Development and validation of a novel survival model for acute myeloid leukemia based on autophagy-related genes

Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques

BACKGROUND

Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) focus on key outcomes such as overall survival (OS), relapse, and graft-versus-host disease (GVHD).

OBJECTIVE

This study aims to evaluate the capability of machine learning (ML) models in predicting OS, relapse, and GVHD in AL patients post-allo-HSCT.

METHODS

Clinical data from 1243 AL patients, with 10 years of follow-up, was utilized to develop 28 ML models. These models incorporated four feature selection methods and seven ML algorithms. Model performance was assessed using the concordance index (c-index) with multivariate analysis.

RESULTS

The multivariate model analysis showed the best FS/ML combinations were UCI_GLMN, IBMA_GLMN and IBMA_CB for OS, UCI_ST, UCI_RSF, UCI GLMB, UCI_GB, UCI_CB, MI_GLMN, IBMA_ST and IBMA GB for relapse, IBMA_GB for aGVHD and Boruta_GB for cGVHD (all p values < 0.0001, mean C-indices in 0.61-0.68)).

CONCLUSION

ML techniques, when combined with clinical variables, demonstrate high accuracy in predicting OS, relapse, and GVHD in AL patients.

The novel prognostic analysis of AML based on ferroptosis and cuproptosis related genes

BACKGROUND

Acute myeloid leukemia (AML) is a hematological malignancy. The aim of this research was to develop a ferroptosis and cuproptosis related novel prognostic signature associated with AML.

METHODS

The ferroptosis and cuproptosis related genes correlated with the prognosis of AML were identified by univariate Cox analysis. The consistent cluster analysis was performed for 150 AML patients in TCGA dataset. The key module genes associated with GSVA score of ferroptosis and cuproptosis were identified by WGCNA. univariate Cox and LASSO regression analysis were adopted to build a ferroptosis and cuproptosis AML prognostic signature. Finally, the expression of five prognostic genes in clinical tissue samples were verified by RT-qPCR.

RESULTS

A grand total of 27 FCRGs associated with AML prognosis were identified.Then, two AML sub-types with significantly different survival were obtained. We found 3 significantly differential expressed immune cells (naive CD4 cells, regulatory T cells and resting mast cells) between two risk sub-groups. Meanwhile, 'IL6 JAK STAT3 signaling' and 'P53 pathway' were enriched in low-risk group. A ferroptosis and cuproptosis related prognostic signature was build based on 8 prognostic genes. RT-qPCR results indicated that there was no significant difference in the expression of OLFML2A and CD109 between AML and normal samples. However, compared to the control group, LGALS1, SOCS1, and RHOC showed significantly lower expression in the AML group.

CONCLUSION

The prognostic signature comprised of OLFML2A, LGALS1, ABCB11, SOCS1, RHOC, CD109, RD3L and PTPN13 based on ferroptosis and cuproptosis was established, which provided theoretical basis for the research of AML.

Crosstalk between autophagy and metabolism: implications for cell survival in acute myeloid leukemia

Acute myeloid leukemia (AML), a prevalent form of leukemia in adults, is often characterized by low response rates to chemotherapy, high recurrence rates, and unfavorable prognosis. A critical barrier in managing refractory or recurrent AML is the resistance to chemotherapy. Increasing evidence indicates that tumor cell metabolism plays a crucial role in AML progression, survival, metastasis, and treatment resistance. Autophagy, an essential regulator of cellular energy metabolism, is increasingly recognized for its role in the metabolic reprogramming of AML. Autophagy sustains leukemia cells during chemotherapy by not only providing energy but also facilitating rapid proliferation through the supply of essential components such as amino acids and nucleotides. Conversely, the metabolic state of AML cells can influence the activity of autophagy. Their mutual coordination helps maintain intrinsic cellular homeostasis, which is a significant contributor to chemotherapy resistance in leukemia cells. This review explores the recent advancements in understanding the interaction between autophagy and metabolism in AML cells, emphasizing their roles in cell survival and drug resistance. A comprehensive understanding of the interplay between autophagy and leukemia cell metabolism can shed light on leukemia cell survival strategies, particularly under adverse conditions such as chemotherapy. This insight may also pave the way for innovative targeted treatment strategies.

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

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

TYRO protein tyrosine kinase-binding protein predicts favorable overall survival in osteosarcoma and correlates with antitumor immunity

To explore the prognostic significance and underlying mechanism of TYRO protein tyrosine kinase-binding protein (TYROBP) in osteosarcoma. Firstly, the expression of TYROBP was analyzed using the t test. The Kaplan-Meier plotter analysis and a receiver operating characteristic curve were performed to evaluate the influence of TYROBP on overall survival (OS). Further, Cox regression analysis was conducted to predict the independent prognostic factors for OS of osteosarcoma patients, and a nomogram was constructed. Then, the relationship between TYROBP and clinicopathological characteristics was determined using statistical methods. Enrichment analyses were conducted to evaluate the biological functions of TYROBP. Finally, the ESTIMATE algorithm was used to assess the association of TYROBP with immune cell infiltration. TYROBP was significantly increased in osteosarcoma (all P < .001). However, the high expression of TYROBP was related to better OS in osteosarcoma patients. Cox regression analysis showed that TYROBP was an independent prognostic factor for predicting OS (P = .005), especially in patients of the male sex, age <18 years, metastasis, and tumor site leg/foot (all P < .05). Besides, TYROBP mRNA expression was significantly associated with the tumor site (P < .01) but had no remarkable relationship with age, gender, and metastasis status (all P > .05). Functional annotation and gene set enrichment analysis (GSEA) revealed that TYROBP was mainly involved in immune-related pathways. Importantly, TYROBP positively correlated with immune scores (P < .001, R = .87). TYROBP served as an independent prognostic biomarker for OS in osteosarcoma. High TYROBP expression might prolong the survival of osteosarcoma patients mainly through promoting antitumor immunity.

