LGALS1 acts as a pro-survival molecule in AML

A novel LGALS1-depended and immune-associated fatty acid metabolism risk model in acute myeloid leukemia stem cells

Leukemia stem cells (LSCs) are recognized as the root cause of leukemia initiation, relapse, and drug resistance. Lipid species are highly abundant and essential component of human cells, which often changed in tumor microenvironment. LSCs remodel lipid metabolism to sustain the stemness. However, there is no useful lipid related biomarker has been approved for clinical practice in AML prediction and treatment. Here, we constructed and verified fatty acid metabolism-related risk score (LFMRS) model based on TCGA database via a series of bioinformatics analysis, univariate COX regression analysis, and multivariate COX regression analysis, and found that the LFMRS model could be an independent risk factor and predict the survival time of AML patients combined with age. Moreover, we revealed that Galectin-1 (LGALS1, the key gene of LFMRS) was highly expressed in LSCs and associated with poor prognosis of AML patients, and LGALS1 repression inhibited AML cell and LSC proliferation, enhanced cell apoptosis, and decreased lipid accumulation in vitro. LGALS1 repression curbed AML progression, lipid accumulation, and CD8+ T and NK cell counts in vivo. Our study sheds light on the roles of LFMRS (especially LGALS1) model in AML, and provides information that may help clinicians improve patient prognosis and develop personalized treatment regimens for AML.

Establishment and validation of a gene mutation-based risk model for predicting prognosis and therapy response in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a malignant clonal proliferative disease of hematopoietic system. Despite tremendous progress in uncovering the AML genome, only a small number of mutations have been incorporated into risk stratification and used as therapeutic targets. In this research, we performed to construct a predictive prognosis risk model for AML patients according to gene mutations.

Methods

Next-generation sequencing (NGS) technology was utilized to detect gene mutation from 118 patients. mRNA expression profiles and related clinical information were mined from TCGA and GEO databases. Consensus cluster analysis was applied to obtain molecular subtypes, and differences in clinicopathological features, prognosis, and immune microenvironment of different clusters were systematically compared. According to the differentially expressed genes (DEGs) between clusters, univariate and LASSO regression analysis were applied to identify gene signatures to build a prognostic risk model. Patients were classified into high-risk (HR) and low-risk (LR) groups according to the median risk score (RS). Differences in prognosis, immune profile, and therapeutic sensitivity between two groups were analyzed. The independent predictive value of RS was assessed and a nomogram was developed.

Results

NGS detected 24 mutated genes, with higher mutation frequencies in CBL (63 %) and SETBP1 (49 %). Two clusters exhibited different immune microenvironments and survival probability (p = 0.0056) were identified. A total of 444 DEGs were screened in two clusters, and a mutation-associated risk model was constructed, including MPO, HGF, SH2B3, SETBP1, HLA-DRB1, LGALS1, and KDM5B. Patients in LR had a superior survival time compared to HR. Predictive performance of this model was confirmed and the developed nomogram further improved the applicability of the risk model with the AUCs for predicting 1-, 3-, 5-year survival rate were 0.829, 0.81 and 0.811, respectively. HR cases were more sensitive to erlotinib, CI-1040, and AZD6244.

Conclusion

These findings supplemented the understanding of gene mutations in AML, and constructed models had good application prospect to provide effective information for predicting prognosis and treatment response of AML.

Identification and validation of a prognostic risk-scoring model for AML based on m7G-associated gene clustering

Background

Acute myeloid leukemia (AML) patients still suffer from poor 5-year survival and relapse after remission. A better prognostic assessment tool is urgently needed. New evidence demonstrates that 7-methylguanosine (m7G) methylation modifications play an important role in AML, however, the exact role of m7G-related genes in the prognosis of AML remains unclear.

Methods

The study obtained AML expression profiles and clinical information from TCGA, GEO, and TARGET databases. Using the patient data from the TCGA cohort as the training set. Consensus clustering was performed based on 29 m7G-related genes. Survival analysis was performed by KM curves. The subgroup characteristic gene sets were screened using WGCNA. And tumor immune infiltration correlation analysis was performed by ssGSEA.

