A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

Construction of lncRNA/Pseudogene-miRNA Network Based on In Silico Approaches for Glycolysis Pathway to Identify Prostate Adenocarcinoma-Related Potential Biomarkers

LncRNAs, pseudogenes, and miRNAs participate a fundamental function in tumorigenesis, metabolism, and invasion of cancer cells, although their regulation of tumor glycolysis in prostate adenocarcinoma (PRAD) is thoroughly not well studied. In this study, we applied transcriptomic, proteomic, and medical information to identify glycolysis-related key genes and modules associated with PRAD. Then, the glycolysis-related lncRNA/lncRNAs/pseudogenes-miRNA-mRNA network was constructed. Analysis of DNA methylation status and expression data determined a DNA methylation-dysregulated three-DE-mRNAs signature for predicting diagnosis, ANGPTL4, GNE, and HSPA in PRAD patients and healthy control. Several lncRNAs/pseudogenes, significantly correlated with the overall survival PVT1, CA5BP1, MIRLET7BHG, SNHG12, and ZNF37BP and disease-free survival status, MALAT1, GUSBP11, MIRLET7BHG, and SNHG1, of patients with PRAD were determined. The methylation profile of DE-lncRNA/pseudogenes was significantly proper for predicting PRAD prognostic model. The transcription level of 6 DE-mRNA ANGPTL4, QSOX1, BIK, CLDN3, DDIT4, and TFF3 was correlated with cancer-related fibroblast infiltration in PRAD. The mutated form of 7 mRNAs, COL5A1, IDH1, HK2, DDIT4, GNE, and QSOX1, was associated with PRAD. In addition to the glycolysis pathway, DE-RNAs play regulatory roles on several pathways, including DNA damage, RTK, cell cycle, RAS/MAPK, TSC/mTOR and PI3K/AKT, AR hormone, and EMT. Overall, our study improves our knowledge of the relation between lncRNAs/pseudogenes and miRNA related to glycolysis and PRAD pathogenesis.

Fatty acid metabolism-related lncRNAs are potential biomarkers for survival prediction in clear cell renal cell carcinoma

Metabolic reprogramming of energy is a newly recognized characteristic of cancer. In our current investigation, we examined the possible predictive importance of long noncoding RNAs (lncRNAs) associated to fatty acid metabolism in clear cell renal cell carcinoma (ccRCC). We conducted an analysis of the gene expression data obtained from patients diagnosed with ccRCC using the Cancer Genome Atlas (TCGA) database and the ArrayExpress database. We performed a screening to identify lncRNAs that are differentially expressed in fatty acid metabolism. Based on these findings, we developed a prognostic risk score model using these fatty acid metabolism-related lncRNAs. We then validated this model using Cox regression analysis, Kaplan-Meier survival analysis, and principal-component analysis (PCA). Furthermore, the prognostic risk score model was successfully validated using both the TCGA cohort and the E-MTAB-1980 cohort. We utilized gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to determine the correlation between fatty acid metabolism and the PPAR signaling pathway in patients with ccRCC at various clinical stages and prognoses. We have discovered compelling evidence of the interaction between immune cells in the tumor microenvironment and tumor cells, which leads to immune evasion and resistance to drugs. This was achieved by the utilization of advanced techniques such as the CIBERSORT method, ESTIMATE R package, ssGSEA algorithm, and TIMER database exploration. Ultimately, we have established a network of competing endogenous RNA (ceRNA) that is related to fatty acid metabolism. The findings of our study suggest that medicines focused on fatty acid metabolism could be clinically significant for individuals with ccRCC. The utilization of this risk model, which is centered around the lncRNAs associated with fatty acid metabolism, could potentially provide valuable prognostic information and hold immunotherapeutic implications for patients with ccRCC.

The Role of Long Noncoding RNAs in Progression of Leukemia: Based on Chromosomal Location

Long non-coding RNA [LncRNA] dysregulation has been seen in many human cancers, including several kinds of leukemia, which is still a fatal disease with a poor prognosis. LncRNAs have been demonstrated to function as tumor suppressors or oncogenes in leukemia. This study covers current research findings on the role of lncRNAs in the prognosis and diagnosis of leukemia. Based on recent results, several lncRNAs are emerging as biomarkers for the prognosis, diagnosis, and even treatment outcome prediction of leukemia and have been shown to play critical roles in controlling leukemia cell activities, such as proliferation, cell death, metastasis, and drug resistance. As a result, lncRNA profiles may have superior predictive and diagnostic potential in leukemia. Accordingly, this review concentrates on the significance of lncRNAs in leukemia progression based on their chromosomal position.

