scholarly journals A bioinformatic analysis of the inhibin-betaglycan-endoglin/CD105 network reveals prognostic value in multiple solid tumors

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249558
Author(s):  
Eduardo Listik ◽  
Ben Horst ◽  
Alex Seok Choi ◽  
Nam. Y. Lee ◽  
Balázs Győrffy ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Bioinformatic Analysis ◽  
Gene Signature ◽  
Breast Cancers ◽  
Luminal A ◽  
Multiple Cancer ◽  
Α Subunit ◽  
Cancer Types ◽  
Lung Adenocarcinomas ◽  
Tgfβ Superfamily

Inhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG/CD105 in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients’ response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.

scholarly journals A meta-analysis of the inhibin network reveals prognostic value in multiple solid tumors

2020 ◽  
Author(s):  
Eduardo Listik ◽  
Ben Horst ◽  
Alex Seok Choi ◽  
Nam. Y. Lee ◽  
Balázs Győrffy ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Meta Analysis ◽  
Gene Signature ◽  
Breast Cancers ◽  
Luminal A ◽  
Multiple Cancer ◽  
Α Subunit ◽  
Cancer Types ◽  
Lung Adenocarcinomas ◽  
Tgfβ Superfamily

ABSTRACTInhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients’ response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.

scholarly journals Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Erdogan Taskesen ◽  
Sjoerd M. H. Huisman ◽  
Ahmed Mahfouz ◽  
Jesse H. Krijthe ◽  
Jeroen de Ridder ◽  
...  
Keyword(s):  
Therapy Response ◽  
Visual Exploration ◽  
Molecular Characteristics ◽  
Cancer Type ◽  
Breast Cancers ◽  
Data Types ◽  
Multiple Cancer ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Cancer Types

Abstract The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.

scholarly journals Survival outcomes are associated with genomic instability in luminal breast cancers

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245042
Author(s):  
Lydia King ◽  
Andrew Flaus ◽  
Emma Holian ◽  
Aaron Golden
Keyword(s):  
Breast Cancer ◽  
Genomic Instability ◽  
Cancer Survival ◽  
Molecular Taxonomy ◽  
Gene Signature ◽  
Therapeutic Interventions ◽  
Survival Outcomes ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Luminal A

Breast cancer is the leading cause of cancer related death among women. Breast cancers are generally diagnosed and treated based on clinical and histopathological features, along with subtype classification determined by the Prosigna Breast Cancer Prognostic Gene Signature Assay (also known as PAM50). Currently the copy number alteration (CNA) landscape of the tumour is not considered. We set out to examine the role of genomic instability (GI) in breast cancer survival since CNAs reflect GI and correlate with survival in other cancers. We focused on the 70% of breast cancers classified as luminal and carried out a comprehensive survival and association analysis using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data to determine whether CNA Score Quartiles derived from absolute CNA counts are associated with survival. Analysis revealed that patients diagnosed with luminal A breast cancer have a CNA landscape associated with disease specific survival, suggesting that CNA Score can provide a statistically robust prognostic factor. Furthermore, stratification of patients into subtypes based on gene expression has shown that luminal A and B cases overlap, and it is in this region we largely observe luminal A cases with reduced survival outlook. Therefore, luminal A breast cancer patients with quantitatively elevated CNA counts may benefit from more aggressive therapy. This demonstrates how individual genomic landscapes can facilitate personalisation of therapeutic interventions to optimise survival outcomes.

scholarly journals Expression Pattern and Prognostic Value of EPHA/EFNA in Breast Cancer by Bioinformatics Analysis: Revealing Its Importance in Chemotherapy

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Zheng Liang ◽  
Xu Wang ◽  
Kaiti Dong ◽  
Xinhua Li ◽  
Chenge Qin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mrna Expression ◽  
Expression Pattern ◽  
Prognostic Value ◽  
Genetic Alterations ◽  
Clinicopathological Parameters ◽  
Breast Cancers ◽  
Luminal A ◽  
Her2 Positive ◽  
Poor Overall Survival

