scholarly journals Identification of cross-talk between m6A/m5C regulators and ferroptosis associated with immune infiltration and prognosis in pan-cancer

2021 ◽  
Author(s):  
Yingli Zhang
Keyword(s):  
Cross Talk ◽  
Immune Cell ◽  
Epigenetic Modification ◽  
Prognostic Significance ◽  
Genomic Alteration ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Combination Strategy ◽  
Non Coding Rna ◽  
Cancer Types

Abstract Although it has been recognized that m6A/5mC methylation and ferroptosis play critical roles in different types of cancers, little is known about the relationship between them. In addition, there is also a growing appreciation that m6A/5mC methylation and ferroptosis may affect immune cell infiltration and activation. This study aimed to reveal the extensive cross-talk between epigenetic modification and ferroptosis. A total of 31 cancer type-specific datasets in TCGA were individually collected by the publicly available web servers for multiple bioinformatic analyses of m6A/5mC regulators and ferroptosis-related genes. Intriguingly, m6A/5mC regulators and ferroptosis-related genes were identified to have considerable global coverage and prognostic significance across multiple cancer types. Moreover, m6A/5mC regulators showed interactive potential with ferroptosis-related genes, and genomic alteration of ferroptosis-related genes coupled with m6A/5mC regulators, at least in pancreatic cancer. Furthermore, m6A/5mC regulators and ferroptosis-related genes were found to be significantly associated with TILs. Finally, m6A/5mC regulators and ferroptosis-related genes exhibited functionally related to each other or co-regulated by TF or non-coding RNA. Together, m6A/5mC methylation and ferroptosis show a wide-ranging connection, and a combination strategy of epigenetic and ferroptosis therapies with ICP inhibitors may benefit more cancer patients in the future.

scholarly journals Long non-coding RNA FAM83H-AS1 acts as a potential oncogenic driver in human ovarian cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaolei Yuan ◽  
Ying Huang ◽  
Man Guo ◽  
Xiaowei Hu ◽  
Peiling Li
Keyword(s):  
Ovarian Cancer ◽  
Normal Control ◽  
Immune Cell ◽  
Multiple Cancer ◽  
Non Coding Rna ◽  
Incidence And Mortality ◽  
Cancer Types ◽  
Oncogenic Driver ◽  
Therapy Target ◽  
Long Non Coding Rna

Abstract Objective Ovarian cancer (OC) is one of the most aggressive women cancers with increasing incidence and mortality rates worldwide. Long non-coding RNAs (lncRNAs) could as major players in OC process. Although FAM83H antisense RNA1 (FAM83H-AS1) is demonstrated play an important roles in a many cancers, the detailed function and mechanism has not been reported in OC. Results We integrated multiple kinds of bioinformatics approaches and experiments validated method to evaluate functions of FAM83H-AS1 in OC. Some differential expressed lncRNAs were identified between OC and normal control tissues. FAM83H-AS1 was one of most differentially expressed lncRNAs and up-regulated in multiple cancer types. Specially, expression of FAM83H-AS1 was higher in OC and showed difference in diverse stages. High FAM83H-AS1 expression is associated with worse pan-cancer and OC outcomes. FAM83H-AS1-centric network including lncRNA-miRNA, lncRNA-protein and lncRNA-mRNA ceRNA network were constructed to infer the function and mechanism of FAM83H-AS1. There were two methylation sites including cg01399317 and cg20519035 located at FAM83H-AS1. The methylation level of cg01399317 was correlated with gene expression of FAM83H-AS1. The expression level of FAM83H-AS1 was correlated with infiltration level of immune cell including macrophage, neutrphil and dendritic cell in OC patients. Lastly, qRT-PCR showed that the expression of FAM83H-AS1 was higher in OC tissues than normal control tissues. Conclusion Collectively, these results indicated that FAM83H-AS1 may act as an oncogenic driver and it may be a potential therapy target in OC.

scholarly journals Long non-coding RNA FAM83H-AS1 acts as an oncogenic driver in human ovarian cancer

2020 ◽  
Author(s):  
Xiaolei Yuan ◽  
Ying Huang ◽  
Man Guo ◽  
Xiaowei Hu ◽  
Peiling Li
Keyword(s):  
Ovarian Cancer ◽  
Normal Control ◽  
Immune Cell ◽  
Multiple Cancer ◽  
Non Coding Rna ◽  
Incidence And Mortality ◽  
Cancer Types ◽  
Oncogenic Driver ◽  
Therapy Target ◽  
Long Non Coding Rna

