Identification and validation of inferior prognostic genes associated with immune signatures and chemotherapy outcome in acute myeloid leukemia

Serum lncRNA ITGB2-AS1 and ICAM-1 as novel biomarkers for rheumatoid arthritis and osteoarthritis diagnosis

BACKGROUND

The complete circulating long non-coding RNAs (lncRNAs) signature of rheumatoid arthritis (RA) and osteoarthritis (OA) is still uncovered. The lncRNA integrin subunit beta 2 (ITGB2)-anti-sense RNA 1 (ITGB2-AS1) affects ITGB2 expression; however, there is a gap in knowledge regarding its expression and clinical usefulness in RA and OA. This study investigated the potential of serum ITGB2-AS1 as a novel diagnostic biomarker and its correlation with ITGB2 expression and its ligand intercellular adhesion molecule-1 (ICAM-1), disease activity, and severity in RA and primary knee OA patients.

SUBJECTS

Forty-three RA patients, 35 knee OA patients, and 22 healthy volunteers were included.

RESULTS

Compared with healthy controls, serum ITGB2-AS1 expression was upregulated in RA patients but wasn't significantly altered in knee OA patients, whereas serum ICAM-1 protein levels were elevated in both diseases. ITGB2-AS1 showed discriminative potential for RA versus controls (AUC = 0.772), while ICAM-1 displayed diagnostic potential for both RA and knee OA versus controls (AUC = 0.804, 0.914, respectively) in receiver-operating characteristic analysis. In the multivariate analysis, serum ITGB2-AS1 and ICAM-1 were associated with the risk of developing RA, while only ICAM-1 was associated with the risk of developing knee OA. A panel combining ITGB2-AS1 and ICAM-1 showed profound diagnostic power for RA (AUC = 0.9, sensitivity = 86.05%, and specificity = 91.67%). Interestingly, serum ITGB2-AS1 positively correlated with disease activity (DAS28) in RA patients and with ITGB2 mRNA expression in both diseases, while ICAM-1 positively correlated with ITGB2 expression in knee OA patients.

CONCLUSION

Our study portrays serum ITGB2-AS1 as a novel potential diagnostic biomarker of RA that correlates with disease activity. A predictive panel combining ITGB2-AS1 and ICAM-1 could have clinical utility in RA diagnosis. We also spotlight the association of ICAM-1 with knee OA diagnosis. The correlation of serum ITGB2-AS1 with ITGB2 expression in both diseases may be insightful for further mechanistic studies.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers

Purpose

The objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis.

Methods

AML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs.

Results

816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups.

Conclusion

LncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer

Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-III respectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70 months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.

Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) represents around 25% of adult acute leukemias. Despite the increasing improvement in the survival rate of ALL patients during the last decade, the heterogeneous clinical and molecular features of this malignancy still represent a major challenge for treatment and achieving better outcomes. To identify aberrantly expressed genes in bone marrow (BM) samples from adults with ALL, transcriptomic analysis was performed using Affymetrix Human Transcriptome Array 2.0 (HTA 2.0). Differentially expressed genes (DEGs) (±2-fold change, p-value < 0.05, and FDR < 0.05) were detected using the Transcriptome Analysis Console. Gene Ontology (GO), Database for Annotation, Visualization, and Integrated Discovery (DAVID), and Ingenuity Pathway Analysis (IPA) were employed to identify gene function and define the enriched pathways of DEGs. The protein-protein interactions (PPIs) of DEGs were constructed. A total of 871 genes were differentially expressed, and DNTT, MYB, EBF1, SOX4, and ERG were the top five up-regulated genes. Meanwhile, the top five down-regulated genes were PTGS2, PPBP, ADGRE3, LUCAT1, and VCAN. An association between ERG, CDK6, and SOX4 expression levels and the probability of relapse and death was observed. Regulation of the immune system, immune response, cellular response to stimulus, as well as apoptosis signaling, inflammation mediated by chemokines and cytokines, and T cell activation were among the most altered biological processes and pathways, respectively. Transcriptome analysis of ALL in adults reveals a group of genes consistently associated with hematological malignancies and underscores their relevance in the development of ALL in adults.

