Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning.

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

5-methylcytosine RNA modification regulators-based patterns and features of immune microenvironment in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a highly heterogeneous malignant disease of the blood cell. The current therapies for AML are unsatisfactory and the molecular mechanisms underlying AML are unclear. 5-methylcytosine (m5C) is an important posttranscriptional modification of mRNA, and is involved in the regulation of mRNA stability, translation, and other aspects of RNA metabolism. However, based on our knowledge of published literature, the role of the m5C regulators has not been explored in AML till date. In this study, we clarified the expression and gene variants of m5C regulators in AML and found that most m5C regulators were differentially expressed and correlated with disease prognosis. We also found that the methylation status of certain m5C regulators (e.g., DNMT3A, DNMT3B) affects the survival of AML patients. Two m5C modification subtypes, and high- and low-risk subgroups identified based on the expression of m5C regulators showed significant differences in the prognosis as well as immune cell infiltration. In addition, most of the m5C regulators were found to be correlated with miRNA expression in AML, as well as IC50 values of many drugs. The miRNA and GSVA analysis were used to identify the different miRNAs and KEGG or hallmark pathways between high- and low-risk subgroups. We also built a prognostic model based on m5C regulators, which was validated by two GSE databases. To verify the reliability of our analysis and conclusions, qPCR was used to identify the expressions of m5C regulators between normal and AML. In summary, we comprehensively explored the molecular characteristics of m5C regulators and built a prognostic model in AML. We proposed new mechanistic insights into the role of m5C in multiple databases and clinical data, which may pave novel ways for the development of therapeutic strategies.

Systems genetics and bioinformatics analyses using ESR1-correlated genes identify potential candidates underlying female bone development.

Estrogen receptor α (ESR1) is involved in E2 signaling and plays a major role in postmenopausal bone loss. However, the molecular network underlying ESR1 has not been explored. We used systems genetics and bioinformatics to identify important genes associated with Esr1 in postmenopausal bone loss. We identified ~2300 Esr1-coexpressed genes in female BXD bone femur, functional analysis of which revealed 'osteoblast signaling' as the most enriched pathway. PPI network led to the identification of 25 'female bone candidates'. The gene-regulatory analysis revealed RUNX2 as a key TF. ANKRD1 and RUNX2 were significantly different between osteoporosis patients and healthy controls. Sp7, Col1a1 and Pth1r correlated with multiple femur bone phenotypes in BXD mice. miR-3121-3p targeted Csf1, Ankrd1, Sp7 and Runx2. ß-estradiol treatment markedly increased the expression of these candidates in mouse osteoblast. Our study revealed that Esr1-correlated genes Ankrd1, Runx2, Csf1 and Sp7 may play important roles in female bone development.

Comparative Root Transcriptome Profiling and Gene Regulatory Network Analysis between Eastern and Western Carrot (Daucus carota L.) Cultivars Reveals Candidate Genes for Vascular Tissue Patterning.

Carrot (Daucus carota L.) is a highly consumed vegetable rich in carotenoids, known for their potent antioxidant, anti-inflammatory, and immune-protecting properties. While genetic and molecular studies have largely focused on wild and Western carrot cultivars (cvs), little is known about the evolutionary interactions between closely related Eastern and Western cvs. In this study, we conducted comparative transcriptome profiling of root tissues from Eastern (UHSBC-23-1) and Western (UHSBC-100) carrot cv. to better understand differentially expressed genes (DEGs) associated with storage root development and vascular cambium (VC) tissue patterning. Through reference-guided TopHat mapping, we achieved an average mapping rate of 73.87% and identified a total of 3544 DEGs (p < 0.05). Functional annotation and gene ontology classification revealed 97 functional categories, including 33 biological processes, 19 cellular components, 45 metabolic processes, and 26 KEGG pathways. Notably, Eastern cv. exhibited enrichment in cell wall, plant-pathogen interaction, and signal transduction terms, while Western cv. showed dominance in photosynthesis, metabolic process, and carbon metabolism terms. Moreover, constructed gene regulatory network (GRN) for both cvs. obtained orthologs with 1222 VC-responsive genes of Arabidopsis thaliana. In Western cv, GRN revealed VC-responsive gene clusters primarily associated with photosynthetic processes and carbon metabolism. In contrast, Eastern cv. exhibited a higher number of stress-responsive genes, and transcription factors (e.g., MYB15, WRKY46, AP2/ERF TF connected via signaling pathways with NAC036) were identified as master regulators of xylem vessel differentiation and secondary cell wall thickening. By elucidating the comparative transcriptome profiles of Eastern and Western cvs. for the first time, our study provides valuable insights into the differentially expressed genes involved in root development and VC tissue patterning. The identification of key regulatory genes and their roles in these processes represents a significant advancement in our understanding of the evolutionary relations and molecular mechanisms underlying secondary growth of carrot and regulation by vascular cambium.

