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

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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Methylation-Based Signatures for Gastroesophageal Tumor Classification

Cancers ◽

10.3390/cancers12051208 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1208 ◽

Cited By ~ 1

Author(s):

Nikolay Alabi ◽

Dropen Sheka ◽

Ashar Siddiqui ◽

Edwin Wang

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Esophageal Cancer ◽

Treatment Options ◽

Gastroesophageal Junction ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Validation Dataset ◽

Transcription Start Sites ◽

Misclassification Rates

Contention exists within the field of oncology with regards to gastroesophageal junction (GEJ) tumors, as in the past, they have been classified as gastric cancer, esophageal cancer, or a combination of both. Misclassifications of GEJ tumors ultimately influence treatment options, which may be rendered ineffective if treating for the wrong cancer attributes. It has been suggested that misclassification rates were as high as 45%, which is greater than reported for junctional cancer occurrences. Here, we aimed to use the methylation profiles of GEJ tumors to improve classifications of GEJ tumors. Four cohorts of DNA methylation profiles, containing ~27,000 (27k) methylation sites per sample, were collected from the Gene Expression Omnibus and The Cancer Genome Atlas. Tumor samples were assigned into discovery (nEC = 185, nGC = 395; EC, esophageal cancer; GC gastric cancer) and validation (nEC = 179, nGC = 369) sets. The optimized Multi-Survival Screening (MSS) algorithm was used to identify methylation biomarkers capable of distinguishing GEJ tumors. Three methylation signatures were identified: They were associated with protein binding, gene expression, and cellular component organization cellular processes, and achieved precision and recall rates of 94.7% and 99.2%, 97.6% and 96.8%, and 96.8% and 97.6%, respectively, in the validation dataset. Interestingly, the methylation sites of the signatures were very close (i.e., 170–270 base pairs) to their downstream transcription start sites (TSSs), suggesting that the methylations near TSSs play much more important roles in tumorigenesis. Here we presented the first set of methylation signatures with a higher predictive power for characterizing gastroesophageal tumors. Thus, they could improve the diagnosis and treatment of gastroesophageal tumors.

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Tumor microenvironment characterization in stage IV gastric cancer

Bioscience Reports ◽

10.1042/bsr20201248 ◽

2021 ◽

Vol 41 (1) ◽

Author(s):

Xianxue Zhang ◽

Feng Yang ◽

Zhenbao Wang

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Stage Iv ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Stage Iv Gastric Cancer ◽

Th 17 ◽

Cancer Genome Atlas

Abstract Immunotherapy is remarkably affected by the immune environment of the principal tumor. Nonetheless, the immune environment’s clinical relevance in stage IV gastric cancer (GC) is largely unknown. The gene expression profiles of 403 stage IV GC patients in the three cohorts: GEO (Gene Expression Omnibus, GSE84437 (n=292) and GSE62254 (n=77), and TCGA (The Cancer Genome Atlas, n=34) were used in the present study. Using four publicly available stage IV GC expression datasets, 29 immune signatures were expression profiled, and on this basis, we classified stage IV GC. The classification was conducted using the hierarchical clustering method. Three stage IV GC subtypes L, M, and H were identified representing low, medium, and high immunity, respectively. Immune correlation analysis of these three types revealed that Immune H exhibited a better prognostic outcome as well as a higher immune score compared with other subtypes. There was a noticeable difference in the three subgroups of HLA genes. Further, on comparing with other subtypes, CD86, CD80, CD274, CTLA4, PDCD1, and PDCD1LG2 had higher expression in the Immunity H subtype. In stage IV GC, potentially positive associations between immune and pathway activities were displayed, due to the enrichment of pathways including TNF signaling, Th-17 cell differentiation, and JAK-STAT signaling pathways in Immunity H vs Immunity L subtypes. External cohorts from TCGA cohort ratified these results. The identification of stage IV GC subtypes has potential clinical implications in stage IV GC treatment.

