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.

Coregulatory effects of multiple histone modifications in key ferroptosis-related genes for lung adenocarcinoma.

Aim: The present study was designed to investigate the coregulatory effects of multiple histone modifications (HMs) on gene expression in lung adenocarcinoma (LUAD). Materials & methods: Ten histones for LUAD were analyzed using ChIP-seq and RNA-seq data. An innovative computational method is proposed to quantify the coregulatory effects of multiple HMs on gene expression to identify strong coregulatory genes and regions. This method was applied to explore the coregulatory mechanisms of key ferroptosis-related genes in LUAD. Results: Nine strong coregulatory regions were identified for six ferroptosis-related genes with diverse coregulatory patterns (CA9, PGD, CDKN2A, PML, OTUB1 and NFE2L2). Conclusion: This quantitative method could be used to identify important HM coregulatory genes and regions that may be epigenetic regulatory targets in cancers.

Potential biomarkers: The hypomethylation of cg18949415 and cg22193385 sites in colon adenocarcinoma.

Overall cancer hypomethylation had been identified in the past, but it is not clear exactly which hypomethylation site is the more important for the occurrence of cancer. To identify key hypomethylation sites, we studied the effect of hypomethylation in twelve regions on gene expression in colon adenocarcinoma (COAD). The key DNA methylation sites of cg18949415, cg22193385 and important genes of C6orf223, KRT7 were found by constructing a prognostic model, survival analysis and random combination prediction a series of in-depth systematic calculations and analyses, and the results were validated by GEO database, immune microenvironment, drug and functional enrichment analysis. Based on the expression values of C6orf223, KRT7 genes and the DNA methylation values of cg18949415, cg22193385 sites, the least diversity increment algorithm were used to predict COAD and normal sample. The 100 % reliability and 97.12 % correctness of predicting tumor samples were obtained in jackknife test. Moreover, we found that C6orf223 gene, cg18949415 site play a more important role than KRT7 gene, cg22193385 site in COAD. In addition, we investigate the impact of key methylation sites on three-dimensional chromatin structure. Our results will be help for experimental studies and may be an epigenetic biomarker for COAD.

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.

The prediction of tumor and normal tissues based on the DNA methylation values of ten key sites.

Abnormal DNA methylation can alter the gene expression to promote or inhibit tumorigenesis in colon adenocarcinoma (COAD). However, the finding important genes and key sites of abnormal DNA methylation which result in the occurrence of COAD is still an eventful task. Here, we studied the effects of DNA methylation in the 12 types of genomic features on the changes of gene expression in COAD, the 10 important COAD-related genes and the key abnormal DNA methylation sites were identified. The effects of important genes on the prognosis were verified by survival analysis. Moreover, it was shown that the important genes were participated in cancer pathways and were hub genes in a co-expression network. Based on the DNA methylation levels in the ten sites, the least diversity increment algorithm for predicting tumor tissues and normal tissues in seventeen cancer types are proposed. The better results are obtained in jackknife test. For example, the predictive accuracies are 94.17 %, 91.28 %, 89.04 % and 88.89 %, respectively, for COAD, rectum adenocarcinoma, pancreatic adenocarcinoma and cholangiocarcinoma. Finally, by computing enrichment score of infiltrating immunocytes and the activity of immune pathways, we found that the genes are highly correlated with immune microenvironment.

Discovered Key CpG Sites by Analyzing DNA Methylation and Gene Expression in Breast Cancer Samples.