Abstract Background Ovarian cancer (OC) is one of the most aggressive women cancers with increasing incidence and mortality rates worldwide. Long non-coding RNAs (lncRNAs) could as major players in OC process. Although FAM83H antisense RNA1 (FAM83H-AS1) is demonstrated play an important roles in a many cancers, the detailed function and mechanism has not been reported in OC. Methods We integrated multiple kinds of bioinformatics approaches and experiments validated method to evaluate functions of FAM83H-AS1 in OC. Results Some differential expressed lncRNAs were identified between OC and normal control tissues. FAM83H-AS1 was one of most differentially expressed lncRNAs and up-regulated in multiple cancer types. Specially, expression of FAM83H-AS1 was higher in OC and showed difference in diverse stages. High FAM83H-AS1 expression is associated with worse pan-cancer and OC outcomes. FAM83H-AS1-centric network including lncRNA-miRNA, lncRNA-protein and lncRNA-mRNA ceRNA network were constructed to infer the function and mechanism of FAM83H-AS1. There were two methylation sites including cg01399317 and cg20519035 located at FAM83H-AS1. The methylation level of cg01399317 was correlated with gene expression of FAM83H-AS1. The expression level of FAM83H-AS1 was correlated with infiltration level of immune cell including macrophage, neutrphil and dendritic cell in OC patients. Lastly, qRT-PCR showed that the expression of FAM83H-AS1 was higher in OC tissues than normal control tissues. Conclusions Collectively, these results indicated that FAM83H-AS1 may act as an oncogenic driver and it may be a potential therapy target in OC.

scholarly journals A Pan-Cancer Study: The Immunological and Prognostic Significance of Aberrant SPP1 Expression on Tumors

2021 ◽  
Author(s):  
Tian peng Huang ◽  
Wei Ye ◽  
Xue jiao Lin
Keyword(s):  
Tumor Immunity ◽  
Cancer Progression ◽  
Immune Cell ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Multiple Cancer ◽  
Multiple Tumor ◽  
Cancer Types

Abstract Background Secretory phosphoprotein 1 (SPP1) is a glyco-phosphoprotein that is widely expressed in a variety of cancer cells. Current studies have identified that SPP1 is differentially expressed in a variety of cancer cell species. However, there are few studies on the level of SPP1 expression in different types of cancer and its clinical significance. Methods In this study, we analyzed SPP1 levels and its significance in 33 different cancer types by using The Cancer Genome Atlas (TCGA) database. The study analyzed the correlation between SPP1 expression and tumor immunity. Results The results showed that SPP1 transcript levels were aberrantly expressed in most tumors. Univariate Cox analysis showed that SPP1 was strongly associated with Overall survival in multiple tumor types. We also found that SPP1 was significantly correlated with tumor immune microenvironment, tumor immune cells, and tumor infiltrating lymphocyte markers. The correlation of SPP1 with Tumor mutational load (TMB) and Microsatellite instability (MSI) also predicts its role in assessing the efficacy of immunotherapy. Gene set enrichment analysis of 33 cancer types provided further evidence for the relationship between SPP1 levels and cancer progression and immune cell infiltration. Conclusion our study concludes that SPP1 plays an important role in tumorigenesis and tumor immunity and can be used as a marker for the assessment of clinical indicators in multiple cancer types.

scholarly journals Long non-coding RNA FAM83H-AS1 acts as an oncogenic driver in human ovarian cancer

2020 ◽  
Author(s):  
Xiaolei Yuan ◽  
Ying Huang ◽  
Man Guo ◽  
Xiaowei Hu ◽  
Peiling Li
Keyword(s):  
Ovarian Cancer ◽  
Normal Control ◽  
Immune Cell ◽  
Multiple Cancer ◽  
Non Coding Rna ◽  
Incidence And Mortality ◽  
Cancer Types ◽  
Oncogenic Driver ◽  
Therapy Target ◽  
Long Non Coding Rna

Abstract Background Ovarian cancer (OC) is one of the most aggressive women cancers with increasing incidence and mortality rates worldwide. Long non-coding RNAs (lncRNAs) could as major players in OC process. Although FAM83H antisense RNA1 (FAM83H-AS1) is demonstrated play an important roles in a many cancers, the detailed function and mechanism has not been reported in OC. Methods We integrated multiple kinds of bioinformatics approaches and experiments validated method to evaluate functions of FAM83H-AS1 in OC. Results Some differential expressed lncRNAs were identified between OC and normal control tissues. FAM83H-AS1 was one of most differentially expressed lncRNAs and up-regulated in multiple cancer types. Specially, expression of FAM83H-AS1 was higher in OC and showed difference in diverse stages. High FAM83H-AS1 expression is associated with worse pan-cancer and OC outcomes. FAM83H-AS1-centric network including lncRNA-miRNA, lncRNA-protein and lncRNA-mRNA ceRNA network were constructed to infer the function and mechanism of FAM83H-AS1. There were two methylation sites including cg01399317 and cg20519035 located at FAM83H-AS1. The methylation level of cg01399317 was correlated with gene expression of FAM83H-AS1. The expression level of FAM83H-AS1 was correlated with infiltration level of immune cell including macrophage, neutrphil and dendritic cell in OC patients. Lastly, qRT-PCR showed that the expression of FAM83H-AS1 was higher in OC tissues than normal control tissues. Conclusions Collectively, these results indicated that FAM83H-AS1 may act as an oncogenic driver and it may be a potential therapy target in OC.