Results

The patients were classified into 3 groups based on m7G-related genebased cluster analysis, and the differential genes were screened by differential analysis and WGCNA. After LASSO regression analysis, 6 characteristic genes (including CBR1, CCDC102A, LGALS1, RD3L, SLC29A2, and TWIST1) were screened, and a prognostic risk-score model was constructed. The survival rate of low-risk patients was significantly higher than that of high-risk patients (p < 0.0001). The area under the curve values at 1, 3, and 5 years in the training set were 0.871, 0.874, and 0.951, respectively, indicating that this predictive model has an excellent predictive effect. In addition, after univariate and multivariate Cox regression screening, histograms were constructed with clinical characteristics and prognostic risk score models to better predict individual survival. Further analysis showed that the prognostic risk score model was associated with immune cell infiltration.

Conclusion

These findings suggest that the scoring model and essential risk genes could provide potential prognostic biomarkers for patients with acute myeloid leukemia.

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

Unraveling the Relevance of Tissue-Specific Decellularized Extracellular Matrix Hydrogels for Vocal Fold Regenerative Biomaterials: A Comprehensive Proteomic and In Vitro Study

Decellularized extracellular matrix (dECM) is a promising material for tissue engineering applications. Tissue-specific dECM is often seen as a favorable material that recapitulates a native-like microenvironment for cellular remodeling. However, the minute quantity of dECM derivable from small organs like the vocal fold (VF) hampers manufacturing scalability. Small intestinal submucosa (SIS), a commercial product with proven regenerative capacity, may be a viable option for VF applications. This study aims to compare dECM hydrogels derived from SIS or VF tissue with respect to protein content and functionality using mass spectrometry-based proteomics and in vitro studies. Proteomic analysis reveals that VF and SIS dECM share 75% of core matrisome proteins. Although VF dECM proteins have greater overlap with native VF, SIS dECM shows less cross-sample variability. Following decellularization, significant reductions of soluble collagen (61%), elastin (81%), and hyaluronan (44%) are noted in VF dECM. SIS dECM contains comparable elastin and hyaluronan but 67% greater soluble collagen than VF dECM. Cells deposit more neo-collagen on SIS than VF-dECM hydrogels, whereas neo-elastin (~50 μg/scaffold) and neo-hyaluronan (~ 6 μg/scaffold) are comparable between the two hydrogels. Overall, SIS dECM possesses reasonably similar proteomic profile and regenerative capacity to VF dECM. SIS dECM is considered a promising alternative for dECM-derived biomaterials for VF regeneration.

The signature based on seven genomic instability-related genes could predict the prognosis of acute myeloid leukemia patients

BACKGROUND

Acute myeloid leukemia (AML) is the most common acute blood malignancy in adults. The complicated and dynamic genomic instability (GI) is the most prominent feature of AML. Our study aimed to explore the prognostic value of GI-related genes in AML patients.

METHODS

The mRNA data and mutation data were downloaded from the TCGA and GEO databases. Differential expression analyses were completed in limma package. GO and KEGG functional enrichment was conducted using clusterProfiler function of R. Univariate Cox and LASSO Cox regression analyses were performed to screen key genes for Risk score model construction. Nomogram was built with rms package.

RESULTS

We identified 114 DEGs between high TMB patients and low TMB AML patients, which were significantly enriched in 429 GO terms and 13 KEGG pathways. Based on the univariate Cox and LASSO Cox regression analyses, seven optimal genes were finally applied for Risk score model construction, including SELE, LGALS1, ITGAX, TMEM200A, SLC25A21, S100A4 and CRIP1. The Risk score could reliably predict the prognosis of AML patients. Age and Risk score were both independent prognostic indicators for AML, and the Nomogram based on them could also reliably predict the OS of AML patients.

CONCLUSIONS

A prognostic signature based on seven GI-related genes and a predictive Nomogram for AML patients are finally successfully constructed.