The activities of the ephrin family in breast cancer (BrCa) are complex. Family A receptors (EPHA) and ligands (EFNA) can act as oncogenes or tumor suppressors and are implicated in chemoresistance. Here, we examined the expression pattern and prognostic value of the EPHA/EFNA family in patients with breast cancer, including patients with different subtypes or different chemotherapy cohorts. In the UALCAN database, the mRNA expression of EPHA1, EPHA10, EFNA1, EFNA3, and EFNA4 was significantly higher, whereas that of EPHA2, EPHA4, EPHA5, and EFNA5 was significantly lower in breast cancer tissues than in paracancerous tissues. The transcriptional levels of EPHA/EFNA family members were correlated with intrinsic subclasses of breast cancer. The relationship between EPHA/EFNA and the clinicopathological parameters of BrCa was analyzed using bc-GenExMiner V4.5. EPHA1, EPHA2, EPHA4, EPHA7, EFNA3, EFNA4, and EFNA5 were upregulated in estrogen receptor- (ER-) and progesterone receptor- (PR-) negative tumors, whereas EPHA3, EPHA6, and EFNA1 were upregulated in ER- and PR-positive tumors. EPHA1, EPHA2, EFNA3, and EFNA4 mRNA expression was significantly higher in human epidermal growth factor receptor 2- (HER2-) positive tumors than in HER2-negative tumors. Triple-negative status was positively correlated with EPHA1, EPHA2, EPHA4, EPHA7, EFNA3, EFNA4, and EFNA5 and negatively correlated with EPHA3 and EPHA10 mRNA expression. Genetic alterations of EPHA/EFNA in breast cancer varied from 1.1% to 10% for individual genes, as determined by the cBioPortal database. The Kaplan–Meier plotter indicated that high EphA7 mRNA expression was associated with poor overall survival (OS) and recurrence-free survival (RFS), especially in the HER2 and luminal A subtypes. EFNA4 was predicted to have poor OS and RFS in breast cancers, especially in luminal B, basal-like subtype, and patients treated with adjuvant chemotherapy. High EPHA3 expression was significantly associated with better OS and RFS, especially in the luminal A subtype, but with poor RFS in BrCa patients receiving chemotherapy. Our findings systematically elucidate the expression pattern and prognostic value of the EPHA/EFNA family in BrCa, which might provide potential prognostic factors and novel targets in BrCa patients, including those with different subtypes or treated with chemotherapy.

Galgo: a bi-objective evolutionary meta-heuristic identifies robust transcriptomic classifiers associated with patient outcome across multiple cancer types

2020 ◽  
Vol 36 (20) ◽  
pp. 5037-5044
Author(s):  
M E Guerrero-Gimenez ◽  
J M Fernandez-Muñoz ◽  
B J Lang ◽  
K M Holton ◽  
D R Ciocca ◽  
...  
Keyword(s):  
Predictive Power ◽  
Patient Survival ◽  
Large Population ◽  
Molecular Classification ◽  
Gene Signature ◽  
Population Based ◽  
Supplementary Information ◽  
Optimization Approach ◽  
Multiple Cancer ◽  
Cancer Types

Abstract Motivation Statistical and machine-learning analyses of tumor transcriptomic profiles offer a powerful resource to gain deeper understanding of tumor subtypes and disease prognosis. Currently, prognostic gene-expression signatures do not exist for all cancer types, and most developed to date have been optimized for individual tumor types. In Galgo, we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patient survival in parallel, which provides greater power to identify tumor transcriptomic phenotypes strongly associated with patient survival. Results To compare the predictive power of the signatures obtained by Galgo with previously studied subtyping methods, we used a meta-analytic approach testing a total of 35 large population-based transcriptomic biobanks of four different cancer types. Galgo-generated colorectal and lung adenocarcinoma signatures were stronger predictors of patient survival compared to published molecular classification schemes. One Galgo-generated breast cancer signature outperformed PAM50, AIMS, SCMGENE and IntClust subtyping predictors. In high-grade serous ovarian cancer, Galgo signatures obtained similar predictive power to a consensus classification method. In all cases, Galgo subtypes reflected enrichment of gene sets related to the hallmarks of the disease, which highlights the biological relevance of the partitions found. Availability and implementation The open-source R package is available on www.github.com/harpomaxx/galgo. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Prognostic Significance of Progesterone Receptor–Positive Tumor Cells Within Immunohistochemically Defined Luminal A Breast Cancer

