KMT2C is Potential Prognostic Biomarker and its Immune Regulating Roles in Pan-Cancer: A Comprehensive Analysis

Abstract Background: A member of histone lysine methyltransferases subfamily, The histone 3 lysine 4 (H3K4) monomethylase KMT2C, has mutations across many cancer types. However, the role of KMT2C in different cancers and its correlation with tumor infiltration and immune therapy indicators remain unknown.Method: Expression and mutation information of KMT2C has been analyzed through the Genotype-Tissue Expression (GTEx), The Cancer Genome Atlas (TCGA) Cancer Cell Line Encyclopedia (CCLE) and International Cancer Genome Consortium (ICGC) database in our study. Prognostic value of KMT2C was evaluated via univariate survival analysisand expression detection in different cancer cells. Result: Survival analysis showed that high expression of KMT2C in some cancer type may be a indication of better outcome, while in other cancer like UVM, patient with high expression of KMT2C suffered from early recurrence. Further, we found there is a strongly link between KMT2C expression and immune cells infiltration, mutation indicators through analysising in the Tumor Immune Evaluation Resource (TIMER) database. Conclusion: The bioinformatics analysis here deliver us a message that KMT2C might be a good molecular biomarker for prognostic and therapeutic evaluation in specific cancer types.

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Putatively cancer-specific exon–exon junctions are shared across patients and present in developmental and other non-cancer cells

NAR Cancer ◽

10.1093/narcan/zcaa001 ◽

2020 ◽

Vol 2 (1) ◽

Cited By ~ 1

Author(s):

Julianne K David ◽

Sean K Maden ◽

Benjamin R Weeder ◽

Reid F Thompson ◽

Abhinav Nellore

Keyword(s):

Rna Splicing ◽

Tissue Expression ◽

The Body ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Rna Seq ◽

Normal Tissues ◽

Cancer Types ◽

Exon Junctions ◽

Cancer Tissues

Abstract This study probes the distribution of putatively cancer-specific junctions across a broad set of publicly available non-cancer human RNA sequencing (RNA-seq) datasets. We compared cancer and non-cancer RNA-seq data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) Project and the Sequence Read Archive. We found that (i) averaging across cancer types, 80.6% of exon–exon junctions thought to be cancer-specific based on comparison with tissue-matched samples (σ = 13.0%) are in fact present in other adult non-cancer tissues throughout the body; (ii) 30.8% of junctions not present in any GTEx or TCGA normal tissues are shared by multiple samples within at least one cancer type cohort, and 87.4% of these distinguish between different cancer types; and (iii) many of these junctions not found in GTEx or TCGA normal tissues (15.4% on average, σ = 2.4%) are also found in embryological and other developmentally associated cells. These findings refine the meaning of RNA splicing event novelty, particularly with respect to the human neoepitope repertoire. Ultimately, cancer-specific exon–exon junctions may have a substantial causal relationship with the biology of disease.

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Putatively cancer-specific alternative splicing is shared across patients and present in developmental and other non-cancer cells

10.1101/754044 ◽

2019 ◽

Author(s):

Julianne K. David ◽

Sean K. Maden ◽

Benjamin R. Weeder ◽

Reid F. Thompson ◽

Abhinav Nellore

Keyword(s):

Rna Splicing ◽

Cell Types ◽

Tissue Expression ◽

The Body ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Rna Seq ◽

Normal Tissues ◽

Cancer Types ◽

Exon Junctions

ABSTRACTWe compared cancer and non-cancer RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) Project, and the Sequence Read Archive (SRA). We found that: 1) averaging across cancer types, 80.6% of exon-exon junctions thought to be cancer-specific based on comparison with tissue-matched samples are in fact present in other adult non-cancer tissues throughout the body; 2) 30.8% of junctions not present in any GTEx or TCGA normal tissues are shared by multiple samples within at least one cancer type cohort, and 87.4% of these distinguish between different cancer types; and 3) many of these junctions not found in GTEx or TCGA normal tissues (15.4% on average) are also found in embryological and other developmentally associated cells. This study probes the distribution of putatively cancer-specific junctions across a broad set of publicly available non-cancer human RNA-seq datasets. Overall, we identify a subset of shared cancer-specific junctions that could represent novel sources of cancer neoantigens. We further describe a framework for characterizing possible origins of these junctions, including potential developmental and embryological sources, as well as cell type-specific markers particularly related to cell types of cancer origin. These findings refine the meaning of RNA splicing event novelty, particularly with respect to the human neoepitope repertoire. Ultimately, cancer-specific exon-exon junctions may affect the anti-cancer immune response and may have a substantial causal relationship with the biology of disease.