A new gene-scoring method for uncovering novel glaucoma-related genes using non-negative matrix factorization based on RNA-seq data.

Early diagnosis and treatment of glaucoma are challenging. The discovery of glaucoma biomarkers based on gene expression data could potentially provide new insights for early diagnosis, monitoring, and treatment options of glaucoma. Non-negative Matrix Factorization (NMF) has been widely used in numerous transcriptome data analyses in order to identify subtypes and biomarkers of different diseases; however, its application in glaucoma biomarker discovery has not been previously reported. Our study applied NMF to extract latent representations of RNA-seq data from BXD mouse strains and sorted the genes based on a novel gene scoring method. The enrichment ratio of the glaucoma-reference genes, extracted from multiple relevant resources, was compared using both the classical differentially expressed gene (DEG) analysis and NMF methods. The complete pipeline was validated using an independent RNA-seq dataset. Findings showed our NMF method significantly improved the enrichment detection of glaucoma genes. The application of NMF with the scoring method showed great promise in the identification of marker genes for glaucoma.

Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis.

A number of processes and pathways have been reported in the development of Group I pulmonary hypertension (Group I PAH); however, novel biomarkers need to be identified for a better diagnosis and management. We employed a robust rank aggregation (RRA) algorithm to shortlist the key differentially expressed genes (DEGs) between Group I PAH patients and controls. An optimal diagnostic model was obtained by comparing seven machine learning algorithms and was verified in an independent dataset. The functional roles of key DEGs and biomarkers were analyzed using various in silico methods. Finally, the biomarkers and a set of key candidates were experimentally validated using patient samples and a cell line model. A total of 48 key DEGs with preferable diagnostic value were identified. A gradient boosting decision tree algorithm was utilized to build a diagnostic model with three biomarkers, PBRM1, CA1, and TXLNG. An immune-cell infiltration analysis revealed significant differences in the relative abundances of seven immune cells between controls and PAH patients and a correlation with the biomarkers. Experimental validation confirmed the upregulation of the three biomarkers in Group I PAH patients. In conclusion, machine learning and a bioinformatics analysis along with experimental techniques identified PBRM1, CA1, and TXLNG as potential biomarkers for Group I PAH.

Weighted gene co-expression network analysis identifies key hub genes and pathways in acute myeloid leukemia.

Introduction: Acute myeloid leukemia (AML) is the most common type of leukemia in adults. However, there is a gap in understanding the molecular basis of the disease, partly because key genes associated with AML have not been extensively explored. In the current study, we aimed to identify genes that have strong association with AML based on a cross-species integrative approach. Methods: We used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules significantly correlated with human AML, and further selected the genes exhibiting a significant difference in expression between AML and healthy mouse. Protein-protein interactions, transcription factors, gene function, genetic regulation, and coding sequence variants were integrated to identify key hub genes in AML. Results: The cross-species approach identified a total of 412 genes associated with both human and mouse AML. Enrichment analysis confirmed an association of these genes with hematopoietic and immune-related functions, phenotypes, processes, and pathways. Further, the integrated analysis approach identified a set of important module genes including Nfe2, Trim27, Mef2c, Ets1, Tal1, Foxo1, and Gata1 in AML. Six of these genes (except ETS1) showed significant differential expression between human AML and healthy samples in an independent microarray dataset. All of these genes are known to be involved in immune/hematopoietic functions, and in transcriptional regulation. In addition, Nfe2, Trim27, Mef2c, and Ets1 harbor coding sequence variants, whereas Nfe2 and Trim27 are cis-regulated, making them attractive candidates for validation. Furthermore, subtype-specific analysis of the hub genes in human AML indicated high expression of NFE2 across all the subtypes (M0 through M7) and enriched expression of ETS1, LEF1, GATA1, and TAL1 in M6 and M7 subtypes. A significant correlation between methylation status and expression level was observed for most of these genes in AML patients. Conclusion: Findings from the current study highlight the importance of our cross-species approach in the identification of multiple key candidate genes in AML, which can be further studied to explore their detailed role in leukemia/AML.

