0.003).

CONCLUSIONS

OMA gene expression analysis offers reproducible and accurate gene expression data for most targeted genes and demonstrates the utility of BAALC expression as a prognostic marker in AML.

Deep learning of 2D-Restructured gene expression representations for improved low-sample therapeutic response prediction

Clinical outcome prediction is important for stratified therapeutics. Machine learning (ML) and deep learning (DL) methods facilitate therapeutic response prediction from transcriptomic profiles of cells and clinical samples. Clinical transcriptomic DL is challenged by the low-sample sizes (34-286 subjects), high-dimensionality (up to 21,653 genes) and unordered nature of clinical transcriptomic data. The established methods rely on ML algorithms at accuracy levels of 0.6-0.8 AUC/ACC values. Low-sample DL algorithms are needed for enhanced prediction capability. Here, an unsupervised manifold-guided algorithm was employed for restructuring transcriptomic data into ordered image-like 2D-representations, followed by efficient DL of these 2D-representations with deep ConvNets. Our DL models significantly outperformed the state-of-the-art (SOTA) ML models on 82% of 17 low-sample benchmark datasets (53% with >0.05 AUC/ACC improvement). They are more robust than the SOTA models in cross-cohort prediction tasks, and in identifying robust biomarkers and response-dependent variational patterns consistent with experimental indications.

Transcriptional Response to Standard AML Drugs Identifies Synergistic Combinations

Unlike genomic alterations, gene expression profiles have not been widely used to refine cancer therapies. We analyzed transcriptional changes in acute myeloid leukemia (AML) cell lines in response to standard first-line AML drugs cytarabine and daunorubicin by means of RNA sequencing. Those changes were highly cell- and treatment-specific. By comparing the changes unique to treatment-sensitive and treatment-resistant AML cells, we enriched for treatment-relevant genes. Those genes were associated with drug response-specific pathways, including calcium ion-dependent exocytosis and chromatin remodeling. Pharmacological mimicking of those changes using EGFR and MEK inhibitors enhanced the response to daunorubicin with minimum standalone cytotoxicity. The synergistic response was observed even in the cell lines beyond those used for the discovery, including a primary AML sample. Additionally, publicly available cytotoxicity data confirmed the synergistic effect of EGFR inhibitors in combination with daunorubicin in all 60 investigated cancer cell lines. In conclusion, we demonstrate the utility of treatment-evoked gene expression changes to formulate rational drug combinations. This approach could improve the standard AML therapy, especially in older patients.

Gene expression profiling unveils the temporal dynamics of CIGB-300-regulated transcriptome in AML cell lines

BACKGROUND

Protein kinase CK2 activity is implicated in the pathogenesis of various hematological malignancies like Acute Myeloid Leukemia (AML) that remains challenging concerning treatment. This kinase has emerged as an attractive molecular target in therapeutic. Antitumoral peptide CIGB-300 blocks CK2 phospho-acceptor sites on their substrates but it also binds to CK2α catalytic subunit. Previous proteomic and phosphoproteomic experiments showed molecular and cellular processes with relevance for the peptide action in diverse AML backgrounds but earlier transcriptional level events might also support the CIGB-300 anti-leukemic effect. Here we used a Clariom S HT assay for gene expression profiling to study the molecular events supporting the anti-leukemic effect of CIGB-300 peptide on HL-60 and OCI-AML3 cell lines.

RESULTS

We found 183 and 802 genes appeared significantly modulated in HL-60 cells at 30 min and 3 h of incubation with CIGB-300 for p < 0.01 and FC >  = │1.5│, respectively; while 221 and 332 genes appeared modulated in OCI-AML3 cells. Importantly, functional enrichment analysis evidenced that genes and transcription factors related to apoptosis, cell cycle, leukocyte differentiation, signaling by cytokines/interleukins, and NF-kB, TNF signaling pathways were significantly represented in AML cells transcriptomic profiles. The influence of CIGB-300 on these biological processes and pathways is dependent on the cellular background, in the first place, and treatment duration. Of note, the impact of the peptide on NF-kB signaling was corroborated by the quantification of selected NF-kB target genes, as well as the measurement of p50 binding activity and soluble TNF-α induction. Quantification of CSF1/M-CSF and CDKN1A/P21 by qPCR supports peptide effects on differentiation and cell cycle.