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Exploration of Lipid Metabolism in Gastric Cancer: A Novel Prognostic Genes Expression Profile

Frontiers in Oncology ◽

10.3389/fonc.2021.712746 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhen Xiong ◽

Yao Lin ◽

Yan Yu ◽

Xianghui Zhou ◽

Jun Fan ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Lipid Metabolism ◽

Expression Profile ◽

External Validation ◽

Ether Lipid ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Gene Profile ◽

Significant Difference

BackgroundAlterations in lipid metabolism are increasingly being recognized. However, the application of lipid metabolism in the prognosis of gastric cancer (GC) has not yet been explored.MethodsA total of 204 lipid metabolism relative genes were analyzed in the GC cohort from The Cancer Genome Atlas (TCGA), and four independent cohorts from Gene Expression Omnibus (GEO) and one cohort from Wuhan Union Hospital were applied for external validation. Differential expression and enrichment analyses were performed between GC and normal tissue. The LASSO-Cox proportional hazard regression model was applied to select prognostic genes and to construct a gene expression profile.ResultsOur research indicated that higher expression level of AKR1B1, PLD1, and UGT8 were correlated with worse prognosis of GC patients, while AGPAT3 was correlated with better prognosis. Furthermore, we developed a gene profile composed of AGPAT3, AKR1B1, PLD1, and UGT8 suggested three groups with a significant difference in overall survival (OS). The profile was successfully validated in an independent cohort and performed well in the immunohistochemical cohort. Furthermore, we found that ether lipid metabolism, glycerophospholipid metabolism, and glycerolipid metabolism were upregulated, and fatty acid β-oxidation and other lipid peroxidation processes were reduced in GC.ConclusionCollectively, we found lipid metabolism is reliable and clinically applicable in predicting the prognosis of GC based on a novel gene profile.

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A novel assessing system for predicting the prognosis of gastric cancer

Epigenomics ◽

10.2217/epi-2019-0151 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1251-1266

Author(s):

Siheng Lin ◽

Rui Zhou ◽

Dongqiang Zeng ◽

Jiani Wu ◽

Jianhua Wu ◽

...

Keyword(s):

Gastric Cancer ◽

Tumor Microenvironment ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Diagnostic Tools ◽

Expression Data ◽

Protein Coding ◽

Kaplan Meier ◽

Cancer Genome Atlas ◽

Gastric Cancer Patients

Aim: To develop novel diagnostic tools that can predict the prognosis of gastric cancer. Material & methods: Using RNA expression data from The Cancer Genome Atlas and Gene Expression Omnibus, we established protein-coding RNAs-noncoding RNAs-tumor microenvironment type (PNM) scores, which contain signatures of tumor protein coding genes (P), tumor noncoding genes (N) and immune/stroma cells in tumor microenvironment (M) to predict the prognosis of gastric cancer. Results & conclusion: Based on PNM scores, gastric cancer patients were divided into three subgroups and Kaplan–Meier survival curves revealed significant differences among the subgroups (p < 0.001). Our study showed that the PNM scores could be used as a robust predicting tool for the prognosis of gastric cancer.

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

PeerJ ◽

10.7717/peerj.11355 ◽

2021 ◽

Vol 9 ◽

pp. e11355

Author(s):

Weilin Peng ◽

Guangxu Tu ◽

Zhenyu Zhao ◽

Boxue He ◽

Qidong Cai ◽

...

Keyword(s):

Gene Expression ◽

Esophageal Adenocarcinoma ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Normal Blood ◽

Diagnostic Model ◽

Transcriptome Data ◽

Blood Samples ◽

Dna Methylome ◽

Human Cancers

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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Interaction between intratumoral ERCC1 expression and adjuvant treatment with S-1 on the survival of patients enrolled in the ACTS-GC study.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.4_suppl.53 ◽

2012 ◽

Vol 30 (4_suppl) ◽

pp. 53-53 ◽

Cited By ~ 2

Author(s):

Koji Kitada ◽

Atsushi Ochiai ◽

Wataru Ichikawa ◽

Masanori Terashima ◽

Issei Kurahashi ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Adjuvant Treatment ◽

