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.

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 OncoRank: A pan-cancer method of combining survival correlations and its application to mRNAs, miRNAs, and lncRNAs

2016 ◽  
Author(s):  
Jordan Anaya
Keyword(s):  
Mirna Target ◽  
Patient Survival ◽  
The Cancer Genome Atlas ◽  
P Values ◽  
Cancer Pathways ◽  
Large Numbers ◽  
Protective Genes ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

OncoRank adopts a method for finding recurrent miRNA-target interactions to find genes with consistent relationships to patient survival across cancers. Genes are first ranked in each cancer by their Cox coefficients, and these ranks are then combined by applying Fisher's method. Using ranks instead of the raw coefficients or p-values allows each cancer to be weighted equally and prevents bias from cancers with large numbers of patients. OncoLnc (http://www.oncolnc.org) is a newly available resource for Cox coefficients and utilizes data from 21 cancers in The Cancer Genome Atlas. Using this resource I applied OncoRank to mRNAs, miRNAs, and lncRNAs and in each case found consistently harmful or protective genes. These genes may be members of central cancer pathways and should be of interest to cancer researchers.

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.

scholarly journals OncoRank: A pan-cancer method of combining survival correlations and its application to mRNAs, miRNAs, and lncRNAs

2016 ◽  
Author(s):  
Jordan Anaya
Keyword(s):  
Mirna Target ◽  
Patient Survival ◽  
The Cancer Genome Atlas ◽  
P Values ◽  
Cancer Pathways ◽  
Large Numbers ◽  
Protective Genes ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

OncoRank adopts a method for finding recurrent miRNA-target interactions to find genes with consistent relationships to patient survival across cancers. Genes are first ranked in each cancer by their Cox coefficients, and these ranks are then combined by applying Fisher's method. Using ranks instead of the raw coefficients or p-values allows each cancer to be weighted equally and prevents bias from cancers with large numbers of patients. OncoLnc (http://www.oncolnc.org) is a newly available resource for Cox coefficients and utilizes data from 21 cancers in The Cancer Genome Atlas. Using this resource I applied OncoRank to mRNAs, miRNAs, and lncRNAs and in each case found consistently harmful or protective genes. These genes may be members of central cancer pathways and should be of interest to cancer researchers.

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 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 Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas

2018 ◽  
Vol 17 (2) ◽  
pp. 476-487 ◽  
Author(s):  
Fengju Chen ◽  
Yiqun Zhang ◽  
Sooryanarayana Varambally ◽  
Chad J. Creighton
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

scholarly journals PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas

2018 ◽  
Vol 19 (10) ◽  
pp. 3250 ◽  
Author(s):  
Anna Sorrentino ◽  
Antonio Federico ◽  
Monica Rienzo ◽  
Patrizia Gazzerro ◽  
Maurizio Bifulco ◽  
...  
Keyword(s):  
Family Members ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Gene ◽  
Rna Seq ◽  
Set Domain ◽  
Primary Tumors ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

The PR/SET domain gene family (PRDM) encodes 19 different transcription factors that share a subtype of the SET domain [Su(var)3-9, enhancer-of-zeste and trithorax] known as the PRDF1-RIZ (PR) homology domain. This domain, with its potential methyltransferase activity, is followed by a variable number of zinc-finger motifs, which likely mediate protein–protein, protein–RNA, or protein–DNA interactions. Intriguingly, almost all PRDM family members express different isoforms, which likely play opposite roles in oncogenesis. Remarkably, several studies have described alterations in most of the family members in malignancies. Here, to obtain a pan-cancer overview of the genomic and transcriptomic alterations of PRDM genes, we reanalyzed the Exome- and RNA-Seq public datasets available at The Cancer Genome Atlas portal. Overall, PRDM2, PRDM3/MECOM, PRDM9, PRDM16 and ZFPM2/FOG2 were the most mutated genes with pan-cancer frequencies of protein-affecting mutations higher than 1%. Moreover, we observed heterogeneity in the mutation frequencies of these genes across tumors, with cancer types also reaching a value of about 20% of mutated samples for a specific PRDM gene. Of note, ZFPM1/FOG1 mutations occurred in 50% of adrenocortical carcinoma patients and were localized in a hotspot region. These findings, together with OncodriveCLUST results, suggest it could be putatively considered a cancer driver gene in this malignancy. Finally, transcriptome analysis from RNA-Seq data of paired samples revealed that transcription of PRDMs was significantly altered in several tumors. Specifically, PRDM12 and PRDM13 were largely overexpressed in many cancers whereas PRDM16 and ZFPM2/FOG2 were often downregulated. Some of these findings were also confirmed by real-time-PCR on primary tumors.

scholarly journals Role of INSL4 Signaling in Sustaining the Growth and Viability of LKB1-Inactivated Lung Cancer

2018 ◽  
Vol 111 (7) ◽  
pp. 664-674 ◽  
Author(s):  
Rongqiang Yang ◽  
Steven W Li ◽  
Zirong Chen ◽  
Xin Zhou ◽  
Wei Ni ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Transcriptome Profiling ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Growth And Survival ◽  
Cancer Genome Atlas ◽  
Lung Adenocarcinomas ◽  
Genome Atlas

Abstract Background The LKB1 tumor suppressor gene is commonly inactivated in non-small cell lung carcinomas (NSCLC), a major form of lung cancer. Targeted therapies for LKB1-inactivated lung cancer are currently unavailable. Identification of critical signaling components downstream of LKB1 inactivation has the potential to uncover rational therapeutic targets. Here we investigated the role of INSL4, a member of the insulin/IGF/relaxin superfamily, in LKB1-inactivated NSCLCs. Methods INSL4 expression was analyzed using global transcriptome profiling, quantitative reverse transcription PCR, western blotting, enzyme-linked immunosorbent assay, and RNA in situ hybridization in human NSCLC cell lines and tumor specimens. INSL4 gene expression and clinical data from The Cancer Genome Atlas lung adenocarcinomas (n = 515) were analyzed using log-rank and Fisher exact tests. INSL4 functions were studied using short hairpin RNA (shRNA) knockdown, overexpression, transcriptome profiling, cell growth, and survival assays in vitro and in vivo. All statistical tests were two-sided. Results INSL4 was identified as a novel downstream target of LKB1 deficiency and its expression was induced through aberrant CRTC-CREB activation. INSL4 was highly induced in LKB1-deficient NSCLC cells (up to 543-fold) and 9 of 41 primary tumors, although undetectable in all normal tissues except the placenta. Lung adenocarcinomas from The Cancer Genome Atlas with high and low INSL4 expression (with the top 10th percentile as cutoff) showed statistically significant differences for advanced tumor stage (P < .001), lymph node metastasis (P = .001), and tumor size (P = .01). The INSL4-high group showed worse survival than the INSL4-low group (P < .001). Sustained INSL4 expression was required for the growth and viability of LKB1-inactivated NSCLC cells in vitro and in a mouse xenograft model (n = 5 mice per group). Expression profiling revealed INSL4 as a critical regulator of cell cycle, growth, and survival. Conclusions LKB1 deficiency induces an autocrine INSL4 signaling that critically supports the growth and survival of lung cancer cells. Therefore, aberrant INSL4 signaling is a promising therapeutic target for LKB1-deficient lung cancers.

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