CONCLUSIONS

We explored for the first time the temporal dynamics of the gene expression profile regulated by CIGB-300 which, along with the antiproliferative mechanism, can stimulate immune responses by increasing immunomodulatory cytokines. We provided fresh molecular clues concerning the antiproliferative effect of CIGB-300 in two relevant AML backgrounds.

Identifying a prognostic model and screening of potential natural compounds for acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is one of the most common hematologic malignancies with a poor prognosis and high recurrence rate. The discovery of new predictive models and therapeutic agents plays a crucial role.

Methods

The differentially expressed gene that was explicitly highly expressed in The Cancer Genome Atlas (TCGA) and GSE9476 transcriptome databases were screened and included in the least absolute shrinkage and selection operator (LASSO) regression model to derive risk coefficients and build a risk score model. Functional enrichment analysis was conducted on the screened hub genes to explore the potential mechanisms. Subsequently, critical genes were incorporated into a nomogram model based on risk scores to analyze prognostic value. Finally, this study combined network pharmacology to find potential natural compounds for hub genes and used molecular docking to verify the binding ability of molecular structures to natural compounds to explore drug development for possible efficacy in AML.

Results

A total of 33 highly expressed genes may be associated with poor prognosis of AML patients. After LASSO and multivariate Cox regression analysis of 33 critical genes, Rho-related BTB domain containing 2 (RHOBTB2), phospholipase A2 (PLA2G4A), interleukin-2 receptor-α (IL2RA), cysteine and glycine-rich protein 1 (CSRP1), and olfactomedin-like 2A (OLFML2A) were found to played a significant role in the prognosis of AML patients. CSRP1 and OLFML2A were independent prognostic factors of AML. The predictive power of these 5 hub genes in combination with clinical features was better than clinical data alone in predicting AML in the column line graphs and had better predictive value at 1, 3, and 5 years. Finally, through network pharmacology and molecular docking, this study found that diosgenin in Guadi docked well with PLA2G4A, beta-sitosterol in Fangji docked well with IL2RA, and OLFML2A docked well with 3,4-di-O-caffeoylquinic acid in Beiliujinu.

Conclusions

The predictive model of RHOBTB2, PLA2G4A, IL2RA, CSRP1, and OLFML2A combined with clinical features can better guide the prognosis of AML. In addition, the stable docking of PLA2G4A, IL2RA, and OLFML2A with natural compounds may provide new options for treating AML.

Role of Biomarkers in the Management of Acute Myeloid Leukemia

Despite many recent advances in treatment options, acute myeloid leukemia (AML) still has a high mortality rate. One important issue in optimizing outcomes for AML patients lies in the limited ability to predict response to specific therapies, duration of response, and likelihood of relapse. With evolving genetic characterization and improving molecular definitions, the ability to predict outcomes and long-term prognosis is slowly improving. The majority of the currently used prognostic assessments relate to molecular and chromosomal abnormalities, as well as response to initial therapy. These risk categories, however, do not account for a large amount of the variability in AML. Laboratory techniques now utilized in the clinic extend beyond bone marrow morphology and single gene sequencing, to next-generation sequencing of large gene panels and multiparameter flow cytometry, among others. Other technologic advances, such as gene expression analysis, have yet to demonstrate enough predictive and prognostic power to be employed in clinical medicine outside of clinical trials, but may be incorporated into the clinic in the future. In this review, we discuss the utility of current biomarkers, and present novel biomarker techniques and strategies that are in development for AML patients. Measurable residual disease (MRD) is a powerful prognostic tool that is increasingly being incorporated into clinical practice, and there are some exciting emerging biomarker technologies that have the potential to improve prognostic power in AML. As AML continues to be a difficult-to-treat disease with poor outcomes in many subtypes, advances in biomarkers that lead to better treatment decisions are greatly needed.