Breast cancer is the most common cancer in the world, and DNA methylation plays a key role in the occurrence and development of breast cancer. However, the effect of DNA methylation in different gene functional regions on gene expression and the effect of gene expression on breast cancer is not completely clear. In our study, we computed and analyzed DNA methylation, gene expression, and clinical data in the TCGA database. Firstly, we calculated the distribution of abnormal DNA methylated probes in 12 regions, found the abnormal DNA methylated probes in down-regulated genes were highly enriched, and the number of hypermethylated probes in the promoter region was 6.5 times than that of hypomethylated probes. Secondly, the correlation coefficients between abnormal DNA methylated values in each functional region of differentially expressed genes and gene expression values were calculated. Then, co-expression analysis of differentially expressed genes was performed, 34 hub genes in cancer-related pathways were obtained, of which 11 genes were regulated by abnormal DNA methylation. Finally, a multivariate Cox regression analysis was performed on 27 probes of 11 genes. Three DNA methylation probes (cg13569051 and cg14399183 of GSN, and cg25274503 of CAV2) related to survival were used to construct a prognostic model, which has a good prognostic ability. Furthermore, we found that the cg25274503 hypermethylation in the promoter region inhibited the expression of the CAV2, and the hypermethylation of cg13569051 and cg14399183 in the 5'UTR region inhibited the expression of GSN. These results may provide possible molecular targets for breast cancer.

Discovering the key genes and important DNA methylation regions in breast cancer.

BACKGROUND: Breast cancer is the malignant tumor with the highest incidence in women. DNA methylation has an important effect on breast cancer, but the effect of abnormal DNA methylation on gene expression in breast cancer is still unclear. Therefore, it is very important to find therapeutic targets related to DNA methylation. RESULTS: In this work, we calculated the DNA methylation distribution and gene expression level in cancer and para-cancerous tissues for breast cancer samples. We found that DNA methylation in key regions is closely related to gene expression by analyzing the relationship between the distribution characteristics of DNA methylation in different regions and the change of gene expression level. Finally, the 18 key genes (17 tumor suppressor genes and 1 oncogene) related to prognosis were confirmed by the survival analysis of clinical data. Some important DNA methylation regions in these genes that result in breast cancer were found. CONCLUSIONS: We believe that 17 TSGs and 1 oncogene may be breast cancer biomarkers regulated by DNA methylation in key regions. These results will help to explore DNA methylation biomarkers as potential therapeutic targets for breast cancer.

The effect of key DNA methylation in different regions on gene expression in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common cancer with high morbidity and mortality. As we all know, the alteration of DNA methylation has a crucial impact on the occurrence of HCC. However, the mechanism of the effect of DNA methylation in different regions on gene expression is still unclear. Here, by computing and analyzing the distribution of differential methylation in 12 different regions in HCC tissues and adjacent normal tissues, not only the hypermethylation of CpG islands and global hypomethylation were found, but also a stable distribution pattern of differential methylation in HCC was found. Then the correlations between DNA methylations in different regions and gene expressions were calculated, and the diversity of correlations in different regions was determined. The key genes of differential methylation and differential expression related to the survival of HCC patients were obtained by using Cox regression analysis, a four-gene prognostic risk scoring model was constructed, and the prognostic performance was well verified. The regions of the differentially methylated CpG sites corresponding to the four key genes were located and their influences on the expression were analyzed. The results indicate that the promoter, first exon, 5'UTR, sixth exon, N_Shore, and S_Shore hypomethylation promotes the expression of key oncogenes, which together lead to the occurrence of HCC. These results might help to study the role of DNA methylation in HCC and provide potential biomarkers for the diagnosis of HCC.

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.

An energy model for recognizing the prokaryotic promoters based on molecular structure.

Promoter is an important functional elements of DNA sequences, which is in charge of gene transcription initiation. Recognizing promoter have important help for understanding the relative life phenomena. Based on the concept that promoter is mainly determined by its sequence and structure, a novel statistical physics model for predicting promoter in Escherichia coli K-12 is proposed. The total energies of DNA local structure of sequence segments in the three benchmark promoter sequence datasets, the sole prediction parameter, are calculated by using principles from statistical physics and information theory. The better results are obtained. And a web-server PhysMPrePro for predicting promoter is established at http://202.207.14.87:8032/bioinformation/PhysMPrePro/index.asp, so that other scientists can easily get their desired results by our web-server.

Effect of the key histone modifications on the expression of genes related to breast cancer.