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Comprehensive Pan-Cancer Analyses of Pyroptosis-Related Genes to Predict Survival and Immunotherapeutic Outcome

Cancers ◽

10.3390/cancers14010237 ◽

2022 ◽

Vol 14 (1) ◽

pp. 237

Author(s):

Qilin Wang ◽

Qian Liu ◽

Sihan Qi ◽

Junyou Zhang ◽

Xian Liu ◽

...

Keyword(s):

Cancer Progression ◽

Risk Model ◽

Tissue Expression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Transcriptional Level ◽

Number Variation ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Novel Strategy

Pyroptosis is a newly characterized type of programmed cell death. However, its function in cancer progression and its response to treatments remain controversial. Here, we extensively and systematically compiled genes associated with pyroptosis, integrated multiomics data and clinical data across 31 cancer types from The Cancer Genome Atlas, and delineated the global alterations in PRGs at the transcriptional level. The underlying transcriptional regulations by copy number variation, miRNAs, and enhancers were elucidated by integrating data from the Genotype-Tissue Expression and International Cancer Genome Consortium. A prognostic risk model, based on the expression of PRGs across 31 cancer types, was constructed. To investigate the role of pyroptosis in immunotherapy, we found five PRGs associated with effectiveness by exploring the RNA-Seq data of patients with immunotherapy, and further identified two small-molecule compounds that are potentially beneficial for immunotherapy. For the first time, from a pyroptosis standpoint, this study establishes a novel strategy to predict cancer patient survival and immunotherapeutic outcomes.

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ASF1B Promotes Oncogenesis in Lung Adenocarcinoma and Other Cancer Types

Frontiers in Oncology ◽

10.3389/fonc.2021.731547 ◽

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.

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OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Nucleic Acids Research ◽

10.1093/nar/gkaa1033 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D1289-D1301 ◽

Cited By ~ 2

Author(s):

Tao Wang ◽

Shasha Ruan ◽

Xiaolu Zhao ◽

Xiaohui Shi ◽

Huajing Teng ◽

...

Keyword(s):

Cancer Genome ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Genes ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Cell Population ◽

Cancer Types ◽

Neutral Mutations ◽

Analysis Platform

Abstract The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

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GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽

10.3390/cells8070675 ◽

2019 ◽

Vol 8 (7) ◽

pp. 675 ◽

Cited By ~ 5

Author(s):

Xia ◽

Liu ◽

Zhang ◽

Guo

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Gene Expression Data ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Cell Line ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Expression Data ◽

Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

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An Accurate Prognostic Model and Treatment for Pancreatic Carcinoma Based on the Tumor Immune Microenvironment

10.21203/rs.3.rs-1121256/v1 ◽

2021 ◽

Author(s):

Shujie Wang ◽

zhenchong li ◽

chunsheng liu ◽

qi zhou ◽

zuyi ma ◽

...

Keyword(s):

Immune Cell ◽

Nk Cell ◽

Immune Therapy ◽

R Package ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Therapy Effect ◽

The Relationship ◽

Worse Prognosis

Abstract BackgroundPancreatic adenocarcinoma (PAAD) is a highly malignant cancer with a poor prognosis. The tumor microenvironment (TME) is closely related to tumorigenesis, progression, and treatment. However, the relationship between TME immune cell genes and prognosis in PAAD is currently unclear.Methodsn this study, we identified three prognostic subtypes based on the TME by using data from The Cancer Genome Atlas (TCGA) database, The International Cancer Genome Consortium (ICGC) database and University of California Santa Cruz (UCSC) database. The Silhouette plot analysis was used to evaluate 758 immune genes expression in PAAD from each database, then to divide all samples into three subtypes (Clusters A, B, C) by Lasso’s binomial logistic regression. We analyzed the relationship between subtypes and prognosis by the survival R package. CIBERSORT was used for evaluating the expression changes of immune cells. We detect the copy number variation areas between two groups through GISTIC 2.0 algorithm. The TIDE network tool was used to predict the response of immune therapy.ResultsWe defined three clusters (Clusters A, B, and C) based on the analysis of immune gene expression. Cluster B got a worse prognosis than the other two clusters. The Cluster B group had the highest level of Macrophages M0 and Macrophage M2. NK cell resting was much higher in Cluster B than other groups in TME. Gene KRAS was mutated in 77% of all samples. Cluster C had a better immune therapy effect than others.ConclusionsWe found a news model to predicted patients’ prognosis who with pancreatic adenocarcinoma. Cluster B had the significant worse prognosis than other groups. Patients in Cluster C could get batter treatment effect by using immunotherapy.

