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

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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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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The identification of a potential prognostic model of colon cancer using integrated bioinformatics analysis

10.21203/rs.3.rs-39410/v1 ◽

2020 ◽

Author(s):

Zhengyu Fang ◽

Sumei Xu ◽

Yiwen Xie ◽

Wenxi Yan

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Prognostic Model ◽

Predictive Power ◽

Gene Signature ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Training Dataset ◽

High Predictive Power

Abstract Background This study aimed to construct prognostic model by screening prognostic gene signature of colon cancer. Methods The gene expression profile data of colon cancer were obtained from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) and differently expressed genes (DEGs) between tumor and control samples were identified. Prognosis-associated genes were then identified and used for the construction of prognostic model. The independent factors that associated with the prognosis of colon in the TCGA cohort was identified. Results Totally, 1153 consistent DEGs were screened out between tumor and normal tissues in the TCGA cohort, GSE44861 and GSE44076 datasets. Among these genes, 12 DEGs were related to the prognosis of colon cancer and were used for constructing the prognostic model. This model presented a high predictive power for the prognosis of colon cancer both in the training dataset and in the validation datasets (AUC > 0.8). Statistical analysis showed that age, pathological T, tumor recurrence, and model status were the independent factors for prognosis of patients with colon cancer in TCGA. Conclusions The 12-gene signature prognostic model had a high predictive power for colon cancer prognosis.

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Overexpression of B7-H3 Is Associated With Poor Prognosis in Laryngeal Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.759528 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yixuan Li ◽

Qian Cai ◽

Ximing Shen ◽

Xiaoting Chen ◽

Zhong Guan

Keyword(s):

Gene Expression ◽

Laryngeal Cancer ◽

Poor Prognosis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Expression Level ◽

Normal Tissues ◽

Qrt Pcr ◽

Node Metastasis ◽

B7 Family

The immune checkpoint molecule, B7-H3, which belongs to the B7 family, has been shown to be overexpressed in various cancers. Its role in tumors is not well defined, and many studies suggest that it is associated with poor clinical outcomes. The effect of B7-H3 on laryngeal cancer has not been reported. This study investigated the expression of B7-H3 in laryngeal squamous cell carcinoma (LSCC), and its relationship with clinicopathological factors and prognosis of LSCC patients. The gene expression quantification data and clinical data of LSCC retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were analyzed to determine the diagnostic and prognostic roles of B7-H3. Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed to determine the gene expression level of B7-H3 between LSCC tissues and paired normal adjacent tissues. In addition, TCGA RNA-seq data was analyzed to evaluate the expression level of B7 family genes. Next, the protein expression of B7-H3 and CD8 in LSCC was determined using immunohistochemistry and immunofluorescence. qRT-PCR results showed that the expression level of B7-H3 mRNA was significantly higher in LSCC tissues than in adjacent normal tissues. Similar results were obtained from the TCGA analysis. The expression of B7-H3 was significantly associated with T stage, lymph node metastasis, and pathological tumor node metastasis (TNM) stage, and it was also an independent factor influencing the overall survival time (OS) of patients with LSCC. In addition, B7-H3 was negatively correlated with CD8+T cells. These results show that B7-H3 is upregulated in LSCC. Therefore, B7-H3 may serve as a biomarker of poor prognosis and a promising therapeutic target in LSCC.

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Prognostic Value of Genes Related with Tumor Microenvironment in Ovarian Cancer

10.21203/rs.3.rs-31574/v1 ◽

2020 ◽

Author(s):

Shimei Li ◽

Jiyi Yao ◽

Shen Zhang ◽

Xinchuan Zhou ◽

Xinbao Zhao ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Tumor Microenvironment ◽

Poor Prognosis ◽

External Validation ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Gene Expression Signatures ◽

Poor Outcomes ◽

Geo Database

Abstract Background Ovarian cancer (OV) is the fifth leading cause of cancer death among females. Growing evidence supports a key role of tumor microenvironment in growth, progress, and metastasis of OV. However, the impacts of gene expression signatures related with OV microenvironment on prognosis have not been well-established . This study aimed to apply ESTIMATE algorithm to extract genes related with tumor microenvironment that predicted poor outcomes in OV patients. Methods The gene expression profile of OV samples were downloaded from The Cancer Genome Atlas (TCGA) database. The immune scores and stromal scores of 469 OV samples were available based on the ESTIMATE algorithm. To better understand impacts of gene expression signatures related with OV microenvironment on prognosis, these samples were categorized based on their ESTIMATE scores into high and low score groups. A different OV cohort from the Gene Expression Omnibus (GEO) database was used for external validation. Results The molecular subtypes in OV patients were correlated with stromal scores, in which the mesenchymal subtype had the highest stromal scores (p < 0.0001). Poor prognosis were found in patients (especially for patients with overall survivals (OS) < 5 years) with higher stromal score (p = 0.0376). 449 differentially expressed genes (DEGs) in stromal scores group were identified and 26 DEGs were significantly associated with poor prognosis in OV patients (p < 0.05). Eventually, 6 genes have further validated to be significantly associated with poor outcomes in 40 patients from a different OV cohort of GEO database (p < 0.05). Conclusion In this study, several genes related with tumor microenvironment that predicted poor prognosis in OV patients were extracted. In addition, some previously overlooked genes could be potential prognostic biomarkers for OV.