C10orf55, CASC2, and SFTA1P lncRNAs Are Potential Biomarkers to Assess Radiation Therapy Response in Head and Neck Cancers

Long non-coding RNAs have proven to be important molecules in carcinogenesis. Due to little knowledge about them, the molecular mechanisms of tumorigenesis are still being explored. The aim of this work was to study the effect of ionizing radiation on the expression of lncRNAs in head and neck squamous cell carcinoma (HNSCC) in patients responding and non-responding to radiotherapy. The experimental model was created using a group of patients with response (RG, n = 75) and no response (NRG, n = 75) to radiotherapy based on the cancer genome atlas (TCGA) data. Using the in silico model, statistically significant lncRNAs were defined and further validated on six HNSCC cell lines irradiated at three different doses. Based on the TCGA model, C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105, with the altered expression between the RG and NRG were observed. Analysis of pathways and immune profile indicated that these lncRNAs were associated with changes in processes, such as epithelial-to-mesenchymal transition, regulation of spindle division, and the p53 pathway, and differences in immune cells score and lymphocyte infiltration signature score. However, only C10orf55, CASC2, and SFTA1P presented statistically altered expression after irradiation in the in vitro model. In conclusion, the expression of lncRNAs is affected by ionization radiation in HNSCC, and these lncRNAs are associated with pathways, which are important for radiation response and immune response. Potentially presented lncRNAs could be used as biomarkers for personalized radiotherapy in the future. However, these results need to be verified based on an in vitro experimental model to show a direct net of interactions.

Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest

Objective: Papillary thyroid carcinoma (PTC) accounts for 80% of thyroid malignancy, and the occurrence of PTC is increasing rapidly. The present study was conducted with the purpose of identifying novel and important gene panels and developing an early diagnostic model for PTC by combining artificial neural network (ANN) and random forest (RF). Methods and results: Samples were searched from the Gene Expression Omnibus (GEO) database, and gene expression datasets (GSE27155, GSE60542, and GSE33630) were collected and processed. GSE27155 and GSE60542 were merged into the training set, and GSE33630 was defined as the validation set. Differentially expressed genes (DEGs) in the training set were obtained by "limma" of R software. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis as well as immune cell infiltration analysis were conducted based on DEGs. Important genes were identified from the DEGs by random forest. Finally, an artificial neural network was used to develop a diagnostic model. Also, the diagnostic model was validated by the validation set, and the area under the receiver operating characteristic curve (AUC) value was satisfactory. Conclusion: A diagnostic model was established by a joint of random forest and artificial neural network based on a novel gene panel. The AUC showed that the diagnostic model had significantly excellent performance.

A six-gene prognostic signature for both adult and pediatric acute myeloid leukemia identified with machine learning

BACKGROUND

Although it is well-known that adult and pediatric acute myeloid leukemias (AMLs) are genetically distinct diseases, they still share certain gene expression profiles. The age-related genetic heterogeneities of AMLs have been well-studied, but the common prognostic signatures and molecular mechanisms of adult and pediatric AMLs are less investigated.

AIM

To identify genes and pathways that are associated with both pediatric and adult AMLs and discover a gene signature for overall survival (OS) prediction.

METHODS

Through mining the transcriptome profiles of The Cancer Genome Atlas (TCGA) data sets of adult cancers and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data of pediatric cancers, we identified genes that are commonly dysregulated in both pediatric and adult AMLs, further discovered a common gene signature, and built two risk score models for TCGA and TARGET cohorts, respectively with L ₀ regularized global AUC (area under the receiver operating characteristic curve) summary maximization.

