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 Higher cytolytic score correlates with an immunosuppressive tumor microenvironment and reduced survival in glioblastoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander F. Haddad ◽  
Jia-Shu Chen ◽  
Taemin Oh ◽  
Matheus P. Pereira ◽  
Rushikesh S. Joshi ◽  
...  
Keyword(s):  
T Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cd8 T Cell ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Sequencing Data ◽  
Gene Score ◽  
T Cell Infiltration ◽  
The Relationship

Abstract Cytolytic score (CYT), calculated from mRNA expression levels of granzyme and perforin, positively correlates with CD8+ T cell infiltration/activity in a variety of cancers. Unlike other cancers, higher CYT has been associated with worse prognosis in glioblastoma (GBM). To address this discrepancy, we sought to investigate the relationship between CYT and immune checkpoint gene score (ICGscore), as well as their correlation with patient survival and tumor immune cell infiltration. Clinical and RNA-sequencing data for patients with newly diagnosed GBM were obtained from The Cancer Genome Atlas. Maximally-selected rank statistics was used to dichotomize subgroups. CIBERSORT was used to estimate abudence of immune cell-types. Spearman correlation was used to characterize the relationship between CYT and ICGscore. Kaplan–Meier curves were generated for survival analysis. Overall, 28/151 patients had high CYT. High CYT was associated with a mesenchymal subtype (p < 0.001) and worse survival (7.45 vs. 12.2 months, p < 0.001). There were no differences in patient demographics, IDH/MGMT mutation status, or treatment. On subgroup analysis, patients with high CYT/ICGscore had significantly increased CD8+ infiltration (p < 0.001), as expected, and worse survival (HR 0.445, p < 0.01). Furthermore, CYT strongly correlated with ICGscore (RS = 0.675, p < 0.001). The high CYT/ICGscore subgroup was associated with greater infiltration of M2 macrophages (p = 0.011) and neutrophils (p = 0.055). Our study highlights a multidimensional immunosuppressive GBM microenvironment in patients with higher CYT and potentially identifies patients with high CYT/ICGscore as a subgroup that may particularly benefit from multi-faceted immunotherapies, given their already elevated tumor CD8+ T cell levels.

scholarly journals ARNTL2, an Immunoregulation-Related Prognostic Biomarker in Pan-Cancer and Lung Adenocarcinoma

2021 ◽  
Author(s):  
Gujie Wu ◽  
Wenmiao Wang ◽  
Zheng Yang ◽  
Qun Xue
Keyword(s):  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Prognostic Biomarker ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Cancer Drugs ◽  
Anti Cancer ◽  
Pan Cancer

Abstract Background ARNTL2 is a member of the PAS superfamily that promotes tumor progression. However, the role of ARNTL2 in lung adenocarcinoma (LUAD) remains unclear. The purpose of our study was to investigate the function of ARNTL2 in LUAD. Methods The expression, clinical features, and prognostic role of ARNTL2 in pan-cancer were evaluated using The Cancer Genome Atlas and Genotype-Tissue Expression data. GSEA and GSVA of ARNTL2 were performed using the R package “clusterProfiler.” The correlation between immune cell infiltration level and ARNTL2 expression was analyzed using two sources of immune cell infiltration data, including the TIMER2 and ImmuCellAI database. Finally,we analyzed the correlation between ARNTL2 and IC50 of 192 drugs. Results ARNTL2 was substantially overexpressed in LUAD and pan-cancer. High ARNTL2 expression predicted poor survival in patients with LUAD. We also found that ARNTL2 expression was positively associated with the infiltration levels of immunosuppressive cells, such as tumor associated macrophages, cancer associated fibroblasts and Tregs. Among the 192 anti-cancer drugs, ARNTL2 expression was positively correlated with IC50 of 114 anti-cancer drugs, such as SB505124, Doramapimod, Nutlin-3a (-), Sabutoclax, AZD5991, PF-4708671, Elephantin, PRIMA-1MET, Sorafenib, Vorinostat, and MK-2206. Conclusions Our results revealed that ARNTL2 is a potential prognostic biomarker in LUAD. An elevated ARNTL2 expression indicates an immunosuppressive microenvironment, and targeted therapies against ARNTL2 have excellent potential.

scholarly journals Predicted CD4+ T cell infiltration levels could indicate better overall survival in sarcoma patients