2013 ◽  
Vol 31 (2) ◽  
pp. 203-209 ◽  
Author(s):  
Aleix Prat ◽  
Maggie Chon U. Cheang ◽  
Miguel Martín ◽  
Joel S. Parker ◽  
Eva Carrasco ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progesterone Receptor ◽  
Tumor Cells ◽  
Prognostic Value ◽  
Prognostic Significance ◽  
Cáncer De Mama ◽  
Breast Cancers ◽  
Luminal A ◽  
Data Set ◽  
Gene And Protein Expression

Purpose Current immunohistochemical (IHC)-based definitions of luminal A and B breast cancers are imperfect when compared with multigene expression-based assays. In this study, we sought to improve the IHC subtyping by examining the pathologic and gene expression characteristics of genomically defined luminal A and B subtypes. Patients and Methods Gene expression and pathologic features were collected from primary tumors across five independent cohorts: British Columbia Cancer Agency (BCCA) tamoxifen-treated only, Grupo Español de Investigación en Cáncer de Mama 9906 trial, BCCA no systemic treatment cohort, PAM50 microarray training data set, and a combined publicly available microarray data set. Optimal cutoffs of percentage of progesterone receptor (PR) –positive tumor cells to predict survival were derived and independently tested. Multivariable Cox models were used to test the prognostic significance. Results Clinicopathologic comparisons among luminal A and B subtypes consistently identified higher rates of PR positivity, human epidermal growth factor receptor 2 (HER2) negativity, and histologic grade 1 in luminal A tumors. Quantitative PR gene and protein expression were also found to be significantly higher in luminal A tumors. An empiric cutoff of more than 20% of PR-positive tumor cells was statistically chosen and proved significant for predicting survival differences within IHC-defined luminal A tumors independently of endocrine therapy administration. Finally, no additional prognostic value within hormonal receptor (HR) –positive/HER2-negative disease was observed with the use of the IHC4 score when intrinsic IHC-based subtypes were used that included the more than 20% PR-positive tumor cells and vice versa. Conclusion Semiquantitative IHC expression of PR adds prognostic value within the current IHC-based luminal A definition by improving the identification of good outcome breast cancers. The new proposed IHC-based definition of luminal A tumors is HR positive/HER2 negative/Ki-67 less than 14%, and PR more than 20%.

scholarly journals Development of a 15-Gene Signature Model as a Prognostic Tool in Sex Hormone-Dependent Cancers

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zhi Xia ◽  
Jian Xiao ◽  
Aibin Liu ◽  
Qiong Chen
Keyword(s):  
Cell Cycle ◽  
Gene Signature ◽  
Sex Hormone ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Net Benefit ◽  
Hub Genes ◽  
Luminal A ◽  
Prognostic Tool ◽  
Cancer Types

