Gene Regulatory Network Perturbation by Genetic and Epigenetic Variation

CancerProteome: a resource to functionally decipher the proteome landscape in cancer

Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.

Comprehensive analysis of PRPF19 immune infiltrates, DNA methylation, senescence-associated secretory phenotype and ceRNA network in bladder cancer

Background

Pre-mRNA processing factor 19 (PRPF19) is an E3 ligase that plays a crucial role in repairing tumor-damaged cells and promoting cell survival. However, the predictive value and biological function of PRPF19 in bladder urothelial carcinoma (BLCA) require further investigation.

Methods

In this study, we utilized transcriptomic data and bladder cancer tissue microarrays to identify the high expression of PRPF19 in BLCA, suggesting its potential as a prognostic biomarker. To gain a better understanding of the role of PRPF19 in the immune microenvironment of BLCA, we performed single cell analysis and employed the LASSO method. Additionally, we examined the methylation profiles of PRPF19 using the SMART website. Our investigation confirmed the correlation between PRPF19 and BLCA cell senescence and stemness. Furthermore, we constructed a PRPF19-miR-125a-5p-LINC02693-MIR4435-2HG ceRNA network using the ENCORI and miRWALK databases.

Results

Our comprehensive analysis reveals that PRPF19 can serve as a prognostic marker for BLCA and is significantly associated with various immune-infiltrating cells in BLCA. Moreover, our findings suggest that PRPF19 influences cellular senescence through the regulation of stemness. Finally, we developed a ceRNA network that has the potential to predict the prognosis of BLCA patients.

Conclusion

We confirmed the prognostic value and multiple biological functions of PRPF19 in BLCA. Furthermore, the specific ceRNA network can be used as a potential therapeutic target for BLCA.

Comprehensive analysis of FOXM1 immune infiltrates, m6a, glycolysis and ceRNA network in human hepatocellular carcinoma

Background

Forkhead box M1 (FOXM1) is a member of the Forkhead box (Fox) transcription factor family. It regulates cell mitosis, cell proliferation, and genome stability. However, the relationship between the expression of FOXM1 and the levels of m6a modification, immune infiltration, glycolysis, and ketone body metabolism in HCC has yet to be fully elucidated.

Methods

Transcriptome and somatic mutation profiles of HCC were downloaded from the TCGA database. Somatic mutations were analyzed by maftools R package and visualized in oncoplots. GO, KEGG and GSEA function enrichment was performed on FOXM1 co-expression using R. We used Cox regression and machine learning algorithms (CIBERSORT, LASSO, random forest, and SVM-RFE) to study the prognostic value of FOXM1 and immune infiltrating characteristic immune cells in HCC. The relationship between FOXM1 and m6A modification, glycolysis, and ketone body metabolism were analyzed by RNA-seq and CHIP-seq. The competing endogenous RNA (ceRNA) network construction relies on the multiMiR R package, ENCORI, and miRNET platforms.

Results

FOXM1 is highly expressed in HCC and is associated with a poorer prognosis. At the same time, the expression level of FOXM1 is significantly related to the T, N, and stage. Subsequently, based on the machine learning strategies, we found that the infiltration level of T follicular helper cells (Tfh) was a risk factor affecting the prognosis of HCC patients. The high infiltration of Tfh was significantly related to the poor overall survival rate of HCC. Besides, the CHIP-seq demonstrated that FOXM1 regulates m6a modification by binding to the promoter of IGF2BP3 and affects the glycolytic process by initiating the transcription of HK2 and PKM in HCC. A ceRNA network was successfully obtained, including FOXM1 - has-miR-125-5p - DANCR/MIR4435-2HG ceRNA network related to the prognosis of HCC.

Conclusion

Our study implicates that the aberrant infiltration of Tfh associated with FOXM1 is a crucial prognostic factor for HCC patients. FOXM1 regulates genes related to m6a modification and glycolysis at the transcriptional level. Furthermore, the specific ceRNA network can be used as a potential therapeutic target for HCC.

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning

Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.