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098153
Author(s):  
Qing Bi ◽  
Yang Liu ◽  
Tao Yuan ◽  
Huizhen Wang ◽  
Bin Li ◽  
...  
Keyword(s):  
Overall Survival ◽  
T Cell ◽  
Survival Data ◽  
Tumor Infiltrating Lymphocytes ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
T Cell Infiltration ◽  
Whole Exome ◽  
Cancer Genome Atlas ◽  
Infiltrating Lymphocytes

Objective The role of tumor-infiltrating lymphocytes (TILs) has not yet been characterized in sarcomas. The aim of this bioinformatics study was to explore the effect of TILs on sarcoma survival and genome alterations. Methods Whole-exome sequencing, transcriptome sequencing, and survival data of sarcoma were obtained from The Cancer Genome Atlas. Immune infiltration scores were calculated using the Tumor Immune Estimation Resource. Potential associations between abundance of infiltrating TILs and survival or genome alterations were examined. Results Levels of CD4+ T cell infiltration were associated with overall survival of patients with pan-sarcomas, and higher CD4+ T cell infiltration levels were associated with better survival. Somatic copy number alterations, rather than mutations, were found to correlate with CD4+ T cell infiltration levels. Conclusions This data mining study indicated that CD4+ T cell infiltration levels predicted from RNA sequencing could predict sarcoma prognosis, and higher levels of CD4+ T cells infiltration indicated a better chance of survival.

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 Pan-cancer analysis of non-coding recurrent mutations and their possible involvement in cancer pathogenesis

NAR Cancer ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Chie Kikutake ◽  
Minako Yoshihara ◽  
Mikita Suyama
Keyword(s):  
The Cancer Genome Atlas ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Huge Number ◽  
Protein Coding ◽  
Coding Regions ◽  
Cancer Pathogenesis ◽  
Recurrent Mutations ◽  
Cancer Genome Atlas ◽  
Pan Cancer

Abstract Cancer-related mutations have been mainly identified in protein-coding regions. Recent studies have demonstrated that mutations in non-coding regions of the genome could also be a risk factor for cancer. However, the non-coding regions comprise 98% of the total length of the human genome and contain a huge number of mutations, making it difficult to interpret their impacts on pathogenesis of cancer. To comprehensively identify cancer-related non-coding mutations, we focused on recurrent mutations in non-coding regions using somatic mutation data from COSMIC and whole-genome sequencing data from The Cancer Genome Atlas (TCGA). We identified 21 574 recurrent mutations in non-coding regions that were shared by at least two different samples from both COSMIC and TCGA databases. Among them, 580 candidate cancer-related non-coding recurrent mutations were identified based on epigenomic and chromatin structure datasets. One of such mutation was located in RREB1 binding site that is thought to interact with TEAD1 promoter. Our results suggest that mutations may disrupt the binding of RREB1 to the candidate enhancer region and increase TEAD1 expression levels. Our findings demonstrate that non-coding recurrent mutations and coding mutations may contribute to the pathogenesis of cancer.

scholarly journals Reprocessing of RNA-sequencing samples from publicly-available data to yield normalized and comparable expression results.

2019 ◽  
Author(s):  
William C. Wright ◽  
Taosheng Chen
Keyword(s):  
Rna Sequencing ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Linear Modeling ◽  
Gene Count ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

Abstract Here we obtained RNA-sequencing data from the publicly-available Pan-Cancer analysis project performed by The Cancer Genome Atlas (TCGA). Data within this project were processed the same experimentally, and analyzed downstream by the UCSC Toil recompute project. We reprocessed the resulting gene count files in batch to obtain normalized expression, which is a step critical for proper and comparable interpretation. We describe the linear modeling and normalization protocol, and provide an example of plotting the results using a gene of interest. We perform the entire protocol using freely available packages within the R framework.

scholarly journals Pan-cancer survey and evaluation of the oncogenic role of NF-κB1

2021 ◽  
Author(s):  
Lihong Huang ◽  
ruoling zheng ◽  
Huasong Gong ◽  
Yongchao Qiao
Keyword(s):  
Endometrial Carcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Data Sets ◽  
Carcinogenic Effect ◽  
Uterine Corpus ◽  
T Cell Infiltration ◽  
Cancer Genome Atlas ◽  
Survey And Evaluation ◽  
Pan Cancer