RESULTS

We identified 57 genes that are differentially expressed and prognostically significant in both adult and childhood AMLs. The top 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched with those 57 genes include transcriptional misregulation, focal adhesion, PI3K-Akt signaling pathway, and signaling pathways regulating pluripotency of stem cells. We further identified a 6-gene signature including genes of ADAMTS3, DNMT3B, NYNRIN, SORT1, ZFHX3, and ZG16B for risk prediction. We constructed a risk score model with one dataset (either TCGA or TARGET) and evaluated its performance with the other. The test AUCs for the risk prediction of TCGA data with a 2-year and 5-year OS cutoffs are 0.762 (P = 2.33e-13, 95% CI: 0.69-0.83) and 0.759 (P = 7.26e-08, 95% CI: 0.66-0.85), respectively, while the test AUCs of TARGET data with the same cutoffs are 0.71 (P = 3.3e-07, 95% CI: 0.62-0.79) and 0.72 (P= 5.25e-09, 95% CI: 0.65-0.80), respectively. We further stratified patients into 3 equal sized prognostic subtypes with the 6-gene risk scores. The P-values of the tertile partitions are 1.74e-07 and 3.28e-08 for the TARGET and TCGA cohorts, respectively, which are significantly better than the standard cytogenetic risk stratification of both cohorts (TARGET: P = 1.64e-06; TCGA: P = 1.79e-05). When validated with two other independent cohorts, the 6-gene risk score models remain a significant predictor for OS. Investigating the common gene expression program is significant in that we may extrapolate the findings from adults to children and avoid unnecessary pediatric clinical trials.

Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia

Risk stratification in acute myeloid leukemia (AML) has been extensively improved thanks to the incorporation of recurrent cytogenomic alterations into risk stratification guidelines. However, mortality rates among fit patients assigned to low or intermediate risk groups are still high. Therefore, significant room exists for the improvement of AML prognostication. In a previous work, we presented the Stellae-123 gene expression signature, which achieved a high accuracy in the prognostication of adult patients with AML. Stellae-123 was particularly accurate to restratify patients bearing high-risk mutations, such as ASXL1, RUNX1 and TP53. The intention of the present work was to evaluate the prognostic performance of Stellae-123 in external cohorts using RNAseq technology. For this, we evaluated the signature in 3 different AML cohorts (2 adult and 1 pediatric). Our results indicate that the prognostic performance of the Stellae-123 signature is reproducible in the 3 cohorts of patients. Additionally, we evidenced that the signature was superior to the European LeukemiaNet 2017 and the pediatric clinical risk scores in the prediction of survival at most of the evaluated time points. Furthermore, integration with age substantially enhanced the accuracy of the model. In conclusion, Stellae-123 is a reproducible machine learning algorithm based on a gene expression signature with promising utility in the field of AML.

A novel signature based on necroptosis-related long non-coding RNAs for predicting prognosis of patients with glioma

Necroptosis is closely related to the occurrence and development of tumors, including glioma. A growing number of studies indicate that targeting necroptosis could be an effective treatment strategy against cancer. Long non-coding RNA (lncRNA) is also believed to play a pivotal role in tumor epigenetics. Therefore, it is necessary to identify the functions of necroptosis-related lncRNAs in glioma. In this study, the transcriptome and clinical characteristic data of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases were collected, and the differentially expressed necroptosis-related lncRNAs in TCGA that have an impact on overall survival (OS) were screened out to construct risk score (RS) formula, which was verified in CGGA. A nomogram was constructed to predict the prognosis of glioma patients based on clinical characteristics and RS. In addition, Gene Set Enrichment Analysis (GSEA) was used to analyze the main enrichment functions of these necroptosis-related lncRNAs and the immune microenvironment. A total of nine necroptosis-related lncRNAs have been identified to construct the RS formula, and the Kaplan–Meier (K-M) survival analysis showed significantly poorer outcomes in the high RS group in both TCGA and CGGA databases. Moreover, the receiver operating characteristic (ROC) curve shows that our prediction RS model has good predictability. Regarding the analysis of the immune microenvironment, significant differences were observed in immune function and immune checkpoint between the high RS group and the low RS group. In conclusion, we constructed a necroptosis-related lncRNA RS model that can effectively predict the prognosis of glioma patients and provided the theoretical basis and the potential therapeutic targets for immunotherapy against gliomas.