Excision Repair ◽

Predictive Marker ◽

Phase Iii ◽

Biomarker Analysis ◽

The Difference ◽

Ercc1 Expression ◽

Gastric Cancer Patients

53 Background: Excision repair cross-complementing group 1 (ERCC1) is thought to be involved in resistance to platinum-based therapy. Recently, expression of this gene was shown to be associated with the outcome in patients with gastric cancer. Biomarker analysis was conducted to evaluate the influence of this gene expression on the outcomes of patients enrolled in the ACTS-GC study, a randomized phase III trial, which demonstrated the efficacy of adjuvant treatment with S-1 after D2 dissection for stage II and III gastric cancer (Sakuramoto et al., NEJM 2007). Methods: Formalin-fixed paraffin-embedded specimens were available for 829 of a total of 1,059 (78.3%) patients. ERCC1 expression was measured by RT-PCR in the macrodissected tumor specimens and normalized to β-actin expression as the reference gene. The expression of ERCC1 was categorized into low and high values at the median. Results: The ERCC1 expression level was quantifiable in 97% of the 829 specimens and the distribution was balanced across the arms. In the S-1 group, overall survival (OS) tended to be longer in the high ERCC1 expression group than in the low ERCC1 expression group (HR, 0.698; 95% CI, 0.469-1.040; p= 0.076). On the contrary, in the surgery group, the OS was shorter in the high ERCC1 expression group than in the low ERCC1 expression group (HR, 1.216; 95% CI, 0.881-1.679; p= 0.232), although the difference was not statistically significant. The HR for OS of S-1 to surgery alone was smaller in the high-ERCC1 (HR, 0.465; 95% CI, 0.320-0.678) than in the low-ERCC1 group (HR, 0.805; 95% CI, 0.568-1.141). Significant interaction between the S-1 group and ERCC1 expression was observed (p= 0.033). Conclusions: This large biomarker study showed that intratumoral ERCC1 gene expression may be a predictive marker in gastric cancer patients receiving postoperative adjuvant chemotherapy with S-1.

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A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽

10.3390/genes12060854 ◽

2021 ◽

Vol 12 (6) ◽

pp. 854

Author(s):

Yishu Wang ◽

Lingyun Xu ◽

Dongmei Ai

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Dna Methylation ◽

Regression Model ◽

The Cancer Genome Atlas ◽

Low Rank ◽

Expression Levels ◽

Rank Regression ◽

Prediction Of Prognosis ◽

Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02329-9 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Wanting Song ◽

Yi Bai ◽

Jialin Zhu ◽

Fanxin Zeng ◽

Chunmeng Yang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Critical Function ◽

Mesenchymal Transition ◽

Patient Prognosis

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

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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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Evaluation of the prognostic value of CBXs in gastric cancer patients

Scientific Reports ◽

10.1038/s41598-021-91649-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mengya He ◽

Limin Yue ◽

Haiyan Wang ◽

Feiyan Yu ◽

Mingyang Yu ◽

...

Keyword(s):

Gastric Cancer ◽

Target Genes ◽

Prognostic Significance ◽

Disease Free Survival ◽

Genetic Alterations ◽

Genetic Mutation ◽

The Cancer Genome Atlas ◽

Normal Tissues ◽

Interactive Analysis ◽

Gastric Cancer Patients

AbstractChromobox (CBX) proteins were suggested to exert epigenetic regulatory and transcriptionally repressing effects on target genes and might play key roles in the carcinogenesis of a variety of carcinomas. Nevertheless, the functions and prognostic significance of CBXs in gastric cancer (GC) remain unclear. The current study investigated the roles of CBXs in the prognosis of GC using the Oncomine, The Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, The Cancer Genome Atlas (TCGA), and cBioPortal databases. CBX1/2/3/4/5 were significantly upregulated in GC tissues compared with normal tissues, and CBX7 was downregulated. Multivariate analysis showed that high mRNA expression levels of CBX3/8 were independent prognostic factors for prolonged OS in GC patients. In addition, the genetic mutation rate of CBXs was 37% in GC patients, and genetic alterations in CBXs showed no association with OS or disease-free survival (DFS) in GC patients. These results indicated that CBX3/8 can be prognostic biomarkers for the survival of GC patients.

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