scholarly journals Expression of Polarity Genes in Human Cancer

2015 ◽  
Vol 14s3 ◽  
pp. CIN.S18964 ◽  
Author(s):  
Wan-Hsin Lin ◽  
Yan W. Asmann ◽  
Panos Z. Anastasiadis
Keyword(s):  
Protein Complexes ◽  
Human Cancer ◽  
Expression Profiles ◽  
Tumor Formation ◽  
Model Systems ◽  
Epithelial Homeostasis ◽  
Directed Cell Migration ◽  
Protein Functions ◽  
Cancer Types ◽  
Polarity Proteins

Polarity protein complexes are crucial for epithelial apical-basal polarity and directed cell migration. Since alterations of these processes are common in cancer, polarity proteins have been proposed to function as tumor suppressors or oncogenic promoters. Here, we review the current understanding of polarity protein functions in epithelial homeostasis, as well as tumor formation and progression. As most previous studies focused on the function of single polarity proteins in simplified model systems, we used a genomics approach to systematically examine and identify the expression profiles of polarity genes in human cancer. The expression profiles of polarity genes were distinct in different human tissues and classified cancer types. Additionally, polarity expression profiles correlated with disease progression and aggressiveness, as well as with identified cancer types, where specific polarity genes were commonly altered. In the case of Scribble, gene expression analysis indicated its common amplification and upregulation in human cancer, suggesting a tumor promoting function.

scholarly journals The Hidden Story on Heterogeneous B-raf V600E Mutation Quantitative Protein Expression in Metastatic Melanoma – Association with Clinical Outcome and Tumor Phenotypes

2019 ◽  
Author(s):  
Lazaro Hiram Betancourt ◽  
A. Marcell Szasz ◽  
Magdalena Kuras ◽  
Jimmy Rodriguez Murillo ◽  
Yutaka Sugihara ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Protein Expression ◽  
Human Cancer ◽  
Expression Profiles ◽  
Drug Efficacy ◽  
Protein Analysis ◽  
Braf V600e ◽  
Current Recommendation ◽  
Cancer Types ◽  
Protein Expression Profiles

In comparison to other human cancer types, malignant melanoma exhibits the greatest amount of heterogeneity. After DNA-based detection of the BRAF V600E mutation in melanoma patients, targeted inhibitor treatment is the current recommendation. This approach, however, does not take the abundance of the therapeutic target, i.e., the B-raf V600E protein, into consideration. As shown by immunohistochemistry, the protein expression profiles of metastatic melanomas do clearly reveal the existence of inter- and intra-tumor variability. Nevertheless, the technique is only semi-quantitative. To quantitate the mutant protein there is a fundamental need for more precise techniques that are aimed at defining the currently non-existent link between the levels of the target protein and subsequent drug efficacy. Using cutting-edge mass spectrometry combined with DNA and mRNA sequencing, the mutated B-raf protein within metastatic tumors was quantitated for the first time. B-raf V600E protein analysis revealed a subjacent layer of heterogeneity for mutation-positive metastatic melanomas. These were characterized into two distinct groups with different tumor morphologies, protein profiles and patient clinical outcomes. This study provides evidence that a higher level of expression for the mutated protein is associated with a more aggressive tumor progression. Our study design that is comprised of surgical isolation of tumors, histopathological characterization, tissue biobanking, and protein analysis may enable the eventual delineation of patient responders/non-responders and the subsequent therapy of malignant melanoma.

scholarly journals The Hidden Story of Heterogeneous B-raf V600E Mutation Quantitative Protein Expression in Metastatic Melanoma—Association with Clinical Outcome and Tumor Phenotypes

Cancers ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1981 ◽  
Author(s):  
Lazaro Hiram Betancourt ◽  
A. Marcell Szasz ◽  
Magdalena Kuras ◽  
Jimmy Rodriguez Murillo ◽  
Yutaka Sugihara ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Protein Expression ◽  
Human Cancer ◽  
Expression Profiles ◽  
Drug Efficacy ◽  
Protein Analysis ◽  
Braf V600e ◽  
Current Recommendation ◽  
Cancer Types ◽  
Protein Expression Profiles