Development of a novel miR-3648-related gene signature as a prognostic biomarker in esophageal adenocarcinoma

Background

Esophageal adenocarcinoma (EA) is a typical immunogenic malignant tumor with a dismal 5-year survival rate lower than 20%. Although miRNA-3648 (miR-3648) is expressed abnormally in EA, its impact on the tumor immune microenvironment remains unknown. In this study, we sought to identify immune-related genes (IRGs) that are targeted by miR-3648 and develop an EA multigene signature.

Methods

The gene expression data of 87 EA tumor samples and 67 normal tissue samples from The Cancer Genome Atlas (TCGA) database and the Genotype-Tissue Expression (GTEx) database were downloaded, respectively. Weighted gene co-expression network analysis (WGCNA), the CIBERSORT algorithm, and Cox regression analysis were applied to identify IRGs and to construct a prognostic signature and nomogram.

Results

MiR-3648 was expectedly highly expressed in EA tumor tissues (P=2.6e-8), and related to the infiltration of activated natural killer cells (NK cells) and activated CD4 T lymphocytes (CD4 cells). A total of 70 miR-3648-targeted genes related to immune cell infiltration were identified. Among them, 4 genes (C10orf55, DLL4, PANX2, and NKAIN1) were closely related to overall survival (OS), and were thus selected to construct a 4-gene risk score (RS). The RS had a superior capability to predict OS [area under the curve (AUC) =0.740 for 1 year; AUC =0.717 for 3 years; AUC =0.622 for 5 years]. A higher score was indicative of a poorer prognosis than a lower score [hazard ratio (HR) =2.71; 95% confidence interval (CI): 1.45-5.09; P=0.002]. Furthermore, the nomogram formed by combining the RS and the TNM classification of malignant tumors (TNM stage) improved the accuracy of survival prediction [Harrell's concordance index (C-index) =0.698].

Conclusions

MiR-3648 may play a critical role in EA pathogenesis. The novel 4-gene signature may serve as a prognostic tool to manage patients with EA.

METTL3 mediates chemoresistance by enhancing AML homing and engraftment via ITGA4

Chemoresistant leukemia relapse is one of the most common causes of death for acute myeloid leukemia (AML) patients and the homing/engraftment in bone marrow (BM) are crucial steps for AML cells to acquire chemoresistance by interacting with stromal cell components. No crosstalk between m⁶A modification and homing/engraftment has been reported. Here, we performed comprehensive high-throughput analyses, including RNA sequencing of CR (complete remission) and relapsed AML patients, and reverse-phase protein arrays of chemoresistant cells to identify METTL3 as a key player regulating AML chemoresistance. Then, METTL3-mediated m⁶A modification was proved to induce the chemoresistance in vitro and in vivo. Furthermore, AML homing/engraftment was discovered being enhanced by upregulated-METTL3 in chemoresistant cells. And the homing/engraftment and drug-resistance associated phenotypes of chemoresistant cells could be reversed by a METTL3 inhibitor. Mechanistically, METTL3 extended the half-life of ITGA4 mRNA by m⁶A methylation, and then, increased expression of ITGA4 protein to enhance homing/engraftment of AML cells. The results provide insights into the function of m⁶A modification on the interaction between AML cells and BM niches and clarify the relationship between METTL3 and AML homing/engraftment, suggesting a therapeutic strategy for the treatment of refractory/relapsed AML with METTL3 inhibitors.

Diverse functions of long noncoding RNAs in acute myeloid leukemia: emerging roles in pathophysiology, prognosis, and treatment resistance

PURPOSE OF REVIEW

Advancements in the next-generation sequencing technologies have identified rare transcripts of long noncoding RNAs (lncRNAs) in the genome of cancers, including in acute myeloid leukemia (AML). The purpose of this review is to highlight the contribution of lncRNAs in AML pathogenesis, prognosis, and chemoresistance.