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Identification of key methylation differentially expressed genes in posterior fossa ependymoma based on epigenomic and transcriptome analysis

Journal of Translational Medicine ◽

10.1186/s12967-021-02834-1 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Guanyi Wang ◽

Yibin Jia ◽

Yuqin Ye ◽

Enming Kang ◽

Huijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Posterior Fossa ◽

Differentially Expressed Genes ◽

Poor Prognosis ◽

Cpg Island ◽

Adaptive Immune Response ◽

R Package ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes

Abstract Background Posterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma (EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis. Methods Using epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database. Results GO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R = 0.69, P = 1 × 10–08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC. Conclusion ssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

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Gene Expression Changes Associated with Dedifferentiation in Liposarcoma Predict Overall Survival

Cancers ◽

10.3390/cancers13123049 ◽

2021 ◽

Vol 13 (12) ◽

pp. 3049

Author(s):

Nicholas Brian Shannon ◽

Qiu Xuan Tan ◽

Joey Wee-Shan Tan ◽

Josephine Hendrikson ◽

Wai Har Ng ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Gene Signature ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Significant Difference ◽

Cancer Genome Atlas ◽

Well Differentiated ◽

Multiple Recurrences ◽

Laser Capture

Up to 10% of well-differentiated liposarcoma (WDLS) progress to dedifferentiated liposarcoma (DDLS). We aimed to identify gene expression changes associated with dedifferentiation and whether these were informative of tumour biology of DDLS. We analysed datasets from the Gene Expression Omnibus (GEO, ID = GSE30929) database to identify differentially expressed genes between WDLS (n = 52) and DDLS (n = 39). We validated the signature on whole and laser-capture microdissected samples from patients with tumours consisting of mixed WDLS and DDLS components. A subset of this signature was applied to an independent dataset from The Cancer Genome Atlas (TCGA, n = 58 DDLS) database to segregate samples based on gene expression and compared for recurrence and overall survival (OS). A 15-gene signature consisting of genes with increased expression in DDLS compared to WDLS was generated. This signature segregated WDLS and DDLS samples from patients with mixed component tumours and across multiple recurrences. A further subset of this signature, consisting of five genes (AQP7, ACACB, FZD4, GPD1, LEP), segregated DDLS in a TCGA cohort with a significant difference in OS (p = 0.019) and recurrence-free survival (RFS) (p = 0.061). The five-gene model stratified DDLS into prognostic groups and outperformed clinical factors in existing models in retroperitoneal DDLS.

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CXCL11 Correlates With Antitumor Immunity and an Improved Prognosis in Colon Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.646252 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yingying Cao ◽

Nanlin Jiao ◽

Tiantian Sun ◽

Yanru Ma ◽

Xinyu Zhang ◽

...

Keyword(s):

Gene Expression ◽

Immune Cells ◽

Antitumor Immunity ◽

Rectal Adenocarcinoma ◽

Colon Adenocarcinoma ◽

Underlying Mechanism ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Pathway Network ◽