scholarly journals Long Non-Coding RNA FAM83H-AS1 Acts as a Potential Oncogenic Driver in Human Ovarian Cancer

2020 ◽  
Author(s):  
Xiaolei Yuan ◽  
Ying Huang ◽  
Man Guo ◽  
Xiaowei Hu ◽  
Peiling Li
Keyword(s):  
Ovarian Cancer ◽  
Normal Control ◽  
Immune Cell ◽  
Multiple Cancer ◽  
Non Coding Rna ◽  
Incidence And Mortality ◽  
Cancer Types ◽  
Oncogenic Driver ◽  
Therapy Target ◽  
Long Non Coding Rna

Abstract Objective Ovarian cancer (OC) is one of the most aggressive women cancers with increasing incidence and mortality rates worldwide. Long non-coding RNAs (lncRNAs) could as major players in OC process. Although FAM83H antisense RNA1 (FAM83H-AS1) is demonstrated play an important roles in a many cancers, the detailed function and mechanism has not been reported in OC. Results We integrated multiple kinds of bioinformatics approaches and experiments validated method to evaluate functions of FAM83H-AS1 in OC. Some differential expressed lncRNAs were identified between OC and normal control tissues. FAM83H-AS1 was one of most differentially expressed lncRNAs and up-regulated in multiple cancer types. Specially, expression of FAM83H-AS1 was higher in OC and showed difference in diverse stages. High FAM83H-AS1 expression is associated with worse pan-cancer and OC outcomes. FAM83H-AS1-centric network including lncRNA-miRNA, lncRNA-protein and lncRNA-mRNA ceRNA network were constructed to infer the function and mechanism of FAM83H-AS1. There were two methylation sites including cg01399317 and cg20519035 located at FAM83H-AS1. The methylation level of cg01399317 was correlated with gene expression of FAM83H-AS1. The expression level of FAM83H-AS1 was correlated with infiltration level of immune cell including macrophage, neutrphil and dendritic cell in OC patients. Lastly, qRT-PCR showed that the expression of FAM83H-AS1 was higher in OC tissues than normal control tissues. Conclusion Collectively, these results indicated that FAM83H-AS1 may act as an oncogenic driver and it may be a potential therapy target in OC.

scholarly journals Long non-coding RNA FAM83H-AS1 acts as a potential oncogenic driver in human ovarian cancer

2021 ◽  
Author(s):  
Xiaolei Yuan ◽  
Ying Huang ◽  
Man Guo ◽  
Xiaowei Hu ◽  
Peiling Li
Keyword(s):  
Ovarian Cancer ◽  
Normal Control ◽  
Immune Cell ◽  
Multiple Cancer ◽  
Non Coding Rna ◽  
Incidence And Mortality ◽  
Cancer Types ◽  
Oncogenic Driver ◽  
Therapy Target ◽  
Long Non Coding Rna

Abstract Objective Ovarian cancer (OC) is one of the most aggressive women cancers with increasing incidence and mortality rates worldwide. Long non-coding RNAs (lncRNAs) could as major players in OC process. Although FAM83H antisense RNA1 (FAM83H-AS1) is demonstrated play an important roles in a many cancers, the detailed function and mechanism has not been reported in OC. Results We integrated multiple kinds of bioinformatics approaches and experiments validated method to evaluate functions of FAM83H-AS1 in OC. Some differential expressed lncRNAs were identified between OC and normal control tissues. FAM83H-AS1 was one of most differentially expressed lncRNAs and up-regulated in multiple cancer types. Specially, expression of FAM83H-AS1 was higher in OC and showed difference in diverse stages. High FAM83H-AS1 expression is associated with worse pan-cancer and OC outcomes. FAM83H-AS1-centric network including lncRNA-miRNA, lncRNA-protein and lncRNA-mRNA ceRNA network were constructed to infer the function and mechanism of FAM83H-AS1. There were two methylation sites including cg01399317 and cg20519035 located at FAM83H-AS1. The methylation level of cg01399317 was correlated with gene expression of FAM83H-AS1. The expression level of FAM83H-AS1 was correlated with infiltration level of immune cell including macrophage, neutrphil and dendritic cell in OC patients. Lastly, qRT-PCR showed that the expression of FAM83H-AS1 was higher in OC tissues than normal control tissues. Conclusion Collectively, these results indicated that FAM83H-AS1 may act as an oncogenic driver and it may be a potential therapy target in OC.

