DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer

Exploring molecular markers and drug candidates for colorectal cancer through comprehensive bioinformatics analysis

Colorectal cancer (CRC) often has a poor prognosis and identifying useful and novel agents for treating CRC is urgently required. This study aimed to examine molecular markers associated with CRC prognosis and to identify potential drug candidates. The differentially expressed genes (DEGs) of CRC in TCGA were identified. The genes associated with CRC, summarized from NCBI-gene, OMIM, and the DEGs, were used to construct a co-expression network by WGCNA. Moreover, the co-expression genes from modules of interest were used to carry out functional enrichment. A total of 2742 DEGs, including 1674 upregulated and 1068 downregulated genes, were identified. Thirteen co-expression modules were constructed with WGCNA. Brown and blue co-expression modules with significant differences in disease phenotype were found. Functional enrichment analysis showed that genes in the brown module were mainly related to cell cycle, cell proliferation, DNA replication, and RNA transport. The genes in the blue module were mainly associated with fatty acid degradation, sulfur metabolism, PPAR signaling pathway and bile secretion. In addition, both the genes in brown and blue were associated with tumor staging. Some prognostic markers and candidate small molecules drugs for CRC treatment were identified. In conclusion, we revealed molecular biomarker profiles in CRC by systematic bioinformatics analysis, constructed regulatory networks of mRNA, ncRNA and transcriptional regulators (TFs), and identified potential drugs targeting hub proteins and TFs.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs

Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.

Prognostic value of comprehensive typing based on m6A and gene cluster in TNBC

BACKGROUND

Triple-negative breast cancer (TNBC) is resistant to targeted therapy with HER2 monoclonal antibodies and endocrine therapy, because it lacks the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC is a subtype of breast cancer with the worst prognosis and the highest mortality rate compared to other subtypes. N6-methyladenosine (m6A) modification is significant in cancer and metastasis, because it can alter gene expression and function at numerous levels, such as RNA splicing, stability, translocation, and translation. There are limited investigations into the connection between TNBC and m6A.

MATERIALS AND METHODS

Breast cancer-related data were retrieved from the Cancer Genome Atlas (TCGA) database, and 116 triple-negative breast cancer cases were identified from the data. The GSE31519 data set, which included 68 cases of TNBC, was obtained from the Gene Expression Omnibus (GEO) database. Survival analysis was used to determine the prognosis of distinct m6A types based on their m6A group, gene group, and m6A score. To investigate the potential mechanism, GO and KEGG analyses were performed on the differentially expressed genes.

RESULTS

The expression of m6A-related genes and their impact on prognosis in TNBC patients were studied. According to the findings, m6A was crucial in determining the prognosis of TNBC patients, and the major m6A-linked genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. YTHDF2, RBM15B and IGFBP3 are associated with poor prognosis, while WTAP is associated with good prognosis. By cluster analysis, the gene cluster and the m6A cluster were beneficial in predicting the prognosis of TNBC patients. The m6A score based on m6A and gene clusters was more effective in predicting the prognosis of TNBC patients. Furthermore, the tumor microenvironment may play an important role in the process of m6A, influencing TNBC prognosis.

CONCLUSIONS

N6-adenylic acid methylation (m6A) was important in altering the prognosis of TNBC patients, and the key m6A-associated genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. Furthermore, the comprehensive typing based on m6A and gene clusters was useful in predicting TNBC patients' prognosis, showing potential as valuable evaluating tools for TNBC.

Prognostic value of comprehensive typing based on m6A and gene cluster.. [DOI: 10.21203/rs.3.rs-1922311/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background Triple-negative breast cancer (TNBC) is resistant to targeted therapy with HER2 monoclonal antibodies and endocrine therapy because it lacks the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC is a subtype of breast cancer with the worst prognosis and the highest mortality rate compared to other subtypes. N6-methyladenosine (m6A) modification is significant in cancer and metastasis because it can alter gene expression and function at numerous levels, such as RNA splicing, stability, translocation, and translation. There has been limited investigation into the connection between TNBC and m6A. Materials and Methods Breast cancer-related data were retrieved from the Cancer Genome Atlas (TCGA) database, and 116 triple-negative breast cancer cases were identified from the data. The GSE31519 dataset, which included 68 cases of TNBC, was obtained from the Gene Expression Omnibus (GEO) database. Survival analysis was used to determine the prognosis of distinct m6A types based on their m6A group, gene group, and m6A score. To investigate the potential mechanism, GO and KEGG analyses were performed on the differentially expressed genes. Results The expression of m6A-related genes and their impact on prognosis in TNBC patients were studied. According to the findings, m6A was crucial in determining the prognosis of TNBC patients, and the major m6A-linked genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. By cluster analysis, the gene cluster and the m6A cluster were beneficial in predicting the prognosis of TNBC patients. The m6A score based on m6A and gene clusters was more effective in predicting the prognosis of TNBC patients. Furthermore, the tumor microenvironment may play an important role in the process of m6A, influencing TNBC prognosis. Conclusion N6-adenylic acid methylation (m6A) was important in altering the prognosis of TNBC patients, and the key m6A-associated genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. Furthermore, the comprehensive typing based on m6A and gene clusters was useful in predicting TNBC patients' prognosis, showing potential as a meaningful evaluating tools for TNBC.

