Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data.
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ABSTRACT:
SUBMITTER: Yaoxing H
PROVIDER: S-EPMC8329722 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics.MethodsWe integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation.ResultsWe comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value.ConclusionsThese findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.
| S-EPMC8073914 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.MethodsGene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytohubba plugin. Their prognostic values were assessed by Kaplan-Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs.ResultsTotal of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.ConclusionsWe have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.
| S-EPMC8201699 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. There are great geographical differences in the incidence of GC, and somatic mutation rates of driver genes are also different. The present study is aimed at screening core prognosis-related candidate genes in Chinese gastric cancer population based on integrated bioinformatics for the early diagnosis and prognosis of GC.MethodsIn the present study, the differentially expressed genes (DEGs) in GC were identified using four microarray datasets from the Gene Expression Omnibus (GEO) database. The samples of these datasets were all from China. Functional enrichment analysis of DEGs was conducted to evaluate the underlying molecular mechanisms involved in GC. Protein-protein interaction (PPI) network and cytoHubba were performed to determine hub genes associated with GC. Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) were performed to validate the hub genes.ResultsA total of 240 DEGs were obtained through the RRA method, including 80 upregulated genes and 160 downregulated genes. Upregulated genes were mainly enriched in extracellular matrix organization, extracellular matrix, and extracellular matrix structural constituent. The downregulated genes were mainly enriched in digestion, extracellular space, and oxidoreductase activity. The KEGG pathway enrichment analysis showed that the upregulated genes were mainly associated with ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway. And downregulated genes were mainly associated with the metabolism of xenobiotics by cytochrome P450, metabolic pathways, and gastric acid secretion. The transcriptional and translational expression levels of the genes including COL1A1, COL5A2, COL12A1, and VCAN were higher in GC tissues than normal tissues.ConclusionA total of four genes including COL1A1, COL5A2, COL12A1, and VCAN were considered potential GC biomarkers in the Chinese population. And ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway were revealed to be important mechanisms of GC. Our findings provide novel insights into the occurrence and progression of GC in the Chinese population.
| S-EPMC7748906 | biostudies-literature
Project description:Objectives Gastric cancer (GC) is the fourth most common cancer worldwide, with the highest incidence and mortality regardless of sex. Despite technological advances in diagnosing and treating gastric cancer, GC still has high incidence and mortality rates. Therefore, continuous research is needed to overcome GC. In various studies, cell lines are used to find and verify the cause of specific diseases. Large-scale genomic studies such as ENCODE and Roadmap epigenomic projects provide multiomics data from various organisms and samples. However, few multi-omics data for gastric tissues and cell lines have been generated. Therefore, we performed RNA-seq, Exome-seq, and ChIP-seq with several gastric cell lines to generate a multi-omics data set in gastric cancer. Data description Multiomic data, such as RNA-seq, Exome-seq, and ChIP-seq, were produced in gastric cancer and normal cell lines. RNA-seq data were generated from nine GC and one normal gastric cell line, mapped to a human reference genome (hg38) using the STAR alignment tool, and quantified with HTseq. Exome sequence data were produced in nine GC and two normal gastric lines. Sequenced reads were mapped and processed using BWA-MEM and GATK, variants were called by stralka2, and annotation was performed using ANNOVAR. Finally, for the ChIP-seq, nine GC cell lines and four GC cell lines were used in two experimental sets; chip-seq was performed to confirm changes in H3K4me3 and H3K27me3. Data was mapped to human reference hg38 with BWA-MEM, and peak calling and annotation were performed using the Homer tool. Since these data provide multi-omics data for GC cell lines, it will be useful for researchers who use the GC cell lines to study.
| S-EPMC10116809 | biostudies-literature
Project description:Snakebite, classified by World Health Organization as a neglected tropical disease, causes more than 100,000 deaths and 2 million injuries per year. Currently, available antivenoms do not bind with strong specificity to target toxins, which means that severe complications can still occur despite treatment. Moreover, the cost of antivenom is expensive. Knowledge of venom compositions is fundamental for producing a specific antivenom that has high effectiveness, low side effects, and ease of manufacture. With advances in mass spectrometry techniques, venom proteomes can now be analyzed in great depth at high efficiency. However, these techniques require genomic and transcriptomic data for interpreting mass spectrometry data. This study aims to establish and incorporate genomics, transcriptomics, and proteomics data to study venomics of a venomous snake, Daboia siamensis. Multiple proteins that have not been reported as venom components of this snake such as hyaluronidase-1, phospholipase B, and waprin were discovered. Thus, multi-omics data are advantageous for venomics studies. These findings will be valuable not only for antivenom production but also for the development of novel therapeutics.
