scholarly journals Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer

2020 ◽  
Vol 21 (22) ◽  
pp. 8837
Author(s):  
Anni Kääriäinen ◽  
Vilma Pesola ◽  
Annalena Dittmann ◽  
Juho Kontio ◽  
Jarkko Koivunen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Biological Processes ◽  
Expression Data ◽  
Therapeutic Approaches ◽  
Associated Proteins ◽  
Cancer Genome Atlas ◽  
Pan Cancer

The expression and regulation of matrisome genes—the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors—is of paramount importance for many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding of the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of “landmark” matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.

A pan-cancer analysis based on weighted gene co-expression network analysis identifies the biomarker utility of lamin B1 in human tumors

2021 ◽  
pp. 1-17
Author(s):  
Youwei Hua ◽  
Zhihui He ◽  
Xu Zhang
Keyword(s):  
Network Analysis ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Lamin B1 ◽  
Cancer Genome Atlas ◽  
Mrna Binding ◽  
Functional Mechanisms ◽  
Pan Cancer

Emerging evidence has revealed a relationship between lamin B1 (LMNB1) and several cancers such as cervical cancer, liver cancer, and prostate cancer. But no systematic pan-cancer analysis is available. Little is known about the clinical significance and biomarker utility of LMNB1. In this study, we first revealed the key role of LMNB1 in esophageal carcinoma (ESCA) through weighted gene co-expression network analysis (WGCNA) and disease-free survival (DFS) analysis. Based on this result and the datasets of the cancer genome atlas (TCGA), we explored the biomarker utility of LMNB1 across thirty-three tumors. We found that LMNB1 was highly expressed in most of the cancers and significant associations existed between LMNB1 expression and prognosis of cases of nearly half of the cancers. We also found that LMNB1 expression was associated with the infiltration level of Macrophages M1 and T cells CD4 memory activated in some cancers. Moreover, LMNB1 was mainly involved in the functional mechanisms of MRNA binding, olfactory transduction, and gene silencing. Our study first provides a pan-cancer study of LMNB1, thereby offering a relatively comprehensive understanding of the biomarker utility of LMNB1 across thirty-three tumors.

scholarly journals The B7H4-PDL1 classifier stratifies immuno-phenotype in cervical cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Lingyan Chen ◽  
Jianfeng Dong ◽  
Zeying Li ◽  
Yu Chen ◽  
Yan Zhang
Keyword(s):  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Sequencing Data ◽  
T Cell Infiltration ◽  
Quantitative Immunofluorescence ◽  
Ki67 Antigen ◽  
Cancer Genome Atlas ◽  
Pan Cancer

Abstract Background It has been revealed that B7H4 is negatively correlated with PDL1 and identifies immuno-cold tumors in glioma. However, the application of the B7H4-PDL1 classifier in cancers has not been well testified. Methods A pan-cancer analysis was conducted to evaluate the immunological role of B7H4 using the RNA-sequencing data downloaded from the Cancer Genome Atlas (TCGA). Immunohistochemistry (IHC) and multiplexed quantitative immunofluorescence (QIF) were performed to validate the primary results revealed by bioinformatics analysis. Results The pan-cancer analysis revealed that B7H4 was negatively correlated with PDL1 expression and immune cell infiltration in CeCa. In addition, patients with high B7H4 exhibited the shortest overall survival (OS) and relapse-free survival (RFS) while those with high PDL1 exhibited a better prognosis. Multiplexed QIF showed that B7H4 was mutually exclusive with PDL1 expression and the B7H4-high group exhibited the lowest CD8 + T cell infiltration. Besides, B7H4-high predicted highly proliferative subtypes, which expressed the highest Ki67 antigen. Moreover, B7H4-high also indicated a lower response to multiple therapies. Conclusions Totally, the B7H4-PDL1 classifier identifies the immunogenicity and predicts proliferative subtypes and limited therapeutic options in CeCa, which may be a convenient and feasible biomarker in clinical practice.

scholarly journals Telomere and Telomerase-Associated Proteins in Endometrial Carcinogenesis and Cancer-Associated Survival