RECENT FINDINGS

Several studies have recently reported that deregulated lncRNAs are novel key players in the development of AML and are associated with AML pathophysiology and may serve as prognostic indicators. A few aberrantly expressed lncRNAs that correlated with the recurrent genetic mutations in AML such as NPM1 and RUNX1 have recently been characterized. Moreover, a few lncRNAs in MLL-rearranged leukemia have been described. Additionally, the involvement of lncRNAs in AML chemoresistance has been postulated.

SUMMARY

Investigating the functional roles of the noncoding regions including lncRNAs, may provide novel insights into the pathophysiology, refine the prognostic schema, and provide novel therapeutic treatment strategies in AML.

CircRNF220, not its linear cognate gene RNF220, regulates cell growth and is associated with relapse in pediatric acute myeloid leukemia

Background

Circular RNAs (circRNAs) constitute a family of transcripts with unique structures and have been confirmed to be critical in tumorigenesis and to be potential biomarkers or therapeutic targets. However, only a few circRNAs have been functionally characterized in pediatric acute myeloid leukemia (AML).

Methods

Here, we investigated the expression pattern of circRNAs in pediatric AML using a circRNA microarray. The characteristics, potential diagnostic value, and prognostic significance of circRNF220 were evaluated. A series of functional experiments were performed to investigate the role of circRNF220 in primary pediatric AML cells. Then we investigated the aberrant transcriptional networks regulated by circRNF220 in primary AML cells by RNA-seq. Furthermore, biotin RNA pulldown assays were implemented to verify the relationship between circRNF220 and miR-30a.

Results

We identified a circRNA, circRNF220, which was specifically abundant in and accumulated in the peripheral blood and bone marrow of pediatric patients with AML. It could distinguish AML from ALL and other hematological malignancies with high sensitivity and specificity. Significantly, circRNF220 expression independently predicted prognosis, while high expression of circRNF220 was an unfavorable prognostic marker for relapse. Furthermore, we characterized the function of circRNF220 and found that circRNF220 knockdown specifically inhibited proliferation and promoted apoptosis in AML cell lines and primary cells. Mechanistically, circRNF220 may act as an endogenous sponge of miR-30a to sequester miR-30a and inhibit its activity, which increases the expression of its targets MYSM1 and IER2 and implicated in AML relapse.

Conclusions

Collectively, these findings demonstrated that circRNF220 could be highly efficient and specific for the accurate diagnosis of pediatric AML, with implications for relapse prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-021-01395-7.

PSD3 is an oncogene that promotes proliferation, migration, invasion, and G1/S transition while inhibits apoptotic in papillary thyroid cancer

Background: The morbidity of thyroid cancer is gradually increasing, meanwhile, the average age of the morbidity population also becomes younger. Mechanisms genomic variations serve an important function for the pathogenesis of many cancer types. Pleckstrin and sec7 domain-containing 3 (PSD3), also known as EFA6R, was shown to be associated with some cancers such as acute myeloid leukemia, breast cancer metastasis, and astrocytoma. But it was unknown that whether PSD3 took effect and how did it work in thyroid cancer. Methods: We guessed that PSD3 might play an important role in thyroid cancer by consulting previous literature. Then, we analyzed the level of PSD3 expression in thyroid malignancy and the connection with clinical manifestation in The Cancer Genome Atlas (TCGA). And RNA extraction, reverse transcription, and real-time quantitative polymerase chain reaction (qRt-PCR) of 40 pairs of local samples were done to verify the result of TCGA. Then, PSD3 was knocked down by small interfering RNA (siRNA) for flowing functional experiments. Results: Bioinformatics and qRt-PCR analysis shown PSD3 was overexpressed in papillary thyroid cancer (PTC) and connected with the histological type (P=0.009) and risk of lymph node metastasis (P=0.016). In vitro assays, we confirmed that down-regulation PSD3 could not only suppress the cell proliferation, colony formation, cell migration, cell invasion, and G1/S cell cycle transition but also promote apoptosis in PTC cells. Conclusion: PSD3 promotes proliferation, migration, invasion, and G1/S transition while inhibits apoptotic in PTC and a possible biomarker in PTC.