Galectin-1 and Galectin-3 in B-Cell Precursor Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemias arising from the malignant transformation of B-cell precursors (BCP-ALLs) are protected against chemotherapy by both intrinsic factors as well as by interactions with bone marrow stromal cells. Galectin-1 and Galectin-3 are lectins with overlapping specificity for binding polyLacNAc glycans. Both are expressed by bone marrow stromal cells and by hematopoietic cells but show different patterns of expression, with Galectin-3 dynamically regulated by extrinsic factors such as chemotherapy. In a comparison of Galectin-1 x Galectin-3 double null mutant to wild-type murine BCP-ALL cells, we found reduced migration, inhibition of proliferation, and increased sensitivity to drug treatment in the double knockout cells. Plant-derived carbohydrates GM-CT-01 and GR-MD-02 were used to inhibit extracellular Galectin-1/-3 binding to BCP-ALL cells in co-culture with stromal cells. Treatment with these compounds attenuated migration of the BCP-ALL cells to stromal cells and sensitized human BCP-ALL cells to vincristine and the targeted tyrosine kinase inhibitor nilotinib. Because N-glycan sialylation catalyzed by the enzyme ST6Gal1 can regulate Galectin cell-surface binding, we also compared the ability of BCP-ALL wild-type and ST6Gal1 knockdown cells to resist vincristine treatment when they were co-cultured with Galectin-1 or Galectin-3 knockout stromal cells. Consistent with previous results, stromal Galectin-3 was important for maintaining BCP-ALL fitness during chemotherapy exposure. In contrast, stromal Galectin-1 did not significantly contribute to drug resistance, and there was no clear effect of ST6Gal1-catalysed N-glycan sialylation. Taken together, our results indicate a complicated joint contribution of Galectin-1 and Galectin-3 to BCP-ALL survival, with different roles for endogenous and stromal produced Galectins. These data indicate it will be important to efficiently block both extracellular and intracellular Galectin-1 and Galectin-3 with the goal of reducing BCP-ALL persistence in the protective bone marrow niche during chemotherapy.

Subclones of bone marrow CD34+ cells in acute myeloid leukemia at diagnosis confer responses of patients to induction chemotherapy

BACKGROUND

Acute myeloid leukemia (AML) is a hematopoietic malignancy with a prognosis that varies with genetic heterogeneity of hematopoietic stem/progenitor cells (HSPCs). Induction chemotherapy with cytarabine and anthracycline has been the standard care for newly diagnosed AML, but about 30% of patients have no response to this regimen. The resistance mechanisms require deeper understanding.

METHODS

In our study, using single-cell RNA sequencing, we analyzed the heterogeneity of bone marrow CD34⁺ cells from newly diagnosed patients with AML who were then divided into sensitive and resistant groups according to their responses to induction chemotherapy with cytarabine and anthracycline. We verified our findings by TCGA database, GEO datasets, and multiparameter flow cytometry.

RESULTS

We established a landscape for AML CD34⁺ cells and identified HSPC types based on the lineage signature genes. Interestingly, we found a cell population with CRIP1^high LGALS1^high S100As^high showing features of granulocyte-monocyte progenitors was associated with poor prognosis of AML. And two cell populations marked by CD34⁺ CD52⁺ or CD34⁺ CD74⁺ DAP12⁺ were related to good response to induction therapy, showing characteristics of hematopoietic stem cells.

CONCLUSION

Our study indicates the subclones of CD34⁺ cells confers for outcomes of AML and provides biomarkers to predict the response of patients with AML to induction chemotherapy.

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.

miR-143-3p represses leukemia cell proliferation by inhibiting KAT6A expression

The present study is designed to investigate the expressions of microRNA-143-3p (miR-143-3p) and Lysine acetyltransferase 6A (KAT6A) in acute myeloid leukemia (AML) samples and AML cell lines and to explore the possible effects and underlying mechanisms of miR-143-3p on the proliferation of AML cells. The expressions of miR-143-3p and KAT6A in AML samples and cell lines were detected by RT-qPCR assay. CCK-8 and flow cytometry were performed to evaluate the role of KAT6A in viability of AML cells. EdU assay was performed to determine the effects of KAT6A on proliferation of AML cells. Western blot analysis was utilized to assess the impacts of KAT6A on proliferation-related protein expressions of AML cells. ELISA assay was adopted to illustrate the influence of KAT6A on inflammatory responses of AML cells. In addition, the relationship between KAT6A and miR-143-3p was predicted by ENCORI and miRWalk, and confirmed by dual-luciferase reporter assay. Moreover, the effects of KAT6A on the proliferation of AML cells mediated with miR-143-3p were carried out by rescue experiment. The expression of KAT6A was significantly upregulated, while miR-134-4p was downregulated both in the AML tissues and in AML cell lines. In addition, the silence of KAT6A significantly inhibited the viability of AML cells. Besides, KAT6A silencing notably suppressed the proliferation of AML cells and reduced the protein expressions of Ki-67 and PCNA. Knockdown of KAT6A notably decreased the expression levels of IL-1β, TNF-α and IL-6, and increased the expression levels of TGF-β and IL-10. Moreover, overexpression of miR-143-3p repressed viability and proliferation of AML cells and overexpression of KAT6A partially reversed the inhibitory effects of miR-143-3p mimic on viability and proliferation of AML cells. miR-143-3p/KAT6A played an essential role in the viability and proliferation of AML cells.