Computational analysis of heat shock proteins and ferroptosis-associated lncRNAs to predict prognosis in acute myeloid leukemia patients

Owing to their functional diversity in many cancers, long noncoding RNAs (lncRNAs) are receiving special attention. LncRNAs not only function as oncogenes or tumor suppressors by participating in various signaling pathways but also serve as predictive markers for various types of cancer, including acute myeloid leukemia (AML). Considering this, we investigated lncRNAs that may act as a mediator between two processes, i.e., heat shock proteins and ferroptosis, which appear to be closely related in tumorigenesis. Using a comprehensive bioinformatics approach, we identified four lncRNAs (AL138716.1, AC000120.1, AC004947.1, and LINC01547) with prognostic value in AML patients. Of interest, two of them (AC000120.1 and LINC01547) have already been reported to be AML-related, and AC004947.1 is considered to have oncogenic potential. In particular, the signature obtained showed a lower survival probability with high-risk patients, and vice versa. To our knowledge, this is the first predictive model of lncRNA that may correlate with the processes of heat shock proteins and ferroptosis in AML. Nevertheless, validation using patient samples is warranted.

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.

A ferroptosis-related gene signature and immune infiltration patterns predict the overall survival in acute myeloid leukemia patients

Targeted therapy for acute myeloid leukemia (AML) is an effective strategy, but currently, there are very limited therapeutic targets for AML treatment. Ferroptosis is strongly related to drug resistance and carcinogenesis. However, there are few reports about ferroptosis in AML. This article explores the relationship between ferroptosis-related gene (FRG) expression and prognosis in AML patients from the FerrDb and the Cancer Genome Atlas (TCGA) databases. The ferroptosis-related gene ARNTL was observed to have high expression and poor prognosis in AML. Receiver operating characteristic curve (ROC) analysis revealed the predictive accuracy of the signature. The area under the time-dependent ROC curve (AUC) was 0.533 at one year, 0.619 at two years, and 0.622 at three years within the training cohort. Moreover, we found that the ARNTL expression is closely associated with tumor-infiltrating immune cells like the macrophages and NK cells. Inhibiting the ARNTL expression suppressed colony formation and induced ferroptosis in AML cells. Overall, the survival prediction model constructed based on ARNTL accurately predicted the survival in AML patients, which could be a potential candidate for diagnosing and treating AML.

Identifying Autophagy-Related lncRNAs and Potential ceRNA Networks in NAFLD

Nonalcoholic fatty liver disease (NAFLD) is a common chronic disease with complex pathogenesis, which brings economic burden to the society, and there is still no effective therapy. Impaired autophagy has been implicated in the development of NAFLD. Long noncoding RNAs (lncRNAs) are also reported to play a role in the pathogenesis of NAFLD. However, the role of autophagy-related lncRNAs in NAFLD disease has not been elucidated. Here, we mined GSE135251, GSE160016, GSE130970 and GSE185062 datasets from the Gene Expression Omnibus database (GEO) and obtained the human autophagy-related gene list from the Human Autophagy Database (HADb) for in-depth bioinformatic analysis. Following differential expression analysis and intersection of the datasets, Pearson correlation analysis was performed on DElncRNAs and autophagy-related DEmRNAs to obtain autophagy-related lncRNAs, and then Starbase3.0 and TargetScan7.2 were used to construct competing endogenous RNAs (ceRNA) regulatory networks. We constructed four lncRNA-dominated ceRNA regulatory networks (PSMG3-AS1, MIRLET7BHG, RP11-136K7.2, LINC00925), and visualized with Cytoscape. Then we performed co-expression analysis of the ceRNA networks and autophagy-related genes, and functionally annotated them with Metascape. Finally, we performed receiver operating characteristic curve (ROC) analysis on lncRNAs and mRNAs within the ceRNA networks. Conclusively, our project is the first to study autophagy-related lncRNAs in NAFLD and finally mined four autophagy-related lncRNAs (PSMG3-AS1, MIRLET7BHG, RP11-136K7.2, LINC00925). We suggested that the four autophagy-related lncRNAs may be closely associated with the occurrence and development of NAFLD through the corresponding ceRNA regulatory networks. This research brings new horizons to the study of NAFLD.