Construction and characterization of rectal cancer-related lncRNA-mRNA ceRNA network reveals prognostic biomarkers in rectal cancer

Rectal cancer is an important cause of cancer‐related deaths worldwide. In this study, the differentially expressed (DE) lncRNAs/mRNAs were first identified and the correlation level between DE lncRNAs and mRNAs were calculated. The results showed that genes of highly correlated lncRNA‐mRNA pairs presented strong prognosis effects, such as GPM6A, METTL24, SCN7A, HAND2‐AS1 and PDZRN4. Then, the rectal cancer‐related lncRNA‐mRNA network was constructed based on the ceRNA theory. Topological analysis of the network revealed that the network was maintained by hub nodes and a hub subnetwork was constructed, including the hub lncRNA MIR143HG and MBNL1‐SA1. Further analysis indicated that the hub subnetwork was highly related to cancer pathways, such as ‘Focal adhesion’ and ‘Wnt signalling pathway’. Hub subnetwork also had significant prognosis capability. A closed lncRNA‐mRNA module was identified by bilateral network clustering. Genes in modules also showed high prognosis effects. Finally, a core lncRNA‐TF crosstalk network was identified to uncover the crosstalk and regulatory mechanisms of lncRNAs and TFs by integrating ceRNA crosstalks and TF binding affinities. Some core genes, such as MEIS1, GLI3 and HAND2‐AS1 were considered as the key regulators in tumourigenesis. Based on the authors’ comprehensive analysis, all these lncRNA‐mRNA crosstalks provided promising clues for biological prognosis of rectal cancer.

The role and mechanism of HLA complex group 11 in cancer

HLA is critical in a variety of diseases, including infectious disease and cancer, and has been used for diagnostic differentiation and immunosurveillance of certain diseases. In addition, emerging evidence suggests that the mutations and dysregulation of lncRNAs are essential contributors in cancers. HLA Complex Group 11 (HCG11) located on MHC region is affiliated with the lncRNA class. Studies have shown that HCG11 could serve as a key regulator in lung cancer, prostate cancer, glioma, cervical cancer and hepatocellular carcinoma. In this review, we summarize the accumulated information on the expression and clinical value of HCG11 in different cancer types, discuss its interactions with microRNAs, mRNAs, and proteins, and discover the biological roles and potential mechanisms of HCG11 in a variety of cellular functions, including cell proliferation, apoptosis, migration, and invasion. Further, we emphasize the possible application of HCG11 in treatment, summarize the studies of HCG11 in chemotherapy resistance and hormone therapy, and propose the significance of further study of HCG11.

MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Networks, Inspired by Capsule Networks

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules-such as gene promoter context, CpG island relationship, or user-defined groupings-and relate them to diagnostic and prognostic outcomes. We demonstrate these models' utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep-learning analyses' interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.

Downregulated mRNA Expression of ZNF385B Is an Independent Predictor of Breast Cancer

Background

ZNF385B, a zinc finger protein, has been known as a potential biomarker in some neurological and hematological studies recently. Although numerous studies have demonstrated the potential function of zinc finger proteins in tumor progression, the effects of ZNF385B in breast cancer (BC) are less studied.

Methods

The Oncomine database and “ESurv” tool were used to explore the differential expression of ZNF385B in pan-cancer. Furthermore, data of patients with BC were downloaded from The Cancer Genome Atlas (TCGA). The receiver operating characteristic (ROC) curve of ZNF385B expression was established to explore the diagnostic value of ZNF385B and to obtain the cut-off value of high or low ZNF385B expression in BC. The chi-square test as well as Fisher exact test was used for identification of the relationships between clinical features and ZNF385B expression. Furthermore, the effects of ZNF385B on BC patients' survival were evaluated by the Kaplan-Meier and Cox regression. Data from the Gene Expression Omnibus (GEO) database were employed to validate the results of TCGA. Protein expression of ZNF385B in BC patient specimens was detected by immunohistochemistry (IHC) staining.