Dynamic transcriptome analyses reveal m6A regulated immune non-coding RNAs during dengue disease progression

Dengue infection is one of the most prevalent arthropod-borne viral diseases, which can result in severe complications. Identification of genes and long non-coding RNAs (lncRNAs) involved in dengue infection would help in deciphering potential mechanisms responsible for the disease progression. We comprehensively analyzed the dynamic transcriptome during dengue disease progression and identified critical genes and lncRNAs with expression perturbations. Our findings revealed that the expression of genes (i.e., CCR10 and GNG7) and lncRNAs (i.e., CTBP1-AS and MAFG-AS1) were potentially regulated by m6A RNA methylation. Interestingly, dengue viral proteins prevalently interact with genes or lncRNAs with expression perturbations, which are involved in cell cycle, inflammation signaling pathways and immune response. Dynamically expressed genes and lncRNAs were likely to locate in the central regions of human protein-protein network, which play crucial roles in mediating signaling spread and helping viral replication. Immune microenvironments analysis revealed that plasma cells levels were increased and T cells infiltrations were decreased during dengue disease progression. Dynamically expressed genes and lncRNAs were correlated with immune cell infiltrations. Moreover, network analysis reveals the associations between dengue viral infections and human complex diseases (i.e., digestive diseases and neoplasms). Our comprehensive transcriptome analysis of dengue disease progression identified potential gene and lncRNA biomarkers, providing novel insights for understanding the pathogenesis of and developing effective therapeutic strategies for dengue infection.

Gain-of-Function Variomics and Multi-omics Network Biology for Precision Medicine

Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more clear that many deleterious mutations could exhibit a "gain-of-function" (GOF) behavior. Systematic investigation of such mutations has been lacking and largely overlooked. Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in disease. Elucidating the functional pathways rewired by GOF mutations will be crucial for prioritizing disease-causing variants and their resultant therapeutic liabilities. In distinct cell types (with varying genotypes), precise signal transduction controls cell decision, including gene regulation and phenotypic output. When signal transduction goes awry due to GOF mutations, it would give rise to various disease types. Quantitative and molecular understanding of network perturbations by GOF mutations may provide explanations for 'missing heritability" in previous genome-wide association studies. We envision that it will be instrumental to push current paradigm toward a thorough functional and quantitative modeling of all GOF mutations and their mechanistic molecular events involved in disease development and progression. Many fundamental questions pertaining to genotype-phenotype relationships remain unresolved. For example, which GOF mutations are key for gene regulation and cellular decisions? What are the GOF mechanisms at various regulation levels? How do interaction networks undergo rewiring upon GOF mutations? Is it possible to leverage GOF mutations to reprogram signal transduction in cells, aiming to cure disease? To begin to address these questions, we will cover a wide range of topics regarding GOF disease mutations and their characterization by multi-omic networks. We highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also discuss advances in bioinformatic and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of GOF mutations.

CpG Site-Specific Methylation-Modulated Divergent Expression of PRSS3 Transcript Variants Facilitates Nongenetic Intratumor Heterogeneity in Human Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most lethal human tumors with extensive intratumor heterogeneity (ITH). Serine protease 3 (PRSS3) is an indispensable member of the trypsin family and has been implicated in the pathogenesis of several malignancies, including HCC. However, the paradoxical effects of PRSS3 on carcinogenesis due to an unclear molecular basis impede the utilization of its biomarker potential. We hereby explored the contribution of PRSS3 transcripts to tumor functional heterogeneity by systematically dissecting the expression of four known splice variants of PRSS3 (PRSS3-SVs, V1~V4) and their functional relevance to HCC.

Methods

The expression and DNA methylation of PRSS3 transcripts and their associated clinical relevance in HCC were analyzed using several publicly available datasets and validated using qPCR-based assays. Functional experiments were performed in gain- and loss-of-function cell models, in which PRSS3 transcript constructs were separately transfected after deleting PRSS3 expression by CRISPR/Cas9 editing.

Results

PRSS3 was aberrantly differentially expressed toward bipolarity from very low (PRSS3Low) to very high (PRSS3High) expression across HCC cell lines and tissues. This was attributable to the disruption of PRSS3-SVs, in which PRSS3-V2 and/or PRSS3-V1 were dominant transcripts leading to PRSS3 expression, whereas PRSS3-V3 and -V4 were rarely or minimally expressed. The expression of PRSS3-V2 or -V1 was inversely associated with site-specific CpG methylation at the PRSS3 promoter region that distinguished HCC cells and tissues phenotypically between hypermethylated low-expression (mPRSS3-SVLow) and hypomethylated high-expression (umPRSS3-SVHigh) groups. PRSS3-SVs displayed distinct functions from oncogenic PRSS3-V2 to tumor-suppressive PRSS3-V1, -V3 or PRSS3-V4 in HCC cells. Clinically, aberrant expression of PRSS3-SVs was translated into divergent relevance in patients with HCC, in which significant epigenetic downregulation of PRSS3-V2 was seen in early HCC and was associated with favorable patient outcome.

