CGPE: A user-friendly gene and pathway explore webserver for public cancer transcriptional data

Digitized for IUPUI ScholarWorks inclusion in 2021.High throughput technology has been widely used by researchers to understand diseases at the molecular level. Database and servers for downloading and analyzing these publicly data is available as well. But there is still lacking tools for facilitating researchers to study the function of genes in pathways views by integrated public omics data

Highly robust model of transcription regulator activity predicts breast cancer overall survival

Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice

Background Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary. Methods We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients. Results A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis. Conclusions Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective

Sesn3 protects against diet‐induced nonalcoholic steatohepatitis in mice via suppression of the TGFβ signal transduction

Sesn3 belongs to the three‐member sestrin protein family. Sestrins have been implicated in anti‐oxidative stress, AMPK and mTOR signal transduction, and metabolic homeostasis. However, the role of Sesn3 in the development of nonalcoholic steatohepatitis (NASH) has not been previously studied. In this work, we generated Sesn3 whole‐body knockout and liver‐specific transgenic mice to investigate the hepatic function of Sesn3 in diet‐induced NASH. With only 4 weeks of dietary treatment, Sesn3 knockout mice developed severe NASH phenotype as characterized by hepatic steatosis, inflammation, and fibrosis. Strikingly, after 8‐week feeding with a NASH‐inducing diet, Sesn3 transgenic mice were largely protected against NASH development. Transcriptomic analysis revealed that multiple extracellular matrix related processes were upregulated including TGFβ signaling and collagen production. Further biochemical and cell biological analyses have illustrated a critical control of the TGFβ‐Smad pathway by Sesn3 at the TGFβ receptor and Smad3 levels. First, Sesn3 inhibits the TGFβ receptor through an interaction with Smad7; second, Sesn3 directly inhibits the Smad3 function through protein‐protein interaction and cytosolic retention

Generation of the tumor-suppressive secretome from tumor cells

Rationale: The progression of cancer cells depends on the soil and building an inhibitory soil might be a therapeutic option. We previously created tumor-suppressive secretomes by activating Wnt signaling in MSCs. Here, we examined whether the anti-tumor secretomes can be produced from tumor cells. Methods: Wnt signaling was activated in tumor cells by overexpressing β-catenin or administering BML284, a Wnt activator. Their conditioned medium (CM) was applied to cancer cells or tissues, and the effects of CM were evaluated. Tumor growth in the mammary fat pad and tibia in C57BL/6 female mice was also evaluated through μCT imaging and histology. Whole-genome proteomics analysis was conducted to determine and characterize novel tumor-suppressing proteins, which were enriched in CM. Results: The overexpression of β-catenin or the administration of BML284 generated tumor-suppressive secretomes from breast, prostate and pancreatic cancer cells. In the mouse model, β-catenin-overexpressing CM reduced tumor growth and tumor-driven bone destruction. This inhibition was also observed with BML284-treated CM. Besides p53 and Trail, proteomics analysis revealed that CM was enriched with enolase 1 (Eno1) and ubiquitin C (Ubc) that presented notable tumor-suppressing actions. Importantly, Eno1 immunoprecipitated CD44, a cell-surface adhesion receptor, and its silencing suppressed Eno1-driven tumor inhibition. A pan-cancer survival analysis revealed that the downregulation of MMP9, Runx2 and Snail by CM had a significant impact on survival outcomes (p < 0.00001). CM presented a selective inhibition of tumor cells compared to non-tumor cells, and it downregulated PD-L1, an immune escape modulator. Conclusions: The tumor-suppressive secretome can be generated from tumor cells, in which β-catenin presented two opposing roles, as an intracellular tumor promoter in tumor cells and a generator of extracellular tumor suppressor in CM. Eno1 was enriched in CM and its interaction with CD44 was involved in Eno1's anti-tumor action. Besides presenting a potential option for treating primary cancers and metastases, the result indicates that aggressive tumors may inhibit the growth of less aggressive tumors via tumor-suppressive secretomes

INPP5D expression is associated with risk for Alzheimer’s disease and induced by plaque-associated microglia