Deconvolution of tumor composition using partially available DNA methylation data

Background

Deciphering proportions of constitutional cell types in tumor tissues is a crucial step for the analysis of tumor heterogeneity and the prediction of response to immunotherapy. In the process of measuring cell population proportions, traditional experimental methods have been greatly hampered by the cost and extensive dropout events. At present, the public availability of large amounts of DNA methylation data makes it possible to use computational methods to predict proportions.

Results

In this paper, we proposed PRMeth, a method to deconvolve tumor mixtures using partially available DNA methylation data. By adopting an iteratively optimized non-negative matrix factorization framework, PRMeth took DNA methylation profiles of a portion of the cell types in the tissue mixtures (including blood and solid tumors) as input to estimate the proportions of all cell types as well as the methylation profiles of unknown cell types simultaneously. We compared PRMeth with five different methods through three benchmark datasets and the results show that PRMeth could infer the proportions of all cell types and recover the methylation profiles of unknown cell types effectively. Then, applying PRMeth to four types of tumors from The Cancer Genome Atlas (TCGA) database, we found that the immune cell proportions estimated by PRMeth were largely consistent with previous studies and met biological significance.

Conclusions

Our method can circumvent the difficulty of obtaining complete DNA methylation reference data and obtain satisfactory deconvolution accuracy, which will be conducive to exploring the new directions of cancer immunotherapy. PRMeth is implemented in R and is freely available from GitHub (https://github.com/hedingqin/PRMeth).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04893-7.

The clinical significance of integrin subunit alpha V in cancers: from small cell lung carcinoma to pan-cancer

BACKGROUND

Little is known about the relationship between integrin subunit alpha V (ITGAV) and cancers, including small cell lung cancer (SCLC).

METHODS

Using large sample size from multiple sources, the clinical roles of ITGAV expression in SCLC were explored using differential expression analysis, receiver operating characteristic curves, Kaplan-Meier curves, etc. RESULTS: Decreased mRNA (SMD = - 1.05) and increased protein levels of ITGAV were detected in SCLC (n = 865). Transcription factors-ZEB2, IK2F1, and EGR2-may regulate ITGAV expression in SCLC, as they had ChIP-Seq (chromatin immunoprecipitation followed by sequencing) peaks upstream of the transcription start site of ITGAV. ITGAV expression made it feasible to distinguish SCLC from non-SCLC (AUC = 0.88, sensitivity = 0.78, specificity = 0.84), and represented a risk role in the prognosis of SCLC (p < 0.05). ITGAV may play a role in cancers by influencing several immunity-related signaling pathways and immune cells. Further, the extensive pan-cancer analysis verified the differential expression of ITGAV and its clinical significance in multiple cancers.

CONCLUSION

ITGAV served as a potential marker for prognosis and identification of cancers including SCLC.

Clinical significance of cyclin-dependent kinase inhibitor 2C expression in cancers: from small cell lung carcinoma to pan-cancers

Background

Cyclin-dependent kinase inhibitor 2C (CDKN2C) was identified to participate in the occurrence and development of multiple cancers; however, its roles in small cell lung carcinoma (SCLC) remain unclear.

Methods

Differential expression analysis of CDKN2C between SCLC and non-SCLC were performed based on 937 samples from multiple centers. The prognosis effects of CDKN2C in patients with SCLC were detected using both Kaplan–Meier curves and log-rank tests. Using receiver-operating characteristic curves, whether CDKN2C expression made it feasible to distinguish SCLC was determined. The potential mechanisms of CDKN2C in SCLC were investigated by gene ontology terms and signaling pathways (Kyoto Encyclopedia of Genes and Genomes). Based on 10,080 samples, a pan-cancer analysis was also performed to determine the roles of CDKN2C in multiple cancers.

Results

For the first time, upregulated CDKN2C expression was detected in SCLC samples at both the mRNA and protein levels (p of Wilcoxon rank-sum test < 0.05; standardized mean difference = 2.86 [95% CI 2.20–3.52]). Transcription factor FOXA1 expression may positively regulate CDKN2C expression levels in SCLC. High CDKN2C expression levels were related to the poor prognosis of patients with SCLC (hazard ratio > 1, p < 0.05) and showed pronounced effects for distinguishing SCLC from non-SCLC (sensitivity, specificity, and area under the curve ≥ 0.95). CDKN2C expression may play a role in the development of SCLC by affecting the cell cycle. Furthermore, the first pan-cancer analysis revealed the differential expression of CDKN2C in 16 cancers (breast invasive carcinoma, etc.) and its independent prognostic significance in nine cancers (e.g., adrenocortical carcinoma). CDKN2C expression was related to the immune microenvironment, suggesting its potential usefulness as a prognostic marker in immunotherapy.

Conclusions

This study identified upregulated CDKN2C expression and its clinical significance in SCLC and other multiple cancers, suggesting its potential usefulness as a biomarker in treating and differentiating cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02036-5.

The dual role of autophagy in acute myeloid leukemia

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