Unlocking the potential: A novel prognostic index signature for acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

The risk model construction of the genes regulated by H3K36me3 and H3K79me2 in breast cancer.

Abnormal histone modifications (HMs) can promote the occurrence of breast cancer. To elucidate the relationship between HMs and gene expression, we analyzed HM binding patterns and calculated their signal changes between breast tumor cells and normal cells. On this basis, the influences of HM signal changes on the expression changes of breast cancer-related genes were estimated by three different methods. The results showed that H3K79me2 and H3K36me3 may contribute more to gene expression changes. Subsequently, 2109 genes with differential H3K79me2 or H3K36me3 levels during cancerogenesis were identified by the Shannon entropy and submitted to perform functional enrichment analyses. Enrichment analyses displayed that these genes were involved in pathways in cancer, human papillomavirus infection, and viral carcinogenesis. Univariate Cox, LASSO, and multivariate Cox regression analyses were then adopted, and nine potential breast cancer-related driver genes were extracted from the genes with differential H3K79me2/H3K36me3 levels in the TCGA cohort. To facilitate the application, the expression levels of nine driver genes were transformed into a risk score model, and its robustness was tested via time-dependent receiver operating characteristic curves in the TCGA dataset and an independent GEO dataset. At last, the distribution levels of H3K79me2 and H3K36me3 in the nine driver genes were reanalyzed in the two cell lines and the regions with significant signal changes were located.

Recognition of driver genes with potential prognostic implications in lung adenocarcinoma based on H3K79me2.

Lung adenocarcinoma is a malignancy with a low overall survival and a poor prognosis. Studies have shown that lung adenocarcinoma progression relates to locus-specific/global changes in histone modifications. To explore the relationship between histone modification and gene expression changes, we focused on 11 histone modifications and quantitatively analyzed their influences on gene expression. We found that, among the studied histone modifications, H3K79me2 displayed the greatest impact on gene expression regulation. Based on the Shannon entropy, 867 genes with differential H3K79me2 levels during tumorigenesis were identified. Enrichment analyses showed that these genes were involved in 16 common cancer pathways and 11 tumors and were target-regulated by trans-regulatory elements, such as Tp53 and WT1. Then, an open-source computational framework was presented (https://github.com/zlq-imu/Identification-of-potential-LUND-driver-genes). Twelve potential driver genes were extracted from the genes with differential H3K79me2 levels during tumorigenesis. The expression levels of these potential driver genes were significantly increased/decreased in tumor cells, as assayed by RT-qPCR. A risk score model comprising these driver genes was further constructed, and this model was strongly negatively associated with the overall survival of patients in different datasets. The proportional hazards assumption and outlier test indicated that this model could robustly distinguish patients with different survival rates. Immune analyses and responses to immunotherapeutic and chemotherapeutic agents showed that patients in the high and low-risk groups may have distinct tendencies for clinical selection. Finally, the regions with clear H3K79me2 signal changes on these driver genes were accurately identified. Our research may offer potential molecular biomarkers for lung adenocarcinoma treatment.

The impact of gene-body H3K36me3 patterns on gene expression level changes in chronic myelogenous leukemia.

Chronic myelogenous leukemia (CML) is a malignant clonal disease of hematopoietic stem cells. Researches have exhibited that the progression of CML is related to histone modifications. Here, we perform the systematic analyses of H3K36me3 patterns and gene expression level changes. We observe that the genes with higher gene-body H3K36me3 levels in normal cells show fewer expression changes during leukemogenesis, while the genes with lower gene-body H3K36me3 levels in normal cells yield obvious expression changes during leukemogenesis (ρ = -0.98, P = 9.30 × 10-8). These findings are conserved in human lung/breast cancers and mouse CML, regardless of gene expression levels and gene lengths. Regulatory element analysis and Random Forest regression display that Hoxd13, Rara, Scl, Smad3, Smad4 and Tgif1 induce the up-regulation of genes with lower H3K36me3 levels (ρ = 0.97, P = 2.35 × 10-56). Enrichment analysis shows that the differentially expressed genes with lower H3K36me3 levels are involved in leukemia-related pathways, such as leukocyte migration and regulation of leukocyte activation. Finally, six driver genes (Tp53, Wt1, Dnmt3a, Cacna1b, Phactr1 and Gbp4) with lower H3K36me3 levels are identified. Our analyses indicate that lower gene-body H3K36me3 levels may serve as a biomarker for the progression of CML.