Results

ZNF385B expression was downregulated in most types of cancer including BC. Low ZNF385B expression was related with survival status, overall survival (OS), and recurrence of BC. ZNF385B had modest diagnostic value, which is indicated by the area under the ROC curve (AUC = 0.671). Patients with lower ZNF385B expression had shorter OS and RFS (relapse-free survival). It had been demonstrated that low ZNF385B expression represented independent prognostic value for OS and RFS by multivariate survival analysis. The similar results were verified by datasets from the GEO database as well. The protein expression of ZNF385B was decreased in patients' samples compared with adjacent tissues by IHC.

Conclusions

Low ZNF385B expression was an independent predictor for worse prognosis of BC patients.

Community-based network analyses reveal emerging connectivity patterns of protein-protein interactions in murine melanoma secretome

Protein-protein interaction networks (PPINs) are static representations of protein connections in which topological features such as subgraphs (communities) may contain proteins functionally related, revealing an additional layer of interactome complexity. We created two PPINs from the secretomes of a paired set of murine melanocytes (a normal melanocyte and its transformed phenotype). Community structures, identified by a graph clustering algorithm, resulted in the identification of subgraphs in both networks. Interestingly, the underlying structure of such communities revealed shared and exclusive proteins (core and exclusive nodes, respectively), in addition to proteins that changed their location within each community (rewired nodes). Functional enrichment analysis of core nodes revealed conserved biological functions in both networks whereas exclusive and rewired nodes in the tumoral phenotype network were enriched in cancer-related processes, including TGFβ signaling. We found a remarkable shift in the tumoral interactome, resulting in an emerging pattern which was driven by the presence of exclusive nodes and may represent functional network motifs. Our findings suggest that the rearrangement in the tumoral interactome may be correlated with the malignant transformation of melanocytes associated with substrate adhesion impediment. The interactions found in core and new/rewired nodes might potentially be targeted for therapeutic intervention in melanoma treatment. SIGNIFICANCE: Malignant transformation is a result of synergistic action of multiple molecular factors in which genetic alterations as well as protein expression play paramount roles. During oncogenesis, cellular crosstalk through the secretion of soluble mediators modulates the phenotype of transformed cells which ultimately enables them to successfully disrupt important signaling pathways, including those related to cell growth and proliferation. Therefore, in this work we profiled the secretomes of a paired set of normal and transformed phenotypes of a murine melanocyte. After assembling the two interactomes, clusters of functionally related proteins (network communities) were observed as well as emerging patterns of network rewiring which may represent an interactome signature of transformed cells. In summary, the significance of this study relies on the understanding of the repertoire of 'normal' and 'tumoral' secretomes and, more importantly, the set of interacting proteins (the interactome) in both of these conditions, which may reveal key components that might be potentially targeted for therapeutic intervention.

Identification of a Prognostic 3-Gene Risk Prediction Model for Thyroid Cancer

Objective: We aimed to screen the genes associated with thyroid cancer (THCA) prognosis, and construct a poly-gene risk prediction model for prognosis prediction and improvement. Methods: The HTSeq-Counts data of THCA were accessed from TCGA database, including 505 cancer samples and 57 normal tissue samples. "edgeR" package was utilized to perform differential analysis, and weighted gene co-expression network analysis (WGCNA) was applied to screen the differential co-expression genes associated with THCA tissue types. Univariant Cox regression analysis was further used for the selection of survival-related genes. Then, LASSO regression model was constructed to analyze the genes, and an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. Results: Three thousand two hundred seven differentially expressed genes (DEGs) were obtained by differential analysis and 23 co-expression genes (|COR| > 0.5, P < 0.05) were gained after WGCNA analysis. In addition, eight genes significantly related to THCA survival were screened by univariant Cox regression analysis, and an optimal prognostic 3-gene risk prediction model was constructed after genes were analyzed by the LASSO regression model. Based on this model, patients were grouped into the high-risk group and low-risk group. Kaplan-Meier curve showed that patients in the low-risk group had much better survival than those in the high-risk group. Moreover, great accuracy of the 3-gene model was revealed by ROC curve and the remarkable correlation between the model and patients' prognosis was verified using the multivariant Cox regression analysis. Conclusion: The prognostic 3-gene model composed by GHR, GPR125, and ATP2C2 three genes can be used as an independent prognostic factor and has better prediction for the survival of THCA patients.