Abstract Although emerging cells or animals based evidence supports an association between nuclear factor kappa-B1 (NF-κB1) cells and cancers, there has no pan-cancer analysis. Therefore, based on TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) data sets, we first studied the potential carcinogenic effect of NF-κB1 in 33 tumors. As we not only found high expression of NF-κB1 in most tumors, but also found that NF-κB1 expression is closely related to the prognosis of tumor patients. Enhanced phosphorylation of S893 was observed in several tumors, such as breast cancer, uterine corpus endometrial carcinoma or lung adenocarcinoma. In thymoma, NF-κB1 expression was relevant to CD8+ T-cell infiltration levels, and tumor-associated fibroblast infiltration has also seen in other tumors, such as uterine corpus endometrial carcinoma or glioblastoma multiforme. In addition, the functional mechanism of NF-κB1 also involves the related functions of protein processing and RNA metabolism. In this study, NF-κB1 was pan-cancer study in order to have a systematic and comprehensive understanding of the carcinogenic effect of NF-κB1 in different tumors.

scholarly journals Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade

2016 ◽  
Author(s):  
Pornpimol Charoentong ◽  
Francesca Finotello ◽  
Mihaela Angelova ◽  
Clemens Mayer ◽  
Mirjana Efremova ◽  
...  
Keyword(s):  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Tumor Escape ◽  
Predictors Of Response ◽  
Cancer Genome Atlas ◽  
Tumor Immunogenicity ◽  
Immune Oncology ◽  
Cancer Germline

SUMMARYThe Cancer Genome Atlas revealed the genomic landscapes of common human cancers. In parallel, immunotherapy with checkpoint blockers is transforming the treatment of advanced cancers. As only a minority of the patients is responsive to checkpoint blockers, the identification of predictive markers and the mechanisms of resistance is a subject of intense research. To facilitate understanding of the tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers, and created The Cancer Immunome Atlas (http://tcia.at). Cellular characterization of the immune infiltrates revealed a role of cancer-germline antigens in spontaneous immunity and showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was superior predictor of response to anti-CTLA-4 and anti-PD-1 antibodies in two independent validation cohorts. Our findings and the developed resource may help informing cancer immunotherapy and facilitate the development of precision immune-oncology.

RNF24 is a Novel Biomarker of Prognosis and Immunological Cell Infiltration in Cancers

2021 ◽  
Author(s):  
Bin Yu ◽  
ma mei
Keyword(s):  
Survival Data ◽  
Immune Cell ◽  
Ring Finger ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Pathway Enrichment Analysis ◽  
Systematic Analysis ◽  
Ppi Networks ◽  
Pan Cancer

Abstract Background The RNF family engaged in diverse biological and pathological processes, including tumorigenesis and cancer advance. However, studies about Ring Finger Protein 24 (RNF24) were limited and have not been reported in cancer. A systematic analysis in pan-cancer is a benefit to understand the function of RNF24. Methods RNF24 expression was evaluated in pan-cancer based on the data from The Cancer Genome Atlas (TCGA) analyzed by TIMER, UALCAN, GEPIA, and HPA. Then, the effect of RNF24 on the prognostic value was assessed by clinical survival data in Kaplan–Meier Plotter and GEPIA. And mutation burden and related survival of RNF24 was observed in cBioPortal. Furthermore, protein-protein interaction (PPI) networks of RNF24 and pathway enrichment analysis were explored on multiple websites. Lastly, relationships between RNF24 expression and immune cells infiltration were analyzed in the TIMER2 online database with various algorithms. Results The mRNA and protein levels of RNF24 were significantly upregulated in most types of cancer compared to normal tissues. And RNF24 was a reliable biomarker to predict prognosis in at least 10 types of cancer, including liver hepatocellular carcinoma (LIHC). In addition, we showed the genetic alteration, PPI networks, and functional pathway of RNF24. Moreover, immune cell infiltration exhibited RNF24 expression negatively linked to CD8+ T cells, but positively to Tregs, MDSCs, HSC, and macrophages in pan-cancer.Conclusions Our pan-cancer analysis revealed RNF24 as an oncogene and its expression predicted OS in multiple human cancers, especially in LIHC. RNF24 might predict the immunotherapy response for cancer patients based on its expression with infiltration of immune and immunosuppressive cells.

scholarly journals CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Judith Abécassis ◽  
Fabien Reyal ◽  
Jean-Philippe Vert
Keyword(s):  
Tumor Heterogeneity ◽  
Cancer Genomics ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Cancer Dataset ◽  
Whole Exome Sequencing Data ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Mutational Processes

AbstractSystematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

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