scholarly journals Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

2022 ◽  
Author(s):  
Malvika Sudhakar ◽  
Raghunathan Rengaswamy ◽  
Karthik Raman
Keyword(s):  
Tumour Progression ◽  
Suppressor Gene ◽  
Tumour Suppressor Gene ◽  
Driver Mutations ◽  
Cancer Type ◽  
Expression Data ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Types ◽  
Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

scholarly journals ASF1B Promotes Oncogenesis in Lung Adenocarcinoma and Other Cancer Types

2021 ◽  
Vol 11 ◽  
Author(s):  
Wencheng Zhang ◽  
Zhouyong Gao ◽  
Mingxiu Guan ◽  
Ning Liu ◽  
Fanjie Meng ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Tumor Promoter ◽  
Regulatory Subunit ◽  
Cell Cycle Distribution ◽  
Cancer Type ◽  
Dependent Manner ◽  
Cancer Types

Anti-silencing function 1B histone chaperone (ASF1B) is known to be an important modulator of oncogenic processes, yet its role in lung adenocarcinoma (LUAD) remains to be defined. In this study, an integrated assessment of The Cancer Genome Atlas (TCGA) and genotype-tissue expression (GTEx) datasets revealed the overexpression of ASF1B in all analyzed cancer types other than LAML. Genetic, epigenetic, microsatellite instability (MSI), and tumor mutational burden (TMB) analysis showed that ASF1B was regulated by single or multiple factors. Kaplan-Meier survival curves suggested that elevated ASF1B expression was associated with better or worse survival in a cancer type-dependent manner. The CIBERSORT algorithm was used to evaluate immune microenvironment composition, and distinct correlations between ASF1B expression and immune cell infiltration were evident when comparing tumor and normal tissue samples. Gene set enrichment analysis (GSEA) indicated that ASF1B was associated with proliferation- and immunity-related pathways. Knocking down ASF1B impaired the proliferation, affected cell cycle distribution, and induced cell apoptosis in LUAD cell lines. In contrast, ASF1B overexpression had no impact on the malignant characteristics of LUAD cells. At the mechanistic level, ASF1B served as an indirect regulator of DNA Polymerase Epsilon 3, Accessory Subunit (POLE3), CDC28 protein kinase regulatory subunit 1(CKS1B), Dihydrofolate reductase (DHFR), as established through proteomic profiling and Immunoprecipitation-Mass Spectrometry (IP-MS) analyses. Overall, these data suggested that ASF1B serves as a tumor promoter and potential target for cancer therapy and provided us with clues to better understand the importance of ASF1B in many types of cancer.

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 Pan-cancer analysis reveals complex tumor-specific alternative polyadenylation

2017 ◽  
Author(s):  
Zhuyi Xue ◽  
René L Warren ◽  
Ewan A Gibb ◽  
Daniel MacMillan ◽  
Johnathan Wong ◽  
...  
Keyword(s):  
Alternative Polyadenylation ◽  
Length Change ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Rna Seq ◽  
Multiple Cancer ◽  
Tissue Samples ◽  
Cancer Genome Atlas ◽  
Specific Alternative ◽  
Cancer Types

AbstractAlternative polyadenylation (APA) of 3’ untranslated regions (3’ UTRs) has been implicated in cancer development. Earlier reports on APA in cancer primarily focused on 3’ UTR length modifications, and the conventional wisdom is that tumor cells preferentially express transcripts with shorter 3’ UTRs. Here, we analyzed the APA patterns of 114 genes, a select list of oncogenes and tumor suppressors, in 9,939 tumor and 729 normal tissue samples across 33 cancer types using RNA-Seq data from The Cancer Genome Atlas, and we found that the APA regulation machinery is much more complicated than what was previously thought. We report 77 cases (gene-cancer type pairs) of differential 3’ UTR cleavage patterns between normal and tumor tissues, involving 33 genes in 13 cancer types. For 15 genes, the tumor-specific cleavage patterns are recurrent across multiple cancer types. While the cleavage patterns in certain genes indicate apparent trends of 3’ UTR shortening in tumor samples, over half of the 77 cases imply 3’ UTR length change trends in cancer that are more complex than simple shortening or lengthening. This work extends the current understanding of APA regulation in cancer, and demonstrates how large volumes of RNA-seq data generated for characterizing cancer cohorts can be mined to investigate this process.

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