2022 ◽  
Vol 23 (2) ◽  
pp. 626
Author(s):  
Lucy Button ◽  
Bryony Rogers ◽  
Emily Thomas ◽  
Alice Bradfield ◽  
Rafah Alnafakh ◽  
...  
Keyword(s):  
Quantitative Polymerase Chain Reaction ◽  
Treatment Strategies ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Altered Expression ◽  
Associated Proteins ◽  
Cancer Genome Atlas ◽  
Novel Treatment Strategies ◽  
Post Menopausal

Risk of relapse of endometrial cancer (EC) after surgical treatment is 13% and recurrent disease carries a poor prognosis. Research into prognostic indicators is essential to improve EC management and outcome. “Immortality” of most cancer cells is dependent on telomerase, but the role of associated proteins in the endometrium is poorly understood. The Cancer Genome Atlas data highlighted telomere/telomerase associated genes (TTAGs) with prognostic relevance in the endometrium, and a recent in silico study identified a group of TTAGs and proteins as key regulators within a network of dysregulated genes in EC. We characterise relevant telomere/telomerase associated proteins (TTAPs) NOP10, NHP2, NOP56, TERF1, TERF2 and TERF2IP in the endometrium using quantitative polymerase chain reaction (qPCR) and immunohistochemistry (IHC). qPCR data demonstrated altered expression of multiple TTAPs; specifically, increased NOP10 (p = 0.03) and reduced NHP2 (p = 0.01), TERF2 (p = 0.01) and TERF2IP (p < 0.003) in EC relative to post-menopausal endometrium. Notably, we report reduced NHP2 in EC compared to post-menopausal endometrium in qPCR and IHC (p = 0.0001) data; with survival analysis indicating high immunoscore is favourable in EC (p = 0.0006). Our findings indicate a potential prognostic role for TTAPs in EC, particularly NHP2. Further evaluation of the prognostic and functional role of the examined TTAPs is warranted to develop novel treatment strategies.

scholarly journals Prognostic and Immunological Role of Gasdermin E in Pan-Cancer Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Zheng Zhang ◽  
Shuangshuang Zhao ◽  
Haizhen Yang ◽  
Yanwei Chen ◽  
Huahui Feng ◽  
...  
Keyword(s):  
The Cancer Genome Atlas ◽  
Survival Prognosis ◽  
Cancer Management ◽  
Cancer Associated Fibroblast ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Functional Mechanisms ◽  
Pan Cancer

Despite accumulating cell- or animal-based experiments providing the relationship between Gasdermin E (GSDME) and human diseases, especially in malignant cancers, no pan-cancer analysis about the function of GSMDE in cancer management can be available up to date. Our research, for the first time, explored the potential carcinogenic role of GSDME across 33 tumors from the public platform of TCGA (The cancer genome atlas) database. GSDME is highly expressed in most malignant cancers, and obvious relationship exists between GSDME level and survival prognosis of cancer patients. The expression of GSDME was statically associated with the cancer-associated fibroblast infiltration in diverse cancer types, such as BLCA, CHOL, GBM, KIRC, LIHC, MESO, STAD, and UCEC. Furthermore, pyroptosis, sensory perception of sound, and defense response to bacterium were involved in the functional mechanisms of GSDME expression from GO analysis. Last but not the least, in vitro experiments were also performed to identify GSDME-induced pyroptosis. Our first pan-cancer analysis of GSDME not only broadens the understanding of the carcinogenic roles of GSDME but also provides a promising therapeutic strategy for benefiting an increasing number of cancerous patients based on GSDME-induced pyroptosis.

scholarly journals Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Cell Reports ◽  
2018 ◽  
Vol 23 (1) ◽  
pp. 172-180.e3 ◽  
Author(s):  
Gregory P. Way ◽  
Francisco Sanchez-Vega ◽  
Konnor La ◽  
Joshua Armenia ◽  
Walid K. Chatila ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Ras Pathway ◽  
Pathway Activation ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

scholarly journals Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer specific driver

