DNA methylome and transcriptome analysis established a model of four differentially methylated positions (DMPs) as a diagnostic marker in esophageal adenocarcinoma early detection

Background Esophageal carcinogenesis involves in alterations of DNA methylation and gene transcription. This study profiled genomic DNA methylome vs. gene expression using transcriptome data on esophageal adenocarcinoma (EAC) tissues from the online databases in order to identify methylation biomarkers in EAC early diagnosis. Materials and Methods The DNA methylome and transcriptome data were downloaded from the UCSC Xena, Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) databases and then bioinformatically analyzed for the differentially methylated positions (DMPs) vs. gene expression between EAC and normal tissues. The highly methylated DMPs vs. reduced gene expression in EAC were selected and then stratified with those of the corresponding normal blood samples and other common human cancers to construct an EAC-specific diagnostic model. The usefulness of this model was further verified in other three GEO datasets of EAC tissues. Result A total of 841 DMPs were associated with expression of 320 genes, some of which were aberrantly methylated in EAC tissues. Further analysis showed that four (cg07589773, cg10474350, cg13011388 and cg15208375 mapped to gene IKZF1, HOXA7, EFS and TSHZ3, respectively) of these 841 DMPs could form and establish a diagnostic model after stratified them with the corresponding normal blood samples and other common human cancers. The data were further validated in other three GEO datasets on EAC tissues in early EAC diagnosis. Conclusion This study revealed a diagnostic model of four genes methylation to diagnose EAC early. Further study will confirm the usefulness of this model in a prospective EAC cases.

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Proliferation Cycle Transcriptomic Signatures are Strongly associated With Gastric Cancer Patient Survival

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.770994 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jianwen Hu ◽

Yanpeng Yang ◽

Yongchen Ma ◽

Yingze Ning ◽

Guowei Chen ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Treatment Options ◽

Disease Diagnosis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Transcriptome Data ◽

Genomic Changes ◽

The Difference ◽

Gastric Cancer Patients

Gastric cancer is one of the most heterogeneous tumors with multi-level molecular disturbances. Sustaining proliferative signaling and evading growth suppressors are two important hallmarks that enable the cancer cells to become tumorigenic and ultimately malignant, which enable tumor growth. Discovering and understanding the difference in tumor proliferation cycle phenotypes can be used to better classify tumors, and provide classification schemes for disease diagnosis and treatment options, which are more in line with the requirements of today’s precision medicine. We collected 691 eligible samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, combined with transcriptome data, to explore different heterogeneous proliferation cycle phenotypes, and further study the potential genomic changes that may lead to these different phenotypes in this study. Interestingly, two subtypes with different clinical and biological characteristics were identified through cluster analysis of gastric cancer transcriptome data. The repeatability of the classification was confirmed in an independent Gene Expression Omnibus validation cohort, and consistent phenotypes were observed. These two phenotypes showed different clinical outcomes, and tumor mutation burden. This classification helped us to better classify gastric cancer patients and provide targeted treatment based on specific transcriptome data.

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Novel Epigenetic Eight-Gene Signature Predictive of Poor Prognosis and MSI-Like Phenotype in Human Metastatic Colorectal Carcinomas

Cancers ◽

10.3390/cancers13010158 ◽

2021 ◽

Vol 13 (1) ◽

pp. 158

Author(s):

Valentina Condelli ◽

Giovanni Calice ◽

Alessandra Cassano ◽

Michele Basso ◽

Maria Grazia Rodriquenz ◽

...