In comparison to other human cancer types, malignant melanoma exhibits the greatest amount of heterogeneity. After DNA-based detection of the BRAF V600E mutation in melanoma patients, targeted inhibitor treatment is the current recommendation. This approach, however, does not take the abundance of the therapeutic target, i.e., the B-raf V600E protein, into consideration. As shown by immunohistochemistry, the protein expression profiles of metastatic melanomas clearly reveal the existence of inter- and intra-tumor variability. Nevertheless, the technique is only semi-quantitative. To quantitate the mutant protein there is a fundamental need for more precise techniques that are aimed at defining the currently non-existent link between the levels of the target protein and subsequent drug efficacy. Using cutting-edge mass spectrometry combined with DNA and mRNA sequencing, the mutated B-raf protein within metastatic tumors was quantitated for the first time. B-raf V600E protein analysis revealed a subjacent layer of heterogeneity for mutation-positive metastatic melanomas. These were characterized into two distinct groups with different tumor morphologies, protein profiles and patient clinical outcomes. This study provides evidence that a higher level of expression in the mutated protein is associated with a more aggressive tumor progression. Our study design, comprised of surgical isolation of tumors, histopathological characterization, tissue biobanking, and protein analysis, may enable the eventual delineation of patient responders/non-responders and subsequent therapy for malignant melanoma.

scholarly journals Overexpression of the E2 ubiquitin–conjugating enzyme UbcH10 causes chromosome missegregation and tumor formation

2010 ◽  
Vol 188 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Janine H. van Ree ◽  
Karthik B. Jeganathan ◽  
Liviu Malureanu ◽  
Jan M. van Deursen
Keyword(s):  
Transgenic Mice ◽  
Ubiquitin Ligase ◽  
Human Cancer ◽  
Chromosome Instability ◽  
Tumor Formation ◽  
Anaphase Promoting Complex ◽  
Central Question ◽  
Ubiquitin Conjugating Enzyme ◽  
Cancer Types ◽  
Conjugating Enzyme

The anaphase-promoting complex/cyclosome (APC/C) E3 ubiquitin ligase functions with the E2 ubiquitin–conjugating enzyme UbcH10 in the orderly progression through mitosis by marking key mitotic regulators for destruction by the 26-S proteasome. UbcH10 is overexpressed in many human cancer types and is associated with tumor progression. However, whether UbcH10 overexpression causes tumor formation is unknown. To address this central question and to define the molecular and cellular consequences of UbcH10 overexpression, we generated a series of transgenic mice in which UbcH10 was overexpressed in graded fashion. In this study, we show that UbcH10 overexpression leads to precocious degradation of cyclin B by the APC/C, supernumerary centrioles, lagging chromosomes, and aneuploidy. Importantly, we find that UbcH10 transgenic mice are prone to carcinogen-induced lung tumors and a broad spectrum of spontaneous tumors. Our results identify UbcH10 as a prominent protooncogene that causes whole chromosome instability and tumor formation over a wide gradient of overexpression levels.

scholarly journals Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi-Hsuan Chuang ◽  
Sing-Han Huang ◽  
Tzu-Mao Hung ◽  
Xiang-Yu Lin ◽  
Jung-Yu Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Cancer Diagnosis ◽  
Protein Interaction ◽  
Human Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Multiple Cancer ◽  
Diagnosis And Prognosis ◽  
Cancer Types

AbstractMany studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein–protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors located in neighboring tissues or in the same cell types, would induce machine make error classification due to the similar gene expression profiles. Furthermore, we observed some patients may exhibit better prognosis if their tumors often misjudged into normal samples. As far as we know, we are the first to generate thousands of cancer networks to predict and classify multiple cancer types with CNN architecture. We believe that our model not only can be applied to cancer diagnosis and prognosis, but also promote the discovery of multiple cancer biomarkers.