Sex hormone dependence is associated with tumor progression and prognosis. Here, we explored the molecular basis of luminal A-like phenotype in sex hormone-dependent cancers. RNA-sequencing data from 8 cancer types were obtained from The Cancer Genome Atlas (TCGA). We investigated the enrichment function of differentially expressed genes (DEGs) in luminal A breast cancer (BRCA). Weighted coexpression network analysis (WGCNA) was used to identify gene modules associated with the luminal A-like phenotype, and we calculated the module’s preservation in 8 cancer types. Module hub genes screened using least absolute shrinkage and selection operator (LASSO) were used to construct a gene signature model for the luminal A-like phenotype, and we assessed the model’s relationship with prognosis, enriched pathways, and immune infiltration using bioinformatics approaches. Compared to other BRCA subtypes, the enrichment functions of upregulated genes in luminal A BRCA were related to hormone biological processes and receptor activity, and the downregulated genes were associated with the cell cycle and nuclear division. A gene module significantly associated with luminal A BRCA was shared by uterine corpus endometrial carcinoma (UCEC), leading to a similar phenotype. Fifteen hub genes were used to construct a gene signature model for the assessment of the luminal A-like phenotype, and the corrected C -statistics and Brier scores were 0.986 and 0.023, respectively. Calibration plots showed good performance, and decision curve analysis indicated a high net benefit of the model. The 15-gene signature model was associated with better overall survival in BRCA and UCEC and was characterized by downregulation of DNA replication, cell cycle and activated CD4 T cells. In conclusion, our study elucidated that BRCA and UCEC share a similar sex hormone-dependent phenotype and constructed a 15-gene signature model for use as a prognostic tool to quantify the probability of the phenotype.

Anticancer Immunotoxins, Challenges, and Approaches

2020 ◽  
Vol 26 ◽  
Author(s):  
Maryam Dashtiahangar ◽  
Leila Rahbarnia ◽  
Safar Farajnia ◽  
Arash Salmaninejad ◽  
Arezoo Gowhari Shabgah ◽  
...  
Keyword(s):  
Therapeutic Strategy ◽  
Antibody Fragment ◽  
Clinical Use ◽  
Multiple Cancer ◽  
Main Obstacle ◽  
Cancer Types ◽  
Recombinant Immunotoxins ◽  
Cancerous Cells ◽  
High Immunogenicity ◽  
Novel Therapeutic

: The development of recombinant immunotoxins (RITs) as a novel therapeutic strategy has made a revolution in the treatment of cancer. RITs are resulting from the fusion of antibodies to toxin proteins for targeting and eliminating cancerous cells by inhibiting protein synthesis. Despite indisputable outcomes of RITs regarding inhibiting multiple cancer types, high immunogenicity has been known as the main obstacle in the clinical use of RITs. Various strategies have been proposed to overcome these limitations, including immunosuppressive therapy, humanization of the antibody fragment moiety, generation of immunotoxins originated from endogenous human cytotoxic enzymes, and modification of the toxin moiety to escape the immune system. This paper devoted to reviewing recent advances in the design of immunotoxins with lower immunogenicity.

scholarly journals Landscape of Chimeric RNAs in Non-Cancerous Cells

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 466
Author(s):  
Chen Chen ◽  
Samuel Haddox ◽  
Yue Tang ◽  
Fujun Qin ◽  
Hui Li
Keyword(s):  
Cancer Cells ◽  
Cell Lines ◽  
Drug Targets ◽  
Neoplastic Cell ◽  
Multiple Cancer ◽  
Chimeric Rnas ◽  
Healthy Donors ◽  
Cancer Types ◽  
Chimeric Rna ◽  
Cancerous Cells

Gene fusions and their products (RNA and protein) have been traditionally recognized as unique features of cancer cells and are used as ideal biomarkers and drug targets for multiple cancer types. However, recent studies have demonstrated that chimeric RNAs generated by intergenic alternative splicing can also be found in normal cells and tissues. In this study, we aim to identify chimeric RNAs in different non-neoplastic cell lines and investigate the landscape and expression of these novel candidate chimeric RNAs. To do so, we used HEK-293T, HUVEC, and LO2 cell lines as models, performed paired-end RNA sequencing, and conducted analyses for chimeric RNA profiles. Several filtering criteria were applied, and the landscape of chimeric RNAs was characterized at multiple levels and from various angles. Further, we experimentally validated 17 chimeric RNAs from different classifications. Finally, we examined a number of validated chimeric RNAs in different cancer and non-cancer cells, including blood from healthy donors, and demonstrated their ubiquitous expression pattern.

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