EWI2 and its relatives in Tetraspanin-enriched membrane domains regulate malignancy.

Experimental studies on immunoglobulin superfamily (IgSF) member EWI2 reveal that it suppresses a variety of solid malignant tumors including brain, lung, skin, and prostate cancers in animal models and inhibits tumor cell movement and growth in vitro. While EWI2 appears to support myeloid leukemia in mouse models and maintain leukemia stem cells. Bioinformatics analyses suggest that EWI2 gene expression is downregulated in glioblastoma but upregulated in melanoma, pancreatic cancer, and liver cancer. The mechanism of action for EWI2 is linked to its inhibition of growth factor receptors and cell adhesion proteins through its associated tetraspanin-enriched membrane domains (TEMDs), by altering the cell surface clustering and endolysosome trafficking/turnover of these transmembrane proteins. Recent studies also show that EWI2 modulates the nuclear translocation of ERK and TFEB to change the activities of these gene expression regulators. For EWI2 relatives including FPRP, IgSF3, and CD101, although their roles in malignant diseases are not fully clear and remain to be determined experimentally, FPRP and IgSF3 likely promote the progression of solid malignant tumors while CD101 seems to modulate immune cells of tumor microenvironment. Distinctive from other tumor regulators, the impacts of EWI subfamily members on solid malignant tumors are likely to be context dependent. In other words, the effect of a given EWI subfamily member on a tumor probably depends on the molecular network and composition of TEMDs in that tumor. Collectively, EWI2 and its relatives are emerged as important regulators of malignant diseases with promising potentials to become anti-cancer therapeutics and cancer therapy targets.

Exploring the Regulation and Function of Rpl3l in the Development of Early-Onset Dilated Cardiomyopathy and Congestive Heart Failure Using Systems Genetics Approach.

BACKGROUND: Cardiomyopathies, diseases affecting the myocardium, are common causes of congestive heart failure (CHF) and sudden cardiac death. Recently, biallelic variants in ribosomal protein L3-like (RPL3L) have been reported to be associated with severe neonatal dilated cardiomyopathy (DCM) and CHF. This study employs a systems genetics approach to gain understanding of the regulatory mechanisms underlying the role of RPL3L in DCM. METHODS: Genetic correlation, expression quantitative trait loci (eQTL) mapping, differential expression analysis and comparative functional analysis were performed using cardiac gene expression data from the patients and murine genetic reference populations (GRPs) of BXD mice (recombinant inbred strains from a cross of C57BL/6J and DBA/2J mice). Additionally, immune infiltration analysis was performed to understand the relationship between DCM, immune cells and RPL3L expression. RESULTS: Systems genetics analysis identified high expression of Rpl3l mRNA, which ranged from 11.31 to 12.16 across murine GRPs of BXD mice, with an ~1.8-fold difference. Pathways such as "diabetic cardiomyopathy", "focal adhesion", "oxidative phosphorylation" and "DCM" were significantly associated with Rpl3l. eQTL mapping suggested Myl4 (Chr 11) and Sdha (Chr 13) as the upstream regulators of Rpl3l. The mRNA expression of Rpl3l, Myl4 and Sdha was significantly correlated with multiple echocardiography traits in BXD mice. Immune infiltration analysis revealed a significant association of RPL3L and SDHA with seven immune cells (CD4, CD8-naive T cell, CD8 T cell, macrophages, cytotoxic T cell, gamma delta T cell and exhausted T cell) that were also differentially infiltrated between heart samples obtained from DCM patients and normal individuals. CONCLUSIONS: RPL3L is highly expressed in the heart tissue of humans and mice. Expression of Rpl3l and its upstream regulators, Myl4 and Sdha, correlate with multiple cardiac function traits in murine GRPs of BXD mice, while RPL3L and SDHA correlate with immune cell infiltration in DCM patient hearts, suggesting important roles for RPL3L in DCM and CHF pathogenesis via immune inflammation, necessitating experimental validations of Myl4 and Sdha in Rpl3l regulation.