Keyword(s):

Gene Expression ◽

Poor Prognosis ◽

Cpg Island ◽

Colon Adenocarcinoma ◽

Gene Signature ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cpg Island Methylator Phenotype ◽

Prognostic Information ◽

Global Methylation

Epigenetics is involved in tumor progression and drug resistance in human colorectal carcinoma (CRC). This study addressed the hypothesis that the DNA methylation profiling may predict the clinical behavior of metastatic CRCs (mCRCs). The global methylation profile of two human mCRC subgroups with significantly different outcome was analyzed and compared with gene expression and methylation data from The Cancer Genome Atlas COlon ADenocarcinoma (TCGA COAD) and the NCBI GENE expression Omnibus repository (GEO) GSE48684 mCRCs datasets to identify a prognostic signature of functionally methylated genes. A novel epigenetic signature of eight hypermethylated genes was characterized that was able to identify mCRCs with poor prognosis, which had a CpG-island methylator phenotype (CIMP)-high and microsatellite instability (MSI)-like phenotype. Interestingly, methylation events were enriched in genes located on the q-arm of chromosomes 13 and 20, two chromosomal regions with gain/loss alterations associated with adenoma-to-carcinoma progression. Finally, the expression of the eight-genes signature and MSI-enriching genes was confirmed in oxaliplatin- and irinotecan-resistant CRC cell lines. These data reveal that the hypermethylation of specific genes may provide prognostic information that is able to identify a subgroup of mCRCs with poor prognosis.

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Functions and clinical significance of KLRG1 in the development of lung adenocarcinoma and immunotherapy

BMC Cancer ◽

10.1186/s12885-021-08510-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaodong Yang ◽

Yuexin Zheng ◽

Zhihai Han ◽

Xiliang Zhang

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Killer Cell ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Normal Lung ◽

Using Data ◽

Low Expression

Abstract Background As a marker of differentiation, Killer cell lectin like receptor G1 (KLRG1) plays an inhibitory role in human NK cells and T cells. However, its clinical role remains inexplicit. This work intended to investigate the predictive ability of KLRG1 on the efficacy of immune-checkpoint inhibitor in the treatment of lung adenocarcinoma (LUAD), as well as contribute to the possible molecular mechanisms of KLRG1 on LUAD development. Methods Using data from the Gene Expression Omnibus, the Cancer Genome Atlas and the Genotype-Tissue Expression, we compared the expression of KLRG1 and its related genes Bruton tyrosine kinase (BTK), C-C motif chemokine receptor 2 (CCR2), Scm polycomb group protein like 4 (SCML4) in LUAD and normal lung tissues. We also established stable LUAD cell lines with KLRG1 gene knockdown and investigated the effect of KLRG1 knockdown on tumor cell proliferation. We further studied the prognostic value of the four factors in terms of overall survival (OS) in LUAD. Using data from the Gene Expression Omnibus, we further investigated the expression of KLRG1 in the patients with different responses after immunotherapy. Results The expression of KLRG1, BTK, CCR2 and SCML4 was significantly downregulated in LUAD tissues compared to normal controls. Knockdown of KLRG1 promoted the proliferation of A549 and H1299 tumor cells. And low expression of these four factors was associated with unfavorable overall survival in patients with LUAD. Furthermore, low expression of KLRG1 also correlated with poor responses to immunotherapy in LUAD patients. Conclusion Based on these findings, we inferred that KLRG1 had significant correlation with immunotherapy response. Meanwhile, KLRG1, BTK, CCR2 and SCML4 might serve as valuable prognostic biomarkers in LUAD.

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BART Cancer: a web resource for transcriptional regulators in cancer genomes

NAR Cancer ◽

10.1093/narcan/zcab011 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Zachary V Thomas ◽

Zhenjia Wang ◽

Chongzhi Zang

Keyword(s):

Gene Expression ◽

Cancer Research ◽

Computational Prediction ◽

Transcriptional Regulators ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Regulation Of Transcription ◽

Data Resource ◽

Web Resource ◽

Cancer Types

Abstract Dysregulation of gene expression plays an important role in cancer development. Identifying transcriptional regulators, including transcription factors and chromatin regulators, that drive the oncogenic gene expression program is a critical task in cancer research. Genomic profiles of active transcriptional regulators from primary cancer samples are limited in the public domain. Here we present BART Cancer (bartcancer.org), an interactive web resource database to display the putative transcriptional regulators that are responsible for differentially regulated genes in 15 different cancer types in The Cancer Genome Atlas (TCGA). BART Cancer integrates over 10000 gene expression profiling RNA-seq datasets from TCGA with over 7000 ChIP-seq datasets from the Cistrome Data Browser database and the Gene Expression Omnibus (GEO). BART Cancer uses Binding Analysis for Regulation of Transcription (BART) for predicting the transcriptional regulators from the differentially expressed genes in cancer samples compared to normal samples. BART Cancer also displays the activities of over 900 transcriptional regulators across cancer types, by integrating computational prediction results from BART and the Cistrome Cancer database. Focusing on transcriptional regulator activities in human cancers, BART Cancer can provide unique insights into epigenetics and transcriptional regulation in cancer, and is a useful data resource for genomics and cancer research communities.