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Cancer Type-Dependent Correlations betweenTP53Mutations and Antitumor Immunity

10.1101/692715 ◽

2019 ◽

Author(s):

Lin Li ◽

Mengyuan Li ◽

Xiaosheng Wang

Keyword(s):

Antitumor Immunity ◽

Colon Adenocarcinoma ◽

Joint Effect ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Mutation Burden ◽

Cancer Genome Atlas ◽

Negative Role ◽

Stomach Adenocarcinoma ◽

Cancer Types

AbstractMany studies have shown thatTP53mutations play a negative role in antitumor immunity. However, a few studies reported thatTP53mutations could promote antitumor immunity. To explain these contradictory findings, we analyzed five cancer cohorts from The Cancer Genome Atlas (TCGA) project. We found thatTP53-mutated cancers had significantly higher levels of antitumor immune signatures thanTP53-wildtype cancers in breast invasive carcinoma (BRCA) and lung adenocarcinoma (LUAD). In contrast,TP53-mutated cancers had significantly lower antitumor immune signature levels thanTP53-wildtype cancers in stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and head and neck squamous cell carcinoma (HNSC). Moreover,TP53-mutated cancers likely had higher tumor mutation burden (TMB) and tumor aneuploidy level (TAL) thanTP53-wildtype cancers. However, the TMB differences were more marked betweenTP53-mutated andTP53-wildtype cancers than the TAL differences in BRCA and LUAD, and the TAL differences were more significant in STAD and COAD. Furthermore, we showed that TMB and TAL had a positive and a negative correlation with antitumor immunity and that TMB affected antitumor immunity more greatly than TAL did in BRCA and LUAD while TAL affected antitumor immunity more strongly than TMB in STAD and HNSC. These findings indicate that the distinct correlations betweenTP53mutations and antitumor immunity in different cancer types are a consequence of the joint effect of the altered TMB and TAL caused byTP53mutations on tumor immunity. Our data suggest that theTP53mutation status could be a useful biomarker for cancer immunotherapy response depending on cancer types.

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Prognostic and immune regulating roles of YIF1B in Pan-Cancer: a potential target for both survival and therapy response evaluation

Bioscience Reports ◽

10.1042/bsr20201384 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Jun Liu ◽

Zheng Chen ◽

Pingsen Zhao ◽

Wenli Li

Keyword(s):

Tumor Growth ◽

Membrane Trafficking ◽

Serotonin Receptor ◽

Immune Cell ◽

Therapy Response ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Poor Overall Survival ◽

Cancer Types ◽

Spearman Test

Abstract The neurotransmitter, serotonin has emerged as a tumor growth factor and immune response regulator through complex signaling pathways. Yip1 Interacting Factor Homolog B (YIF1B) is a membrane protein involved in serotonin receptor (HTR) membrane trafficking and signal transmission in neuropathy. Participation of YIF1B in serotonin-induced tumor growth and immune regulation has not been previously investigated. Data for analysis of YIF1B mRNA expression were downloaded from the website portals: The Cancer Genome Atlas (TCGA), GTEx, Cancer Cell Line Encyclopedia (CCLE) and International Cancer Genome Consortium (ICGC), including clinical and mutational information. Survival analysis included the Kaplan–Meier method for calculation of the cumulative incidence of the survival event and the log rank method for comparison of survival curves between groups. Infiltration levels of immune cells were calculated and correlated with YIF1B expression using the Spearman correlation test to evaluate significance. Further correlation analyses between YIF1B expression and mutation indicators such as tumor mutation burden (TMB), microsatellite instability (MSI), and mismatch repair (MMR) were also examined by the Spearman test. YIF1B expression was elevated in most cancer types and this high expression was indicative of poor overall survival (OS) and death-specific survival. In breast invasive carcinoma (BRCA) and liver hepatocellular carcinoma (LIHC), high YIF1B expression correlated with a poor disease-free interval (DFI), indicating a role in malignancy progression. There was a positive relationship between YIF1B expression and immune cell infiltration in several cancer types, and YIF1B also positively correlated with TMB, MSI, and methylation in some cancer types, linking its expression to possible evaluation of therapy response. The bioinformatics analyses have, therefore, established YIF1B as a good biomarker for prognostic and therapeutic evaluation.

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Pan-cancer analysis reveals complex tumor-specific alternative polyadenylation

10.1101/160960 ◽

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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