Identification of key genes and important histone modifications in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world. It has been reported that HCC is closely related to the changes of histone modifications. However, finding histone modification patterns in key genes which related to HCC is still an important task. In our study, the patterns of 11 kinds of histone modifications in the promoter regions for the different types of genes were analyzed by hierarchical screening for hepatocyte (normal) cell line and HepG2 (tumor) cell line. The important histone modifications and their key modification regions in different types of genes were found. The results indicate that these important genes may play a pivotal role in the occurrence of HCC. By analyzing the differences of histone modifications and gene expression levels for these important genes between the two cell lines, we found that the signals of H3K4me3, H3K27ac, H3K9ac, and H3K4me2 in HCC are significantly stronger. The changed regions of important histone modifications in 17 key genes were also identified. For example, the H3K4me3 signals increased 150 times in regions (-1500, -500) bp and (0, 1000) bp of ARHGAP5 in tumor cell line than in normal cell line. Finally, a prognostic risk scoring model was constructed, and the effects of key genes on the prognosis of HCC were verified by the survival analysis. Our results may provide a more precise potential therapeutic targets for identifying key genes and histone modifications in HCC as new biomarkers.

Identification of Key Histone Modifications and Their Regulatory Regions on Gene Expression Level Changes in Chronic Myelogenous Leukemia.

Chronic myelogenous leukemia (CML) is a type of cancer with a series of characteristics that make it particularly suitable for observations on leukemogenesis. Research have exhibited that the occurrence and progression of CML are associated with the dynamic alterations of histone modification (HM) patterns. In this study, we analyze the distribution patterns of 11 HM signals and calculate the signal changes of these HMs in CML cell lines as compared with that in normal cell lines. Meanwhile, the impacts of HM signal changes on expression level changes of CML-related genes are investigated. Based on the alterations of HM signals between CML and normal cell lines, the up- and down-regulated genes are predicted by the random forest algorithm to identify the key HMs and their regulatory regions. Research show that H3K79me2, H3K36me3, and H3K27ac are key HMs to expression level changes of CML-related genes in leukemogenesis. Especially H3K79me2 and H3K36me3 perform their important functions in all 100 bins studied. Our research reveals that H3K79me2 and H3K36me3 may be the core HMs for the clinical treatment of CML.

Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue.

Breast cancer has a high mortality rate for females. Aberrant DNA methylation plays a crucial role in the occurrence and progression of breast carcinoma. By comparing DNA methylation differences between tumor breast tissue and normal breast tissue, we calculate and analyze the distributions of the hyper- and hypomethylation sites in different function regions. Results indicate that enhancer regions are often hypomethylated in breast cancer. CpG islands (CGIs) are mainly hypermethylated, while the flanking CGI (shores and shelves) is more easily hypomethylated. The hypomethylation in gene body region is related to the upregulation of gene expression, and the hypomethylation of enhancer regions is closely associated with gene expression upregulation in breast cancer. Some key hypomethylation sites in enhancer regions and key hypermethylation sites in CGIs for regulating key genes are, respectively, found, such as oncogenes ESR1 and ERBB2 and tumor suppressor genes FBLN2, CEBPA, and FAT4. This suggests that the recognizing methylation status of these genes will be useful for the diagnosis of breast cancer.

Genome-wide analysis of H3K36me3 and its regulations to cancer-related genes expression in human cell lines.

H3K36me3 is a histone modification known to mark active genes. To further understand the effects of H3K36me3 on gene expression levels, we develop predictive models to compute the correlation between the binding signal of H3K36me3 in each bin and the gene expression levels. We find that the bins with stronger H3K36me3 averaged-binding signals present higher correlative strengths with the expression levels of gene. And the higher correlative strengths appear in the downstream regions of the transcription start site. Moreover, we systematically compare the predictive abilities of 11 histone modifications to gene expression levels. The results show that H3K36me3 achieves a higher predictive ability than other modifications, and the higher predictive ability is robust across different mammalian cells and gene groups. Finally, in contrast to the two normal cell lines, our analysis finds that the predictive abilities of H3K36me3 are enhanced in 10 of the 13 bins for oncogenes and are decreased in 10 of 16 bins for tumor-suppressor genes in the cancer cell.

Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells.

Transcription factors and histone modifications are vital for the regulation of gene expression. Hence, to estimate the effects of transcription factors binding and histone modifications on gene expression, we construct a statistical model for the genome-wide 15 transcription factors binding data, 10 histone modifications profiles and DNase-I hypersensitivity data in three mammalian. Remarkably, our results show POLR2A and H3K36me3 can highly and consistently predict gene expression in three cell lines. And H3K4me3, H3K27me3 and H3K9ac are more reliable predictors than other histone modifications in human embryonic stem cells. Moreover, genome-wide statistical redundancies exist within and between transcription factors and histone modifications, and these phenomena may be caused by the regulation mechanism. In further study, we find that even though transcription factors and histone modifications offer similar effects on expression levels of genome-wide genes, the effects of transcription factors and histone modifications on predictive abilities are different for genes in independent biological processes.

Predicting gene expression level by the transcription factor binding signals in human embryonic stem cells.

The transcription factor (TF) binding signals play important role in the control of gene expression. In this study, to elucidate the relationship between the transcription factor binding signals and gene expression, we firstly analyze the distributions of 57 kinds of TFs' binding signals in human H1 embryonic stem cells. Their distributions in highly and lowly expressed genes are further compared. On this basis, a statistic model of predicting gene expression level is constructed by using 57 kinds of transcription factor synthetic indexes (TFSIs). Then, the TF's Down-regulated and Up-regulated genes are predicted and the statistics significance is estimated by one-sided Kolmogorov-Smirnov test. Based on the stepwise regression analysis, the "optimal" TFSIs are selected out, and the better results for predicting the expression level of genes with high CpG content promoters (HCPs) and low CpG content promoters (LCPs) are obtained.

Gene expression classification using epigenetic features and DNA sequence composition in the human embryonic stem cell line H1.

Epigenetic factors are known to correlate with gene expression in the existing studies. However, quantitative models that accurately classify the highly and lowly expressed genes based on epigenetic factors are currently lacking. In this study, a new machine learning method combines histone modifications, DNA methylation, DNA accessibility, transcription factors, and trinucleotide composition with support vector machines (SVM) is developed in the context of human embryonic stem cell line (H1). The results indicate that the predictive accuracy will be markedly improved when the epigenetic features are considered. The predictive accuracy and Matthews correlation coefficient of the best model are as high as 95.96% and 0.92 for 10-fold cross-validation test, and 95.58% and 0.92 for independent dataset test, respectively. Our model provides a good way to judge a gene is either highly or lowly expressed gene by using genetic and epigenetic data, when the expression data of the gene is lacking. And a web-server GECES for our analysis method is established at http://202.207.14.87:8032/fuwu/GECES/index.asp, so that other scientists can easily get their desired results by our web-server, without going through the mathematical details.

Association analysis between the distributions of histone modifications and gene expression in the human embryonic stem cell.

It is well known that histone modifications are associated with gene expression. In order to further study this relationship, 16 kinds of Chip-seq histone modification data and mRNA-seq data of the human embryonic stem cell H1 are chosen. The distributions of histone modifications in the regions flanking transcription start sites (TSSs) for highly expressed and lowly expressed genes are computed, respectively. And four types of distributions of histone modifications in regions flanking TSSs and the spatial patterning of the correlations between histone modifications and gene expression are detected. Our results suggest that the correlations between the regions overlapped by peaks are higher than the non-overlapped ones for each histone modification. In addition, to obtain the effect of the cooperative action of histone modification on gene expression, five histone modification clusters are found in highly expressed and lowly expressed genes, histone modification and gene expression interaction network is constructed. To further explore which region is the main target region for the specific histone modification, the human genes are divided into five functional regions. The results indicate that histone modifications are mostly located in the promoters of highly expressed genes versus the exons of lowly expressed genes, and exons have a smaller range of normalized tag counts than other gene elements in the two groups of genes. Finally, the type specificity and regional bias of histone modifications for 11 key transcription factor genes regulating the stem cell renewal are analyzed.