Chemokine Ligand

The chemokine ligand C-X-C motif chemokine ligand 11 (CXCL11) is involved in the progression of various cancers, but its biological roles in colorectal cancer (CRC) remain confused. Therefore, the prognostic value and underlying mechanism of CXCL11 in CRC were preliminarily evaluated. Three independent datasets were used for mRNA-related analysis: one dataset from the Cancer Genome Atlas (TCGA, n = 451) and two single-cell RNA sequencing (scRNA-seq) datasets from Gene Expression Omnibus (GEO): GSE146771 and GSE132465. In addition, a colon adenocarcinoma (COAD) patient cohort (the Yijishan Hospital cohort, YJSHC, n = 108) was utilized for analysis of cell infiltration by immunohistochemistry. We determined the distribution of CXCL11 in tumor tissue across all TCGA cancers and found that CXCL11 expression was significantly upregulated in both COAD and rectal adenocarcinoma (READ). However, the upregulation of CXCL11 mRNA was associated with a better prognosis in COAD, but not in READ. Within the YJSHC, the patients with a high abundance of intratumoral CXCL11+ cells had prolonged survival (p = 0.001). Furthermore, we found that the high CXCL11 expression group had a higher proportion of antitumor immune cells, and a lower proportion of protumor immune cells. Additionally, we discovered the changes of gene expression and enriched immune pathway network mediated by CXCL11. Interestingly, both cytotoxic genes (IFNG, GZMA, GZMB, GZMK, GZMM, and PRF1) and immunosuppressive molecules, including PD-L1, were positively correlated with CXCL11 expression. CXCL11, which promoted antitumor immunity to benefit survival, was identified as an independent prognostic biomarker in patients with COAD.

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Identification of Key Methylation Differentially Expressed Genes in Posterior Fossa Ependymoma Based on Epigenomic and Transcriptome Analysis

10.21203/rs.3.rs-148618/v1 ◽

2021 ◽

Author(s):

Guanyi Wang ◽

Yibin Jia ◽

Yuqing Ye ◽

Enming Kang ◽

Huijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Posterior Fossa ◽

Differentially Expressed Genes ◽

Poor Prognosis ◽

Cpg Island ◽

Adaptive Immune Response ◽

R Package ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes

Abstract BackgroundPosterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma(EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis. MethodsUsing epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database. ResultsGO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R=0.69, P=1 x 10-08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC. ConclusionssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

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FXYD5 is a Marker for Poor Prognosis and a Potential Driver for Metastasis in Ovarian Carcinomas

Cancer Informatics ◽

10.4137/cin.s30565 ◽

2015 ◽

Vol 14 ◽

pp. CIN.S30565 ◽

Cited By ~ 6

Author(s):

Pichai Raman ◽

Timothy Purwin ◽

Richard Pestell ◽

Aydin Tozeren

Keyword(s):

Gene Expression ◽

Copy Number ◽

Poor Prognosis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Ovarian Carcinomas ◽

Tissue Arrays ◽

Cancer Genome Atlas ◽

Underlying Causes ◽

Web Platform

Ovarian cancer (OC) is a leading cause of cancer mortality, but aside from a few well-studied mutations, very little is known about its underlying causes. As such, we performed survival analysis on ovarian copy number amplifications and gene expression datasets presented by The Cancer Genome Atlas in order to identify potential drivers and markers of aggressive OC. Additionally, two independent datasets from the Gene Expression Omnibus web platform were used to validate the identified markers. Based on our analysis, we identified FXYD5, a glycoprotein known to reduce cell adhesion, as a potential driver of metastasis and a significant predictor of mortality in OC. As a marker of poor outcome, the protein has effective antibodies against it for use in tissue arrays. FXYD5 bridges together a wide variety of cancers, including ovarian, breast cancer stage II, thyroid, colorectal, pancreatic, and head and neck cancers for metastasis studies.

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An independent poor-prognosis subtype of hepatocellular carcinoma based on the tumor microenvironment

Journal of International Medical Research ◽

10.1177/0300060520980646 ◽

2021 ◽

Vol 49 (2) ◽

pp. 030006052098064

Author(s):

Junfeng Wang ◽

Jianying Lou ◽

Lei Fu ◽

Qu Jin

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Tumor Microenvironment ◽

Poor Prognosis ◽

Molecular Mechanisms ◽

Immune Cell ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Using Data

Background Hepatocellular carcinoma (HCC) is a highly malignant tumor with a particularly poor prognosis. The tumor microenvironment (TME) is closely associated with tumorigenesis, progression, and treatment. However, the relationship between TME genes and HCC patient prognosis is poorly understood. Methods In this study, we identified two prognostic subtypes based on the TME using data from The Cancer Genome Atlas and Gene Expression Omnibus. The Microenvironment Cell Populations-counter method was used to evaluate immune cell infiltration in HCC. Differentially expressed genes between molecular subtypes were calculated with the Limma package, and clusterProfiler was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses to identify genes related to the independent subtypes. We also integrated mRNA expression data into our bioinformatics analysis. Results We identified 4227 TME-associated genes and 640 genes related to the prognosis of HCC. We defined two major subtypes (Clusters 1 and 2) based on the analysis of TME-associated gene expression. Cluster 1 was characterized by increased expression of immune-associated genes and a worse prognosis than Cluster 2. Conclusions The identification of these HCC subtypes based on the TME provides further insight into the molecular mechanisms and prediction of HCC prognosis.

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