Selection of a reference gene for studies on lipid-related aquatic adaptations of toothed whales (Grampus griseus)

Toothed whales are one group of marine mammals that has developed special adaptations, such as echolocation for predation, to successfully live in a dynamic aquatic environment. Their fat metabolism may differ from that of other mammals because toothed whales have acoustic fats. Gene expression in the metabolic pathways of animals can change with respect to their evolution and environment. A real-time quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for studying the relative expressions of genes. However, since the accuracy of RT-qPCR data is totally dependent on the reference gene, the selection of the reference gene is an essential step. In this study, 10 candidate reference genes (ZC3H10, FTL, LGALS1, RPL27, GAPDH, FTH1, DCN, TCTP, NDUS5, and UBIM) were initially tested for amplification efficiency using RT-qPCR. After excluding DCN, the remaining nine genes, which are nearly 100% efficient, were selected for the gene stability analysis. Stable reference genes across eight different fat tissue, liver, and muscle samples from Grampus griseus were identified by four algorithms, which were provided in Genorm, NormFinder, BestKeeper, and Delta CT. Finally, a RefFinder comprehensive ranking was performed based on the stability values, and the nine genes were ranked as follows: LGALS1 > FTL > GAPDH > ZC3H10 > FTH1 > NDUS5 > TCTP > RPL27 > UBIM. The LGALS1 and FTL genes were identified as the most stable novel reference genes. The third-ranked gene, GAPDH, is a well-known housekeeping gene for mammals. Ultimately, we suggest the use of LGALS1 as a reliable novel reference gene for genomics studies on the lipid-related aquatic adaptations of toothed whales.

Targeted gene expression profiling of inverted papilloma and squamous cell carcinoma

BACKGROUND

Inverted papilloma (IP) is a sinonasal tumor with a well-known potential for malignant transformation. The purpose of this study was to identify the genes and pathways associated with IP, with progression to carcinoma-in-situ and invasive carcinoma.

METHODS

To determine genes and molecular pathways that may indicate progression and correlate with histologic changes, we analyzed six IP without dysplasia, five IP with carcinoma-in-situ, and 13 squamous cell carcinoma ex-IP by targeted sequencing. The HTG EdgeSeq Oncology Biomarker Panel coupled with next-generation sequencing was used to evaluate 2560 transcripts associated with solid tumors.

RESULTS

Progressive upregulation of 11 genes were observed (CALD1, COL1A1, COL3A1, COL4A2, COL5A2, FN1, ITGA5, LGALS1, MMP11, SERPINH1, SPARC) in the order of invasive carcinoma > carcinoma-in-situ > IP without dysplasia. When compared with IP without dysplasia, more genes are differentially expressed in invasive carcinoma than carcinoma-in-situ samples (341 downregulated/333 upregulated vs. 195 downregulated/156 upregulated). Gene set enrichment analysis determined three gene sets in common between the cohorts (epithelial mesenchymal transition, extracellular matrix organization, and coagulation).

CONCLUSIONS

Progressive upregulation of genes specific to IP malignant degeneration has significant clinical implications. This panel of 11 genes will improve concordance of histologic classification, which can directly impact treatment and patient outcomes. Additionally, future studies on larger tumor sets may observe upregulation in the gene panel that preceded histologic changes, which may be useful for further risk stratification.