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.

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.

Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia

Background: Emerging evidence has proven that ferroptosis plays an important role in the development of acute myeloid leukemia (AML), whereas the exact role of ferroptosis-associated genes in AML patients’ prognosis remained unclear.

Materials and Methods: Gene expression profiles and corresponding clinical information of AML cases were obtained from the TCGA (TCGA-LAML), GEO (GSE71014), and TARGET databases (TARGET-AML). Patients in the TCGA cohort were well-grouped into two clusters based on ferroptosis-related genes, and differentially expressed genes were screened between the two clusters. Univariate Cox and LASSO regression analyses were applied to select prognosis-related genes for the construction of a prognostic risk-scoring model. Survival analysis was analyzed by Kaplan–Meier and receiver operator characteristic curves. Furthermore, we explored the correlation of the prognostic risk-scoring model with immune infiltration and chemotherapy response. Risk gene expression level was detected by quantitative reverse transcription polymerase chain reaction.

Results: Eighteen signature genes, including ZSCAN4, ASTN1, CCL23, DLL3, EFNB3, FAM155B, FOXL1, HMX2, HRASLS, LGALS1, LHX6, MXRA5, PCDHB12, PRINS, TMEM56, TWIST1, ZFPM2, and ZNF560, were developed to construct a prognostic risk-scoring model. AML patients could be grouped into high- and low-risk groups, and low-risk patients showed better survival than high-risk patients. Area under the curve values of 1, 3, and 5 years were 0.81, 0.827, and 0.786 in the training set, respectively, indicating a good predictive efficacy. In addition, age and risk score were the independent prognostic factors after univariate and multivariate Cox regression analyses. A nomogram containing clinical factors and prognostic risk-scoring model was constructed to better estimate individual survival. Further analyses demonstrated that risk score was associated with the immune infiltration and response to chemotherapy. Our experiment data revealed that LGALS1 and TMEM56 showed notably decreased expression in AML samples than that of the normal samples.

Conclusion: Our study shows that the prognostic risk-scoring model and key risk gene may provide potential prognostic biomarkers and therapeutic option for AML patients.

Identification of m6A-Related lncRNAs Associated With Prognoses and Immune Responses in Acute Myeloid Leukemia

Background: Acute myeloid leukemia (AML) remains the most common type of hematopoietic malignancy in adults and has an unfavorable outcome. Herein, we aimed to construct an N6-methylandenosine (m6A)-related long noncoding RNAs (lncRNAs) signature to accurately predict the prognosis of patients with AML using the data downloaded from The Cancer Genome Atlas (TCGA) database.

Methods: The RNA-seq and clinical data were obtained from the TCGA AML cohort. First, Pearson correlation analysis was performed to identify the m6A-related lncRNAs. Next, univariate Cox regression analysis was used to determine the candidate lncRNAs with prognostic value. Then, feature selection was carried out by Least absolute shrinkage and selection operator (LASSO) analysis, and seven eligible m6A-related lncRNAs were included to construct the prognostic risk signature. Kaplan–Meier and receiver operating characteristic (ROC) curve analyses were performed to evaluate the predictive capacity of the risk signature both in the training and testing datasets. A nomogram was used to predict 1-year, 2-year, and 3-year overall survival (OS) of AML patients. Next, the expression levels of lncRNAs in the signature were validated in AML samples by qRT-PCR. Functional enrichment analyses were carried out to identify probable biological processes and cellular pathways. The ceRNA network was developed to explore the downstream targets and mechanisms of m6A-related lncRNAs in AML.

Results: Seven m6A-related lncRNAs were identified as a prognostic signature. The low-risk group hold significantly prolonged OS. The nomogram showed excellent accuracy of the signature for predicting 1-year, 2-year and 3-year OS (AUC = 0.769, 0.820, and 0.800, respectively). Moreover, the risk scores were significantly correlated with enrichment in cancer hallmark- and malignancy-related pathways and immunotherapy response in AML patients.

Conclusion: We developed and validated a novel risk signature with m6A-related lncRNAs which could predict prognosis accurately and reflect the immunotherapy response in AML patients.