Targeting integrins in drug-resistant acute myeloid leukaemia

Acute myeloid leukaemia (AML) continues to have a poor prognosis, warranting new therapeutic strategies. The bone marrow (BM) microenvironment consists of niches that interact with not only normal haematopoietic stem cells (HSC) but also leukaemia cells like AML. There are many adhesion molecules in the BM microenvironment; therein, integrins have been of central interest. AML cells express integrins that bind to ligands in the microenvironment, enabling adhesion of leukaemia cells in the microenvironment, thereby initiating intracellular signalling pathways that are associated with cell migration, cell proliferation, survival, and drug resistance that has been described to mediate cell adhesion-mediated drug resistance (CAM-DR). Identifying and targeting integrins in AML to interrupt interactions with the microenvironment have been pursued as a strategy to overcome CAM-DR. Here, we focus on the BM microenvironment and review the role of integrins in CAM-DR of AML and discuss integrin-targeting strategies. LINKED ARTICLES: This article is part of a themed issue on Cancer Microenvironment and Pharmacological Interventions. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.2/issuetoc.

Constructing discriminative feature space for LncRNA-protein interaction based on deep autoencoder and marginal fisher analysis

Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space; (2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein interactions. To resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. The code and data are available on https://github.com/D0ub1e-D/DFRPI.

Surveying lncRNA-lncRNA cooperations reveals dominant effect on tumor immunity cross cancers

Long non-coding RNAs (lncRNAs) can crosstalk with each other by post-transcriptionally co-regulating genes involved in the same or similar functions; however, the regulatory principles and biological insights in tumor-immune are still unclear. Here, we show a multiple-step model to identify lncRNA-lncRNA immune cooperation based on co-regulating functional modules by integrating multi-omics data across 20 cancer types. Moreover, lncRNA immune cooperative networks (LICNs) are constructed, which are likely to modulate tumor-immune microenvironment by regulating immune-related functions. We highlight conserved and rewired network hubs which can regulate interactions between immune cells and tumor cells by targeting ligands and activating or inhibitory receptors such as PDCD1, CTLA4 and CD86. Immune cooperative lncRNAs (IC-lncRNAs) playing central roles in many cancers also tend to target known anticancer drug targets. In addition, these IC-lncRNAs tend to be highly expressed in immune cell populations and are significantly correlated with immune cell infiltration. The similar immune mechanisms cross cancers are revealed by the LICNs. Finally, we identify two subtypes of skin cutaneous melanoma with different immune context and prognosis based on IC-lncRNAs. In summary, this study contributes to a comprehensive understanding of the cooperative behaviours of lncRNAs and accelerating discovery of lncRNA-based biomarkers in cancer.

Identifying patterns of immune related cells and genes in the peripheral blood of acute myocardial infarction patients using a small cohort

Background

The immune system plays a vital role in the pathophysiology of acute myocardial infarction (AMI). However, the exact immune related mechanism is still unclear. This research study aimed to identify key immune-related genes involved in AMI.

Methods

CIBERSORT, a deconvolution algorithm, was used to determine the proportions of 22 subsets of immune cells in blood samples. The weighted gene co-expression network analysis (WGCNA) was used to identify key modules that are significantly associated with AMI. Then, CIBERSORT combined with WGCNA were used to identify key immune-modules. The protein–protein interaction (PPI) network was constructed and Molecular Complex Detection (MCODE) combined with cytoHubba plugins were used to identify key immune-related genes that may play an important role in the occurrence and progression of AMI.

Results

The CIBERSORT results suggested that there was a decrease in the infiltration of CD8 + T cells, gamma delta (γδ) T cells, and resting mast cells, along with an increase in the infiltration of neutrophils and M0 macrophages in AMI patients. Then, two modules (midnightblue and lightyellow) that were significantly correlated with AMI were identified, and the salmon module was found to be significantly associated with memory B cells. Gene enrichment analysis indicated that the 1,171 genes included in the salmon module are mainly involved in immune-related biological processes. MCODE analysis was used to identify four different MCODE complexes in the salmon module, while four hub genes (EEF1B2, RAC2, SPI1, and ITGAM) were found to be significantly correlated with AMI. The correlation analysis between the key genes and infiltrating immune cells showed that SPI1 and ITGAM were positively associated with neutrophils and M0 macrophages, while they were negatively associated with CD8 + T cells, γδ T cells, regulatory T cells (Tregs), and resting mast cells. The RT-qPCR validation results found that the expression of the ITGAM and SPI1 genes were significantly elevated in the AMI samples compared with the samples from healthy individuals, and the ROC curve analysis showed that ITGAM and SPI1 had a high diagnostic efficiency for the recognition of AMI.

Conclusions

Immune cell infiltration plays a crucial role in the occurrence and development of AMI. ITGAM and SPI1 are key immune-related genes that are potential novel targets for the prevention and treatment of AMI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03517-1.