Conclusions

These results provide the first evidence for the transcriptional and functional characterization of PRSS3 transcripts in HCC. Aberrant expression of divergent PRSS3-SVs disrupted by site-specific CpG methylation may integrate the effects of oncogenic PRSS3-V2 and tumor-suppressive PRSS3-V1, resulting in the molecular diversity and functional plasticity of PRSS3 in HCC. Dysregulated expression of PRSS3-V2 by site-specific CpG methylation may have potential diagnostic value for patients with early HCC.

Shedding light on the hidden human proteome expands immunopeptidome in cancer

Unrestrained cellular growth and immune escape of a tumor are associated with the incidental errors of the genome and transcriptome. Advances in next-generation sequencing have identified thousands of genomic and transcriptomic aberrations that generate variant peptides that assemble the hidden proteome, further expanding the immunopeptidome. Emerging next-generation sequencing technologies and a number of computational methods estimated the abundance of immune infiltration from bulk transcriptome have advanced our understanding of tumor microenvironments. Here, we will characterize several major types of tumor-specific antigens arising from single-nucleotide variants, insertions and deletions, gene fusion, alternative splicing, RNA editing and non-coding RNAs. Finally, we summarize the current state-of-the-art computational and experimental approaches or resources and provide an integrative pipeline for the identification of candidate tumor antigens. Together, the systematic investigation of the hidden proteome in cancer will help facilitate the development of effective and durable immunotherapy targets for cancer.

Global RNA editing identification and characterization during human pluripotent-to-cardiomyocyte differentiation

RNA editing is widely involved in stem cell differentiation and development; however, RNA editing events during human cardiomyocyte differentiation have not yet been characterized and elucidated. Here, we identified genome-wide RNA editing sites and systemically characterized their genomic distribution during four stages of human cardiomyocyte differentiation. It was found that the expression level of ADAR1 affected the global number of adenosine to inosine (A-to-I) editing sites but not the editing degree. Next, we identified 43, 163, 544, and 141 RNA editing sites that contribute to changes in amino acid sequences, variation in alternative splicing, alterations in miRNA-target binding, and changes in gene expression, respectively. Generally, RNA editing showed a stage-specific pattern with 211 stage-shared editing sites. Interestingly, cardiac muscle contraction and heart-disease-related pathways were enriched by cardio-specific editing genes, emphasizing the connection between cardiomyocyte differentiation and heart diseases from the perspective of RNA editing. Finally, it was found that these RNA editing sites are also related to several congenital and noncongenital heart diseases. Together, our study provides a new perspective on cardiomyocyte differentiation and offers more opportunities to understand the mechanisms underlying cell fate determination, which can promote the development of cardiac regenerative medicine and therapies for human heart diseases.

Single-Cell RNA-Seq of Cisplatin-Treated Adult Stria Vascularis Identifies Cell Type-Specific Regulatory Networks and Novel Therapeutic Gene Targets

The endocochlear potential (EP) generated by the stria vascularis (SV) is necessary for hair cell mechanotransduction in the mammalian cochlea. We sought to create a model of EP dysfunction for the purposes of transcriptional analysis and treatment testing. By administering a single dose of cisplatin, a commonly prescribed cancer treatment drug with ototoxic side effects, to the adult mouse, we acutely disrupt EP generation. By combining these data with single cell RNA-sequencing findings, we identify transcriptional changes induced by cisplatin exposure, and by extension transcriptional changes accompanying EP reduction, in the major cell types of the SV. We use these data to identify gene regulatory networks unique to cisplatin treated SV, as well as the differentially expressed and druggable gene targets within those networks. Our results reconstruct transcriptional responses that occur in gene expression on the cellular level while identifying possible targets for interventions not only in cisplatin ototoxicity but also in EP dysfunction.

ERG transcription factors have a splicing regulatory function involving RBFOX2 that is altered in the EWS-FLI1 oncogenic fusion