Abnormal histone modifications (HMs) and transcription factors (TFs) can alter the expression of cancer-related genes to promote tumorigenesis. We studied the variations of 11 HMs and 2 TFs in human breast cancer cells (MCF-7) compared to human normal mammary epithelial cells (HMEC), and the effects of HMs/TFs in various regions of the genome on the expression changes of breast cancer-related genes. Based on HMs and TFs signals' differences between MCF-7 and HMEC flanking TSSs, the up- and down-regulated genes in MCF-7 were predicted by Random Forest, and important HMs and regions were found. Results indicate that H3K79me2, H3K27ac, and H3K4me1 are particularly important for the changes of gene expression in MCF-7. Especially, H3K79me2 around the 60-th bin flanking TSSs may be the key for regulating gene expression. Our studies reveal H3K79me2 may be a core HM for breast cancer.

Revealing Gene Function and Transcription Relationship by Reconstructing Gene-Level Chromatin Interaction.

Chromatin is hierarchically organized in human interphase nuclei. Dynamic chromatin interactions are thought to influence gene transcription and cell fate determination. A consensus concept is that genes may form transcription factories within nucleus by spatially interaction. However, it is still not well known whether the function-related genes co-locate in three-dimensional (3D) space for co-transcription. Especially, there is a lack of visualization method that directly reflect the relationship between gene spatial interaction, gene function and co-transcription. In this study, we constructed three kinds of matrices based on gene ontology annotations, high-through chromosome conformation capture (Hi-C) data and RNA-seq data from twenty human tissues and cell lines. The comparative analysis for gene pairs revealed that 3D genome organization influences gene transcription predominantly at local scale. We found that the local genes within family clusters have similar transcription patterns. We also found that spatial reorganization of a histone gene cluster could control gene transcription. These observations suggest that function-related genes are close in space and activated or repressed together. Our work provided a framework for genome-wide studying the relationship between gene function, co-transcription and spatial interaction.

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.

Recognition of the long range enhancer-promoter interactions by further adding DNA structure properties and transcription factor binding motifs in human cell lines.

The enhancer-promoter interactions (EPIs) with strong tissue-specificity play an important role in cis-regulatory mechanism of human cell lines. However, it still remains a challenging work to predict these interactions so far. Due to that these interactions are regulated by the cooperativeness of diverse functional genomic signatures, DNA spatial structure and DNA sequence elements. In this paper, by adding DNA structure properties and transcription factor binding motifs, we presented an improved computational method to predict EPIs in human cell lines. In comparison with the results of other group on the same datasets, our best accuracies by cross-validation test were about 15%-24% higher in the same cell lines, and the accuracies by independent test were about 11%-15% higher in new cell lines. Meanwhile, we found that transcription factor binding motifs and DNA structure properties have important information that would largely determine long range EPIs prediction. From the distribution comparisons, we also found their distinct differences between interacting and non-interacting sets in each cell line. Then, the correlation analysis and network models for relationships among top-ranked functional genomic signatures indicated that diverse genomic signatures would cooperatively establish a complex regulatory network to facilitate long range EPIs. The experimental results provided additional insights about the roles of DNA intrinsic properties and functional genomic signatures in EPIs prediction.

Recognition of long-range enhancer-promoter interactions by adding genomic signatures of segmented regulatory regions.

Enhancer-promoter interaction (EPI) is an important cis-regulatory mechanism in the regulation of tissue-specific gene expression. However, it still has limitation to precisely identity these interactions so far. In this paper, using diverse genomic features for various regulatory regions, we presented a computational approach to predict EPIs with improved accuracies. Meanwhile, we comprehensively studied more potential regulatory factors that are important to EPIs prediction, such as nucleosome occupancy, enhancer RNA; and found the cell line-specificity and region-specificity of the contributions of diverse regulatory signatures. By adding genomic signatures of segmented regulatory regions, our best accuracies of cross-validation test were about 11%-16% higher than the previous results, indicating the location-specificity of genomic signatures in a regulatory region for predicting EPIs. Additionally, more training samples and related features can provide reliable performances in new cell lines. Consequently, our study provided additional insights into the roles of diverse signature features for predicting long-range EPIs.

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.