Role of CDH23 as a prognostic biomarker and its relationship with immune infiltration in acute myeloid leukemia

Background

Cadherin-23 (CDH23) plays an important role in intercellular adhesion and is involved in the progression of several types of cancer. However, the biological functions and effect of CDH23 expression on the prognosis of patients with acute myeloid leukemia (AML) are unexplored. Herein, we aim to characterize the role and molecular functions of CDH23 in AML.

Methods

We downloaded the transcriptomic profiles and clinical data from the Cancer Genome Atlas and Beat AML trial. The expression level of CDH23 was assessed using Gene Expression Profiling Interactive Analysis (GEPIA). Kaplan-Meier survival analysis was used to assess prognostic value of CDH23. Correlation and biological function analyses were performed using LinkedOmics and GeneMANIA. Relationship of CDH23 with immune infiltration level was determined using Tumor Immune Estimation Resource (TIMER).

Results

We found that the CDH23 expression was aberrantly upregulated in patients with AML and could be used as an independent risk factor of overall survival using Cox multivariate analysis. Notably, we observed a negative correlation between CDH23 expression and immune cell infiltration abundance by calculating the immune and stromal scores. In addition, functional enrichment analysis established that CDH23 plays a crucial role in tumor immunity.

Conclusions

Our findings indicate that upregulated CDH23 expression corresponds to decreased overall survival of patients with AML. CDH23 may be involved in mediating tumor immune environment, and this highlights the potential of CDH23 as a therapeutic target in AML.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09532-1.

Systematic analysis of prognostic significance, functional enrichment and immune implication of STK10 in acute myeloid leukemia

Background

Despite deeper understanding of the genetic landscape of acute myeloid leukemia (AML), the improvement of survival is still a great challenge. STK10 is overexpressed in several cancers with functions varying according to cancer types. But the functions of STK10 in AML has never been reported.

Methods

We analyzed the expression, prognosis and potential functions of STK10 utilizing public web servers. Metascape and the String database were used for functional and protein–protein interaction analyses.

Results

We found STK10 was enriched in blood & immune cells and overexpressed in AML. High STK10 expression was associated with poor overall survival, which was also identified in the subgroups of patients ≤ 60 years old and patients with non-high-risk cytogenetics. We demonstrated genes associated with STK10 were enriched in blood, spleen and bone marrow, influencing the immune function and biological process of AML. ITGB2 and ITGAM might directly interact with STK10 and were associated with poor prognosis. Besides, STK10 was associated with the infiltration of immune cells and immune checkpoints, like HLA-E, CD274 and GAL-9.

Conclusions

The present study was the original description of STK10 in AML and set the stage for developing STK10 as a new prognostic marker or therapeutic target for AML.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01251-7.

HCK is a Potential Prognostic Biomarker that Correlates with Immune Cell Infiltration in Acute Myeloid Leukemia

Background. The tumor microenvironment (TME) plays a significant role in the progression and prognosis of acute myeloid leukemia (AML). This study is aimed at exploring TME-associated biomarkers and identify their potential mechanism in the microenvironment of AML. Method. In this study, the stromal, immune, and ESTIMATE scores of AML patients were evaluated with the ESTIMATE and CIBERSORT algorithms; then, the AML samples were divided into high- and low-score groups. We evaluated the association between clinicopathological characteristics, survival rate, and the stromal/immune/ESTIMATE scores. Furthermore, we identified TME-associated differentially expressed genes (DEGs) then carried out pathway enrichment analysis, protein-protein interaction (PPI) network, Cox regression analysis, and Kaplan-Meier survival analysis to select the most crucial genes. In addition, we further explored the potential mechanism of HCK in the AML microenvironment. Results. We identified 624 TME-associated DEGs and found that HCK was the most promising biomarker associated with AML. The results of the gene set enrichment analysis (GSEA) indicated that HCK was mainly involved in immune and inflammation-related signaling pathways. In addition, CIBERSORT analysis showed that HCK was closely related to tumor immune infiltration, with HCK expression associated with various infiltrating immune cells, including B cells, T cells, tumor-associated macrophages (TAM), NK cells, plasma cells, eosinophils, and neutrophils. Furthermore, HCK expression was closely related with ELN risk stratification in patients with AML. Conclusion. HCK could regulate immune cell infiltration in the microenvironment of AML and may act as a potential biomarker for the treatment and prognosis of AML patients.