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, robust microgliosis, neuroinflammation, and neuronal loss. Genome-wide association studies recently highlighted a prominent role for microglia in late-onset AD (LOAD). Specifically, inositol polyphosphate-5-phosphatase (INPP5D), also known as SHIP1, is selectively expressed in brain microglia and has been reported to be associated with LOAD. Although INPP5D is likely a crucial player in AD pathophysiology, its role in disease onset and progression remains unclear. We performed differential gene expression analysis to investigate INPP5D expression in AD and its association with plaque density and microglial markers using transcriptomic (RNA-Seq) data from the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) cohort. We also performed quantitative real-time PCR, immunoblotting, and immunofluorescence assays to assess INPP5D expression in the 5xFAD amyloid mouse model. Differential gene expression analysis found that INPP5D expression was upregulated in LOAD and positively correlated with amyloid plaque density. In addition, in 5xFAD mice, Inpp5d expression increased as the disease progressed, and selectively in plaque-associated microglia. Increased Inpp5d expression levels in 5xFAD mice were abolished entirely by depleting microglia with the colony-stimulating factor receptor-1 antagonist PLX5622. Our findings show that INPP5D expression increases as AD progresses, predominantly in plaque-associated microglia. Importantly, we provide the first evidence that increased INPP5D expression might be a risk factor in AD, highlighting INPP5D as a potential therapeutic target. Moreover, we have shown that the 5xFAD mouse model is appropriate for studying INPP5D in AD

Intron retention-induced neoantigen load correlates with unfavorable prognosis in multiple myeloma

Neoantigen peptides arising from genetic alterations may serve as targets for personalized cancer vaccines and as positive predictors of response to immune checkpoint therapy. Mutations in genes regulating RNA splicing are common in hematological malignancies leading to dysregulated splicing and intron retention (IR). In this study, we investigated IR as a potential source of tumor neoantigens in multiple myeloma (MM) patients and the relationship of IR-induced neoantigens (IR-neoAg) with clinical outcomes. MM-specific IR events were identified in RNA-sequencing data from the Multiple Myeloma Research Foundation CoMMpass study after removing IR events that also occurred in normal plasma cells. We quantified the IR-neoAg load by assessing IR-induced novel peptides that were predicted to bind to major histocompatibility complex (MHC) molecules. We found that high IR-neoAg load was associated with poor overall survival in both newly diagnosed and relapsed MM patients. Further analyses revealed that poor outcome in MM patients with high IR-neoAg load was associated with high expression levels of T-cell co-inhibitory molecules and elevated interferon signaling activity. We also found that MM cells exhibiting high IR levels had lower MHC-II protein abundance and treatment of MM cells with a spliceosome inhibitor resulted in increased MHC-I protein abundance. Our findings suggest that IR-neoAg may represent a novel biomarker of MM patient clinical outcome and further that targeting RNA splicing may serve as a potential therapeutic strategy to prevent MM immune escape and promote response to checkpoint blockade

Intron Retention Induced Neoantigen as Biomarkers in Diseases

Indiana University-Purdue University Indianapolis (IUPUI)Alternative splicing is a regulatory mechanism that generates multiple mRNA transcripts from a single gene, allowing significant expansion in proteome diversity. Disruption of splicing mechanisms has a large impact on the transcriptome and is a significant driver of complex diseases by producing condition-specific transcripts. Recent studies have reported that mis-spliced RNA transcripts can be another major source of neoantigens directly associated with immune responses. Particularly, aberrant peptides derived from unspliced introns can be presented by the major histocompatibility complex (MHC) class I molecules on the cell surface and elicit immunogenicity. In this dissertation, we first developed an integrated computational pipeline for identifying IR-induced neoantigens (IR-neoAg) from RNA sequencing (RNA-Seq) data. Our workflow also included a random forest classifier for prioritizing the neoepitopes with the highest likelihood to induce a T cell response. Second, we analyzed IR neoantigen using RNA-Seq data for multiple myeloma patients from the MMRF study. Our results suggested that the IR-neoAg load could serve as a prognosis biomarker, and immunosuppression in the myeloma microenvironment might offset the increasing neoantigen load effect. Thirdly, we demonstrated that high IR-neoAg predicts better overall survival in TCGA pancreatic cancer patients. Moreover, our results indicated the IR-neoAg load might be useful in identifying pancreatic cancer patients who might benefit from immune checkpoint blockade (ICB) therapy. Finally, we explored the association of IR-induced neo-peptides with neurodegeneration disease pathology and susceptibility. In conclusion, we presented a state-of-art computational solution for identifying IR-neoAgs, which might aid neoantigen-based vaccine development and the prediction of patient immunotherapy responses. Our studies provide remarkable insights into the roles of alternative splicing in complex diseases by directly mediating immune responses.2023-08-1