Chaperonin as the normal controls and pathological anti-stress response in the human reproductive system

2016 ◽  
pp. 126-129
Author(s):  
M. Makarenko ◽  
◽  
D. Hovsyeyev ◽  
L. Sydoryk ◽  
◽  
...  
Keyword(s):  
Reproductive System ◽  
Stress Proteins ◽  
Physiological Stress ◽  
Protein Complexes ◽  
Reproductive Function ◽  
Intercellular Signaling ◽  
Toll Like Receptors ◽  
Wide Range ◽  
Protein Functions ◽  
And Function

Different kinds of physiological stress cause mass changes in the cells, including the changes in the structure and function of the protein complexes and in separate molecules. The protein functions is determined by its folding (the spatial conclusion), which depends on the functioning of proteins of thermal shock- molecular chaperons (HSPs) or depends on the stress proteins, that are high-conservative; specialized proteins that are responsible for the correct proteinaceous folding. The family of the molecular chaperones/ chaperonins/ Hsp60 has a special place due to the its unique properties of activating the signaling cascades through the system of Toll-like receptors; it also stimulates the cells to produce anti- inflammatory cytokines, defensins, molecules of cell adhesion and the molecules of MHC; it functions as the intercellular signaling molecule. The pathological role of Hsp60 is established in a wide range of illnesses, from diabetes to atherosclerosis, where Hsp60 takes part in the regulation of both apoptosis and the autoimmune processes. The presence of the HSPs was found in different tissues that are related to the reproductive system. Key words: molecular chaperons (HSPs), Toll-like receptors, reproductive function, natural auto antibody.

scholarly journals MicroRNA-21 Plays Multiple Oncometabolic Roles in the Process of NAFLD-Related Hepatocellular Carcinoma via PI3K/AKT, TGF-β, and STAT3 Signaling

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 940
Author(s):  
Chi-Yu Lai ◽  
Kun-Yun Yeh ◽  
Chiu-Ya Lin ◽  
Yang-Wen Hsieh ◽  
Hsin-Hung Lai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Signaling Pathways ◽  
Stellate Cell ◽  
Human Cancer ◽  
Tumor Formation ◽  
Transgenic Zebrafish ◽  
Zebrafish Model ◽  
Lipid Metabolism Disorder ◽  
Full Spectrum ◽  
Stat3 Signaling

MicroRNA-21 (miR-21) is one of the most frequently upregulated miRNAs in liver diseases such as nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). However, mechanistic pathways that connect NAFLD and HCC remain elusive. We developed a doxycycline (Dox)-inducible transgenic zebrafish model (LmiR21) which exhibited an upregulation of miR-21 in the liver, which in turn induced the full spectrum of NAFLD, including steatosis, inflammation, fibrosis, and HCC, in the LmiR21 fish. Diethylnitrosamine (DEN) treatment led to accelerated liver tumor formation and exacerbated their aggressiveness. Moreover, prolonged miR-21 expression for up to ten months induced nonalcoholic steatohepatitis (NASH)-related HCC (NAHCC). Immunoblotting and immunostaining confirmed the presence of miR-21 regulatory proteins (i.e., PTEN, SMAD7, p-AKT, p-SMAD3, and p-STAT3) in human nonviral HCC tissues and LmiR21 models. Thus, we demonstrated that miR-21 can induce NAHCC via at least three mechanisms: First, the occurrence of hepatic steatosis increases with the decrease of ptenb, pparaa, and activation of the PI3K/AKT pathway; second, miR-21 induces hepatic inflammation (or NASH) through an increase in inflammatory gene expression via STAT3 signaling pathways, and induces liver fibrosis through hepatic stellate cell (HSC) activation and collagen deposition via TGF-β/Smad3/Smad7 signaling pathways; finally, oncogenic activation of Smad3/Stat3 signaling pathways induces HCC. Our LmiR21 models showed similar molecular pathology to the human cancer samples in terms of initiation of lipid metabolism disorder, inflammation, fibrosis and activation of the PI3K/AKT, TGF-β/SMADs and STAT3 (PTS) oncogenic signaling pathways. Our findings indicate that miR-21 plays critical roles in the mechanistic perspectives of NAHCC development via the PTS signaling networks.

scholarly journals Cellular Fitness Phenotypes of Cancer Target Genes from Oncobiology to Cancer Therapeutics

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 433
Author(s):  
Bijesh George ◽  
P. Mukundan Pillai ◽  
Aswathy Mary Paul ◽  
Revikumar Amjesh ◽  
Kim Leitzel ◽  
...  
Keyword(s):  
Drug Targets ◽  
Target Genes ◽  
Human Cancer ◽  
Cancer Therapeutics ◽  
Survival Duration ◽  
Cancer Drug ◽  
Targeted Therapeutics ◽  
Cellular Targets ◽  
Human Cancer Cells ◽  
Cancer Types