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Development and validation of a 13-m6a related lncRNA prognostic signature for lung adenocarcinoma

10.21203/rs.3.rs-771653/v1 ◽

2021 ◽

Author(s):

Pingfan Wu ◽

Xiaowen Zhao ◽

Ling Xue ◽

Xiaojing Yang ◽

Yuxiang Shi ◽

...

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Treatment Strategies ◽

Risk Groups ◽

Gene Expression Omnibus ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

P53 Signaling ◽

Selection Operator

Abstract Considerable evidence suggests that N6-methyladenosine (m6A) is involved in the regulation of long non-coding RNA (lncRNA), whichparticipates in the occurrence, development and prognosis of tumorscancerBut the relationship between m6A regulators-related lncRNA (mRlncRNA) and lung adenocarcinoma (LUAD) remains unclear. This study aims to determine a feature based on mRlncRNA for prognostic evaluation of LUAD patients. By integrating the gene expression data of LUAD and normal samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, the m6A gene and mRlncRNA with imbalanced expression were screened out. Then we used the least absolute shrinkage and selection operator (LASSO) to obtain the 13-lncRNA prognostic signature in the TCGA training cohort. Patients were divided into two risk groups based on the risk score of lncRNAs characteristics, and their overall survival (OS) was significantly different. The predictive power of this signature was verified in TCGA testing cohort and entire TCGA cohort. These landmark lncRNAs were involved in several biologiocal processes and pathways related to cell cycle, DNA replication, P53 signaling pathway and mismatch repair. Besides, the high-risk group was low-response to cisplatin, while high-response to mitomycin, docetaxel and immunotherapy. In conclusion, we identified a 13-mRlncRNA model associated with prognosis and treatment sensitivity in LUAD, which may provide clues about the influence of m6A on lncRNA in LUAD and promote the further improvement of LUAD individualized treatment strategies.

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Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

Bioscience Reports ◽

10.1042/bsr20201704 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Wei Han ◽

Biao Huang ◽

Xiao-Yu Zhao ◽

Guo-Liang Shen

Keyword(s):

Gene Expression ◽

Tumor Microenvironment ◽

Metastatic Melanoma ◽

Interaction Network ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

Subtype Identification ◽

Cancer Genome Atlas ◽

Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

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OSmfs: An Online Interactive Tool to Evaluate Prognostic Markers for Myxofibrosarcoma

Genes ◽

10.3390/genes11121523 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1523

Author(s):

Huimin Li ◽

Longxiang Xie ◽

Qiang Wang ◽

Yifang Dang ◽

Xiaoxiao Sun ◽

...

Keyword(s):

Gene Expression ◽

Poor Prognosis ◽

Analysis Data ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Prognostic Biomarkers ◽

Rank Test ◽

Web Tool ◽

Kaplan Meier ◽

Cox Analysis

Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan–Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma.

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OSkirc: a web tool for identifying prognostic biomarkers in kidney renal clear cell carcinoma

Future Oncology ◽

10.2217/fon-2019-0296 ◽

2019 ◽

Vol 15 (27) ◽

pp. 3103-3110 ◽

Cited By ~ 15

Author(s):

Longxiang Xie ◽

Qiang Wang ◽

Yifang Dang ◽

Linna Ge ◽

Xiaoxiao Sun ◽

...

