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

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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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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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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A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

Cancer Cell International ◽

10.1186/s12935-021-01823-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xin Xu ◽

Yida Lu ◽

Youliang Wu ◽

Mingliang Wang ◽

Xiaodong Wang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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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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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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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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A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

10.21203/rs.3.rs-52209/v2 ◽

2021 ◽

Author(s):

Xin Xu ◽

Yida Lu ◽

Youliang Wu ◽

Mingliang Wang ◽

Xiaodong Wang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Immune Related Genes

Abstract Background: Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC.Method: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.Results: A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment.Conclusions: Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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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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