ERG family proteins (ERG, FLI1 and FEV) are a subfamily of ETS transcription factors with key roles in physiology and development. In Ewing sarcoma, the oncogenic fusion protein EWS-FLI1 regulates both transcription and alternative splicing of pre-messenger RNAs. However, whether wild-type ERG family proteins might regulate splicing is unknown. Here, we show that wild-type ERG proteins associate with spliceosomal components, are found on nascent RNAs, and induce alternative splicing when recruited onto a reporter minigene. Transcriptomic analysis revealed that ERG and FLI1 regulate large numbers of alternative spliced exons (ASEs) enriched with RBFOX2 motifs and co-regulated by this splicing factor. ERG and FLI1 are associated with RBFOX2 via their conserved carboxy-terminal domain, which is present in EWS-FLI1. Accordingly, EWS-FLI1 is also associated with RBFOX2 and regulates ASEs enriched in RBFOX2 motifs. However, in contrast to wild-type ERG and FLI1, EWS-FLI1 often antagonizes RBFOX2 effects on exon inclusion. In particular, EWS-FLI1 reduces RBFOX2 binding to the ADD3 pre-mRNA, thus increasing its long isoform, which represses the mesenchymal phenotype of Ewing sarcoma cells. Our findings reveal a RBFOX2-mediated splicing regulatory function of wild-type ERG family proteins, that is altered in EWS-FLI1 and contributes to the Ewing sarcoma cell phenotype.

Alternative splicing perturbation landscape identifies RNA binding proteins as potential therapeutic targets in cancer

Alternative splicing (AS) plays an important role in gene regulation, and AS perturbations are frequently observed in cancer. RNA binding protein (RBP) is one of the molecular determinants of AS, and perturbations in RBP-gene network activity are causally associated with cancer development. Here, we performed a systematic analysis to characterize the perturbations in AS events across 18 cancer types. We showed that AS alterations were prevalent in cancer and involved in cancer-related pathways. Given that the extent of AS perturbation was associated with disease severity, we proposed a computational pipeline to identify RBP regulators. Pan-cancer analysis identified a number of conserved RBP regulators, which play important roles in regulating AS of genes involved in cancer hallmark pathways. Our application analysis revealed that the expression of 68 RBP regulators helped in cancer subtyping. Specifically, we identified four subtypes of kidney cancer with differences in cancer hallmark pathway activities and prognosis. Finally, we identified the small molecules that can potentially target the RBP genes and suggested potential candidates for cancer therapy. In summary, our comprehensive AS perturbation landscape analysis identified RBPs as potential therapeutic targets in cancer and provided novel insights into the regulatory functions of RBPs in cancer.

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types

Accumulating evidence has demonstrated that transcriptional regulation is affected by DNA methylation. Understanding the perturbation of DNA methylation-mediated regulation between transcriptional factors (TFs) and targets is crucial for human diseases. However, the global landscape of DNA methylation-mediated transcriptional dysregulation (DMTD) across cancers has not been portrayed. Here, we systematically identified DMTD by integrative analysis of transcriptome, methylome and regulatome across 22 human cancer types. Our results revealed that transcriptional regulation was affected by DNA methylation, involving hundreds of methylation-sensitive TFs (MethTFs). In addition, pan-cancer MethTFs, the regulatory activity of which is generally affected by DNA methylation across cancers, exhibit dominant functional characteristics and regulate several cancer hallmarks. Moreover, pan-cancer MethTFs were found to be affected by DNA methylation in a complex pattern. Finally, we investigated the cooperation among MethTFs and identified a network module that consisted of 43 MethTFs with prognostic potential. In summary, we systematically dissected the transcriptional dysregulation mediated by DNA methylation across cancer types, and our results provide a valuable resource for both epigenetic and transcriptional regulation communities.

Integrated Analysis of the Functions and Prognostic Values of RNA Binding Proteins in Lung Squamous Cell Carcinoma

Lung cancer is the leading cause of cancer-related deaths worldwide. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma (LUSC) remain unclear. In this study, we obtained gene expression data and corresponding clinical information for LUSC from The Cancer Genome Atlas (TCGA) database, identified aberrantly expressed RBPs between tumors and normal tissue, and conducted a series of bioinformatics analyses to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained, comprising 59 downregulated and 241 upregulated RBPs. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with mRNA metabolic processes, RNA processing, RNA modification, regulation of translation, the TGF-beta signaling pathway, and the Toll-like receptor signaling pathway. Nine RBP genes (A1CF, EIF2B5, LSM1, LSM7, MBNL2, RSRC1, TRMU, TTF2, and ZCCHC5) were identified as prognosis-associated hub genes by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan–Meier survival, and multivariate Cox regression analyses, and were used to construct the prognostic model. Further analysis demonstrated that high risk scores for patients were significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.712 at 3 years and 0.696 at 5 years. We also developed a nomogram based on nine RBP genes, with internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis in LUSC. Our results provide novel insights into the pathogenesis of LUSC. The nine-RBP gene signature showed predictive value for LUSC prognosis, with potential applications in clinical decision-making and individualized treatment.