Endometrial receptivity: a revisit to functional genomics studies on human endometrium and creation of HGEx-ERdb.

BACKGROUND: Endometrium acquires structural and functional competence for embryo implantation only during the receptive phase of menstrual cycle in fertile women. Sizeable data are available to indicate that this ability is acquired by modulation in the expression of several genes/gene products. However, there exists little consensus on the identity, number of expressed/not-detected genes and their pattern of expression (up or down regulation). METHODS: Literature search was carried out to retrieve the data on endometrial expression of genes/proteins in various conditions. Data were compiled to generate a comprehensive database, Human Gene Expression Endometrial Receptivity database (HGEx-ERdb). The database was used to identify the Receptivity Associated Genes (RAGs) which display the similar pattern of expression across different investigations. Transcript levels of select RAGs encoding cell adhesion proteins were compared between two human endometrial epithelial cell lines; RL95-2 and HEC-1-A by quantitative real time polymerase chain reaction (q-RT-PCR). Further select RAGs were investigated for their expression in pre-receptive (n = 4) and receptive phase (n = 4) human endometrial tissues by immunohistochemical studies. JAr spheroid attachment assays were carried out to assess the functional significance of two RAGs. RESULTS: HGEx-ERdb (http://resource.ibab.ac.in/HGEx-ERdb/) helped identification of 179 RAGs, of which 151 genes were consistently expressed and upregulated and 28 consistently not-detected and downregulated in receptive phase as compared to pre-receptive phase. q-RT-PCR confirmed significantly higher (p<0.005) expression of Thrombospondin1 (THBS1), CD36 and Mucin 16 transcripts, in RL95-2 as compared to HEC-1-A. Further, the pretreatment with antibodies against CD36 and COMP led to a reduction in the percentage of JAr spheroids attached to RL95-2. Immunohistochemical studies demonstrated significantly higher (p<0.05) expression of endometrial THBS1, Cartilage Oligomeric Matrix Protein (COMP) and CD36 in the receptive phase as compared to pre-receptive phase human endometrial tissues. CONCLUSION: HGEx-ERdb is a catalogue of 19,285 genes, reported for their expression in human endometrium. Further 179 genes were identified as the RAGs. Expression analysis of some RAGs validated the utility of approach employed in creation of HGEx-ERdb. Studies aimed towards defining the specific functions of RAGs and their potential networks may yield relevant information about the major 'nodes' which regulate endometrial receptivity.

MGEx-Udb: a mammalian uterus database for expression-based cataloguing of genes across conditions, including endometriosis and cervical cancer.

BACKGROUND: Gene expression profiling of uterus tissue has been performed in various contexts, but a significant amount of the data remains underutilized as it is not covered by the existing general resources. METHODOLOGY/PRINCIPAL FINDINGS: We curated 2254 datasets from 325 uterus related mass scale gene expression studies on human, mouse, rat, cow and pig species. We then computationally derived a 'reliability score' for each gene's expression status (transcribed/dormant), for each possible combination of conditions and locations, based on the extent of agreement or disagreement across datasets. The data and derived information has been compiled into the Mammalian Gene Expression Uterus database (MGEx-Udb, http://resource.ibab.ac.in/MGEx-Udb/). The database can be queried with gene names/IDs, sub-tissue locations, as well as various conditions such as the cervical cancer, endometrial cycles and disorders, and experimental treatments. Accordingly, the output would be a) transcribed and dormant genes listed for the queried condition/location, or b) expression profile of the gene of interest in various uterine conditions. The results also include the reliability score for the expression status of each gene. MGEx-Udb also provides information related to Gene Ontology annotations, protein-protein interactions, transcripts, promoters, and expression status by other sequencing techniques, and facilitates various other types of analysis of the individual genes or co-expressed gene clusters. CONCLUSIONS/SIGNIFICANCE: In brief, MGEx-Udb enables easy cataloguing of co-expressed genes and also facilitates bio-marker discovery for various uterine conditions.