To define the growing significance of cellular targets and/or effectors of cancer drugs, we examined the fitness dependency of cellular targets and effectors of cancer drug targets across human cancer cells from 19 cancer types. We observed that the deletion of 35 out of 47 cellular effectors and/or targets of oncology drugs did not result in the expected loss of cell fitness in appropriate cancer types for which drugs targeting or utilizing these molecules for their actions were approved. Additionally, our analysis recognized 43 cellular molecules as fitness genes in several cancer types in which these drugs were not approved, and thus, providing clues for repurposing certain approved oncology drugs in such cancer types. For example, we found a widespread upregulation and fitness dependency of several components of the mevalonate and purine biosynthesis pathways (currently targeted by bisphosphonates, statins, and pemetrexed in certain cancers) and an association between the overexpression of these molecules and reduction in the overall survival duration of patients with breast and other hard-to-treat cancers, for which such drugs are not approved. In brief, the present analysis raised cautions about off-target and undesirable effects of certain oncology drugs in a subset of cancers where the intended cellular effectors of drug might not be good fitness genes and that this study offers a potential rationale for repurposing certain approved oncology drugs for targeted therapeutics in additional cancer types.

scholarly journals Transporter associated with antigen processing 1 (TAP1) expression and prognostic analysis in breast, lung, liver, and ovarian cancer

2021 ◽  
Author(s):  
Anika Tabassum ◽  
Md. Nazmus Samdani ◽  
Tarak Chandra Dhali ◽  
Rahat Alam ◽  
Foysal Ahammad ◽  
...  
Keyword(s):  
Expression Pattern ◽  
Cancer Progression ◽  
Antigen Processing ◽  
Human Cancer ◽  
Analytical Data ◽  
Significant Negative Correlation ◽  
Cancer Tissue ◽  
Human Cancer Cell Lines ◽  
Prognostic Analysis ◽  
Cancer Types

Abstract Transporter associated with antigen processing 1 (TAP1) is a transporter protein that represent tumor antigen in the MHC I or HLA complex. Any defect in the TAP1 gene resulting in inadequate tumor tracking. TAP1 influences multidrug resistance (MDR) in human cancer cell lines and hinders the treatment during chemotherapeutic. The association of TAP1 in cancer progression remains mostly unknown and further study of the gene in relation with cancer need to conduct. Thus, the study has designed to analyze the association between the TAP1 with cancer by computationally. The expression pattern of the gene has determined by using ONCOMINE, GENT2, and GEPIA2 online platforms. The protein level of TAP1 was examined by the help of Human Protein Atlas. Samples with different clinical outcomes were investigated to evaluate the expression and promoter methylation in cancer vs. normal tissues by using UALCAN server. The copy number alteration, mutation frequency, and expression level of the gene in different cancer were analyzed by using cBioPortal server. The PrognoScan and KM plotter platforms were used to perform the survival analysis and represented graphically. Additionally, pathway and gene ontology (GO) features correlated to the TAP1 gene were analyzed and presented by bar charts. After arranging the data in a single panel like correlating expression to prognosis, mutational and alterations characteristic, and pathways analysis, we observed some interesting insights that emphasized the importance of the gene in cancer progression. The study found the relationship between the TAP1 expression pattern and prognosis in different cancer tissues and shows how TAP1 affects the clinical characteristics. The analytical data presented in the study is vital to learn about the effect of TAP1 in tumor tissue, where previously studies showing contradicting expression of TAP1 in cancer tissue. The analyzed data can also be utilized further to evade the threats against chemotherapy. Overall, the study provided a new aspect to consider the role of TAP1 gene in cancer progression and survival status. Key messages • This study demonstrated, for the first time, a correlation between the TAP1 gene and tumor progression. • An upregulation of TAP1 mRNA was demonstrated in various cancer types. • This study reported a significant negative correlation for TAP1 gene expression and the survival rate in different cancer types.

scholarly journals Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

2021 ◽  
Vol 20 ◽  
pp. 117693512110024
Author(s):  
Jason D Wells ◽  
Jacqueline R Griffin ◽  
Todd W Miller
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Human Tumors ◽  
Molecular Characteristics ◽  
Survival Times ◽  
Chemotherapy Drugs ◽  
Testing Dataset ◽  
Cancer Models ◽  
Cancer Types ◽  
Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

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