2021 ◽  
Author(s):  
Pathum Kossinna ◽  
Weijia Cai ◽  
Xuewen Lu ◽  
Carrie S Shemanko ◽  
Qingrun Zhang
Keyword(s):  
Genome Instability ◽  
Core Gene ◽  
The Cancer Genome Atlas ◽  
Cancer Pathways ◽  
Network Analyses ◽  
Complex Interactions ◽  
Cancer Genome Atlas ◽  
Oncogenic Driver ◽  
Pan Cancer

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (1) co-expression network analyses focusing on correlations between genes; (2) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: a slight change of parameterization or dataset could lead to dramatic alternations of outcomes. Here, we propose Stabilized Core gene and Pathway Election, or SCOPE, a tool integrating bootstrapped LASSO and co-expression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations towards complex interactions using transcriptome data.

scholarly journals An Integrative Pan-Cancer Analysis of the Oncogenic Role of COPB2 in Human Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Biao Wu ◽  
Yumeng Wu ◽  
Xianlin Guo ◽  
Yanping Yue ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Comprehensive Understanding ◽  
Chemotherapy Sensitivity ◽  
Tumor Study ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Patient Prognosis ◽  
Pan Cancer

Several studies have suggested that coatomer protein complex subunit beta 2 (COPB2) may act as an oncogene in various cancer types. However, no systematic pan-cancer analysis has been performed to date. Therefore, the present study analyzed the potential oncogenic role of COPB2 using TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) datasets. The majority of the cancer types overexpressed the COPB2 protein, and its expression significantly correlated with tumor prognosis. In certain tumors, such as those found in breast and ovarian tissues, phosphorylated S859 exhibited high expression. It was found that mutations of the COPB2 protein in kidney and endometrial cancers exhibited a significant impact on patient prognosis. It is interesting to note that COPB2 expression correlated with the number of cancer-associated fibroblasts in certain tumors, such as cervical and endocervical cancers and colon adenocarcinomas. In addition, COPB2 was involved in the transport of substances and correlated with chemotherapy sensitivity. This is considered the first pan-tumor study, which provided a relatively comprehensive understanding of the mechanism by which COPB2 promotes cancer growth.

scholarly journals Mining The Cancer Genome Atlas gene expression data for lineage markers in distinguishing bladder urothelial carcinoma and prostate adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ewe Seng Ch’ng
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
The Cancer Genome Atlas ◽  
Relative Importance ◽  
Expression Data ◽  
Gene Expressions ◽  
Urothelial Carcinomas ◽  
Cancer Genome Atlas ◽  
Lineage Markers ◽  
Genome Atlas

AbstractDistinguishing bladder urothelial carcinomas from prostate adenocarcinomas for poorly differentiated carcinomas derived from the bladder neck entails the use of a panel of lineage markers to help make this distinction. Publicly available The Cancer Genome Atlas (TCGA) gene expression data provides an avenue to examine utilities of these markers. This study aimed to verify expressions of urothelial and prostate lineage markers in the respective carcinomas and to seek the relative importance of these markers in making this distinction. Gene expressions of these markers were downloaded from TCGA Pan-Cancer database for bladder and prostate carcinomas. Differential gene expressions of these markers were analyzed. Standard linear discriminant analyses were applied to establish the relative importance of these markers in lineage determination and to construct the model best in making the distinction. This study shows that all urothelial lineage genes except for the gene for uroplakin III were significantly expressed in bladder urothelial carcinomas (p < 0.001). In descending order of importance to distinguish from prostate adenocarcinomas, genes for uroplakin II, S100P, GATA3 and thrombomodulin had high discriminant loadings (> 0.3). All prostate lineage genes were significantly expressed in prostate adenocarcinomas(p < 0.001). In descending order of importance to distinguish from bladder urothelial carcinomas, genes for NKX3.1, prostate specific antigen (PSA), prostate-specific acid phosphatase, prostein, and prostate-specific membrane antigen had high discriminant loadings (> 0.3). Combination of gene expressions for uroplakin II, S100P, NKX3.1 and PSA approached 100% accuracy in tumor classification both in the training and validation sets. Mining gene expression data, a combination of four lineage markers helps distinguish between bladder urothelial carcinomas and prostate adenocarcinomas.

Sign in / Sign up

Export Citation Format

Share Document