| S-EPMC9338987 | biostudies-literature
Project description:Motivation:Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression. Results:We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS' competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types. Availability and implementation:NetICS is available at https://github.com/cbg-ethz/netics. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6041755 | biostudies-literature
Project description:PurposeTo identify a gene signature for the prognosis of breast cancer using high-throughput analysis.MethodsRNASeq, single nucleotide polymorphism (SNP), copy number variation (CNV) data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into training set or verification set. Genes related to breast cancer prognosis and differentially expressed genes (DEGs) with CNV or SNP were screened from training set, then integrated together for feature selection of identify robust biomarkers using RandomForest. Finally, a gene-related prognostic model was established and its performance was verified in TCGA test set, Gene Expression Omnibus (GEO) validation set and breast cancer subtypes.ResultsA total of 2287 prognosis-related genes, 131 genes with amplified copy numbers, 724 gens with copy number deletions, and 280 genes with significant mutations screened from Genomic Variants were closely correlated with the development of breast cancer. A total of 120 candidate genes were obtained by integrating genes from Genomic Variants and those related to prognosis, then 6 characteristic genes (CD24, PRRG1, IQSEC3, MRGPRX, RCC2, and CASP8) were top-ranked by RandomForest for feature selection, noticeably, several of these have been previously reported to be associated with the progression of breast cancer. Cox regression analysis was performed to establish a 6-gene signature, which can stratify the risk of samples from training set, test set and external validation set, moreover, the five-year survival AUC of the model in the training set and validation set was both higher than 0.65. Thus, the 6-gene signature developed in the current study could serve as an independent prognostic factor for breast cancer patients.ConclusionThis study constructed a 6-gene signature as a novel prognostic marker for predicting the survival of breast cancer patients, providing new diagnostic/prognostic biomarkers and therapeutic targets for breast cancer patients.
| S-EPMC7654770 | biostudies-literature
Project description:Parkinson's disease (PD) is the second most frequent neurogenic disease after Alzheimer's disease. The clinical manifestations include mostly motor disorders, such as bradykinesia, myotonia, and static tremors. Since the cause of this pathological features remain unclear, there is currently no radical treatment for PD. Environmental and genetic factors are thought to contribute to the pathology of PD. To identify the genetic factors, some studies employed the Genome-Wide Association Studies (GWAS) method and detected certain genes closely related to PD. However, the functions of these gene mutants in the development of PD are unknown. Combining GWAS and expression Quantitative Trait Loci (eQTL) analysis, the biological meaning of mutation could be explained to some extent. Therefore, the present investigation used Summary data-based Mendelian Randomization (SMR) analysis to integrate of two PD GWAS datasets and four eQTL datasets with the objective of identifying casual genes. Using this strategy, we found six Single Nucleotide Polymorphism (SNP) loci which could cause the development of PD through altering the susceptibility gene expression, and three risk genes: Synuclein Alpha (SNCA), Mitochondrial Poly(A) Polymerase (MTPAP), and RP11-305E6.4. We proved the accuracy of results through case studies and inferred the functions of these genes in PD. Overall, this study provides insights into the genetic mechanism behind PD, which is crucial for the study of the development of this disease and its diagnosis and treatment.
| S-EPMC8355633 | biostudies-literature
Project description:Background and Objective: Despite striking advances in multimodality management, gastric cancer (GC) remains the third cause of cancer mortality globally and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The study aimed to identify potential key genes associated with the pathogenesis and prognosis of GC. Methods: Differentially expressed genes between GC and normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GC were identified by employing protein-protein interaction network and Cox proportional hazards model analyses. Results: We identified nine hub genes (TOP2A, COL1A1, COL1A2, NDC80, COL3A1, CDKN3, CEP55, TPX2, and TIMP1) which might be tightly correlated with the pathogenesis of GC. A prognostic gene signature consisted of CST2, AADAC, SERPINE1, COL8A1, SMPD3, ASPN, ITGBL1, MAP7D2, and PLEKHS1 was constructed with a good performance in predicting overall survivals. Conclusion: The findings of this study would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GC.
| S-EPMC6056647 | biostudies-literature
Project description:Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.
| S-EPMC7797537 | biostudies-literature