Keyword(s):

Gene Expression ◽

Cell Carcinoma ◽

Web Application ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

P Value ◽

Survival Plot

Aim: To develop a free and quick analysis online tool that allows users to easily investigate the prognostic potencies of interesting genes in kidney renal clear cell carcinoma (KIRC). Patients & methods: A total of 629 KIRC cases with gene expression profiling data and clinical follow-up information are collected from public Gene Expression Omnibus and The Cancer Genome Atlas databases. Results: One web application called Online consensus Survival analysis for KIRC (OSkirc) that can be used for exploring the prognostic implications of interesting genes in KIRC was constructed. By OSkirc, users could simply input the gene symbol to receive the Kaplan–Meier survival plot with hazard ratio and log-rank p-value. Conclusion: OSkirc is extremely valuable for basic and translational researchers to screen and validate the prognostic potencies of genes for KIRC, publicly accessible at http://bioinfo.henu.edu.cn/KIRC/KIRCList.jsp

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mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Genes ◽

10.3390/genes11030257 ◽

2020 ◽

Vol 11 (3) ◽

pp. 257 ◽

Cited By ~ 6

Author(s):

Yitong Zhang ◽

Joseph Ta-Chien Tseng ◽

I-Chia Lien ◽

Fenglan Li ◽

Wei Wu ◽

...

Keyword(s):

Gene Expression ◽

Stem Cell ◽

Lung Adenocarcinoma ◽

Tumor Necrosis Factor Receptor ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Clinical Stages ◽

Key Genes ◽

Geo Database

Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

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Elucidating the pathogenesis of esophageal adenocarcinoma through meta-analysis of public data.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.4_suppl.71 ◽

2019 ◽

Vol 37 (4_suppl) ◽

pp. 71-71

Author(s):

Kamal Khorfan ◽

Jihad Aljabban ◽

Saad Syed ◽

Hussam Salhi ◽

Aderinola Adejare ◽

...

Keyword(s):

Gene Expression ◽

Esophageal Adenocarcinoma ◽

Poor Prognosis ◽

Meta Analysis ◽

Statistical Significance ◽

Suppressor Gene ◽

Gene Expression Omnibus ◽

Incidence And Mortality ◽

Public Data ◽

Log Ratio

71 Background: Esophageal adenocarcinoma (EAC) is the sixth most common cause of cancer-related deaths worldwide. It commonly arises in the setting of reflux disease and Barett’s esophagus. It remains incurable and holds a poor prognosis. Dissecting the genetic signature of EAC can pave new therapeutic avenues. Methods: We employed our Search Tag Analyze Resource (STARGEO) platform to conduct meta-analysis using the National Center for Biotechnology’s (NCBI) Gene Expression Omnibus (GEO). We tagged 151 tumor samples from EAC patients and 62 normal esophageal samples as a control. We then analyzed the signature in Ingenuity Pathway Analysis, restricting genes that showed statistical significance (p < 0.05) and an absolute experimental log ratio greater than 0.15 between tumor and control. Results: Our analysis revealed granulocyte adhesion and diapedesis and FXR/RXR signaling as top canonical pathways. TGFB1, TNF, and beta-estradiol were top upstream regulators with predicted activation. TGFB1 and TNF expression have been correlated with poor prognosis in EAC. Also, beta-estradiol has tumorigenic activity in several cancers but has not been investigated in EAC. Among the top upregulated genes were oncogenic genes such as tetraspanin 8, the antiapoptotic factor OLFM4, and the protease cathepsin E (CTSE). SPINK1, a trypsin inhibitor with recently suggested role in cancer, and the choline transporter SLC44A4, a drug target for pancreatic cancer, were also upregulated. The most downregulated genes included alcohol dehydrogenase 7, associated with EAC in alcohol-drinkers, and the pro-apoptotic gene CRCT1. We also found downregulation of the serine peptidase inhibitor SPINK7. SPINK7 is involved in maintaining epithelial-barrier integrity and is implicated in eosinophilic esophagitis pathogenesis. Lastly, there was downregulation of the candidate tumor suppressor gene transglutaminase 3. Conclusions: Despite screening efforts, EAC incidence and mortality continues to increase as does the need for better treatment. This meta-analysis defines the significant gene expression changes within causal disease processes to provide markers for detection, prognostic insight, and therapeutic value for EAC.

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